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ISBN 1-84544-843-X
ISSN 1469-1930
Volume 6 Number 4 2005
Journal of
Intellectual Capital Management consulting practice on intellectual capital Guest Editor: Bernard Marr
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Journal of Intellectual Capital
ISSN 1469-1930 Volume 6 Number 4 2005
Management consulting practice on intellectual capital Guest Editor Bernard Marr
Access this journal online __________________________ 467 Editorial advisory board ___________________________ 468 Management consulting practice on intellectual capital: editorial and introduction to special issue Bernard Marr __________________________________________________
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Implementing the KPMG Value Explorer: critical success factors for applying IC measurement tools Daniel Andriesson_______________________________________________
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Intellectual capital: management approach in ICS Ltd S. Pike, L. Fernstro¨m and G. Roos _________________________________
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An integrated framework for visualising intellectual capital Christina Boedker, James Guthrie and Suresh Cuganesan _______________
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Data envelopment analysis as method for evaluating intellectual capital Karl-Heinz Leitner, Michaela Schaffhauser-Linzatti, Rainer Stowasser and Karin Wagner__________________________________________________
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CONTENTS
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Journal of Intellectual Capital Vol. 6 No. 4, 2005 p. 468 # Emerald Group Publishing Limited 1469-1930
EDITORIAL ADVISORY BOARD Guy Ahonen Professor in Knowledge Management, Head of Department, Hanken Business School, Finland Sabin Azua Mendia Managing Director, BearingPoint, Spain Margareta Barchan Co-founder and Executive Board Member, Celemi, Sweden Derek Binney Chief Knowledge and Technology Officer, CSC Australia, Australia David H. Brett CEO and Founder, Knexa, Canada Annie Brooking Chief Executive Officer, Lux Inflecta, Iceland Wendi Bukowitz Product Development, Mellon, Human Resources and Investor Solutions, USA Leif Edvinsson Managing Director, Universal Networking Intellectual Capital AB, Sweden Luiz Antonio Joia Associate Professor, Brazilian School of Public and Business Administration, Getulio Vargas Foundation, Brazil Baruch Lev Philip Bardes Professor of Accounting and Finance, Stern School of Business, New York University, USA Bernard Marr Research Fellow at the Center for Business Performance, Cranfield School of Management, UK and Visiting Professor of Intellectual Capital, University of Basilicata, Italy
Jose´ Marı´a Viedma Marti Professor of Business Administration, Polytechnic University of Catalonia, Spain and President of Intellectual Capital Management Systems (ICMS), Spain Jan Mouritsen Department of Operations Management, Copenhagen Business School, Denmark Klaus North Professor of International Management, Wiesbaden University of Applied Sciences, Germany Sharon L. Oriel Director, Global Intellectual Capital Tech Center, The Dow Chemical Company, USA Richard Petty Lecturer, Faculty of Business and Economics, The University of Hong Kong, Hong Kong, China Kurt P. Ramin Commercial Director, International Accounting Standards Committee Foundation, UK Go¨ran Roos Chairman, Intellectual Capital Services Ltd, UK Hubert Saint-Onge Principal, SAINTONGE/ALLIANCE Inc., Canada Patrick H. Sullivan Sr President, Intellectual Capital Management Group Inc., USA Karl-Erik Sveiby Professor, Swedish School of Economics and Business Administration, Helsinki, Finland
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Management consulting practice on intellectual capital
Editorial and introduction
Editorial and introduction to special issue Bernard Marr
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Centre for Business Performance, Cranfield School of Management, Cranfield, UK Abstract Purpose – With intellectual capital and intangible assets high on the agenda of executives around the world, and little practical evidence of good practice in measuring and managing these assets, there is a great need for help. This editorial to a special issue on the topic introduces the problem and highlights key issues. The special issue provides an overview of how management consulting companies acting in this space suggest tackling the problem. The purpose is therefore to bring together the approaches of different management consulting firms and to make their differences explicit. Design/methodology/approach – All major general management consulting firms as well as specialist consulting firms focusing in the area of intellectual capital and intangible assets were directly invited to submit a paper for this special issue. The call for papers was also made publicly available in the journal and through e-mail campaigns by Emerald. All submissions underwent a double-blind refereed selection process. Findings – Even though many submissions were received for this special issue, most of the authors were not able to demonstrate a sufficient understanding of the constructs nor were they able to justify the tools and methodologies developed. Reviewers were made aware of the practical background of many of the authors and it was ensured that sufficient and constructive feedback was provided. Even with various rounds of reviews many papers had to be rejected as they resembled marketing brochures rather then logical discussions. This unfortunately shows that there still is a massive skills gap in the industry and companies should be careful before they engage with any management consulting firm to help them measuring or managing their intangibles. Practical implications – The focus of potential papers was not academic rigor (as opposed to the Special Issue Vol. 5 No 2) but the provision of an overview of the state of the art in intellectual capital consulting practice. The papers therefore provide practitioners with good insights into current practice. Originality/value – This special issue is the first to bring together in a structured and rigorous format different management consulting approaches to the measurement and management of intellectual capital and intangible assets. Keywords Management consultancy, Intellectual capital, Intangible assets Paper type Viewpoint
Intellectual capital today and consulting firm’s interest in the topic Today, many organizations recognize the importance of intellectual capital as a principal driver of firm performance and a core differentiator. Also governments are recognizing the importance of intellectual capital. The European Union aims for their membership countries to invest a minimum of three percent of their GDP into research and development initiatives. In the UK, for example, Prime Minister Tony Blair wrote in a recent Government White Paper that creativity and inventiveness is the greatest
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source of economic success but that too many firms have failed to put enough emphasis on R&D and developing skills. Patricia Hewitt, Secretary of State for Trade and Industry, added in a recent DTI report that increasingly it is the intangible factors that underpin innovation and the best-performing businesses. An increasing number of firms start to report more of the intangible aspects of their business, even without the force of regulations. This trend is especially observable in Europe with various initiatives by the European Commission (e.g. projects such as METITUM, E *KNOW NET, PRISM). Another example is presented by the Danish Department of Trade and Industry, which produced guidelines of how companies can produce intellectual capital reports. In Austria, the Government has passed a law that all universities have to report on their intellectual capital, in the UK companies will be forced to produce an Operating and Financial Review outlining many intangible elements of their business, and countries as diverse as Iceland, Germany, or Spain have started their own initiatives. At the same time accounting guidelines are being developed and standards are being questioned and reviewed. With the introduction of the International Accounting Standards more emphasis will be placed on accounting for intangible components and stricter compliance rules force companies to report on other intangible aspects of their performance. Leading software companies such as SAP, Hyperion, Oracle, 4GHI or Peoplesoft are developing applications to address this, and even governments are beginning to measure the intellectual capital of cities, regions, and countries. Many consulting companies have discovered different areas of this increasing awareness and interest in intellectual capital to offer their services like PricewaterhouseCoopers who offer their services to help companies in their value reporting initiatives to increase transparency in corporate reporting or WatsonWyatt who offer human capital audits. In recent reports or marketing material from different consulting firms this trend is apparent: Accenture writes that today’s economy depends on the ability of companies to create, capture, and leverage intellectual capital faster than the competition. Cap Gemini Ernst & Young believes that intangibles are the key drivers for competitive advantage and KPMG states that most general business risks derive from intangibles and organizations therefore need to manage their intangibles very carefully. PricewaterhouseCoopers writes that in a globalized world, the intellectual capital in any organization becomes essential and its correct distribution at all organizational levels requires the best strategy integrated solutions, processes and technology. Even though the leading management consulting firms recognize the importance of intellectual capital – they seem to suffer from the same predicament as the field as a whole. Intellectual capital is defined differently and the concept is often fuzzy. As a result, many firms provide point solutions only addressing particular isolated aspects of a firm’s intellectual capital such as: . help with implementing accounting for some intangibles; . legal advice of how to protect intellectual property such as patents, copy rights, etc.; . guidance on building customer or stakeholder relationships; . improved stakeholder dialogue and value reporting; . human capital or capabilities assessments; and . solutions for valuing brands.
Even though these are all important areas, the danger is that organizations are missing out on the big picture. What is often not clearly understood is that intellectual capital is a truly multidisciplinary field. Below we will expand on this problem. Misunderstanding intellectual capital as a barrier for convergence The multidimensional nature of intellectual capital, as defined by many members of the community, is often not well understood which means definitions are not always very clear and neither are the boundaries of what people mean when they talk about intellectual capital. In a recent book to address exactly the multidimensional nature of intellectual capital, Marr (2005) writes that it could happen that when one talks to accountants they might refer to intangibles as “non-financial fixed assets that do not have physical substance but are identifiable and controlled by the entity through custody and legal rights” as defined by the Accounting Standards Board in FRS 10, their main standard for reporting intangibles and goodwill. Such a stringent definition excludes many commonly accepted intangibles like customer satisfaction, knowledge and skills of employees as they cannot be controlled by the firm in an “accounting” sense. If one then went to a HR manager she might refer to intellectual capital as skills, knowledge, and attitude of employees. A marketing manager might argue that intellectual capital such as brand recognition and customer satisfaction are at the heart of business success, whereas the IT manager might view key intangibles as being software applications and network capabilities. Furthermore, different words are being used to describe very similar constructs from different perspectives, which adds to the confusion. In accounting, most people would refer to intangible assets to explain the non-financial and non-physical drivers of success. In Economics the phrase knowledge assets is often used to describe similar ideas and in strategic management they use intellectual or intangible resources or capabilities. The potential power of the field of intellectual capital is to create a truly inter-disciplinary view of these different constructs and ideas. When intellectual capital is defined by members of the intellectual capital community, it is often divided into various components, which refer to the skills and competencies of people in the organisations (human capital), then components referring to relationships with customers or other stakeholders (relationship capital), and components referring to organisational culture, routines and practices, or intellectual property (organisational or structural capital). Even though these components are often defined or bundled slightly differently, it shows how broad the scope of the concept of intellectual capital really is. One key role of members of this community is to make the concept of intellectual capital more accessible to the different fields who often clearly recognise the importance of intellectual capital components, but miss out the big picture and therefore the interdependencies and interconnections between the different elements. Much emphasis has recently been placed on the interactions and interdependencies of different intellectual capital components. Firms are now realizing that for example, by valuing their brands companies only get a partial view of the truth since their brand value is linked to other crucial aspects such as their processes that produce high-quality products and services, their relationship, the reputation, and the competencies of their employees. Examples such as Arthur Andersen show how quickly a well-recognized brand can disappear over night if some of the other organizational components are
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missing. What the field of intellectual capital has to offer is a more comprehensive view of the organizational elements and how they deliver value and competitive advantage. By converging some of the point solutions into a more strategic overall package, consulting firms would be able offer their clients a truer and more insightful help. The current misunderstandings and the isolated point solutions offered by many, mostly major firms, does seriously make one question the thought leadership claimed in much of their marketing material. There is a huge opportunity here for consulting firms to make a major impact, what is needed is some help from the intellectual capital community to shape these solutions into the right format. Aim of this special issue The aim of this special issue was to bring together the approaches of the different management consulting firms and to make their differences explicit. The call for papers was sent to all major consulting firms, specialist firms, and made publicly available in the journal and through e-mail campaigns by Emerald. All submissions underwent a double-blind refereed selection process. Authors were asked to demonstrate a thorough understanding of the subject and include the following: (1) A historic description of how the concept of intellectual capital evolved in your firm, how it entered the agenda, and how its importance evolved. (2) A definition of intellectual capital as a concept and its components (if applicable a firm-specific definition). (3) If applicable, a description of how the definition of the concept of intellectual capital evolved (e.g. did the definition become more specific or broader?). (4) The evolution of tools and approaches developed to understand and manage intellectual capital (e.g. identify, measure, value, report). (5) An overview of the current approach(es) towards managing intellectual capital with a special emphasis on actual case studies. (6) If possible, provide justifications for the tools and approaches used and evidence of its impacts. (7) A look into the future – where do you see the field heading to? Will the importance of intellectual capital increase further? What will be the implications? The focus of potential papers was therefore not academic rigor (as opposed to the Special Issue Vol. 5 No 2) but the provision of an overview of the state of the art in intellectual capital consulting practice. Even though we received many submissions for this special issue, most of the authors were not able to demonstrate a sufficient understanding of the constructs nor were they able to justify the tools and methodologies developed. We ensured that reviewers were aware of the practical background of many of the authors and that sufficient and constructive feedback was provided. Even with various rounds of reviews papers were finally rejected as they resembled marketing brochures rather then logical discussions. However, this special issue on consulting practice on intellectual capital includes a list of exciting papers which demonstrate a sub-set of the many activities going on in
this area. The first paper by Daniel Andriessen discusses the implementation challenges of the KPMG Value Explorerw. The paper reports on an empirical investigation of the critical success factors for implementing an intellectual capital valuation method. The second paper by Stephen Pike, Lisa Fernsto¨m, and Go¨ran Roos discusses the theoretical roots of the intellectual capital approach adopted by ICS Limited. Their approach is a strategic one founded in the thinking of the resource-based view of the firm. The third paper by Christina Boedker, James Guthrie, and Suresh Cuganesan outlines an integrated framework for the visualization of intellectual capital. The fourth paper in this special issue is by Karl-Heinz Leitner, Michaela Schaffhauser-Linzatti, Rainer Stowasser, and Karin Wagner and discusses data envelopment analysis as a tool to assess intellectual capital generation in the Austrian University Sector. The fifth paper by Thomas Housel and Sarah Nelson outlines a valuation tool based on complexity and information theory to quantify the value creation intellectual capital in firms. The sixth paper is by Eggert Claessen and discusses the progress of a Nordic project on harmonizing the reporting of intellectual capital in small and medium size IT companies. In the seventh paper Kristine Jacobsen, Peder Hofman-Bang, and Reidar Nordby, Jr outline the IC Rating Model developed by Intellectual Capital Sweden and show its application in a case study. The final paper in this special issue is by Roland Burgman, Go¨ran Roos, John Ballow, and Robert Thomas who describe the future value management approach developed by Accenture and AssetEconomics. Overall, I hope that this special issue as well as the dialogue with many other major consulting firms will prompt consultants to better understand the nature of intellectual capital and the potential for cross-disciplinary learning. Reference Marr, B. (Ed.) (2005), Perspectives on Intellectual Capital – Interdisciplinary Insights into Management, Measurement and Reporting, Elsevier, Boston, MA.
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The current issue and full text archive of this journal is available at www.emeraldinsight.com/1469-1930.htm
Implementing the KPMG Value Explorer Critical success factors for applying IC measurement tools Daniel Andriesson INHOLLAND University of Professional Education, Diemen, The Netherlands Abstract Purpose – The purpose of this paper is to describe the results of an empirical study into the critical success factors for implementing an intellectual capital valuation method, the KPMG Value Explorer. Design/methodology/approach – For this study the design approach was used as research methodology. Findings – The research shows the strengths and weaknesses of the method and identifies four general critical success factors for the implementation of intellectual capital valuation and measurement tools. Research limitations/implications – The research was based on six case studies. Application of the method with other companies may provide further grounding of the conclusions. Practical implications – The research shows that practitioners who want to implement an intellectual capital valuation or measurement method must: perform a proper diagnosis of the problem at hand; have knowledge of the strengths and weaknesses of the method they want to use; understand the application domain of the method – the class of problems and the class of contexts for which the method needs to provide a solution; and possess the necessary skills to implement the method. Originality/value – Successfully implementing a method for the valuation or measurement of intellectual capital is not an easy task. Practitioners yet receive little support from the intellectual capital research community. Little research has been done into the factors that influence the success of a method. This paper is a first attempt at systematically identifying some of the factors for the successful implementation of an intellectual capital valuation or measurement method. Keywords Intellectual capital, Research methods, Measurement, Critical success factors Paper type Research paper
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Introduction Over the last ten years many methods have been proposed for the measurement or valuation of intellectual capital (IC). (For overviews see: Bontis, 2001; Bontis et al., 1999; Luthy, 1998; Petty and Guthrie, 2000; Sveiby, 2002 and Andriessen, 2004a). Little empirical research has been done on how these types of methods are being implemented and what the critical success factors are for successful implementation. As a result there is an abundance of information on how to measure or value IC, but there is little knowledge how to successfully apply these methods in practice. Implementing a new measurement method is an intervention into the daily operation of a company. How successful are these interventions? What are their effects? Implementation of these methods requires certain skills and conditions. What are some of those skills and conditions? What are some of the mistake to avoid? What are critical success factors?
The field of IC research has come to the phase where it needs to start evaluating the success and effects of its methods. In addition it needs to give practitioners tested guidelines on how to successfully implement and use these methods. In this paper I present a systematic analysis of the implementation and effectiveness of the KPMG Value Explorer. The Value Explorer is a method for the identification and (financial) valuation of intangible resources developed by the Knowledge Advisory Services team of KMPG The Netherlands. In 1997, KPMG The Netherlands founded an innovation unit focused at the impact of the knowledge economy on businesses. This Knowledge Advisory Services (KAS) unit developed new management approaches and assisted clients in three areas: developing knowledge-based strategies, improving knowledge sharing, and measuring and reporting intellectual capital. In 1998 KAS participated in a pilot study initiated by the by the Dutch Ministry of Economic Affairs (Ministry of Economic Affairs, 1999) to develop a new approach for the measurement and reporting of intangibles. The purpose of the study was to give four accounting firms the opportunity to develop new approaches to intangibles reporting and try these with a number of their clients. KPMG developed the Value Explorer (Bontis et al., 1999; Andriessen and Tissen, 2000; Andriessen, 2001). No competitive research was conducted prior to its development. Developing the tool was not a strategic decision but came out of the opportunity offered by the Ministry. The Value Explorer was used by the KAS unit in a number of client engagements, six of which are used as case studies in this paper. After the KAS unit was abolished in 2003 the tool was no longer used within KPMG. I still use it as a strategic tool. First I describe the research methodology used for designing, implementing and testing the method. Then I provide a brief outline of the method itself. I continue by reporting the findings from six companies where the method was used. I conclude by summarizing the critical success factors for implementing an IC measurement method. Methodology The design approach I used the design approach (Andriessen, 2004a,c; Van Aken, 2004; Weggeman, 1995) as my research methodology for this study. I used the reflective cycle to generate design knowledge about the method. Figure 1 shows an overview of the reflective cycle. The reflective cycle starts with a general diagnosis and description of the problem. The second step in the reflective cycle is designing a first draft of a method that helps solve the problem. For this the design cycle is used which consists of the following four activities. First, create a general diagnosis and description of the problem. This gives an impression of the application domain of the method. The application domain describes the class of problems the method needs to address and the class of contexts to which it needs to be applicable. Second, develop the requirements for the method based on the class of problems and the class of contexts, as well as demands from clients, from users, and from the environment. Third, draft a first version of the method based on these requirements and on available theory. Finally, check whether this design meets the requirements. This evaluation may lead to changes in the design, but also to changes in the problem definition and the requirements. According to Van Aken (1996), a researcher should continue this process until an adequate design is created.
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Figure 1. The reflective cycle
The third step in the reflective cycle is the selection of a case to test the draft method. The fourth step in the reflective cycle is to use the method to solve the case-specific problem using the regulative cycle. The regulative cycle consists of five activities. First, diagnose the specific situation to define the problem in its context. Second, develop specific requirements that supplement the general requirements. Third, make amendments to the method. Fourth, implement the method. And fifth, evaluate the outcome of the method. This evaluation often leads to further modifications of the design, but also to changes in the way the problem originally was perceived and sometimes to changes in the set of specific requirements. The fifth step in the reflective cycle is to reflect on the results using three evaluation questions: (1) Was the case part of the application domain? (2) What did this case reveal about the success of the method? (3) What did this case reveal about improvements to the method? As a sixth step in the reflective cycle, design knowledge is developed in three areas. First, knowledge about the class of problems for which the method was designed. This may lead to further refinement of the problem definition. Second, knowledge about the class of contexts for which the method is applicable. The so-called “indications” and “contraindications” demonstrated under what circumstances the method produces proper results. They are the conditions for success that need to be fulfilled. Third, insight into the means-end relationships that underlie the method and that produce its results.
Cases The Value Explorer was implemented at six medium size companies (Table I). The first three cases – Bank Ltd, Electro Ltd and Automotive Ltd – were part of a study funded by the Dutch Ministry of Economic Affairs that took place in 1998 and 1999. These companies were selected because they were medium-size knowledge-intensive businesses covering various industries. The fourth case was Logistic Services BU. The management of Logistic Services BU wanted to value its core competencies. The fifth case was Professional Services LLP. It wanted to report intellectual capital in its annual report. Lastly, Consulting Department was a small consulting unit within a larger financial institution. They wanted to determine their strengths and weaknesses as part of their decision process about becoming an independent consulting firm.
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The method Introduction The Value Explorer is based on the concept of core competencies to identify the strategically important intellectual capital in an organization. In a previous work (Andriessen, 2004a) I published a revised and improved version of the method, named the Weightless Wealth Toolkit. Steps The Value Explorer offers a five-step approach: (1) Identify the intellectual capital by making a list of the core competencies of the organization. (2) Conduct a value assessment by using a checklist that assesses the added value, competitiveness, potential, sustainability and robustness of those core competencies. (3) Perform a financial valuation of the intellectual capital by allocating a portion of the expected normalized earnings of the organization to the identified core competencies. (4) Develop a management agenda based on the findings making recommendations to management on how to improve the value of the intellectual capital. (5) Create a report for management using a value dashboard. I have reported the full do-it-yourself method in Andriessen (2004a). Let me describe these steps in a little more detail. Case study
Industry
Type of organization
Bank Ltd Electro Ltd Automotive Ltd Logistic Services BU Professional Services LLP Consulting Department
Banking Engineering Automotive Logistics Professional services Banking
Subsidiary of listed company Subsidiary of listed company Private company Department of listed company Professional partnership Department of subsidiary of listed company
Table I. Overview of case studies
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The first step is to identify the IC in the company. Of course, there are a large number of intangibles. And while many of these may exist in a company (in fact, the majority will exist in a company, whether you realize it or not), not all of them are equally important. What we need to do is track down those intangibles, which add value to the company. In the first step towards establishing the value of intellectual capital, we have to decide which intangibles are the most relevant to us. Not only to find out the economic value of a company but because those intangibles which add value to your company are the ones which are of strategic importance to your future success. Such intangibles, however, should never be viewed in isolation. It is only when they combine that the economic synergy is created. All this brings us to the critical question: how do you decide which of the many intangibles within a company are of strategic importance? And the answer is to define core competencies of the company. A core competence is a skill cluster which lies at the centre of competitive success and which contributes to the long-term corporate prosperity. It is a bundle of various types of intellectual capital, including skills and tacit knowledge, values and norms, technology and explicit knowledge, processes and reputation. Listing a company’s core competencies can identify this intellectual capital. For example, one of the core competencies of Electro Ltd was its ability to design energy conversion systems, which was a combination of implicit and explicit knowledge, certain design processes and the company’s reputation in this field. The second step is to determine how today’s core competencies – often built up over a long period and representing a considerable investment in time, money, people, and skills – equip a company for competitive success in a changing market. When discussing core competencies, traditional literature maintains that unless a suggested core competence meets all the criteria laid down for it, it should not be considered as such. While this is fine in theory, it does not always work in practice. In extreme cases, an analysis along “traditional” theoretical lines may show that a company has no core competencies at all. For many companies a company competence may qualify as a core competence, even if it does not meet all criteria laid down. In our view, the strength of each of the competencies is of far greater relevance than the name we give to them. Such strength is not consistent. It varies. A competence may be very strong in one area, but weaker in another. For managers, it is important to be able to assess the varying strengths of the competencies they have defined. And for this reason the Value Explorer contained a list of criteria, which will help to determine the practical strength of each competence. The third step is to put a monetary value on the identified intellectual capital. There are three ways to do a financial valuation: the cost approach, the market approach and the income approach. The cost approach is based on the economic principles of substitution and price equilibrium. These principles assert that an investor will pay no more for an investment than the cost to obtain an investment of equal utility (Reilly and Schweihs, 1999). Thus, the price of a new resource is commensurate with the economic value of the service that that resource can provide during its life. The market approach is based on the economic principles of competition and equilibrium. These principles assert that in a free and unrestricted market, supply and demand factors will drive the price of any good to a point of equilibrium. In the market approach, an analysis is made of similar resources that have recently been sold or licensed. These market data are used to estimate a market value. The income approach is based on the economic principle of anticipation. The value of intangible resources is the value of the expected economic income generated by these resources.
Each approach has its strengths and weaknesses. The problem with the cost approach is that in many cases cost is not a good indication of value. Many of the most important factors that drive value are not reflected in this approach. In the market approach, an analysis is made of similar resources that have recently been sold or licensed. The market data are used to estimate a market value. The market approach can only be used if data are available on the transaction of intangible resources that are similar to the subject resources. When the subject resources are unique, which is often the case, this approach is not appropriate. The income approach is based on a projection of economic income and thereby on somehow predicting the future. Therefore, it always contains a level of uncertainty and subjectivity. “All income approach analyses are based on the premise that the analyst can project economic income with a reasonable degree of certainty. The term reasonable degree of certainty is, by its very nature, subjective” (Reilly and Schweihs, 1999, p.182). The Value Explorer uses an income approach. It analyses the expected earnings of a company. It then assesses the contribution of the identified intellectual capital to the creation of these earnings, taking into account other forms of capital (financial assets, tangible assets) that are used to produce these earnings. It uses a discount rate to calculate the present value of the intellectual capital earnings. The fourth step is to analyse all data and draw a management agenda. The identification of the core competencies, the assessment of their strengths and weaknesses and the financial valuation will have given valuable insight into problems and challenges that management need to respond to. These are prioritised and written down in a management agenda. Finally the management agenda is combined with a graphical representation of the outcome of the assessment and valuation (see Figure 2 for an example for Bank Ltd).
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Figure 2. Value Dashboard of Bank Ltd
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Findings Motives for using the Value Explorer When KPMG went to market with the Value Explorer we found that clients had a variety of motives to use the method. These motives can be grouped into two categories (see also Andriessen, 2004b): improving internal management and improving external reporting. Companies that wanted to improve their operations were primarily interested in the Value Explorer to support their strategy development process. These companies wanted to identify the hidden drivers of their success to better leverage them and to create an intellectual capital-based company strategy. Companies that wanted to improve their external reporting were intrigued by the possibility of providing evidence to their stakeholders on their unique competencies and hidden assets. Success of the Value Explorer In the six case studies the success of the method was limited (see Table II). In two cases the limited success was the result of poor implementation: At Bank Ltd we stopped the implementation process before it was finished because the team ran out of budget. Although the process was never properly finished, the end report was used in the decision-making process about Bank Ltd’s independence. However, according to the CEO, its contribution to the decision was limited. At Automotive Ltd the manager/owner of the company stopped the process because of other priorities. We were not able to convince him otherwise. When the implementation was successful, in only one case the problem was solved. Consulting Department became a successful, independent company. According to the manager, the method had been very important in facilitating the discussion about independence. It helped to make explicit important considerations for outsourcing. In three other cases, the problem was not solved. The general manager of Electro Ltd had been very satisfied with the results at the time of the final presentation. However, circumstances beyond our control changed the situation completely and the company filed for bankruptcy. At Logistics Services BU, a similar thing happened. The method
Problem type
Problem definition
Internal management Electro Ltd Develop a strategy based on available technologies and skills Logistic Create a future for Logistic Services BU Services Ltd Consulting Create a future for Consulting Department Department Automotive Ltd Improve strategy-making process Table II. Appraisal of the success of the method in six case studies
External reporting Bank Ltd Remain independent within holding company Professional Report on intangibles Services LLP
Successful implementation?
Problem solved?
Contribution of method?
Yes
Not available
Yes
Wrong problem No
Yes
Yes
Big
No
No
None
No
Yes
Limited
Yes
No
Not available
Some
contributed to the decision to effect a management buyout. However, in the end, key players decided not to join the new company and the buyout was cancelled. According to two participants, the method contributed to the decision-making process. It created enthusiasm and energy within the group, and it helped to develop a proper business case because it created insight into the four core competencies and their strengths and weaknesses. At Professional Services LLP, all the necessary conditions for successful implementation were met. However, we discovered that the method did not produce results that could be reported easily externally. More on this result will follow below.
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Necessary conditions for success To explain these disappointing results we need to take a look at the reflective cycle again. The feedback arrows in Figure 1 indicate four errors that can be made when designing and implementing a method: (1) we did not diagnose the situation correctly and we have identified the wrong problem; (2) we used a poor method that was unsuccessful and we need to fix it; (3) the case did not match the application domain of the method. In other words, we selected the wrong tool for the job; (4) we implemented the method poorly. Figure 3 summarizes these errors, redefined as necessary conditions for a successful implementation of a method. Let us look at each of these necessary conditions in detail to see if they can explain the performance of the Value Explorer in the six cases. Problem definitions Each of the six companies had a different motive for applying the method. Four companies wanted to improve their internal management. Two wanted to use the method for external reporting purposes. However, the exact problem was not always immediately clear. Internal management. Electro Ltd, was an organization in turmoil. In the previous seven years, this electric installation and engineering company had had five general managers, each one leaving within a year. The company was self-centred, product-oriented and lacked market-focus. For the previous two years, the number of contracts won had declined rapidly. The profits from national and international
Figure 3. Necessary conditions for a successful implementation of a management method
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projects were under severe pressure. The newly appointed general manager was working on a turnaround, improving the market-orientation and sales capability of the company and developing a strategy focused on specific product/market combinations. The General Manager wanted to use the method to develop the new strategy, set priorities and determine focus. However, it turned out Electro Ltd had a severe cash flow problem. This problem became urgent just after the project was finished. The cash flow problem was never solved and the company went bankrupt. In a sense, the method was solving the wrong problem. At Automotive Ltd key players did not share the initial problem definition. The main contact was through the financial controller. He was working hard to formalize a number of processes within the company. When the company was small, it could run its operation in a rather informal way. Now, as it grew bigger, there was a need for more transparency and rules and regulations. One of the controller’s ambitions was to improve the strategic decision-making process. Until then, the owner had made all strategic decisions based on limited market research and without an explicit corporate strategy. The financial controller hoped that a discussion on intellectual capital would help make the strategy process more explicit. Talking to the owner, the KPMG team did not sense this need, nor did the team notice that the owner was worried about any other specific problem. He was willing to co-operate, as long as it would not consume too much of his time or the time of his staff. When it did, he terminated the implementation of the method. Both Logistic Services BU and Consulting Department were reconsidering their position. Management wanted to develop a new future for the company, based on the company’s intangible strength. However, the management of these companies did not know what the strength of the company was and wanted to have insight into its future potential. In both cases the Value Explorer proved to be helpful. External reporting. Bank Ltd was an independent private bank that was part of a worldwide financial institution. As a small private bank, it nurtured its independence and objectivity in serving clients. Management faced the challenge of convincing the holding company that Bank Ltd’s independent position within the Holding and the bank’s distinct style and identity were vital for its future success. It wanted to use the Value Explorer to give the holding company insight into the importance of the bank’s intellectual capital, to promote a ’non-intervention’ policy on behalf of the holding company and to secure independence in the future. As the CEO phrased it: “What is the value of our independence?”. The method proved to be useful, however, as we will see, we made some mistakes implementing it. Professional Services LLP offered a wide range of consulting and auditing services to its clients. It was well aware of the transition in the global economy from an industrial to an economy based on intellectual capital. At the end of the millennium, it wanted to express this transition in its annual report. Professional Services LLP had the idea that this would be a nice theme for its annual report. The idea was to analyse the intellectual capital of the firm, assess its strengths and weaknesses and use this information to report externally, proving to the outside world that the company had prepared for the future. As we will see we found the Value Explorer was not the appropriate tool for this job.
Quality of the method Strengths of the Value Explorer. At the four cases where the Value Explorer was implemented successfully, we found the method had a number of strengths. These can be grouped into the five steps. The first step of the method is the identification of intellectual capital with the help of core competencies. The Value Explorer searches for the combined power of intangible resources. It determines the way individual intangibles contribute to a company’s uniqueness and cumulative capabilities. It determines which intangibles are important and how they contribute to company success. We found that the use of core competencies to identify intangible resources provides a new and positive view on a company, and a common language that can explain the company’s success, install a sense of pride, boost the company’s self-confidence, and identify new opportunities. The second step of the method is the value assessment of the core competencies using five checklists. We found that the value assessment helps to create a realistic view on the capabilities of a company that are genuine core competencies. In addition, the assessment highlights strengths and weaknesses of core competencies. These weaknesses can be the starting point for improvement initiatives. The third step is the financial valuation of the core competencies. The financial valuation highlights the absolute importance of intangibles. Both the CEO of Bank Ltd and the manager of Consulting Department acknowledged the importance of the monetary value figure in conveying the significance of intangible resources to other stakeholders. The manager of Consulting Department phrased it as follows: “Within the financial services industry, people speak the language of money. If something has no monetary value attached to it, it is not considered important” (personal communication). The added value of the financial valuation of intangible resources lies in the fact that numbers attract management attention. This finding is in line with the view of Mouritsen et al. (2001) about the importance of indicators in intellectual capital statements. They state that these indicators are especially important because they demonstrate seriousness on the part of top management. In addition, the financial valuation shows the relative importance of the core competencies. The financial valuation uses money as a common denominator to compare the usefulness of the competencies. This can help when making decisions about investments in intellectual capital. The fourth step of the method is the management agenda. The management agenda reflects the implications of the findings for management. It provides an action plan on how to strengthen the company’s intellectual capital. We found that the management agenda can help to make the important step from valuation to action, making the method practical and meaningful. The fifth step of the method is the end report, which contains the value dashboard. We found that the value dashboard of the method helps to communicate the findings in an effective and comprehensive way by providing insight into the strengths, weaknesses, and value drivers of core competencies in one comprehensive picture. Weaknesses of the Value Explorer. We also found the Value Explorer has certain weaknesses. First, the version of the method that was used lacked a diagnosis phase. It did not include a step in which the analyst checks whether the problems of the company fit the class of problems for which the method was designed. We found the method
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“jumped to solutions” (Kerssens, 1999), did not prevent pigeonholing (Perrow, 1970), or, phrased differently, the method suffered from the “child-with-a-hammer-syndrome”[1]. Second, we found that the step from creating an inventory of intangibles and capabilities to defining core competencies is still a more or less creative and unguided step. The personal skills of the analyst play an important role. The existing guidelines for this step did leave room for personal preferences, diminishing the reliability of the outcome. Third, we found that the results of the method are internally focused. The method describes important intellectual capital of a company without looking at the environment. Roos et al. (2001) distinguish between two approaches to strategy: external analysis and the resource-based view. They state that a strategy process should combine the best of both approaches. The method takes care of the resource-based view, identifying the valuable resources of the company. However, before a company can develop a new strategy, an external analysis of major environmental, competitive forces must be made. Right method for the job We found the Value Explorer has certain strengths, however, the question remains, under what circumstances is it the right tool for the job? There are two sides to this question. What class of problems is the method able to solve and under what circumstances can it be successful? The findings from the case studies indicate that the Value Explorer is not an appropriate tool for the external reporting of intellectual capital. We found that the results of the method are not self-evident and must be accompanied by an extensive reading instruction. Interpretation of the results requires insight into the underlying method. Furthermore, clients are reluctant to publish the results. Professional Services LLP considered the reporting of financial valuations risky. In addition, supporting evidence for core competencies often includes data about competitors. Professional Services LLP was reluctant to report these data because it might provoke criticism. The method highlights a company’s strengths but also its weaknesses. Professional Services LLP and Electro Ltd were hesitant to report these weaknesses to the outside world. Finally, these companies considered data about their core competencies confidential information. As the CEO of Electro Ltd put it: “I will not published this information for the next six years” (personal communication). In three cases we found that the method was a useful tool to help improve the way a company is managed. We found that the method can help in solving problems of future orientation and strategy development, by helping to create resource-based strategies for companies that lack insight into or are insecure about the intangible resources that make these companies successful. A second factor that influences the application domain of a management method is the class of contexts in which the method can be used. We found that the method worked well for knowledge-intensive, middle-size companies employing from 50 to 1,000 employees. The tests showed it also works with smaller units that are part of a bigger company (Logistics Services Ltd, Consulting Department). Tests also proved it can be used with bigger companies (Professional Services LLP), providing that the analyst focuses on the core competencies of the company that various departments have in common. The tests highlighted that the following conditions must be fulfilled
to ensure a successful implementation. The company must have an issue about its future direction. If there is no clear issue, as in the case of Automotive Ltd, it is less likely that the method will produce useful results. In addition, management of the company must have a certain willingness to reflect on the organization and to review critically the organization’s strengths and weaknesses. Management must have enough time to participate – at least to join in the interviews and visit the end presentation. At Automotive Ltd these two conditions were not met, which in part explains the early termination of the project. Finally, management must have the willingness, as well as the mental ability, to look at the company from an intangible perspective. This, too, was lacking at Automotive Ltd Quality of the implementation The last necessary condition for a successful implementation of an IC method is the quality of the implementation itself. One can have a company with an urgent problem and use a good method that is suitable for the case at hand and still be unsuccessful because the method is not implemented properly. De Caluwe´ and Stoppelenburg (2003) identify six process criteria for successful implementation of methods by outside consultants: (1) level of involvement of the consultant and the client system with the assignment; (2) intensity of communication between the consultant and the client system; (3) degree to which the approach is being developed along the way; (4) extent to which the consultant provides concrete directions to the client system; (5) level of equivalence between the consultant and the client system; and (6) extent to which a specific method was used We used these criteria to assess the quality of the implementation. We found that in two cases the quality of the way we implemented the method did not meet these criteria. At Bank Ltd two of the conditions for a successful implementation were not fulfilled: We had not involved important players of the client system at crucial stages of the implementation, and there was lack of communication between our implementation team and the client system on the input and output of the valuation. The mistake we made was that at the meeting where we presented the results of the method, we told the management of Bank Ltd that the draft report was the final result of the study. We would only correct major mistakes. If the bank wanted additional research, analyses, or calculations, it would have to pay us more. The management team was very surprised. To their expectation, this report was merely the feedback of the results of the second workshop. It was the first time that the management team had seen the results of the valuation. They had additional questions and suggestions for improvement, and were disappointed that we did not want to do any additional analysis. They thought the results of the analysis were interesting, but the project was not yet finished. This mistake had a big impact on the success of the method. When I asked the CEO of Bank Ltd about the implementation two years after the project was finished, he showed not so much disappointment about the method and its potential results, but disappointment about the fact that the project was not finished properly.
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At Automotive Ltd we found three of the conditions for successful implementation were not fulfilled. The level of involvement of the client system with the engagement was minimal. The communication between the implementation team and the client system was deliberately kept to a minimum in order not to take too much of the client’s time. There was no equivalence between the client system and our implementation team. These factors explain part of the lack of success. The lack of a clear and urgent problem, and the very pragmatic mind-set of the owner were other important factors. As a consequence the owner was not convinced that the implementation was very useful and he terminated the project. Conclusions Based on the implementation of one particular method for the valuation of intellectual capital at six companies I draw the following conclusions about critical success factors for implementing an intellectual capital valuation method. I formulate my conclusions as hypotheses. First there is the need for a proper diagnosis of the problem at hand. The valuation of intellectual capital can help to solve several types of company problems (Andriessen, 2004a). We need to do a thorough diagnosis to determine the specified problem of the situation at hand. This is especially essential when our intention is to improve the internal management of your organization. There can be many reasons why a company is performing sub optimally or poorly. There can be many ways to optimise a company’s performance. It is not sufficient merely to identify the problem at hand as an internal management problem. Instead, we should analyse the specific context of your organization and diagnose its unique situation. We may find that the intellectual capital perspective is an appropriate perspective to diagnose the problem. Using this perspective implies focusing on the intellectual capital of a company, the way it is managed, its strengths and weaknesses, and its potential. However, to avoid pigeonholing, we must be aware that other perspectives may be equally or more appropriate. Otherwise, there is a clear risk that an inappropriate or unimportant problem will be solved, as we saw in the case of Electro Ltd Second, we must understand the strengths and weaknesses of the method we intend to use. This includes the internal validity of the method. Many of the existing methods have internal validity weaknesses (Andriessen, 2004a). In addition we must have knowledge of the weaknesses of IC measurement methods in the way they work in practice. Unfortunately not much research has been done into the practical weaknesses of existing methods. Third, we must clearly understand the application domain of the method: the class of problems and the class of contexts for which the method provides solutions. What problems can it solve and for what kind of problems is it not the appropriate tool? This question is crucial to avoid pigeonholing. In what circumstances and under what conditions can it be used? This includes critical conditions for success like for example the IC-intensiveness of the company, its size, the willingness of management to be involved and the presence of the appropriate skills set to make sense of and use the results. Fourth, we must posses the necessary skills to implement the method. This is true whether one implements a method as a manager or as a consultant. As a consultant this skill-set includes basic consulting skills on communicating with and involving the
client, diagnosing the situation, creating a tailor-made solution, providing concrete directions and creating a level of equivalence. However as a manager implementing an IC measurement method requires similar skills. Implementing such a method in your own company also requires for example buy-in from important stakeholders and a proper problem diagnosis. Successfully implementing a method for the valuation or measurement of intellectual capital is not an easy task. Practitioners yet receive little support from the intellectual capital research community. Little research has been done into the factors that influence the success of a method. This paper is a first attempt at systematically identifying some of the factors for the successful implementation of an intellectual capital valuation or measurement tool. This paper focused on the KPMG Value Explorer. We need more of this kind of research about other available methods. Note 1. Give a child a hammer and, to the child, suddenly everything becomes a nail. References Andriessen, D. (2001), “Weightless wealth: four modifications to standard IC theory”, Journal of Intellectual Capital, Vol. 2 No. 3, pp. 204-14. Andriessen, D. (2004a), Making Sense of Intellectual Capital, Butterworth Heinemann, Burlington, VT. Andriessen, D. (2004b), “IC valuation and measurement; classifying the state of the art”, Journal of Intellectual Capital, Vol. 5 No. 2, pp. 230-42. Andriessen, D. (2004c), “Reconciling the rigor-relevance dilemma in intellectual capital research”, The Learning Organization, Vol. 11 Nos 4/5, pp. 393-401. Andriessen, D. and Tissen, R. (2000), Weightless Wealth: Find Your Real Value in a Future of Intangibles Assets, Financial Times Prentice Hall, London. Bontis, N. (2001), “Assessing knowledge assets: a review of the models used to measure intellectual capital”, International Journal of Management Reviews, Vol. 3 No. 1, pp. 41-60. Bontis, N., Dragonetti, N.C., Jacobsen, K. and Roos, G. (1999), “The Knowledge Toolbox: a review of the tools available to measure and manage intangible resources”, European Management Journal, Vol. 17 No. 4, pp. 391-401. De Caluwe´, L. and Stoppelenburg, A. (2003), “Organisatieadvies bij de Rijksoverheid; kwaliteit onderzocht”, Tijdschrift voor Management en Organisatie, Vol. 57 No. 1, pp. 25-52. Kerssens, I.C. (1999), “ Systematic design of R&D performance measuring systems”, thesis, University of Twente, Enschede. Luthy, D.H. (1998), “Intellectual capital and its measurement”, Proceedings of the Asian Pacific Interdisciplinary Research in Accounting Conference (APIRA), Osaka, available at: www3. bus.osaka-cu.ac.jp/apira98/archives/htmls/25.htm Ministry of Economic Affairs (1999), Intangible Assets: Balancing Accounts with Knowledge, Information Department of the Ministry of Economic Affairs, The Hague. Mouritsen, J., Larsen, H.T. and Bukh, P.N. (2001), “Valuing the future: intellectual capital supplements at Skandia”, Accounting, Auditing & Accountability Journal, Vol. 14 No. 14, pp. 399-422. Perrow, C. (1970), Organizational Analysis: A Sociological Review, Wadsworth, Belmont, CA.
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Petty, R. and Guthrie, J. (2000), “Intellectual capital literature overview: measurement, reporting and management”, Journal of Intellectual Capital, Vol. 1 No. 2, pp. 155-76. Reilly, R. and Schweihs, R. (1999), Valuing Intangible Assets, McGraw-Hill, New York, NY. Roos, G., Bainbridge, A. and Jacobsen, K. (2001), “Intellectual capital analysis as a strategic tool”, Strategy and Leadership Journal, Vol. 29 No. 3, pp. 21-6. Sveiby, K.E. (2002), “Methods for measuring intangible assets”, available at: www.sveiby.com/ articles/IntangibleMethods.htm Van Aken, J.E. (1996), “Methodologische vraagstukken bij het ontwerpen van bedrijfskundige systemen”, Bedrijfskunde, jaargang, Vol. 68 No. 2, pp. 14-22. Van Aken, J.E. (2004), “Management research based on the paradigm of the design sciences: the quest for field-tested and grounded technological rules”, Journal of Management Studies, Vol. 41 No. 2, pp. 219-46. Weggeman, M. (1995), “Creatieve Ambitie Ontwikkeling”, PhD thesis, Tilburg University Press, Tilburg.
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Intellectual capital Management approach in ICS Ltd
Management approach in ICS Ltd
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Abstract Purpose – The purpose of this paper is to demonstrate how the ICS intellectual capital methodology was developed starting from the underpinning academic theory. Design/methodology/approach – The approach is founded upon a number of theoretical strands. The basic intellectual capital approach is based on a development of the resource based theory of the firm. Most intellectual capital approaches have problems with meaningful measurement. ICS addresses the valuation of intellectual capital resources by using axiology and multi-attribute value theory to produce a valuation framework and measurement theory to ensure that the results are reliable. Findings – The ICS intellectual capital approach generates navigators (maps) of how resources are used in companies which have proven to be very useful. It has also demonstrated the value of deeper analysis of the intellectual capital resources. The measurement part, which is often used independently (known as the Conjoint Value Hierarchy (CVH), is shown as a powerful aid to decision making as well as to more straightforward valuation. Research limitations/implications – The limitations are that the navigator and its associated analyses are non-rigorous while the CVH is rigorous, transparent and auditable. This mismatch can lead to problem and the challenge is to integrate them. Originality/value – While parts have been reported previously, this paper is the first integrated review of ICS’ methodology. Keywords Intellectual capital, Measurement Paper type Research paper
The development of our concept of intellectual capital The paper begins with a review of the historical background to resource-based accounting and intellectual capital. This is then used as the starting point for a description of the intellectual capital approach used in ICS Ltd[1]. Aspects of measurement are then discussed as they apply to intellectual capital methodologies. The paper concludes with a review of the key barriers to the development of intellectual capital thinking as seen by ICS Ltd and some initial views on the important issues to be talked as matters of priority. Intellectual capital has a surprisingly long history, one founded in the meso-economics of the first third of the twentieth century which was then developed in the second third into the micro-economic (firm-based) views. Chamberlin and Robinson (Chamberlin, 1933; Robinson, 1933) and later Penrose (1959) were contributors in this early work. Schumpeter’s work of 1912 (Schumpeter, 1934) predates this work and sees the use of new resource combinations by entrepreneurs as the foundation of cyclical economic growth. However, Schumpeter’s perspective was macro-economic and invention, as distinct from innovation, was treated as exogenous to the firm. Examination of the contributions of Robinson and Chamberlin show how the concepts they developed have survived to the present. For example, Chamberlin
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identified that some of the key capabilities of firms included technical know-how, reputation, brand awareness, the ability of managers to work together and particularly, patents and trademarks, many of these are common in relatively recent strategy and marketing literature (Day, 1994; Hall, 1992). Edith Penrose’s much cited work on the theory of the growth of the firm dismissed the view that a firm was just an administrative unit and saw it instead as productive resources at the disposal of managers. She suggested that a firm is best gauged by some measure of the productive resources it employs. This led directly to the development of ideas concerning competitive advantage in the last third of twentieth century. The concept of sustainable competitive advantage based on the utilisation of resources is simple and stems from the assumption that the desired outcome of management is a sustainable competitive advantage. Sustainable competitive advantage demands the possession of certain key resources and that they have characteristics such as quality or value, high barriers to duplication and so on (Barney, 1991). Sustainable competitive advantage can be achieved if the firm effectively deploys and maintains these resources in its field or operation. The key issue and the most important feature of useful intellectual capital[2] approaches is one of exposing strategic choice. Penrose’s work provided further guidance for the development of intellectual capital as an approach to business management. For example the clear definition of what a resource can be and how it differs from activities and services is crucial. This led to the notion that services yielded by resources depend on the resources that are used. A given resource can be used in different combinations with other resources to give different services or generate a variety of other resources. Furthermore, the development of a firm is constrained to an extent by the nature and qualities of the resource it currently posses. This thinking led others to consider the development and deployment of resources (Amit and Schoemaker, 1993; Barney, 1986, Barney and Zajac, 1994, Lei et al., 1996; Schoemaker, 1992) and the relationship between resources and the scope of the firm (Chatterjee and Wernerfelt, 1991; Markides and Williamson, 1996; Prahalad and Hamel, 1990; Robins and Wiersema, 1995). The term “resource-based view of the firm” was first used in 1984 in a paper (Wernerfelt, 1984) which was later awarded the Strategic Management Journal award for best paper. It was also in this decade that the rise of the new economy gathered pace and the traditional Porterian structures were found to be inadequate to describe firms and the performances of firms even in the same industry (Cubbin, 1988; Hansen and Wernerfelt, 1989). This later observation immediately brought in researchers concerned with strategy and strategic decision making (Amit and Schoemaker, 1993; Barney, 1986, 1991; Dierickx and Cool, 1989; Lippman and Rumelt, 1982; Peteraf, 1993; Reed and DeFillippi, 1990). Interest in these issues was not confined to academics. In Sweden, consultants, company chief executives and others convened what became known as the Konrad Group[3] to review the role of resources, both tangible and intangible in value creating or maintaining sustainable competitive advantage in firms. They issued a first publication in January 1988 entitled the New Annual Report and issued their final report in 1989 presenting the first method on intangible measurement, The Invisible Balance Sheet (Den Osynliga Balansra¨kningen). The publication presents key indicators for accounting control and valuation of know-how companies.
The issue of achieving and maintaining sustainable competitive advantage by means of combining and using resources naturally leads to the question of how the goodness or suitability of the resources should be described and measured. Barney (1991) proposes four conditions: value, rareness, inimitability and non-substitutability. Grant (1991) argues that levels of durability, transparency, transferability and replicability are important while Collis and Montgomery (1995) suggest five tests: inimitability, durability, appropriability, substitutability and competitive superiority. Amit and Schoemaker (1993) go even further, producing a list of eight criteria including complementarity, scarcity, low tradability, inimitability, limited substitutability, appropriability, durability and overlap with strategic industry factors. In the development of intellectual capital at ICS, supporting thinking was also brought in, notably to assist with the treatment of value extraction from innovation (Teece, 1986) and human resources (Handy, 1989). The first methodology was published in 1997 (Roos et al., 1997). To support the work overall and give a different perspective from the Konrad Group work, the work of Itami (1987) was used. Itami’s treatment of the “mobilization” of resources being particularly helpful in the formulation of thinking of resource transformations. It was obvious at this stage that different managers in companies had different views on the importance of the resource transformations in the company and key outside stakeholders, such as investors, may take yet another view. The ability to accommodate multiple stakeholder view is therefore important. The preceding paragraphs have touched on a number of issues that need to be consolidated into a homogeneous whole if it is to yield a practical approach to business management. These can be summarized as follows: . It is strategic. . It is about all a firm’s resources. . It is about their characteristics and quality. . It is about how they are used in combinations to create value. . It is about how value is seen by a wide selection of stakeholders. . It is about how they are developed to ensure sustainable competitive advantage. The measurement of intellectual capital has been problematical but it is the acid test of a working theory. Without measurement and the ability to predict then intellectual capital as a means of managing and explaining a firm’s performance remains a hypothesis since it fails the classic test of the scientific method. A key question at this stage is to determine what should be measured. There is increasing pressure from regulators, especially the Financial Accounting Standards Board in the USA who have been pressing and moving towards the mandatory disclosure of certain elements of intangible resources and the recording of elements of goodwill in mergers and acquisition; see FAS141 (Financial Accounting Standards Board, 2001a) and 142 (Financial Accounting Standards Board, 2001b). Thus, there is a growing requirement to disclose data on intangible resources. The ideal solution is to construct a single measurement system that is comprehensive inside the firm and is modular (permitting the simple use of parts throughout the firm) and which permits the disclosure of benchmarkable data (across the business sector of the firm thereby allowing meaningful comparison in the market) without compromising strategic intent.
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When addressing intangible resources and their contribution it is clear that financial style measures are inappropriate. While some measures of intangible resources such as hiring and wage costs might be comparable, the value derived from any employee depends on how he or she is used and such is the complexity of the value contribution that ascribe a financial value to him or her is meaningless. Thus the wider concept of “value” has to be invoked. The study of value, known as axiology, moral philosophy and values stretches back to the ancient Greeks and, like many other things, seems to have lain largely dormant between the end of the classical period until renaissance times in the West. In the nineteenth century, the positivist movement sought to put science onto firm philosophical (rather than technical) foundations and in the early twentieth century, the logical positivists of the “Vienna circle” developed this further admitting only theories, methodologies or approaches that had either a basis in logic (such as mathematics) or could be “proved” experimentally (such as the other natural sciences). This second group can be problematic on the grounds of implied subjectivity. However, it can lead to the admission of axiology (see Frondizi, 1971), the study of value, into the fold of acceptability from the positivist point of view. Axiology is and must be a general approach applicable to all questions of value but it has constraints in exploitation. Fortunately, most commentators admit economic value into the fold. Zu´niga (2000) in her thesis on a general theory of value, discusses economic value and uses an example to demonstrate compliance. Both Zu´niga and, much earlier, Menger (1883) discuss the nature of “goods” and value in an economic sense and distinguish between first order goods (which have immediate value) and higher order goods (which are enablers). They also consider the issue of independence of view and argue that while value and valuations may be personal, all observers may be wrong in their valuations since there is an ultimate ex post arbiter of value which is the market. Axiology requires extension from its simple philosophical roots to enable it to be used in complex situations such as the valuation of intellectual capital in companies. Multi-attribute value theory (MAVT) (Keeney and Raiffa, 1993) is the most widely used theory in solving multi-attribute decision-making problems. Practically all approaches to multi-attribute decision making explicitly or implicitly make use of the concept of the relative importance of criteria the weights of criteria or attributes within a hierarchical system of value. While there are many approaches to the issues of weighting or determining the relative importance of attribute, the pair-wise comparison methodology of Saaty (1980) has been found to give good results and is widely used. The foundation of measurement is measurement theory, a branch of applied mathematics. If it is adhered to then reliable measures can be obtained which mangers can use. If it is not, and adherence appears uncommon in non-financial measurement, then what results is a selection of probably misleading results of no real use to managers. While indicators, as distinct from proper measures, have their place as rough guides, care has to be exercized in their use. Table I compares measures and indicators. ICS has developed a simplified indicator system known as the IC Index for internal managerial use but this is not discussed in this paper, since it is extensively covered in other publications (Roos et al., 1997; Roos and Jacobsen, 1999, Bontis et al., 1999; Bontis,
Measurement system Advantages Accurate if built properly Produces a complete view of the object Data can be disclosed Results can be benchmarked Can be the basis of derived measures Can be used with other business models Transparent and auditable Takes multiple views of value into account Disadvantages Takes care and time to set up Data requirement can be large Data quality requirements are stringent
Indicators Quick to build Easy to operate
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Purpose specific Cannot be benchmarked with safety Takes a single “average” view of value Cannot be aggregated to value complex objects Possibility of duplication
2000; Pike and Roos, 2000; Neely et al., 2002; Marr et al., 2002; Pike and Roos, 2004; Marr et al., 2004). Measurement theory has its very early roots in ancient Greece but the ideas of the modern theory of measurement date from the ninteenth century work of Helmholtz(1887) and others. However, its formalisation is a surprisingly recent event. The catalyst for the formalization of measurement theory is generally accepted to be the psychologist S.S. Stevens (Stevens, 1946), but it was not until the 1960s that measurement was fully axiomatized with the publications of Scott and Suppes (1958) and Suppes and Zinnes (1963). In pragmatic applications of measurement theory, a multi-stage process is generally involved in which the representation of the object to be measured and the measurements system are kept separate. In the first stage, an empirical relation system is specified to define the relations among the attributes of the studied entity, in this case, the company. Second, an isomorphic numerical relation system is defined, to provide values for the measures of the attributes and relations among these values. The general form of these measurement systems is hierarchical with a precisely defined business value context at the top. The pragmatic rules and requirements of an empirical relation system suited to modern business systems have been set out by M’Pherson and Pike (2001) and extended later (Pike and Roos, 2003). In the empirical system, there are two rules that must be observed: (1) That at every level of detail, the sum of the meanings adds up and completely describes the meaning of the company and what it does. This is the condition of “completeness” and ensures that nothing (important) is missed out. (2) Every attribute at every level is independent in terms of meaning from every other at the same level. This is the condition of “distinctness” and prevents double counting. M’Pherson and Pike (2001) then continue with the numerical isomorph in which real measures are found which satisfy the desired measures in the empirical system.
Table I. Comparison of proper measurement and indicators
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Unfortunately, some of the attributes of value that should be measured are hard to observe in practice and proxies must be used for them. . That the proxies are “agreeable” and do not change the meaning of the attributes above them in the hierarchy by violating either of the first two rules. This means that the real measurement system truly reflect the intention of what is to be measured. . The “commensurability” condition ensures that data is all on the same 0 to 1 scale and is all defined on a ratio scale. This means that there is no chance that any of the results or later statistical post-processing are invalidated because of poor data. . The mathematical constraints on the aggregation function are often lumped under and “independence” banner and assure that ill-conceived combination schemes which would produce wrong answers are not permitted. By combining measurement theory with axiology and multi-attribute value theory it is possible to develop bespoke or general measurement systems to measure business performance and “account” for the value in intangible resources and their use. Our concept of intellectual capital ICS defines intellectual capital[4] as any intangible resources or transformations of those resources, which are under some level of control of the company that adds to a company’s value creation (Roos et al., 1997). Resources The researchers of the 1980s and early 1990s produced a variety of definitions. It is generally agreed that tangible resources are either monetary or physical, are owned by the firm, behave with diminishing returns and can be valued with reasonable agreement according to generally accepted accounting principles. Intangible resources in contrast may or may not be owned by the firm, they might only be partially under the firm’s control, may they have complex return characteristics and they are hard to value. Indeed, Edvinsson and Malone (1997), while credited with equating market value to the sum of intellectual capital resources and tangible resource results, were not the first to suggest such an erroneous relationship. Grant (1991) suggested that the IP of pharmaceutical companies as tradable entities was the cause of the significant difference between market value and book value. The definition of resources must surely be one of the most embarrassing features of the intellectual capital movement. Over the past ten years there have been a great number of formulae for describing intangible resources with few exploring much below a level where there are two groups of tangible resources and another two or three groups of intangible resources. None so far have backed their definitions with semantics or mathematics. This means that methodologies are not interchangeable and comparisons cannot be made between any two firms, as there is no guarantee that like resources are being compared. The ICS methodology has a hierarchical menu of resources starting with “level 1” which comprises monetary, physical, human, organizational and relational resources. These are defined by a set of underlying resources at finer levels of granularity. Thus the menu continues with some 30 standard “level 2” resources which are supported in
turn by up to 100 firm-specific “level 3” resources. While the list may seem a long one, it is customized for different firms at level 3 only. Furthermore, while “level 3” resources are used in the precise definition of the “level 2” resources, in practice, it is usually unnecessary to consider more than about 25 resources since the efficiency, effectiveness, opportunities, threats to the firm are almost always visible even at this level of granularity. Although the definitions are not semantically validated, some effort has been expended in making their meanings complete and distinct, as this is a pre-requisite for later measurement activities. Resources, and some of their general characteristics, are shown in Figure 1, a telecom company providing the example. They are arranged in what is known as a distinction tree but it should be remembered that this is really nothing other than a presentational device since it is the combinations of and interactions between different resources that is important in the creation of value in firms.
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Resource characteristics ICS has adopted a variant of Barney’s (1991) resource descriptions. In practice, the influences of the resource characteristics in creating value are not uniform. For some types of resource such as intellectual property, inimitability, non-substitutability may be of much greater importance than other characteristics whereas for human resources, inimitability is often impossible to attain and is therefore not an important issue. This means that two measures of goodness for each of the resource characteristics must be found. These are: how important is each characteristic and how does the resource perform against a reasonable test of goodness. ICS takes a weighted average of the results to illustrate results but retains the more complete data.
Figure 1. Resources and their characteristics for a telecom company
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Resource deployment As stated by Penrose (1959) and noted above, it is the combinations and use of resources that generates value through the creation of other saleable resources or services. It is important to realize that there is no correlation between the amount of a given resource that the organization has at its disposal and the value that the organization can create as the efficiency of deployment and the quality of the resources, not to mention the actions of competitors, are all factors which will affect the generation of value. All resources in an organization are interconnected in some way or other and value is created through the transformation of one resource into another, for example, products to money, creativity to new processes, relationships to reduced search costs, brands to increased revenues and so on. The requirement in the analysis of a firm is to identify and evaluate the firm’s unique transformations structure. However, there are two problems that are encountered: (1) Few of the firm’s resources are additive in the way they are “used”. For example, doubling the number of people does not double the human resource value. This means that the relationship between the amounts of resources involved in a transformation and the amount of the resource(s) produced is complex. (2) Outwardly similar transformations may actually be rather different in detail. For example, parallel and similar production lines may be dependent on resource quality to very different degrees. This makes backward interpretations of aggregated results problematical and small volume transformations may be missed altogether even though they are important. The conclusion is that it is easier and safer to avoid an attempt at a full explanation of value creating processes in firms but rather to confine the analysis to the principles of what is occurring, that is, to look for what is important rather than attempting an exhaustive numerical illustration. Experience shows that a wealth of informative results can be obtained by analysing what is important rather than undertaking a detailed analysis. The issue of inadvertently ignoring small volume but high importance transformations because they seem insignificant beside high volume, low importance activities (like repetitive production) is obviously fundamental and is avoided. The Intellectual Capital Navigator (ICN) is a numerical and visual representation of how management views the deployment of resources to create value in the organization. The ICN is about identifying transformations from one resource into another. It is important to remember that all transformations are possible although in a given organization, they are not all relevant. Based on our experience, in an analysis of a firm which has been described by 25 resources, for example, only a few, possibly less than 20 of the 625 possible transformations will be important. Figure 2 shows a simple and generalized transformation matrix at “level 1” with examples of all 25 possible transformations. The matrix is turned into a numerical representation of the firm in a two-step process. The first step is to consider resources with managers with broad experience of the form and seek their views on the relative importance of the resources in creating value for the firm. This is necessarily a top-level appreciation but serves to weight the lower level and more detailed views of
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Figure 2. Generic resource transformations at “level 1”
functional managers. The second stage is to consider resource utilization with functional managers possessing a localized but deep knowledge of parts of the firm. In this way a numerical matrix with numbers representing the importance of resources in creating value is developed. The matrices developed in this way are frequently sparse as many of the possible transformations are irrelevant or impossible in a given firm. Figure 3 shows a numerical ICN at “level 1”. The figures were taken from the analysis of the research department of a pharmaceutical company and it is clearly obvious how important human resource (laboratory competence) and organizational resources (protected intellectual property) are in the creation of value. Navigators can be presented at any level of granularity and with any level of filtering. Filtering of the results is undertaken to remove clutter from the picture by excluding transformations relatively unimportant to company managers. When the navigator is drawn, analysis is then possible. Several features can be seen in navigators but perhaps the most obvious and interesting is the adherence or otherwise to the recognized forms of firm value creating architecture (Stabell and Fjeldstad 1998). Stabell and Fjeldstad (1998) postulate three basic firm architectures: the (Porterian) value shop typical of production orientated companies, the value shop typical of professional service companies and the value network typical of market facilitating companies. Production orientated companies are typified by important physical to physical transformations supported by human and organizational to physical influences. Professional service companies exhibit a triangular structure involving human, organizational and relational resources, that is, a learning process. Market facilitating companies can either be physically based (such as the physical resources of a telecom firm) or organizationally based (such as the software and processes of an
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Figure 3. Numerical navigator of the research department of a pharmaceutical company
on-line market place). While the underlying structure is variable, all market facilitating companies have a strong relational to relational transformation representing transactions with relational to monetary and relational to human links representing earning and feed-back respectively. Of course, the degree, balance and completeness of these basic structural forms are a measure of how effectively the company is following its chosen operating form. Figure 4 shows the “level 1” navigator from the pharmaceutical company and the triangular pattern involving human, organizational and relational resources is at once evident although the involvement of the relational resources is weak suggesting that research direction is exercized by managerial whim rather than by market force. In the figure, the size of the circles represents the importance of the resource. The arrows represent the transformations and the thickness of the lines represents the importance of the transformations. Further analyses based on the navigator matrix can be carried out. One of the most revealing is to inspect the balance between how influential a resource is in a firm and the extent to which it is influenced by other resources. Resources with a ratio greater than unity are sources of value since they influence more than they are influenced. Resources with a ratio less than unity are termed sinks of value. The ratios for all resources (usually expressed as the logarithm of the ratio) can be plotted against the absolute importance of the resources in what is known as an “effector plot”. Figure 5 shows the effector plot for the pharmaceutical company at “level 2”. The ratio is on the vertical axis and the absolute importance is on the horizontal axis. The grey zones are zones which together (arbitrarily) occupy 25 per cent of the plot area and concern the more important resources. The upper triangle represents resources which are
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Figure 4. Level 1 navigator for the pharmaceutical company R&D department
Figure 5. The “level 2” “effector” plot for the pharma R&D department
important and a great source of value while in the lower triangle they are important but are significant sinks of value. Resources in the grey zones are worth of further analysis and our analysis is as follows: The appearance of resources in the lower grey zone is an important question since it points to major areas of inefficiency in the firm. Important resources (as they
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Figure 6. Hot-spot plot for the pharma R&D department
appear to the right) are not generating value. In the top grey zone the issues are completely different since an effective firm is one that derives value from its important resources. It is intuitively obvious that average or trend of the placement of the resources on this plot should be from bottom-left to top-right although actual dispositions are always going to deviate away from this “ideal”. What is important for resources in the top-right triangle is that they are robust and truly are the source of sustainable competitive advantage that is required. It is here that the analysis of resource characteristics can be overlaid with the navigator matrix data to yield a modified “effector” plot which is referred to as a “hot-spot” plot. Resources which are not robust require rapid management action to ensure sustainable competitive advantage. Figure 6 shows a hot-spot plot. In this plot, only the more important resources only have been highlighted and the plot now contains markers with black, grey or white points. Black points represent resources whose weighted average robustness was 0.333 or less. Grey resource points have a robustness score greater than 0.333 but less than 0.666 and white resource points have robustness scores of greater than 0.666. Clearly this company has issues with the robustness of the R&D budget and their managers. Finances are perennial favourites for scrutiny but with the managers it was found that there was an unacceptably large turnover rate and, given their mode of influence in the firm, became a priority area for detailed analysis and improvement. So far, the intellectual capital analysis of the firm has taken the form of a snap-shot view with no consideration of the strategic intent of the company. Earlier in the paper it was noted that the resource-based view of the firm encompassed a number of issues, one of these was the strategic nature of the approach. In order to set targets and elucidate development pathways a forward-look is required, that is, a management view of what is intended for the future (which may involve divestment, organic growth, acquisitions, mergers or combinations of some or all of them). To accommodate the strategic intent, a second navigator matrix representing the desired end state can be constructed and analyzed in parallel with the current position. Targets and measures will be considered in the next section but the link between the two states can be visualized using a second variant of the “effector” plot. In this case,
the current and target positions can be plotted together with any number of intermediate conditions which will arise in time as the firm transitions along its chosen path from the current to the end position. The crucial feature of the analysis is to ensure that at no time is there any serious and unintentional incursion into the grey zones on the effector plot. In effect, a trajectory for each resource is drawn. Assuming there are no problems with trajectories, the ICS methodology continues by addressing measurement. This is carried out in one of two ways depending on whether simple “rough” internal targets are sufficient or whether an accurate appraisal is needed, possibly up to the standard required for disclosure. Measurement and reporting Over the last 10-15 years, a great number of systems have been devised to help managers with business performance and with a special emphasis on non-financial measures. According to Luthy (1998) and Williams (2000), methodologies may be categorized into four groups. These are: (1) Direct intellectual capital methods (DIC). (2) Market capitalization methods (MCM). (3) Return on assets methods (ROA). (4) Scorecard methods (SC). Sveiby (2004) uses this classification system, but without the distinction of a proper measurement group, or MS group, and has generated a list of methodologies. MS is an approach that aims at completeness and reliability with an explicit treatment of all aspects of intangible value. Of the others, the MCM and ROA approaches have an element of rigour in that they rely on financial figures which, if not perfect, are auditable. DIC, and to a lesser extent SC methods, offer the potential to create a more comprehensive picture of an organization’s health than financial metrics, since they can be easily applied at any level of an organization. This is because they are directly aimed at management support and DIC is intended to be holistic. Unfortunately, none of these approaches complies with measurement theory (Pike and Roos, 2004) and can at best be indicator systems. ICS employs two approaches to measurement, one is an indicator system belonging roughly to the DIC category and the second is a system known as CVH (Conjoint Value Hierarchy) which is a fully compliant measurement system (MS category) used to measure the value contribution of intangible resources. The CVH is based upon relational measurement theory in which an empirical system is developed to model the intellectual capital in the company and then a numerical isomorph is produced to calculate the results. Figure 7 shows the empirical relation system for a fast-food company operating through franchized restaurants. This structure was developed according to the methodology described above and converted into an operational numerical system. In this case, the weightings used were provided by a group of external stakeholders; a group of market analysts. Thus the stakeholder group as would normally be defined by Agle et al. (1999) was incomplete. It should also be noted that the structure shown in Figure 7 contains few attributes described using the language found in Figure 1. This is especially true in the instrumental group above the uppermost black line. The reason for this is that in most
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Figure 7. CVH measurement structure for a fast food company
cases, the value of an intangible resource stems not from its ownership or control but from its use. Thus what is measured is the value created as an outcome of normal firm activities. To see the intellectual capital resources at work required the inspection of the underlying navigator. However, it should be noted that in a study concerning what investors wanted to know about a firm carried out by Mavrinac and Siesfeld (1997), no immediately recognizable intellectual capital resource appeared in the top 10 items investors wanted disclosed. The CVH methodology was applied in a case study of a fast food company with the objective of determining the value of the company in question, taking only the perspective of the (share price) market makers. The purpose of this example is to demonstrate measurement of intangible resources and the predictive capabilities of the CVH in strategic issues. In the case study, the company was subjected to three possible futures in order to determine the robustness of its present configuration. In addition to the possible futures, a “do nothing” base run was carried out as a comparator. The four runs were as follows: (1) A base run in which no parameters were altered. (2) Scenario 1 in which advertising was cut by 15 per cent (3) Scenario 2 in which there was a 40 per cent cut in advertising and 62.5 per cent of this saving devoted to improving human resources through training.
(4) Scenario 3 in which in addition to the changes in Scenario 2, there was a one-off spend of 37.5 per cent of the saved money applied to organizational capital in the form of market research in the second year of the simulation and then that 37.5 per cent saving is returned to shareholders in the third year and thereafter. These four runs constituted a logical comparable series. This means that the results from the three scenario runs could be compared against each other and also against the base run. In particular, it allows for direct comparison of the cost of the options and the value benefits gained. This is a true calculation of value for money involving intellectual capital assets. The value for money plots for the firm are shown in Figure 8. The plots show combinations of the financial output with the output from the CVH at the top level. The top graph in each group shows the change in value for the scenario compared with the base run. The middle graph shows the total revenue change between the base run and the scenario in billions of dollars on the left-hand y-axis while the net saving for each scenario in millions of dollars is shown on the right-hand axis of the top graph. The lower graph of each scenario trio shows the quotient of the value change and the spending change: . Scenario 1. From the left-hand column of plots, it can be seen that there is an initial slight fall in value followed by a recovery. There is also a modest growth in income over the period of the simulation coupled with an overall discretionary spend saving over the period. Over the same period, as described above, there is a very slight value change leading to a near level value for money change. . Scenario 2. The second column of graphs shows the results for Scenario 2 and show the loss of top-line benefit compared to Scenario 1 stemming from the loss of brand presence. This is compounded by worse value results leading to a negative value for money across the time period. Clearly this is a poor option. . Scenario 3. The third column shows stronger top-line growth and a positive value for money change making this the best value for money option of the three scenarios. Figure 8 shows that the CVH can differentiate between strategic options for real companies and express the results with meaningful calculations of value and value for money. In the example, the company would clearly not consider Scenario 2 further due to the negative value for money outcome and Scenario 1 is probably too mild to make the meaningful improvement required. Scenario 3 would be investigated in further detail and with other changes to optimize the value for money outcome and to establish the robustness of the scenario. Given that the CVH fulfills the requirements of reliability and repeatability, it is also a viable method to use in reporting intangible resources. It can supply standardized information within market sectors and permit comparison while at the same time protecting data that are sensitive to the company (Pike et al., 2003). Reflections on ICS’s approach The paper has demonstrated the development of ICS’s approach and its theoretical foundations. In common with all IC approaches, the IC Navigator[5] (Fernstro¨m et al., 2005) and the indicator system used with it known as the IC Index are strategic
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Figure 8. Value for money of thethree scenarios compared to the base run
approaches rather then specific measurement approaches. The CVH, originating from measurement theory and axiology is a high-precision measurement approach. As a consequence the applications of these approaches are different since they address different issues. ICS often encounters concerns client expectations about what they can do with the approaches and the results that each gives. Table II compares some of the things that the approaches can and cannot do.
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The future of intellectual capital Although we have seen that the roots of intellectual capital stretch back over 70 years to the economists of the 1930s, for most people the awakening to intellectual capital came with Stewart’s article in 1994 (Stewart, 1994). Intellectual capital was born with a wealth of literature and learning behind it. The fields of economics, strategy theory, decision making, philosophy and mathematics were all well developed and available to support serious thought about the management and measurement of firms using the new intellectual capital approach. In the ten years that have passed since Stewart’s article it may fairly be claimed that progress has been slow at best. The evidence for this is that the number of firms using intellectual capital methodologies world-wide is very low and those disclosing intellectual capital in reports is even lower. It would be unrealistic to pretend that the methodologies developed in ICS are the final word in intellectual capital methodologies. From our consulting experience, we believe that the challenges that beset the intellectual capital movement are just two in number but are of such importance that they have contributed significantly to the poor take-up of intellectual capital as a management approach. The first is the dire situation regarding taxonomies. For the last ten years and even now, many papers and presentations involving intellectual capital contain sections devoted to defining the terms being used. Even at the coarsest level (“level 1”) there are many different categorisations of resources, for example customer capital versus What it can do IC Navigator/IC Index Provide a simple, clear and useful appreciation of how business work and could work (Navigator) thereby provide input into any strategic discussion Provide simple and clear sets of targets for managers (Index) Be applied to parts or all of a company Be applied to any organization whether profit driven or not
What it cannot do Measurement since the IC-Index is indicator based Integrate directly with financial measures
CVH Provides a detailed and accurate appreciation of the value of Be extended simply so that unplanned and radical alternatives can be assessed a company Provide multiple stakeholder views Integrate with any other model Used for disclosure purposes Be applied to all or parts of a company or an industry Measure things other than companies.
Table II. Applications of the ICS methodologies
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relational capital, organizational capital versus structural capital and so on. To an extent, this might be considered a presentational issue since “level 1” is not a working level, however, there is little agreement at “level 2” on terminology amongst the DIC or SC type methodologies. If firms are to embrace intellectual capital in larger numbers, generally understood definitions will be needed and the process of standardization must include business schools as well as firms. Furthermore, if firms are to disclose elements of their intellectual capital resources and their usage in the future then the need for agreement on the definitions of terms (avoiding lacunae or overlaps in meaning) is even stronger. The second challenge stems from the introductory section of this paper. That is that there is a broad field of contributory disciplines embodied in intellectual capital even as it stands today. What is certain is that the development path for intellectual capital will include at least all of these and probably more and this is especially true in the area of measurement. The solutions to the challenges facing the intellectual capital movement will be found by bringing in techniques from other disciplines. The task is not an easy one, many hypotheses involving external contributions will have to be attempted before anything like a complete and secure transit of Bacon’s scientific pathway of inductive reasoning is completed. But completed it will have to be if intellectual capital is not to fade. Notes 1. Intellectual Capital Services Ltd is a consulting company and think-tank with business offerings in, amongst others, the areas of strategic management. In common with other small companies, it has sought intellectual excellence and rigour as a distinct feature. Thus the methodologies ICS Ltd develops stand on sound academic footings. 2. The term “intellectual capital” was first used by John Galbraith (Canada) in 1969, in a letter to Michael Kalecki, although the meaning intended by Galbraith was undoubtedly different from that in use today. 3. They called it the Konrad Group because they first met on 12 November 1987 and 12 November is Konrad Day in the Swedish calendar. 4. For those interested in a more detailed outline of these approaches and how to apply them using numerous real life cases we refer the reader to the book entitled Managing Intellectual Capital in Practice by Roos et al. (2005). 5. The IC Index is an indicator system based on IC Navigator analyses at the present time and at some key point in the future. The IC Index system identifies the key resources and transformations that must be measured to ensure they follow the desired trajectory between the present and the future. Results are produced at several levels of detail. References Agle, B., Mitchell, R. and Sonnenfield, J. (1999), “What matters to CEOs? An investigation into stakeholder attributes and salience, corporate performance and CEO values”, Academy of Management Journal, Vol. 42 No. 5, pp. 507-25. Amit, R. and Schoemaker, P. (1993), “Strategic assets and organisational rent”, Strategic Management Journal, Vol. 14 No. 1, pp. 33-46. Barney, J. (1986), “Strategic factor markets: expectations, luck and business strategy”, Management Science, Vol. 32 No. 10, pp. 1231-41.
Barney, J. (1991), “Firm resources and sustained competitive advantage”, Journal of Management, Vol. 17 No. 1, pp. 99-120. Barney, J. and Zajac, E. (1994), “Competitive organisational behaviour: toward an organisationally-based theory of competitive advantage”, Strategic Management Journal, Vol. 15, pp. 5-9. Bontis, N. (2000), “Assessing knowledge assets: a review of the models used to measure intellectual capital”, Framework Paper 00-01, Queen’s Management Research Centre for Knowledge-Based Enterprises, Queen’s School of Business, Queen’s University at Kingston, April. Bontis, N., Dragonetti, N.C., Jacobsen, K. and Roos, G. (1999), “The knowledge toolbox; a review of the tools available to measure and manage intangible resources”, European Management Journal, Vol. 17, August. Chamberlin, E. (1933), The Theory of Monopolistic Competition, Harvard University Press, Cambridge, MA. Chatterjee, S. and Wernerfelt, B. (1991), “The link between resources and type of diversification: theory and evidence”, Strategic Management Journal, Vol. 12 No. 1, pp. 33-48. Collis, D. and Montgomery, C. (1995), “Competing on resources: strategy in the 1990s”, Harvard Business Review, Vol. 73, July-August, pp. 118-28. Cubbin, J. (1988), “Is it better to be a weak firm in a strong industry or a strong firm in weak industry?”, No. 49, Centre for Business Strategy, London Business School, London. Day, G. (1994), “The capabilities of market-driven organisations”, Journal of Marketing, Vol. 58, pp. 37-52. Dierickx, I. and Cool, K. (1989), “Asset stock accumulation and sustainability of competitive advantage”, Management Science, Vol. 35 No. 1, pp. 1504-11. Edvinsson, L. and Malone, M.S. (1997), Intellectual Capital: Realizing Your Company’s True Value by Finding Its Hidden Brainpower, Harper Collins, New York, NY. Fernstro¨m, L., Pike, S. and Roos, G. (2005), Den Verdiskapende Organisasjonen: Intellektuell Kapital i Praksis, Fagborforlaget (forthcoming). Financial Accounting Standards Board (2001a), Business Combinations, FAS 141, FASB, Norwalk, CT. Financial Accounting Standards Board (2001b), Goodwill and Other Intangible Assets, FAS 142, FASB, Norwalk, CT. Frondizi, R. (1971), What is Value?, Open Court Publishing, La Salle, IL. Grant, R. (1991), “The resource-based theory of competitive advantage: implications for strategy formulation”, California Management Review, Vol. 33 No. 3, pp. 114-35. Hall, R. (1992), “The strategic analysis of intangible resources”, Strategic Management Journal, Vol. 13 No. 2, pp. 135-44. Handy, C. (1989), The Age of Unreason, Century Hutchinson, London. Hansen, G. and Wernerfelt, B. (1989), “Determinants of firm performance: the relative importance of economic and organisational factors”, Strategic Management Journal, Vol. 10 No. 5, pp. 399-411. Helmholtz, H. (1887), Zahlen und Messen, in Philosophische Aufsatze, Fues’s Verlag, Leipzig. Itami, H. (1987), Mobilizing Invisible Assets, Harvard University Press, Cambridge, MA. Keeney, R. and Raiffa, H. (1993), Decisions With Multiple Objectives: Preferences and Value Tradeoffs, Cambridge University Press, Cambridge.
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Lei, D., Hitt, M. and Bettis, R. (1996), “Dynamic core competences through meta-learning and strategic context”, Journal of Management, Vol. 22 No. 4, pp. 549-69. Lippman, S. and Rumelt, R. (1982), “Uncertain imitability: an analysis of interfirm differences in efficiency under competition”, Bell Journal of Economics, Vol. 13, pp. 418-38. Luthy, D. (1998), “Intellectual capital and its measurement”, Proceedings of the Asian Pacific Interdisciplinary Research in Accounting Conference (APIRA), Osaka, Japan. M’Pherson, P. and Pike, S. (2001), “Accounting, empirical measurement and intellectual capital”, Journal of Intellectual Capital, Vol. 2 No. 3, pp. 246-60. Markides, C. and Williamson, P. (1996), “Corporate diversification and organisational structure: a resource-based view”, Academy of Management Journal, Vol. 39 No. 2, pp. 340-67. Marr, B., Gray, D. and Schliuma, G. (2004), “Measuring and valuing intangible assets – what, why, and how”, in Bourne, M. (Ed.), Handbook of Performance Measurement, Gee, London. Marr, B., Schiuma, G. and Neely, A. (2002), “Intellectual capital – defining key performance indicators for organisational knowledge assets”, Business Process Management Journal, (special issue on “Performance measurement in the twenty-first century”). Mavrinac, S. and Siesfeld, G. (1997), “Measures that matter”, Enterprise Value in the Knowledge Economy, Ernst & Young Center for Business Innovation. Menger, C. (1883), “Problems of economics and sociology”, translated by Nock. F. (1963), University of Illinois and published in 1985 in Investigations into the Methods of the Social Sciences with Special Reference to Economics. Neely, A., Gray, D., Kennerley, M. and Marr, B. (2002), Measuring Corporate Management and Leadership Capability, Cranfield School of Management, Cranfield, report commissioned by the Council for Excellence in Management and Leadership from the Centre for Business Performance, Cranfield School of Management Centre for Business Performance, Cranfield. Penrose, E. (1959), The Theory of Growth of the Firm, Blackwell, Oxford. Peteraf, M. (1993), “The cornerstones of competitive advantage: a resource-based view”, Strategic Management Journal, Vol. 14 No. 3, pp. 179-91. Pike, S. and Roos, G. (2000), “An introduction to intellectual capital”, Works Institute Journal, Vol. 42, October, pp. 21-7 (in Japanese). Pike, S. and Roos, G. (2004), “Mathematics and Modern Business Management”, Journal of Intellectual Capital, Vol. 5 No. 2, pp. 243-56. Pike, S., Rylander, A. and Roos, G. (2003), “Intellectual capital management and disclosure”, in Bontis, N. and Choo, C.W. (Eds), The Strategic Management of Intellectual Capital and Organizational Knowledge: A Selection of Readings, Oxford University Press, Oxford. Prahalad, C. and Hamel, G. (1990), “The core competence of the corporation”, Harvard Business Review, Vol. 68, May-June, pp. 79-91. Reed, R. and DeFillippi, R. (1990), “Causal ambiguity, barriers to imitation and sustainable competitive advantage”, Academy of Management Review, Vol. 15 No. 1, pp. 88-102. Robins, J. and Wiersema, M. (1995), “A resource-based approach to the multi-business firm: Empirical analysis of portfolio interrelationships and corporate financial performance”, Strategic Management Journal, Vol. 16, pp. 277-99. Robinson, J. (1933), The Economics of Imperfect Competition, Macmillan, London. Roos, G. and Jacobsen, K. (1999), “Management in a complex stakeholder organisation; a case study of the application of the IC-process to a Branch of the Commonwealth Public Service”, Monash Mt. Eliza Business Review, Vol. 2 No. 1.
Roos, G., Burgman, R., Pike, S. and Fernstro¨m, L. (2005), Managing Intellectual Capital in Practice, Elsevier, Amsterdam. Roos, G., Edvinsson, L., Roos, J. and Dragonetti, N.C. (1997), Intellectual Capital: Navigating the New business Landscape, Macmillan, London. Saaty, T. (1980), The Analytical Hierarchy Process: Planning, Priority Setting, Resource Allocation, McGraw-Hill, New York, NY. Schoemaker, P. (1992), “How to link strategic vision to core capabilities”, Sloan Management Review, Vol. 34, pp. 67-81. Schumpeter, J. (1934), The Theory of Economic Development, 1912, Harvard University Press, Cambridge, MA, trans Redvers Opie (first published 1912). Scott, D. and Suppes, P. (1958), “Foundational aspects of theories of measurement”, Journal of Symbolic Logic, Vol. 23, pp. 113-28. Stabell, B. and Fjeldstad, Ø. (1998), “Configuring value for competitive advantage: on chains, shops, and networks”, Strategic Management Journal, Vol. 19 No. 5, pp. 413-37. Stevens, S.S. (1946), “On the theory of scales of measurement”, Science, p. 103. Stewart, T. (1994), “Your company’s most valuable asset: intellectual capital”, Fortune Magazine, October (cover story). Suppes, P. and Zinnes, J. (1963), “Basic measurement theory”, in Luce, R., Bush, R. and Galanter, E. (Eds), Handbook of Mathematical Psychology, Vol. 1, Wiley, New York, NY. Sveiby, K.-E. (2004), “Methods for measuring intangible assets”, available at: www.sveiby.com/ articles/IntangibleMethods.htm Teece, D. (1986), “Profiting from technical innovation: implications for integration, collaboration, licensing and public policy”, Research Policy, Vol. 15 No. 6, pp. 285-305. Wernerfelt, B. (1984), “A resource-based view of the firm”, Strategic Management Journal, Vol. 5, pp. 171-80. Williams, M. (2000), “Is a company’s intellectual capital performance and intellectual capital disclosure practices related? Evidence from publicly listed companies from the FTSE 100,” paper presented at McMasters Intellectual Capital Conference, Hamilton, January. Zu´niga, G. (2000), “A general theory of value: axiology in the Central European philosophical tradition”, PhD thesis, State University of New York, Buffalo, NY.
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An integrated framework for visualising intellectual capital Christina Boedker
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School of Accounting, University of New South Wales, Sydney, Australia
James Guthrie School of Accounting, University of Sydney, Sydney, Australia, and
Suresh Cuganesan Macquarie Graduate School of Management, Macquarie University, Sydney, Australia Abstract Purpose – The purpose of this article is to trace the techniques and consulting methods developed and deployed by an Australian project team during an investigation of a client organisation’s intellectual capital management, measurement and reporting (ICMMR) practices. The article aims to highlight the benefits of adopting an integrated approach to investigating intellectual capital (IC) and proposes the Intellectual Capital Value Creation (ICVC) framework as an analytical model for extending the breadth and depth of existing management consulting and research practices into ICMMR. Design/methodology/approach – The methods deployed by the project team during the consulting project included semi-structured interviews and content analyses. Furthermore, the ICVC framework was developed and deployed as an analytical model to facilitate the investigation of the client organisation’s ICMMR practices. Findings – To the client organisation, the ICVC framework proved beneficial in that it enabled senior management to visualise their knowledge resources and how these contribute to organisational value creation. To the project team, the ICVC framework facilitated the identification of organisational knowledge management gaps, highlighting weaknesses in the client organisation’s utilisation of its knowledge resources. The framework provides a structured approach for investigating organisations’ ICMMR practices and locating and analysing these within a strategic context. Originality/value – The paper highlights to management consultants and others the importance of investigating client organisations’ ICMMR practices in an integrated manner and demonstrates to organisations the strategic significance of making “visible” their invisible sources of value creation. Keywords Intellectual capital, Knowledge management, Management consultancy, Research methods, Project teams, Australia Paper type Case study
1. Introduction Intellectual capital (IC) and related knowledge resources are much featured items on the agendas of business executives and public policy makers. Questions in foci pertain
Journal of Intellectual Capital Vol. 6 No. 4, 2005 pp. 510-527 q Emerald Group Publishing Limited 1469-1930 DOI 10.1108/14691930510628799
The authors would like to thank the NSW Department of Lands for participating in the project and for their financial support and commitment, the Australian Government Consultative Committee on Knowledge Capital for their ongoing support, the Centre for Management of Knowledge Capital at Macquarie Graduate School of Management, the Macquarie University external collaborative grant research scheme, Melissa Jamcotchian and Fiona Crawford for their research and editorial assistance and the anonymous reviewers of this paper for their constructive comments.
to “what constitutes IC?”, “how to strategically manage knowledge resources?”, “how to establish guidelines for reporting IC?”, and “how to value and measure such ‘invisible’ organisational resources?”. The growing interest in IC is driven by a broader range of socio-economic changes pertaining to increasingly sophisticated customers, the surge in service based industries, changing patterns of interpersonal activities and the emergence of the network society, being digital, virtual and interconnected (Petty and Guthrie, 2000; Ordo´nez de Pablos, 2002; Fincham and Roslender, 2003). These broader socio-economic changes have implications for how organisations manage their resources and are causing a shift in organisational value drivers, with knowledge resources taking precedence over traditional physical resources in the pursuit of competitive advantage (Marr et al., 2004, p. 312). However, despite the growing acknowledgement of the strategic significance of IC, there is limited understanding of how organisations manage, measure and report their knowledge resources (Guthrie, 2001; Fincham and Roslender, 2003). Roos (2005, p. 2) explains that “despite the widely recognised importance of IC as a vital source of competitive advantage, there is little understanding of how organisations actually create IC by dynamically managing knowledge”. There is a growing need to provide practical examples illustrating how organisations manage, measure and report their knowledge resources, how they benefit from doing so and how they may improve their ICMMR activities and capabilities. It is essential to “gain a better conceptual and operational appreciation of what it means to strategically manage knowledge for sustained competitive advantage” (McCann and Buckner, 2004, p. 61). To management consultants and researchers, this requires the development of new analytical models, research techniques and staff competencies. This paper addresses this need and outlines how an Australian project team investigated a client organisation’s ICMMR practices. The overarching objective of the paper is to outline the techniques and consulting methods developed and deployed by the project team during the IC project. This is achieved through a number of sub-objectives, pertaining to: . a classification of IC and definitions of knowledge management (KM) and related KM activities; . a review of the analytical framework and consulting methods deployed to investigate the client organisation’s ICMMR practices; and . an outline of the outcome of the analyses, illustrating the client organisation’s knowledge management gaps. The paper proposes the Intellectual Capital Value Creation (ICVC) framework as an analytical model for investigating client organisations’ ICMMR practices and highlights, via illustrations from its application, its relevance, use and potential impact. The ICVC framework was particularly beneficial in that it made “visible” the client organisation’s invisible sources of value creation and facilitated the identification of three knowledge management gaps. The paper is structured as follows. Section 2 introduces the client organisation and the consulting objectives. Section 3 provides a brief review of contemporary activities and trends in the field of IC. Section 4 outlines perspectives on, and definitions of, IC and KM. Section 5 details the ICVC framework. Section 6 outlines the consulting methods deployed to investigate the client organisation’s ICMMR practices. Section 7
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briefly illustrates the outcomes of the knowledge management gaps analyses. Section 8 concludes the paper and highlights future prospects for the field of IC. 2. Client organisation and consulting objectives The client organisation is an Australian public sector organisation employing 1500 employees. The project was conducted over a seven month period. It was headed by a team of consultants and researchers and facilitated as a pilot study through the Australian Government Consultative Committee on Knowledge Capital (AGCCKC). The client organisation’s motivations for engaging in ICMMR were driven by a number of changes in its operating environment including an ageing workforce, organisational restructuring, and the introduction of “New Public Management” reforms resulting in the instigation of public trading enterprise structures and more stringent financial performance requirements (Guthrie et al., 2003)[1]. These broader changes inspired the executive team to seek new ways in which to improve the organisation’s performance. Senior management was particularly interested in identifying the organisation’s invisible sources of value creation and making these known to external stakeholders, such as customers, New South Wales (NSW) Treasury and the community. ICMMR was perceived to be a means to provide external stakeholders with a broader perspective on the organisation’s value creating abilities and activities. It was a management tactic deployed to make visible the organisation’s knowledge resources and KM activities. In particular, the intention of the organisation’s senior executives was to demonstrate to NSW Treasury the value of the organisation’s knowledge resources and KM activities, which thus far had not been captured in budget papers and financial accounting reports. Other key motivations driving the executive team’s interest in ICMMR pertained to: . improving resource allocation, decision making and the effectiveness of capital; . retaining the expert knowledge held by senior staff scheduled for retirement; . initiating a process of self-reflection and the re-establishment of the organisation’s corporate identity; and . building a stronger corporate image and positioning the department as an innovative, learning organisation, which sets a benchmark for other public sector organisations. Based on the client brief, the project team developed the following three consulting objectives: (1) IC management. How does the organisation prioritise, enact, manage and develop its knowledge resources? Is the management of IC done in a strategic manner relating organisational knowledge resources and KM activities to the organisation’s strategic management challenges? Is the management of IC done in an integrated manner, taking into consideration the direct and indirect relationships that exist between the organisation’s resources? (2) IC measurement. To what extent does the organisation measure the composition and performance of its knowledge resources and KM activities? Are IC indicators incorporated in strategic planning processes and used to inform decision making and resource allocation?
(3) IC reporting. What is the type and level of IC reported in the organisation’s internal business management and strategy documents and annual reports? Does the organisation inform its external stakeholders about its strategic management challenges, KM activities and the composition and performance of its knowledge resources?
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3. Contemporary trends in ICMMR IC and related knowledge management activities have become increasingly important to organisations in their pursuit of value creation and competitive advantage. Reflecting this, in recent years there has been an emergence of IC reporting guidelines and acts, which inform and educate organisations on how to report their knowledge resources and KM activities. In Scandinavia, the Danish Ministry of Science, Technology and Innovation has published IC reporting guidelines illustrating to organisations the content, structure and format of IC reports (Mouritsen et al., 2003). The Danish guidelines are based on a pilot project, in which over 100 organisations participated in preparing IC reports. In the UK, the UK Department of Trade and Industry has proposed a compulsory reporting requirement for UK organisations to include an Operating and Review section in their annual reports from 2005. The objective is to provide a more strategic and forward-looking perspective, highlighting the importance of intangible, largely human, assets (CIPD, 2004). In Austria, the Austrian University Act 2002, which came into force on 1 January 1 2004, requires state universities to prepare and disclose IC reports. The IC report “informs about the past development of the university as well as forecasts of [sic.] performance outcomes” (Schaffhauser-Linzatti, 2004, p. 2). It is designed to provide an inventory of the IC that exists within the university and serves as an important basis for the university’s budgetary reimbursement. In Australia, the government has set up the Australian Government Consultative Committee on Knowledge Capital (AGCCKC) with a view to “produce a set of comprehensive knowledge capital standards whose application across the public and private sectors will contribute to the development of Australia as a competitive knowledge economy” (Australian Government Consultative Committee on Knowledge Capital, 2004, p. 2). The AGCCKC has instigated pilot studies, which aim at testing frameworks for reporting and valuing IC. At an industry level, Standards Australia (2003) has released an interim Standard on Knowledge Management, which outlines KM processes and concepts. Empirical research into IC is also on the increase, both in the USA, Europe and Australia. For example, in the USA, McCann and Buckner (2004) undertook a research study into IC consisting of 222 completed surveys. Among others, the study found that the best performing organisations: “viewed intellectual capital as a competitive asset to be actively managed; had adopted explicit measures for assessing intellectual capital; had cultures that supported the sharing of knowledge; and provided rewards and incentives tied to knowledge creation, application, and sharing” (McCann and Buckner, 2004, p. 59).
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However, a recent survey by PricewaterhouseCoopers (2004) finds that “mid-sized Australian businesses have not realised their true value by taking up the opportunities resting in their intangible assets, both on and off the balance sheet”. The survey encourages businesses to conduct a thorough review of their intangibles to determine which soft assets are important to their business’ competitive advantage. Likewise, a case study by McKinsey and Co. into the KM activities of a US-based company (Capozzi et al., 2003) highlights the need for organisations to become better at devising and implementing KM strategies and practices. The study argues that organisations must start managing their knowledge more effectively to put themselves in a stronger position. This brief review of trends in ICMMR demonstrates that IC and related KM activities are becoming increasingly important to organisations in their pursuit of value creation and competitive advantage. However, the review also highlights that there is a growing need to provide practical examples, which exemplifies how organisations manage, measure and report their knowledge resources, how they benefit from doing so and how they can improve their ICMMR activities and capabilities. To management consultants and researchers, this requires the development of new analytical models and consulting methods and competencies. It also requires the establishment of a common language with which to discuss IC, as discussed in the following section. 4. Definitions of and perspectives on ICMMR Agreeing upon a common language with which to discuss IC is a challenge to practitioners, policy makers, management consultants and researchers within the field of IC. This is partly due to the embryonic nature of this area of management practice and partly due to the inherent difficulties associated with establishing universally acceptable definitions (Leon, 2002). Contemporary literature on IC shows that a plethora of terminologies are being used to inform the discussion of ICMMR. Some of the most frequently used terminologies include: knowledge resources; knowledge assets; knowledge based assets; intellectual resources; intangibles; and, intellectual capital. Often these terminologies are used interchangeably and ambiguously. This ambiguity poses a challenge to practitioners and management consultants aiming to establish IC as a plausible field of management concern. To reduce the level of ambiguity surrounding IC, the Australian project team introduced a tripartite model of IC. The model was used to frame the investigation of the client organisation’s ICMMR practices. It classifies IC into: Internal Capital; External Capital; and Human Capital, as illustrated in Figure 1. The IC sub-categories featured in the tripartite model of IC were adapted from Petty and Guthrie’s (2000, p. 166)[2] IC model. The tripartite model of IC was beneficial to the client organisation in that it simplified the meaning of IC and translated IC into a language easily understood by the senior executives interviewed during the project. It reduced the uncertainty and ambiguity commonly experienced by practitioners wanting to engage in the IC discourse. In regards to KM, Petty and Guthrie’s (2000, p. 159) definition “that knowledge management is about the management of the intellectual capital controlled by a company” and that “knowledge management, as a function, describes the act of
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Figure 1. Tripartite model of IC
managing the object, intellectual capital” was used by the project team. The terminology “knowledge resources” was used interchangeably with the terminology “intellectual capital”. This definition correlates with Fincham and Roslender’s (2003, p. 3) argument that “the imperative to manage knowledge coincides with that of managing intellectual capital”. Furthermore, KM activities were defined as tactics and initiatives taken by the organisation to identify, enact, develop and dispose of its knowledge resources. In regards to identifying the value of IC, contemporary literature shows the existence of two lines of thinking, known as the stock and the flow approaches (Guthrie et al., 1999; Guthrie and Ricceri, 2002). The first approach, the stock approach, is concerned with calculating a dollar value of intangibles (Guthrie and Ricceri, 2002, pp. 5-9). It provides a snapshot of stocks of IC that is suitable for comparisons between companies. “It represents an attempt to fill the gap between market and book value by finding ways of determining the market assessment of the value of an organisation’s stock of IC” (Guthrie and Ricceri, 2002, p. 8). The second approach, the flow approach (Guthrie and Ricceri, 2002, pp. 9-13) views IC as being concerned with identifying the knowledge resources that drive value creation, rather than assigning a specific $-value to the resources. It is based on the notion that future financial performance is better predicted by non-financial than by financial indicators. Fincham and Roslender (2003, pp. 10-11) extend this line of reasoning and distinguish between “value realisation” and “value creation”. Value realisation is concerned with the historical value generated by an organisation. It correlates with the stock approach. In contrast, value creation is concerned with the capacity of an organisation to deliver sustainable competitive advantage now and in the future. It correlates with the flow approach. The value creation approach is not bound by the necessity of identifying a transaction basis for inclusion in any account or report and does not seek to incorporate value into the balance sheet using traditional financial measures. Instead, the focus of the value creation approach is on providing
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information, which captures and represents an organisation’s future value creation capacity. The project team’s analysis of the client organisation’s ICMMR practices was conducted in accordance with the value creation approach. The team focused on identifying the organisation’s sources of value creation and how these influence its current and future value creation capacity. This entailed making “visible” the organisation’s invisible knowledge resources and assessing how these were managed, measured and reported. From this value creation perspective, IC management is conceptualised as a process of organisational discovery and development (Roos et al., 1997). Here, “value does not [only] imply calculating a value, but to understand the creation and development of value” (Mouritsen, 2004, p. 261). “What is important about intellectual capital is the implicit importance, not of the investment in the stock of intellectual capital, but of the flow – the utilisation of that stock in pursuing the purposes of management” (Collier, 2001, p. 441). The objective of IC measurement, from this value creation perspective, is not to assign a financial value of IC but rather to enable management to monitor the performance of the organisation’s knowledge resources and KM activities over time (Mouritsen et al., 2003; Fincham and Roslender, 2003). IC measurement is, in this regard, “a means to verify a company’s ability to achieve its strategic intent” (Chen et al., 2004, p. 196). In regards to IC reporting, from this value creation perspective, an IC statement is seen as an inscription device and a centre of translation, which makes knowledge visible (Mouritsen et al., 2001). It does so by summarising the organisation’s efforts to develop and use knowledge resources, by reporting on the mechanisms put in place to make knowledge manageable and by telling a story of how the resources of the organisation are composed and bundled together in order to create value (Mouritsen et al., 2001). This perspective correlates with Fincham and Roslender’s (2003, p. 12) argument that business reporting is no longer solely about the financial representation and the valuation of assets. Instead, its emphasis is: . . .on telling the story of how different assets and values within the organisation evolve jointly and coalesce. The new business reporting is a theory of what creates value, one that is set in narrative form, albeit a reliable and valid form (Fincham and Roslender, 2003, p.12).
5. A framework for investigating ICMMR practices The project team developed the ICVC framework (see Figure 2) as an analytical model to facilitate the investigation of the client organisation’s ICMMR practices. The ICVC framework was informed by the consulting objectives outlined in section 2. The ICVC framework was inspired by two existing IC models: Petty and Guthrie’s (2000) tripartite model of IC; and Mouritsen et al.’s (2003) IC statement model. The ICVC framework is structured as follows: . The y-axis elements are derived from Petty and Guthrie’s (2000) tripartite model of IC, categorising IC into: external, internal and human capital. . The x-axis elements are adapted from the reporting categories of Mouritsen et al’s (2003) IC statement model. They detail the: organisation’s strategic management challenges; knowledge resources enacted, and the knowledge management
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Figure 2. ICVC framework and gaps analyses
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activities implemented, by management to respond to the management challenges; and indicators or measures assigned to measure the composition and performance of the knowledge resources and KM activities vis-a`-vis the management challenges. The z-axis elements detail the research methods including the semi-structured interviews and content analysis. These methods are discussed in more detail in section 6 below.
The ICVC framework proved particularly beneficial to the project team in that it facilitated the assessment of organisational knowledge management gaps. As illustrated in Figure 2, three knowledge management gaps were investigated: (1) Gap 1: Strategic management challenges vs knowledge management initiatives. Does the organisation respond to its strategic management challenges through the implementation of KM activities, including the acquisition, disposal, enactment and development of its knowledge resources? (2) Gap 2: Knowledge management activities vs IC indicators. Does the organisation measure the composition and performance of its knowledge resources and KM activities? (3) Gap 3: Internal IC management issues and practices vs external IC reporting practices. Does the organisation report to its external stakeholders its strategic management challenges, KM activities and IC indicators via its annual reports? The ICVC framework was used to link the organisation’s knowledge resources and KM activities to its strategic management challenges and, hence, its ability to create value now and in the future. The ICVC framework is thus similar to recent models developed
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within the IC discipline, which also attempt to link IC to organisational value creation. Popular models include among others the: Balanced Scorecard and strategy maps (Kaplan and Norton, 1996, 2004); value creation maps (Marr et al., 2004); and IC-Navigators (Fernstrom et al., 2004). Comparing the ICVC framework to these models, similarities include the strong strategic focus (as per the Balanced Scorecard) and consideration of inter-relationships between different knowledge resources (as per the value creation maps and IC-Navigators). In contrast, the ICVC does not attempt to force a causal relation to value in financial terms, as is the case with the Balanced Scorecard. Furthermore, it identifies inter-relationships between different knowledge resources through a consideration of how these resources are co-implicated in the strategic management challenges rather than through the development of visual linkages, as per the value creation maps and IC-navigators. The ICVC framework is also significantly different to those IC models offered by management consulting firms in Australia. Common approaches in this regard appear to focus on assigning monetary values to IC resources or emphasising particular aspects of IC categories or ICMMR activities only. For example, Deloitte offers a specialised human capital consulting service (Deloitte, 2005) while PricewaterhouseCoopers includes IC as part of a broader investigation into ValueReportingTM (Morris et al., 1998). In contrast to these, the ICVC framework presents a more holistic approach to examining client organisations’ ICMMR practices, incorporating all functional aspects of IC (i.e. internal, external and human capital) and three key IC activities (i.e. IC management, measurement and reporting). The ICVC framework neither attempts to pre-define the knowledge resources or activities to be considered or how they impact on value creation. Rather, the establishment of the knowledge resources is done through consulting and research methods that capture and reflect the unique value creation context and logic of the client organisation. This contrasts with models such as the Value-Creation Index (Baum et al., 2000) and the Value Creation Scoreboard (Lev, 2001), both of which identify a set of non-financial measures or drivers that are statistically associated with indicators of value such as share prices. Furthermore, an explicit and differentiating element of the ICVC framework is the evaluation of alignment or gaps in client organisations’ ICMMR practices. The project team’s experiences with using the ICVC framework to investigate the client organisation’s ICMMR practices are discussed in more detail in section 7.
6. Consulting and research methods and processes The consulting and research methods deployed to analyse the client organisation’s ICMMR practices are illustrated on the z-axis in the ICVC framework. The three methods adopted include: (1) semi-structured interviews with fifteen senior managers and executives; (2) content analysis of the department’s annual reports (2000 – 03); and (3) reviews of the organisation’s internal business management and strategy documents including the: Corporate Plan (2003-06); Divisional Business Plans (2004); and Target Business Model (2003) document[3].
The use of multiple consulting and research methods facilitated a more comprehensive investigation of the client organisation’s ICMMR practices, revealing gaps in its KM practices. Each of the three methods employed are discussed briefly below. Semi-structured interviews The objective of the semi-structured interviews was to gain an understanding of how the organisation and its members enact, manage, measure, report and develop their knowledge resources and whether this is done in a strategic and integrated manner (see consulting objective 1 in section 2). To achieve this objective, the interviewees were asked to: . identify the organisation’s strategic management challenges (column 1 in the ICVC); . comment on the knowledge resources deemed to be important to the organisation and the KM activities implemented by management to respond to the management challenges (column 2 in the ICVC); and . outline the IC measures or indicators, if any, assigned to assess the composition and performance of the knowledge resources or KM activities (column 3 in the ICVC). A benefit of using the ICVC framework to guide the semi-structured interviews was that it established a linkage between IC and organisational value creation. It did so by asking the interviewees to comment on the ways in which they respond to the organisation’s management challenges and how they enact, utilise, develop and dispose off the organisation’s knowledge resources. The ICVC framework helped frame the mindset of the interviewees to view their organisation and managerial activities from an IC perspective. It brought day to day tactical activities to a strategic level and enabled them to relate the organisation’s management, measurement and reporting activities to its strategic intents. The semi-structured interviews were integral to establishing the organisation’s ICMMR activities and played an important role in facilitating the three gaps analyses, discussed previously in section 5. External reporting: content analysis of the client organisation’s annual reports Content analysis was deployed as a research method to analyse the level and type of IC reported in the client organisation’s annual reports and internal business management and strategy documents[4]. The outcome of the content analyses was a quantitative summary of the levels and types of IC reported to external stakeholders in its annual reports. The analyses enabled the project team to derive patterns in the presentation and reporting of information and gain an insight into which resources and activities are important to the organisation. A rationale for applying this method to analyse annual reports is that annual reports are viewed as communication devices, which tell a story of how the organisation and its resources are enacted, utilised, developed and disposed off. The starting point for conducting the content analyses entailed classifying IC information into categories and sub-categories according to a pre-defined coding scheme. The annual reports were thereafter analysed in accordance with the coding scheme and the level of reporting of IC within each pre-defined category was recorded. One of the benefits of content analysis is that it ensures published information is analysed systematically and reliably (Guthrie et al., 2004).
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Internal reporting: content analysis and reviews of internal documents The review of internal documents entailed reading through the organisation’s Target Business Model (2003), the Corporate Plan (2003-06) and the Divisional Business Plans (2004). Content analysis was applied to the Corporate Plan (2003-06) in accordance with the method used to analyse the annual reports, discussed above. Together with the semi-structured interviews, the review of the internal business management and strategy documents played an important role in establishing how the organisation manages, measures and reports its IC internally. The divisional business plans and the corporate plans were particularly useful to the project team in establishing whether the organisation manages its knowledge resources in a strategic and integrated manner. They provided an insight into which knowledge resources and KM activities are prioritised within the organisation’s strategic management framework and informed all three gap analyses. A benefit of using the ICVC framework to conduct content analyses on annual reports and internal documents is that it details whether the organisation measures the composition and performance of its knowledge resources and KM activities. This is done by means of the “IC Measures or Indicators” reporting category featured on the x-axis of the ICVC (see column 3). The decision by the project team to include this reporting category was based on the assumption that measuring the performance of the knowledge resources and KM activities is necessary to evaluate whether the resources and/or activities create or destroy value for the organisation. 7. Knowledge management gaps and practical implications The project team’s investigation of the client organisation’s ICMMR practices illustrated that all three knowledge management gaps were found to be present at the client organisation, indicating weaknesses in the utilisation of its knowledge resources. Gap 1 showed that the organisation responded poorly to six out of twelve of its strategic management challenges, indicating that it does not manage all areas of IC in a strategic manner. Gap 2 showed that the organisation does little to measure the composition and performance of its knowledge resources and KM activities and illustrated that IC measures are not used to inform decision making and resource allocation. Gap 3 showed inconsistency between the organisation’s internal IC management issues and practices and its external IC reporting practices, indicating that external stakeholders are not fully informed about the organisation’s internal IC management issues and practices[5]. The use of the ICVC framework to identify gap 1 is illustrated in more detail below. The analysis of gap 1 was based on a comparison of columns 1 and 2 in the ICVC framework. The objective of this analysis was to assess the extent to which the organisation responds to its strategic management challenges through the implementation of KM activities across the three IC categories (i.e. internal, external and human capital). The analysis was based on the assumption that value creation is a
function of the ways in which the organisation manages its knowledge resources vis-a`-vis its strategic management challenges. Informed by the semi-structured interviews and the review of the organisation’s internal business management and strategy documents, the analysis of gap 1 showed that the organisation faced 12 strategic management challenges. These are illustrated graphically in Figure 3. The analysis also illustrated that six out of the twelve strategic management challenges were not addressed by the organisation through the implementation of KM activities. These strategic management challenges [6] are shaded grey in Figure 3. The lack of attention to six out of 12 strategic management challenges highlighted weaknesses in the organisation’s utilisation of its knowledge resources. It illustrated that strategically significant knowledge resources and KM activities, identified by the senior executives during the interviews, were not prioritised by the organisation within its strategic management framework. The analysis was, in this regard, beneficial to the executive team at the client organisation in that it introduced a new perspective from which to understand and analyse their organisation, enabling them to visualise the organisation’s knowledge resources and how these contribute to, or subtract from, organisational value creation. It demonstrated to the executives the strategic significance of making visible the organisation’s invisible sources of value creation. On the basis of the visualisation of the organisation’s knowledge resources and the identification of its knowledge management gaps, the project team was able to devise a series of recommendations and action plans for how to improve the utilisation of the organisation’s knowledge resources. To illustrate, in brief, the client recommendations for KM gap 1 pertained to: . External capital. Strengthening the corporate image and communicate to external stakeholders and Treasury, in particular, the significance and contribution of the organisation’s knowledge resources and KM activities to value creation. . Internal capital. Building structural agility and develop a dynamic, outward looking, engaged, team based, knowledge culture with a view to enhance the
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Figure 3. Knowledge management gap no.1: responses to strategic management challenges
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timeliness, reliability and responsiveness of customer services and improve organisational innovativeness and development. Human capital. Enhancing employee motivations to improve operational efficiency and organisational learning, facilitating knowledge identification, sharing and retention to capture expert knowledge and reduce the risks associated with the ageing of the workforce.
In summary, the ICVC framework offered five main advantages to the project team. First, the framework is rooted in Sveiby’s (1997) original tripartite categorisation of IC, a widely accepted classification and definition of IC categories. Also, it is informed by Mouritsen et al.’s (2003) IC statement model, which has been tested by over 100 Danish organisations. The roots of the ICVC framework enhanced its credibility. Furthermore, it reduced the level of dissonance among the interviewees as several executives expressed familiarity with the components of the framework, and in particularly the categories featured in the tripartite model of IC. The ICVC framework provided a broad, yet easy to understand, classification and definition of IC, thereby “demystifying” IC and making it easy for the client organisation to comprehend ICMMR. Second, the ICVC framework linked IC to value creation by tracing the development and creation of value rather than seeking to assign a financial, stock value to the knowledge resources. It enabled the project team to assess how effective the executive team is at managing and developing the organisation’s knowledge resources vis-a`-vis its strategic management challenges. The project team was able to identify the knowledge resources that drive value creation at the client organisation and assess how effectively these are enacted, managed, utilised and developed within the organisation’s strategic context. Third, the framework facilitated an integrated approach to organisational resource analysis and management (Marr et al., 2004) by relating knowledge resources and KM activities across the three IC categories featured on the y-axis of the ICVC (i.e. internal, external and human capital) to value creation. This was achieved by the project team requesting the interviewees to identify the knowledge resources enacted to respond to the management challenge across all three IC categories, thereby encouraging cross-functional integration and horizontal, as opposed to vertical, thinking. In doing so, the framework helped illustrate the interrelations and interdependencies that existed between the client organisation’s resources regardless of their nature (i.e. tangible or intangible) or functional location (i.e. operations, HR, finance, etc). Fourth, the project team’s experience with the application of the ICVC framework suggested that the framework can be used in a variety of ways by management consultants, researchers and client organisations. For instance, it can be used as: an analytical framework for consultants and researchers to analyse client organisations’ ICMMR practices; an internal management tool for managing organisational resources in an integrated and strategic manner; and, a reporting tool to provide external stakeholders with a broader perspective on the organisation’s value creating activities and abilities in the form of an IC report. Last, the ICVC framework enabled the executive team at the client organisation to visualise the organisation’s knowledge resources and how these contribute to or subtract from organisational value creation. It introduced a new perspective from
which to understand and analyse the organisation and enabled the senior executives to gain a better understanding of the strategic significance of the organisation’s knowledge resources and KM activities. 8. Conclusion and future prospects of IC This paper has responded to the growing need to illustrate how organisations manage, measure and report their IC, how they benefit from doing so and how they may improve their ICMMR activities and capabilities to enhance the utilisation of their knowledge resources. The paper has presented the ICVC framework as an integrated management consulting framework for investigating client organisations’ ICMMR practice. It has illustrated the benefits of the ICVC framework in visualising client organisations’ invisible sources of value creation and assessing the degree of alignment between the various components of organisational ICMMR. The application, use and relevance of the ICVC framework has been illustrated through a case study of an Australian public sector organisation seeking new ways in which to improve its performance, strengthen its corporate image, and secure its expert knowledge. A combination of different methods was utilised to facilitate the process, comprising semi-structured interviews and content analysis of internal business documents and annual reports. Specifically, the ICVC framework proved beneficial to examining the existence and extent of three knowledge management gaps pertaining to: (1) strategic management challenges vs KM activities; (2) KM activities vs IC indicators; and (3) internal IC management issues and practices vs external IC reporting practices. The analyses highlighted weaknesses in the client organisation’s ICMMR practices with all three knowledge management gaps detected. Based on these findings, the project team provided the client organisation with a series of recommendations as to how to improve the utilisation of its knowledge resources with a view to enhancing value creation and competitive advantage. As a result of this initial study, the client organisation has decided to develop IC reports for inclusion in its annual reporting documentation in 2004/05 and to initiate the development of an IC scorecard to improve organisational resource allocation and managerial decision-making processes. Furthermore, the project team has commenced collaborative projects with five other Australian public, private and third-sector organisations with a view to deploying the ICVC framework in their organisations. Common to these organisations is the recognition that knowledge resources and ICMMR activities are increasingly important to securing financial resources from governments and/or other sources of capital and for improving the basis for organisational resource allocation and decision making. As such, organisations seek better understandings and improvements of their value creation processes and an identification of the organisational resources that are key to their ability to survive and compete more effectively. A common challenge to these organisations is the absence of a clear understanding of how these management and development process should be commenced and navigated. The ICVC framework provides one means for organisations to commence this journey.
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However, potential barriers to the wider dissemination of the ICVC framework exist. One main barrier is a pre-occupation among corporations with valuing IC. Evidence in Australia indicates share-market investors and analysts focus on value realisation in financial terms, rather than longer-term value creation (Morris et al., 1998). This narrow focus on value realisation is among others influenced by the transition to international accounting standards in 2005 (Buffini and Fenton-Jones, 2004). The second main barrier to the proliferation of the ICVC framework comprises the required engagement of the client organisation in the consulting processes and the continuous commitment to the ICMMR activities after the mandate has been awarded. A distinctive feature of the ICVC framework is that it is developed and implemented in conjunction with senior management at participating client organisations. Doing so requires engagement, involvement and commitment by stakeholders from across all functional areas of the organisation. This is a time consuming process, which may not appeal to “time-poor” executives and employees. In contrast, alternative IC frameworks, which contain pre-defined features and processes that can be quickly implemented, may seem more attractive to organisations in search of fast, short term solutions. Countering such “off the shelf” consulting packages is the loss of organisational learning and development, an invaluable aspect and a significant benefit of the ICVC framework and consulting processes deployed by the Australian project team. Overcoming such barriers requires education and heightened awareness of ICMMR activities among practitioners, management consultants, researchers and public policy makers. Current initiatives undertaken by the AGCCKC and other institutions detailed earlier provide important stimuli in this regard. The conduct and development of pilot studies, which illustrate the organisational benefits and challenges associated with implementing ICMMR frameworks and activities play an important role in yielding the awareness required to establish ICMMR as managerial priorities. Furthermore, significant monetary funding of innovation programs by the Australian government (Commonwealth Government of Australia, 2004) are important signals to the broader economy of the importance of ICMMR. Hence, the observations of the project team indicate that the IC movement in Australia is set to increase. Present and growing pressures for organisations to improve their managerial practices in relation to the strategic management, measurement and reporting of their knowledge resources suggest an increasing market for IC management consulting and the potential use of the ICVC framework. Notes 1. Please see Guthrie et al. (2005) for more details on the client organisation’s motivations for engaging in ICMMR. 2. Petty and Guthrie’s (2000, p. 166) tripartite model of IC was adapted from Sveiby’s (1997) original classification of IC. 3. The Corporate Plan (2003-06), Divisional Business Plans (2004) and Target Business Model (2003) documents are internal documents provided in confidence by the client organisation. 4. Content analysis of annual reports has frequently been used by researchers in the field of intellectual capital reporting, including Guthrie and Petty (2000) and Yongvanich and Guthrie (2004).
5. For more details on the inconsistencies between the client organisation’s internal IC management issues and practices and its external IC reporting practices, please see, Boedker et al. (2005). 6. It should be noted that the organisation had in place some KM activities pertaining to the strategic management challenge called ‘Knowledge Identification, Sharing and Retention’. These activities were however fragmented and done in pockets with no overarching strategy or action plan in place. This research finding was informed by the semi-structured interviews and the review of the Divisional Business Plans (2004), neither of which showed any mentioning of the organsiation’s knowledge identification, sharing and retention activities. For more information on these activities, please see, Boedker et al. (2005). References Australian Government Consultative Committee on Knowledge Capital (2004), “Draft project plan”, quoted with the permission of the AGCCKC. Baum, G., Ittner, C., Larcker, D., Low, J., Siesfeld, T. and Malone, M. (2000), “Introducing the new Value Creation Index”, Forbes ASAP, April, available at: www.forbes.com Boedker, C., Guthrie, J. and Cuganesan, S. (2005), “The strategic significance of intellectual capital information in annual reporting”, MGSM working paper. Buffini, F. and Fenton-Jones, M. (2004), “Accounting rules to hit firms hard”, Australian Financial Review, 19 March, p. 1, 6. Capozzi, M.M., Lowell, S.M. and Silverman, L. (2003), “Knowledge management comes to philanthropy”, The McKinsey Quarterly, special edition, available at: www.mckinsey.com Chen, J., Zhu, Z. and Xie, H.Y. (2004), “Measuring intellectual capital: a new model and empirical study”, Journal of Intellectual Capital, Vol. 5 No. 1, pp. 195-212. CIPD (2004), Government Proposal on Financial Reporting Lack Focus on People, Says CIPD, press release, 6 August, available at: www.cipd.co.uk Collier, P.M. (2001), “Valuing intellectual capacity in the police”, Accounting, Auditing & Accountability Journal, Vol. 14 No. 4, pp. 437-55. Commonwealth Government of Australia (2004), Backing Australia’s Ability, Commonwealth Government, Canberra. Deloitte (2005), “Human capital: maximising the value of your human capital”, available at: www.deloitte.com/dtt/section_node/0%2C1042%2Csid%25253D54774%2C00.html Fernstrom, L., Pike, S. and Roos, G. (2004), “Understanding the truly value creating resources – the case of a pharmaceutical company”, International Journal of Learning and Intellectual Capital, Vol. 1 No. 1, pp. 105-20. Fincham, R. and Roslender, R. (2003), The Management of Intellectual Capital and its Implications for Business Reporting, Research Committee of The Institute of Chartered Accountants of Scotland, Edinburgh. Guthrie, J. (2001), “The management, measurement and the reporting of intellectual capital”, Journal Of Intellectual Capital, Vol. 2 No. 1, pp. 27-41. Guthrie, J. and Petty, R. (2000), “Intellectual capital: Australian annual reporting practices”, Journal of Intellectual Capital, Vol. 1 No. 3, pp. 241-51. Guthrie, J. and Ricceri, F. (2002), “Quantify Intellectual Capital: Measuring and Reporting to Demonstrate Value of KM to Stakeholders”, paper presented at KM Australia: Building and Improving on Knowledge Management Initiative For Commercial Proficiency Conference, Sydney, 4 December.
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Guthrie, J., Cuganesan, S. and Boedker, C. (2005), “An Australian public sector organisation’s transition to extended performance reporting”, MGSM working paper. Guthrie, J., Parker, L. and English, L. (2003), “A review of new public financial management change in Australia”, Australian Accounting Review, Vol. 13 No. 2, pp. 3-9. Guthrie, J., Petty, R., Ferrier, F. and Wells, R. (1999), “There is no accounting for intellectual capital in Australia: a review of annual reporting practices and the internal measurement of intangibles within Australian organisations”, paper presented at the International Symposium on Measuring and Reporting of Intellectual Capital: Experiences, Issues and Prospects, OECD, Amsterdam, June. Guthrie, J., Petty, R., Yongvanich, K. and Ricceri, F. (2004), “Using content analysis as a research method to inquire into intellectual capital reporting”, Journal of Intellectual Capital, Vol. 5 No. 2, pp. 282-93. Kaplan, R.S. and Norton, D.P. (1996), The Strategy Focused Organisation, Harvard Business School Press, Boston, MA. Kaplan, R.S. and Norton, D.P. (2004), Strategy Maps: Converting Intangible Assets into Tangible Outcomes, Harvard Business School Press, Boston, MA. Leon, M.V. (2002), “Intellectual capital: managerial perceptions of organisational knowledge resources”, Journal of Intellectual Capital, Vol. 3 No. 2, pp. 149-66. Lev, B. (2001), Intangibles: Management, Measurement and Reporting, Brookings Institutions Press, Washington, DC. Marr, B., Schiuma, G. and Neely, A. (2004), “The dynamics of value creation: mapping your intellectual performance drivers”, Journal of Intellectual Capital, Vol. 5 No. 2, pp. 312-25. McCann, J.E. III and Buckner, M. (2004), “Strategically integrating knowledge management initiatives”, Journal of Knowledge Management, Vol. 8 No. 1, pp. 47-63. Morris, G., Eccles, R.G. and Falconer, I.D. (1998), Value Reporting in Australia, available at: http:// store.barometersurveys.com/docs/ValueReporting_White_Paper(Text).pdf Mouritsen, J. (2004), “Measuring and intervening: how do we theorise intellectual capital management?”, Journal of Intellectual Capital, Vol. 5 No. 2, pp. 257-67. Mouritsen, J. et al. (2003), Intellectual Capital Statements: the New Guideline, Danish Ministry of Science, Technology and Innovation, Copenhagen, available at: www. videnskabsministeriet.dk/cgi-bin/theme-list.cgi?theme_id ¼ 100650&_lang ¼ uk (accessed April, 2004). Mouritsen, J., Larsen, H.T. and Bukh, P.N.D. (2001), “Intellectual capital and the ‘capable firm’: narrating, visualising and numbering for managing knowledge”, Accounting, Organization and Society, Vol. 26, pp. 735-62. Ordo´nez de Pablos, P. (2002), “Evidence of intellectual capital measurement from Asia, Europe and the Middle East”, Journal of Intellectual Capital, Vol. 3 No. 3, pp. 287-302. Petty, R. and Guthrie, J. (2000), “Intellectual capital literature review: measurement, reporting and management”, Journal of Intellectual Capital, Vol. 1 No. 2, pp. 155-76. PricewaterhouseCoopers (2004), Business Insights Survey: Survey of Australia’s Mid-Sized Businesses, 2004 – Wave 2, PricewaterhouseCoopers. Roos, G. (2005), “Epistemological cultures and knowledge transfer within and between organisations”, in Bukh, P.N., Christensen, K.S. and Mouritsen, J. (Eds), Knowledge Management and Intellectual Capital: Establishing a Field of Practice, Palgrave Macmillan, London. Roos, J., Roos, G., Dragonetti, N.C. and Edvinsson, L. (1997), Intellectual Capital, Navigating the New Business Landscape, Macmillan Business, London.
Schaffhauser-Linzatti, M. (2004), “Intellectual capital reporting for austrian universities – a thrilling work in progress”, European Institute for Advanced Studies in Management (EIASM) Workshop on the Process of Reform of the University Across Europe, Certosa di Pontignano, Siena, 24-26 May, available at: www.eiasm.org/documents/abstracts/2824.doc Standards Australia (2003), Interim Knowledge Management Standard, Standards Australia, Sydney. Sveiby, K.E. (1997), The New Organizational Wealth: Managing & Measuring Knowledge Based Assets, Berrett-Koehler, San Francisco, CA. Yongvanich, K. and Guthrie, J. (2004), “Extended performance reporting: an examination of the Australian mining industry”, MGSM Working Papers in Management, WP 2004-15.
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Karl-Heinz Leitner Department of Technology Policy, ARC Systems Research GmbH, Seibersdorf, Austria
Michaela Schaffhauser-Linzatti Faculty of Business, Economics and Statistics, University of Vienna, Vienna, Austria
Rainer Stowasser Office Austrian Science Board, Vienna, Austria, and
Karin Wagner Oesterreichische Nationalbank (Austrian Central Bank), Vienna, Austria Abstract Purpose – The purpose of this paper is to demonstrate the usefulness of data envelopment analysis (DEA) as a consulting and management tool that fulfils the requirements of quantitatively and comprehensively evaluating and benchmarking the efficiency of intellectual capital (IC). Design/methodology/approach – DEA is applied for a sample of input and output data of all technical and natural science departments of Austrian universities. Correlation and factor analyses are carried out to select appropriate variables of the sample. DEA estimates the production function of the units under evaluation in relation to peer units, which are identified as fully efficient. Findings – Results illustrate the existence of scale efficiencies of Austrian university departments and show a large heterogeneity within and among universities as well as between different fields of study with respect to their efficiency. Research limitations/implications – DEA is mainly appropriate for larger samples inside an organisation or among different organisations. The method can be easily transferred to similar management situations in other types of organisations or industries, where the efficiency of IC should be assessed. Practical implications – The results reveal detailed improvement or reduction amounts of each input and output of the evaluated organisational units and indicate areas for managerial action at Austrian universities. Originality/value – For the first time DEA is applied for evaluating and benchmarking IC of Austrian universities. DEA is proposed as consulting and management tool for evaluation IC performance. Keywords Data analysis, Intellectual capital, Universities, Austria Journal of Intellectual Capital Vol. 6 No. 4, 2005 pp. 528-543 q Emerald Group Publishing Limited 1469-1930 DOI 10.1108/14691930510628807
Paper type Research paper
The authors thank Julia Prikoszovits from the Austrian Rectors’ Conference as well as our reviewers for helpful comments and support.
1. Introduction In the last few years, management and reporting of intellectual capital (IC) has not only received the attention of private firms, but also of public institutions, research organisations, regions, and countries. In spite of growing academic literature on IC management, consultants still lack quantitative and financial methods for analysing IC in practice. There exist numerous methodical approaches that evaluate single aspects of IC, e.g. human resources or customer capital, yet, satisfying approaches that integrate IC as one unitary asset are still lacking. Indicator-based IC management systems often used by consulting firms such as the Balanced Scorecard (Kaplan and Norton, 1992), the Intangible Asset Monitor (Sveiby, 1997) or the IC Navigator (Steward, 1997) have shortcomings in evaluating and benchmarking a set of IC indicators and calculate the performance and exploitation of IC of organisations or organisational departments based on different scales of indicators. We are the first to propose data envelopment analysis (DEA) as an IC management and consultancy tool for evaluating and benchmarking IC for all Austrian universities. Hereby, we measure the value of an organisation’s IC by calculating its relative efficiency within a set of comparable units. This allows to benchmark and rank these units with respect to their potential efficient use of IC. DEA has been successfully used for efficiency analysis in many fields, for instance for banks (Wheelock and Wilson, 1995), start-up companies (Fuertes Callen et al., 2005) or various industrial sectors (Lovell, 1993). DEA is an approach to estimate the production function of organisations and organisational units and enables the assessment of their efficiency. The advantages of this method are that it can cope with variables of different scales and that it incorporates multiple input and multiple output ratios (the majority of the other approaches is based on one single output per one single input ratios). Most of all, due to its benchmarking approach, DEA represents a competitive analysis by matching actual practice with reference targets. Hereby, it reveals the strengths and weaknesses of an organisational unit under evaluation in relation to peer units, which are identified as fully efficient. For all inefficient organisational units, consultants can easily find and quantify improvement potentials (e.g. cost reductions, revenue increases) for each IC component included in the evaluation. Furthermore it enables ranking, as this is an evaluation relative to all units under consideration. Although in this paper the method of DEA is applied for universities, the overall usefulness for managers, investors, and consultants should be pointed out. The Austrian Ministry for Education, Science and Culture[1] has recognised that the efficient use of IC is essential for university performance. It adopted the idea of IC reporting to enhance transparency, to foster the management of intangible resources, and to set incentives for performance orientation. According to the current university law (Universitaetsgesetz 2002 – UG 2002, passed in August 2002), all universities will have to issue annual IC reports by 2006. Within this IC report, each university will have to publish input, output, and performance indicators for industry, teaching, research, and for other forms of outputs. The IC report should serve as a management instrument for the university as well as a communication instrument between universities and the Ministry. The reorganisation of Austrian universities reveals a high demand for such a new instrument since universities are provided with greater autonomy and thus have to take decisions on the resource allocations with respect to their tangible and intangible assets. Moreover, the Austrian science and education
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policy is interested in more comprehensive information on the development and effective use of its intangible resources. Since the IC reports of Austrian universities will have to publish a well-defined set of input and output indicators defined by law, statistical and quantitative methods have become feasible for the analysis of this type of data. DEA, as an instrument for analysing IC data, aims at determining the efficiency of departments of different universities in certain scientific fields. Despite vast literature on evaluation systems (e.g. Roessner, 2000), so far only a few studies have analysed relationships of various indicators of universities by statistical methods (e.g. Teodorescu, 2000; Fairweather, 2002) or even applied these indicators to determine the efficiency of universities (e.g. Athanassopolous and Shale, 1997; Fandel, 2003). In our empirical study, we apply an input-oriented DEA model for all technical and natural science departments in Austrian universities. Its objective is to minimise inputs for a given level of outputs. All public Austrian universities are obliged to provide the Ministry with structural and performance data annually; for this study we have used data for the years 2000 and 2001. We show that DEA is a suitable instrument for the IC management to distinguish between efficient and inefficient departments, to rank them according to their performance, and to reveal their improvement capacities. This information is necessary to develop key aspects of future activities and to reallocate financial funds. On the one hand DEA can be applied internally by a university to evaluate and benchmark IC only of its own departments, on the other hand it is an interesting instrument to compare departments of different universities. Both fields of application will come to the fore. Our study, which is conducted with the support of the Austrian Rectors’ Conference (the association of all university rectors), concentrates on a cross university sample. The present paper is structured as follows. Section 2 gives a short overview of the university reform in Austria and the specific role and aims of IC reporting and the consultancy support provided so far by consultants in Austria. Section 3 describes DEA as suitable methodology to evaluate IC. Section 4 concentrates on the design of the analysis as well as the data base, the sample and time period applied for the empirical study. In section 5, we present the results of a DEA conducted for technical and natural science departments. Section 6 summarises the findings and concludes that DEA is an important method to evaluate the performance of universities. 2. IC reporting in Austrian universities The reorganisation of Austrian universities is based on the principles of New Public Management with its premises of increased autonomy, output orientation and performance-based funding (Titscher et al., 2000). The university law UG 2002 specifies the organisational framework of all public Austrian universities with respect to funding, governance, management structures, evaluation, accreditation, and rights of university staff. One important element of the UG 2002 to realise the new paradigm is the introduction of performance contracts. These contracts, which are drawn up for the period of three years, define the duties of both the university (studies offered, human resources, research programs, co-operations and social goals) and the Ministry (funding), and assign a global budget for the following three years. Funding, which is based on four criteria, namely need, demand, performance, and social goals, is negotiated between universities and the Ministry. In addition, every year the
universities will have to generate a performance report to provide information on the development and achievement of the targets agreed on within the contract. Apart from the development of a performance contract and a performance report, an IC report (called Wissensbilanz) has to be published annually, which contains predefined information. While the performance report only deals with the topics addressed within the performance contract, the idea behind the IC report is to give the university the opportunity to report to other stakeholders on its full range of activities not restricted by a few, legally defined measures (Schaffhauser-Linzatti, nd). IC reports have to be prepared for the whole university and – probably – for predefined scientific disciplines, but it is up to the university to produce reports on more detailed levels such as departments, too. The UG 2002 states that the IC report explicitly has to show: (1) the university’s activities, social goals and self-imposed objectives and strategies; (2) its intellectual capital, broken down into human, structural and relationship capital; and (3) the processes set out in the performance agreement, including their outputs and impacts. This framework conceptualises the transformation process of intangible resources when carrying out different activities (research, education, etc.) that result in the production of different outputs according to the general and specific goals. Research and education are two major outputs of a university. However, additional outputs such as training or commercialisation of research might also be aims of a university. Like other IC models, especially applied by research organisations in Austria and Germany (Leitner and Warden, 2004), the Austrian university model can be labelled as process-oriented approach which does not only focus on the different forms of intangible assets or IC but also on the question how these investments are used by the university and how they influence the outputs. This approach is similar to models of innovation and research processes described by the innovation and evaluation literature, which frequently separate inputs, outputs, and processes (Dodgson and Hinze, 2000; Roessner, 2000). Besides providing stakeholders with detailed information, IC reporting for Austrian universities should provide information for the management of intangible resources. In the course of the preparation of an IC report, universities, among others, have to discuss the targets and strategy they have to develop, to interpret indicators, and therefore learn about their knowledge production process. Hereby, the Ministry should also benefit from a better overview of the development of the national university system, the strengths and weaknesses in specific fields and thus get information for effectively adapting the national science and education policy. The underlying thesis is that a proper management of IC at universities has a significant impact on the performance and efficient use of the invested financial funds. The political discussion process as to how the IC report shall be precisely structured is not finished yet. Currently, the Ministry and the Rectors’ Conference are in the process of defining and selecting indicators for different disciplines. So far, some university departments and one university have already published IC reports voluntarily on recommendation of the project team (Leitner et al., 2001): The Danube
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University Krems, the Department of Economics and Business Management at the Montanuniversita¨t Leoben, and the Department of General and Tourism Management of the University of Innsbruck. The IC reports published or currently developed by universities have been partly produced in co-operation with smaller Austrian consulting firms[2]. These consulting firms use the traditional approaches to select and interpret IC indicators as described in the IC management literature (e.g. Roos et al., 1998; Steward, 1997; Sveiby, 1997). None of them applies DEA or similar quantitative methods for analysing and interpreting IC data. In general, only a few smaller Austrian consulting firms offer DEA analysis at all, e.g. for production planning or efficiency analyses of the energy sector. However, none of them is offering consultancy for management accounting in general or IC management in specific. Within the IC projects for the universities, the consulting firms provide support for moderating the managerial process of introducing an IC management system and develop IC reports based on the framework depicted by the law. Thereby, the consultants start with the definition of goals and strategies as the first phase followed by the definition and selection of indicators. Hereby, assistance for implementing information systems and software for the management of data is also provided. Finally, consultants help universities to interpret and value IC indicators. Moreover, the consultants give support with respect to the structure and the layout of an IC report based on the national and international reference standards. The consulting firms apply methods proposed for implementing indicator-based IC management systems such as the identification of key success factors, the development of strategic maps to visualise the links between different forms of IC, and conduct general rules for defining indicators such as the requirement of validity and robustness. But most often these indicators are ratios constructed by division of one output per one input. Consultants evaluate IC by comparing the current data with past data or by monitoring the achievements of indicator targets but neither give support for the interpretation and evaluation of IC metrics by providing benchmark data from other universities and countries nor include the efficiency of the IC exploitation. Thus, at the moment, they are not able to assess the performance in a more objective and comprehensive way, in particular it is not possible to evaluate the performance of different organisational departments by calculating the efficiency based on multiple inputs and outputs. Herein lies the specific advantage of the DEA proposed in this paper. DEA allows the comparison of the performance of different departments, disciplines or fields of studies, and illustrates areas for performance improvement and thus for managerial actions. Moreover, an input-oriented DEA model allows to analyse the impact of a change of various input variables, which can be interpreted as different forms of intellectual capital, on the different outputs. Thereby, questions such as the impact of the size, discipline, or specialisation of a department on specific outputs can be addressed. 3. Methodology of DEA In economic terms, DEA compares the performance of decision-making units (DMUs) acting under the same technology. This characteristic is especially valuable for non-profit units as it does not need any price information as prerequisite. DEA evaluates efficiency, i.e. the relation of inputs and outputs, and hereby determines the
overall efficiency which consists of the pure technical efficiency and the allocative efficiency. Figure 1 presents the principle ideas of DEA. Hereby, each point represents a DMU. It explains the pure technical and allocative efficiency by showing the production of one output by two inputs. An efficient DMU is a point on the isoquant built by the points A to D and by the sections in between these points. Hence, none of the four points A to D can improve one of its input values without deteriorating the other one (the graph displays the input-oriented view – the target is to minimise inputs). An additional amount of one of the inputs at constant output values (e.g. G) would lead to inefficiency (in comparison to C, G uses by far more of both inputs for the production of the same output value). The pure technical efficiency for G is measured by the ratio 00GP , Therefore, the inefficiency of G is evaluated by using a whereby P lies on the line BC. combination of B and C. G can improve its efficiency by “going” to P, i.e. by reducing inputs. The pure technical efficiency only partially detects inefficiency for DMUs on isoquant sections parallel to the axes (e.g. F) which is, however, recorded by DEA in a second step. Here, a further reduction of input 1 is possible. Although B is technically efficient, in view of given prices, it is allocatively inefficient (as it does not lie on line p, the given price level). With moving towards C, the input-mix changes and adjusts itself to the given price level. The first and very crucial step in conducting a DEA is the determination of inputs and outputs. The selection which indicator is to be classified as input or output might be quite difficult. Inputs are characterised by the fact that fewer of them is better (e.g. fewer costs occur), whereas outputs by the fact the more the better (e.g. more publications). An important assumption of DEA is the fact that if a given DMU A is able to produce the amount of yA outputs by using xA inputs, then other DMUs – using the same technology – should at least be able to produce the same output if they are efficient. Given DMUs are combined to an artificial composite DMU with composite inputs and outputs. This artificial DMU lies on the efficiency frontier. The main part of the DEA model is to find for each – real – DMU the corresponding – efficient – point
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Figure 1. Technical and allocative efficiency
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on the isoquant. If this point, the so-called “virtual DMU”, is better than the original DMU (as it either produces more outputs by using the same input quantities or it achieves the same output with fewer input quantities), then the original DMU is inefficient. There exist different models for each situation of returns to scale. The standard DEA model with constant returns to scale is referred to as the CCR model (after Charnes et al., 1978). An alternative model assuming variable returns to scale is referred to as BCC model (after Banker et al., 1984). The oriented models aim at reducing inputs at given output levels (in the case of input-oriented models) or at improving outputs at given input levels (in the case of output-oriented models). These models focus on proportional improvement and reduction potentials (there are further models which display improvement potentials not proportionally for all inputs/outputs but for each input and output separately). The “classical” efficiency measure of Farrell (1957); Farrell was the first to formulate the ratio was based on pioneer work of Koopmans, 1951, and Debreu, 1959) max
output input
was generalised by Charnes et al. (1978) for multiple inputs and outputs. In an economic context, often simple ratios like output per worker or output per working hour (ratios, where the numerator or denominator consists of only one input or output) are used. But they show no comprehensive picture. The danger to assign revenues to one factor although they were derived from another one cannot occur when using multiple inputs and outputs. Within the DEA model, each input and output is weighted. They are combined to a virtual composite input or output. A further advantage of DEA is its ability to be unit-invariant – the relative ranks among the DMUs do not change when changing the underlying data units (pieces, monetary units, persons:, e.g. a change from millions USD to billions USD), whereas simple output/input calculations depend on the units chosen. So, the DEA model (maximisation of productivity) has the following form: max
sum of weighted outputs sum of weighted inputs
The higher this ratio, the more efficient the DMU. Hereby, DEA evaluates n different, independent DMUs acting under the same technology. The efficiency of each DMU is measured relative to the other DMUs. All DMUs are below or on the efficiency frontier. Each DMU uses m different inputs and s different outputs. DMU jð j ¼ 1; ::::; nÞ takes the quantity xij of input i and the quantity yrj of output r. It is xij $ 0 and yrj $ 0, and each DMU has at least one positive input and output value. Mathematically, this means that at least one xij . 0 and one yrj . 0 exists ;j ¼ 1; :::; n. Let ur and vi be the weight of output r and of input i, respectively, and yrk and xik being the observed values of the DMU k under evaluation. As for universities, all department heads would give high weights to those inputs or outputs where they are good or better than the others and low weights to those where they do not show good data. DEA solves this problem by the fact that no weights are fixed at the beginning. They enter the model as unknown variables – they are calculated within the analysis.
After some mathematical transformations one gets the – from now on – linear model (e.g. for DMU k under evaluation): X mr yrk þ u* max z ¼
Data envelopment analysis
r
subject to: s X r¼1
mr yrj 2
m X
535 ni xij þ u* # 0 for j ¼ 1; :::; n
i¼1 m X
ni xik ¼ 1
i¼1
mr $ 1; r ¼ 1; :::; s
ni $ 1; i ¼ 1; :::; m u* ¼ 0 for the model assuming constant returns to scale ðCRSÞ u* free for the model assuming vaiable returns to scale ðVRSÞ: For each DMU such a linear problem has to be solved[3]. On the one hand, it gives the efficiency score u * of the respective DMU – it is the reduction factor of the inputs of the DMU. On the other hand, it shows the weights[4]. For u * , 1, the DMU is inefficient. Inefficient DMUs are projected to the efficient point (x_ !P ; y_ !P ) on the efficiency frontier. After the calculation of these coordination points, for each DMU the amount can be given by which the input has to be reduced or by which the output has to be expanded, to get an efficient DMU. This reduction/expansion amount is
jij ¼ xij 2 xPij and hrj ¼ yPrj 2 yrj : 4. Data and design of the study For our study we applied DEA as it extends the single-input, single-output efficiency analysis as often used within evaluation and performance analyses to a multi-input, multi-output measure. DEA has already been used in various countries for evaluating research activities of universities (e.g. Vakkuri and Ma¨lkia¨, 1996, Sinuany-Stern et al., 1994, Wagner, 2002). Furthermore, we chose the input-oriented BCC-model as in practice inputs are much easier to control in the short run than outputs. Ahn and Seiford (1990) discuss the problems occurring and specifications needed within DEA for university operations. They show that on the one hand DEA is insensitive to the aggregation of variables (an important aspect given the fact that due to the heterogeneity of the university departments and their research activity a rather big number of indicators is needed) and on the other hand insensitive to model selection. Seiford and Thrall (1990) focus on
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a comparison of CCR and BCC models. To decide among the various DEA-models, which situation to assume for the returns to scale, we investigated relations between input and output variables. Strikingly, we found little “economies of scale”, i.e. bigger departments are not performing much better and, by excluding the room indicator, we saw that there was no linear growth. Departments with much staff and room seem to have a coordination problem, especially when they have more than one location. So, the use of constant returns to scale was inappropriate and we applied variable returns to scale. One of the guiding principles of DEA is to choose comparable evaluation units, i.e. DMUs with an identical input/output structure. To get meaningful results, it should be: number of inputs þ numberofoutputs # numberofDMUs 4 3. Hence, it was necessary to identify a sample of comparable DMUs as well as to restrict the number of input and output variables. We concentrated on the technical and natural science departments of all universities in Austria because more than for other fields of study, such departments are installed at many different universities, the total number of the departments allows a reasonable application of DEA, and the tasks of the departments are diversified among industrial co-operation, teaching, and research. In fact, such departments exist within seven Austrian universities: (1) Graz University of Technology (Technische Universita¨t Graz). (2) Johannes Kepler University of Linz (Johannes Kepler Universita¨t Linz). (3) Karl-Franzens University of Graz (Universita¨t Graz). (4) Leopold-Franzens University of Innsbruck (Universita¨t Innsbruck). (5) Paris-Lodron University of Salzburg (Universita¨t Salzburg). (6) University of Vienna (Universita¨t Wien). (7) Vienna University of Technology (Technische Universita¨t Wien). The fields of study comprise: . biology; . chemistry; . formal sciences; . geography; . geosciences; . informatics; and . physics After some statistical tests, we excluded the departments of psychology and engineering as their production structure is completely different from the other technical and natural science departments. Further, we did not consider departments that either do not have their own administrative support and/or do not perform both research and teaching. At last, the sample comprised 133 DMUs. In agreement with the Austrian Rectors’ Conference we were allowed to use the original, disaggregated ABIV data. The ABIV (Arbeitsberichte des Institutsvorstandes, work report of the heads of departments) can be regarded as
the predecessor of the IC report. Under the previous university law, it served as general university statistics and included 48 structural and performance indicators of the Austrian universities. After having checked them for plausibility, we incorporated quantitative ABIV data of the years 2000 and 2001, as only for these two years (nearly) complete data sets exist. The calculations were done for each of these two years separately. In a first step, we aggregated the full set of the ABIV data to key indicators that are significantly relevant to control efficiency. Hereby, we applied a correlation analysis for all ABIV indicators for the years 2000 and 2001. As money was not given on the level of the DMUs, we decided to use staff and room as input indicators, since these two account for about 80 per cent of the budget. With respect to research outputs, we deducted the most relevant variables by dropping those with high correlation and by using the ones that discriminated the DMUs in different dimensions. We calculated the correlations with the full sample of all Austrian university departments in all fields of research on a general level as well as on the level of fields of study separately. Hereby, we saw that staff correlates positively, even though weakly, with the departments’ output. Furthermore, we could not detect any meaningful correlation between the size on the one hand and teaching and research on the other hand, except for published papers. Moreover, the data shows that there is no connection between the size of a department and the amount of third-party research grants. Interestingly enough, our results do not support the common argument that intensive teaching obligation negatively influences scientists in their research as we did not find negative correlation between the number of examinations and research output. Furthermore, departments supervising many PhD-students seem to incorporate their specific knowledge better in the department’s research. Based on the correlation calculations, we decided to use the following indicators for the DEA of the technical and natural science departments: Inputs . staff; and . room. Outputs . industry-specific: financial funds provided by third parties, finished (ordered) projects ad personam, finished (ordered) projects of the department; . teaching-specific: number of examinations, number of finished, supervised diploma theses; and . research-specific: monographs, original papers, project reports, patents, presentations, other publications, number of finished, supervised PhD-theses 5. Results As a first step, we calculated not just the efficiency values but also the targets and improvements and, in addition, the respective peer unit(s) for each DMU separately. Afterwards, we conducted a sensitivity analysis to prove that our results are robust over various models and variables applied. Table I summarises the results. If not indicated differently, the results refer to the year 2000.
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Table I. Austrian university DMUs 2 0 0 0 1 0 1 0 2 2 4
1 0 0 0 0 0 2 1 1 1 3
3 4 2 9
0 0 4 1 0 4 0
4 3 1 0 1 0 0
1 2 2 5
1 0 2 0 0 2 0
1 3 0 0 0 0 1
7 13 5 25
4 5 3 1 1 1 10
3 3 3 4 2 2 8
3 18 4 25
1 4 5 1 4 4 6
1 7 2 2 2 1 10
Research 2000 2001
4 4 2 10
4 2 0 0 1 2 1
0 2 1 2 1 0 4
5 3 6 14
3 5 0 0 0 3 3
3 1 1 2 4 0 3
All rounder 2000 2001
6 62 18 86
15 20 11 3 14 11 12
14 12 10 15 12 8 15
3 62 20 85
17 18 11 4 11 9 15
6 9 12 7 11 9 11
No specialisation 2000 2001
Notes: The percentages of DMUs of the sample is given, rounded, in parentheses. Specialisation: industry – efficiency measured on basis industrial outputs $85 per cent; teaching – efficiency measured on basis teaching output $ 85 per cent; research – efficiency measured on basis research output $85 per cent; all rounder – efficiency of at least two specialisations $ 85 per cent; Size: small – 1-10 employees; medium – 11-35 employees; large – . 35 employees
0 1 0 0 0 1 2
0 1 0 0 1 1 0
Teaching 2000 2001
538
University Graz University of Technology (16) Vienna University of Technology (16) Karl-Franzens- Universitaet Graz (11) Leopold-Franzens-Universitaet Innsbruck (13) Joannes Kepler Universitaet Linz (13) Paris-Londron Universitaet Salzburg (8) University of Vienna (20) Field of science Biology (18) Chemistry (20) Formal sciences (14) Geography (4) Geosciences (12) Informatics (14) Physics (19) Size Small (2000: 16; 2001: 9) Medium (2000: 63; 2001: 21) Large (2000: 21; 2001: 26) Total (133 DMUs)
Industry 2000 2001
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Within our sample, nearly half of all DMUs (2000: 48 per cent; 2001: 47 per cent) can be called efficient (a DMU is described as efficient if its efficiency score is at least 85 per cent). However, DEA reveals great performance differences among the universities. For example, the Vienna University of Technology and the University of Vienna are top with 72 per cent and 59 per cent efficient DMUs, while most of the DMUs of the University of Salzburg (27 per cent) or the University of Innsbruck (38 per cent) do not use their given resources satisfactorily. Among the fields of study, efficiency does not differ to such an extent. Geography is best with 60 per cent efficient DMUs, close to the latter is chemistry with 44 per cent. Far behind, the least efficient field of study is geosciences with 12.5 per cent which, however, increases its efficient DMUs to 38 per cent in 2001. While the general characteristics of the DMUs, like field of study or involvement in the curricula, remained nearly unchanged, a significant number of the efficient DMUs differs in size. Hereby, the number of small DMUs decreased from 16 per cent to 9 per cent, the number of large DMUs increased from 21 per cent to 26 per cent. The number of medium-sized DMUs nearly stayed the same, although the DMUs within this subsample changed in one out of ten cases from efficient to inefficient and vice versa. It is interesting to observe that 71 per cent of all small DMUs (2001: 75 per cent) and 68 per cent of the large DMUs (2001: 59 per cent) perform efficiently, the medium-sized DMUs perform comparatively worse with about one-third of efficient DMUs. To reveal specific performance focuses, we identified – with the DEA model – DMUs that either specialise in industry, teaching, or research, qualify as allrounder, or which do not specialise at all. Hereby, only 2 per cent of all DMUs concentrate on industry (2001: 3 per cent) and 7 per cent on teaching (2001: 4 per cent), while about 20 per cent emphasise research in both years. The fact that only 8 per cent of the DMUs (2001: 11 per cent) can be classified as allrounders is astonishing as, accordingly to the university law, DMUs should concentrate on industry, teaching, and research in the same way. Two-thirds of all DMUs do not specialise at all, as far as the DEA results show. Among universities, we recognised different focuses within each year. For example, in 2000 the Graz University of Technology hosted 44 per cent of all efficient teaching DMUs and only 12 per cent of all research DMUs, while the University of Vienna provided 32 per cent of all research DMUs and 40 per cent of all all rounders, but had no teaching emphases. However, the DEA results point out that specialisation can change in the following year. This insight leads to the conclusion that it is advisable not to apply data from one single year, but to evaluate long-term specialisation by data sets that represent information on at least three years (which, e.g. corresponds with the time required to write and publish a paper). Splitting up each field of study into its characteristic qualifications, formal sciences and informatics especially focus on teaching over both years, while physics and chemistry concentrate on research. Furthermore, it is interesting to analyse the influence of the DMU’s size on its specialisation. Medium-sized DMUs, as the largest subsample, represent the largest share in each specialisation group. Regarding their size, small DMUs focus on specialisation in industrial corporation (2000: 5 per cent, 2001: 0 per cent), teaching (2000: 14 per cent, 2001: 8 per cent), research (2000: 36 per cent, 2001: 25 per cent), and allrounders (2000: 18 per cent, 2001: 41 per cent) more than the medium-sized and large DMUs; only 27 per cent of the small DMUs (2001: 25 per cent) are non-specialists in contrast to the medium-sized DMUs with 74 per cent non-specialists (2001: 72 per cent).
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One of the major advantages of DEA is its ability to show the improvement/reduction amounts necessary to achieve efficiency. For managers this is crucial as they get a comprehensive and precise picture of the situation of the inefficient DMUs. In Table II, we show such improvement potentials representatively for one DMU (anonymous for data protection reasons: DMU17), but of course we calculated them for each DMU of the sample. At last, a sensitivity analysis proved that our results are robust with regard to the various DEA models VRS and CRS, to the various input and output variables applied, and over the period. In general, the more variables applied, the higher the number of efficient DMUs. Two DMUs are always efficient, independent of the model and the applied variables. Summarising the descriptive results for Austrian university DMUs, we conclude that DEA helps to reveal the different impacts of DMU characteristics, e.g. university or field of science, DMU’s size, on performance efficiency and hence on the application of IC. Although all universities are embedded in an identical political and financial framework, different internal structures might influence efficiency, which is subject to further studies, however. The field of study determines specialisation and, to a lesser extent, efficiency. The differences shown by our results may on the one hand point to different emphases within the scientific communities or to different predetermined targets, e.g. curricula. Most of all, the number of employees seems to be a relevant factor. Hereby, small DMUs perform more efficiently and focus on a specific area of activity, e.g. industry, teaching, or research. Additionally, our DEA results support the university management with improvement potentials, targets, and peer DMUs. Hence, DEA offers a detailed steering and controlling tool to specify possible changes in structure and resource allocation.
Variable
Table II. DEA: improvement potentials
Targets for DMU17 Efficiency score: 72.64 Actual Target
Staff 19.4 14.1 Room 1148.7 834.4 Financial funds provided by third parties 2,825,000.0 3,941,876.9 Finished (ordered) projects ad personam 1.0 2.1 Finished (ordered) projects of the department 1.5 5.6 Monographs 1.5 1.5 Original papers 23.0 28.2 Project papers 5.5 6.8 Patents 1.0 1.0 Presentations 46.5 46.5 Other publications 1.0 6.5 Number of examinations 330.0 330.0 Number of finished supervised diploma theses 7.0 11.8 Number of finished supervised PhD theses 4.0 4.0 Notes: Peers for DMU 17: DMU29, DMU40, DMU72, DMU86, DMU93
2000, VRS all inputs, all outputs To gain (%) Achieved (%) 27.4 27.4 39.5 109.2 272.0 0.0 22.8 23.4 0.0 0.0 550.7 0.0 68.6 0.0
72.6 72.6 71.7 47.8 26.9 100 81.4 81.0 100.0 100.0 15.4 100.0 59.3 100.0
6. Conclusion Worldwide, IC has been recognised as one of the most important resources for profit and non-profit organisations. To manage IC with regard to its relevance, it first has to be evaluated. In a second step, to identify further performance gaps and improve operational performance, benchmarking is proposed and applied as an important procedure (Marr, 2004). To do so, consultants and managers have so far been using widespread tools like IC Reports or Balanced Scorecards, which help to underline the relevance of IC. However, these tools also have limitations, e.g. with respect to the calculation, comparability and evaluation of the efficient use of IC. These drawbacks have led to the search for alternative consulting tools. The requirements of these tools go beyond those of the existing instruments with respect to their ability to handle multiple inputs and multiple outputs and the opportunity to get relative efficiency results. We present DEA as a consulting instrument that fulfils the requirements of quantitatively and comprehensively evaluating and benchmarking IC performance. It evaluates IC efficiency, which allows to check how efficient specific decision-making units manage their resources in comparison to other units. Hence, consultants get a single, explicit result, a performance picture of the evaluated unit in relation to other comparable units. Furthermore, DEA offers improvement possibilities for each input and output separately by benchmarking IC efficiency with other DMUs in the sample. DEA is a nonparametric model based on the method of linear programming. Being nonparametric represents one of its major advantages, as it requires no parameters to be fixed in advance and no a priori assumptions of the functional connections. Further, DEA provides models for various situations of returns to scale (constant, variable, non-increasing, non-decreasing). It requires, however, a sufficient number of comparable DMUs and the existence of a common set of defined indicators for the units to compare, as it is given for the Austrian universities. Thus, the method is mainly appropriate for larger samples inside an organisation or among different organisations. A further prerequisite before starting a DEA (e.g. Salerno, 2003) is the specification of the underlying variables and the determination which of these variables are defined as inputs and outputs, a necessity for all comprehensive evaluation models and not only for DEA. To show the usefulness and applicability of DEA for IC consulting, we performed an empirical study on the efficiency of university departments in Austria. The sample includes all natural and technical science departments. First, we identified input and output variables, relevant for managing such departments, with the help of correlation and factor analysis. Then, we applied a BCC-model with variable returns to scale. Afterwards, we conducted a sensitivity analysis which shows that the results hold under multiple conditions. The DEA results do not only illustrate the existence of scale efficiencies of Austrian university departments, but also show a large heterogeneity within and among universities as well as between different fields of study and thus indicate demand for managerial actions. However, they do not reflect the reasons for this heterogeneity and inefficiency. In this connection additional statistical analyses might identify specific structures and management practices on the departmental level that are of interest for IC management. Thus, our results indicate that DEA reveals the necessity for IC management. It does not replace the meanwhile classical IC evaluating instruments like IC Reports, but goes beyond their scope and helps to analyse and interpret them. DEA supports consultants and managers to find improvement gaps for the IC
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management of universities by revealing specific items and questions, for instance the optimal size of a department, the heterogeneity of departments, or the sense and usefulness of specialisation, and by benchmarking IC efficiency with other DMUs within and outside one’s university. Additionally, it supplies the management with information on improvement potentials for each evaluated DMU. Based on this information, it is possible for managers to restructure the DMU’s financial and intangible resources in order to allocate them more efficiently. We recommend DEA to consultants as a powerful tool to evaluate and benchmark IC within a single organisation as well as to compare departments of different universities. Notes 1. The Austrian Ministry for Education, Science and culture will be referred to as “Ministry”. 2. Success factory management coaching GmbH in Graz, ESPRiT Consuting GmbH and Knowledge Management Austria in Vienna as well as ARC system research GmbH in Seibersdorf carry out IC projects for universities. 3. According to linear programming theory the number of the restrictions of the dual problem is the same as the number of variables of the primal problem. Both models give the same solution, but due to numerical reasons the dual model is much easier to solve. 4. So, within the model one tries to find the weights ur and vi, which displays the ratio of inputs to outputs of the respective DMU in the best light, under the condition that these weights assign to no other DMU a greater ratio than 1 or an efficiency score higher than 100 per cent. References Ahn, T. and Seiford, L.M. (1990), “Sensitivity of DEA to models and variables sets in a hypothesis test setting: the efficiency of university operations”, in Ijiri, Y. (Ed.), Creative and Innovative Approaches to the Science of Management, Quorum Books, Westport, CT, pp. 191-208. Athanassopolous, A.D. and Shale, E. (1997), “Assessing the comparative efficiency of higher education institutions in the UK by means of data envelopment analysis”, Education Economics, Vol. 5 No. 2, pp. 117-34. Banker, R.D., Charnes, A. and Cooper, W.W. (1984), “Some models for estimating technical and scale inefficiences in data envelopment analysis”, Management Science, Vol. 30 No. 9, pp. 1078-92. Charnes, A., Cooper, W.W. and Rhodes, E. (1978), “Measuring the efficiency of decision making units”, European Journal of Operational Research, Vol. 2, pp. 429-44. Debreu, G. (1959), Theory of Value: An Axiomatic Analysis of Economic Equilibrium, Wiley, New York, NY. Dodgson, M. and Hinze, S. (2000), “Indicators used to measure the innovation process: defects and possible remedies”, Research Evaluation, Vol. 8 No. 2, pp. 101-14. Fairweather, J.S. (2002), “The mythologies of faculty productivity. implications for institutional policy and decision making”, The Journal of Higher Education, Vol. 73 No. 1, pp. 26-48. Fandel, G. (2003), “Zur Leistung nordrhein-westfa¨lischer Universita¨ten. Gegenu¨berstellung einer Verteilungslo¨sung und der Effizienzmaße einer Data Envelopment Analysis”, in Backes-Gellner, U. and Schmidtke, C. (Eds), Hochschulo¨konomie – Analysen interner Steuerungsprobleme und gesamtwirtschaftlicher Effekte, Berlin. Farrell, M.J. (1957), “The measurement of productive efficiency”, Journal of the Royal Statistical Society, Vol. 120, pp. 253-81.
Fuertes Callen, Y., Mar Molinero, C. and Serrano Cinca, C. (2005), “Measuring DEA efficiency in internet companies”, Decision Support Systems, Vol. 38 No. 4, pp. 557-73. Kaplan, R.S. and Norton, D.P. (1992), “The Balanced Scorecard – measures that drive performance”, Harvard Business Review, Vol. 70 No. 1, pp. 71-9. Koopmans, T.C. (1951), Activity Analysis of Production and Allocation, Wiley, New York, NY. Leitner, K-H. and Warden, C. (2004), “Managing and reporting knowledge-based resources and processes in research organisations: specifics, lessons learned and perspectives”, Management Accounting Research, Vol. 15, pp. 33-51. Leitner, K.-H., Sammer, M., Graggober, M., Schartinger, D. and Zielowski, C. (2001), “Wissensbilanzierung fu¨r Universita¨ten, Auftragsprojekt fu¨r das Bundesministerium fu¨r Bildung, Wissenschaft und Kunst”, available at: www.weltklasse-uni.ac.at Lovell, C.A.K. (1993), “Production frontiers and productive efficiency”, in Fried, H.O., Lovell, C.A.K. and Schmidt, S.S. (Eds), The Measurement of Productive Efficiency: Techniques and Applications, Oxford University Press, New York, NY. Marr, B. (2004), “Measuring and benchmarking intellectual capital”, Benchmarking: An International Journal, Vol. 11 No. 6, pp. 559-70. Roessner, D. (2000), “Quantitative and qualitative methods and measures in the evaluation of research”, Research Evaluation, Vol. 8 No. 2, pp. 125-32. Roos, J., Roos, G., Dragonetti, N.C. and Edvinsson, L. (1998), Intellectual Capital, University Press, New York, NY. Salerno, C. (2003), “What we know about the efficiency of higher education institutions: the best evidence”, Beleidsgerichte studies hoger onderwijs en wetenschappelijk onderzoek, University of Twente, Enschede. Schaffhauser-Linzatti, M.M. (nd), “Welches Interesse verbindet Sie mit der universita¨ren Wissensbilanz?”, in Universitaet Wien (Ed.), Wissensbilanzierung an der Universitaet Wien, Vienna (working title). Seiford, L.M. and Thrall, R.M. (1990), “Recent developments in DEA: the mathematical programming approach to frontier analysis”, Journal of Econometrics, Vol. 46, pp. 7-38. Sinuany-Stern, Z., Mehrez, A. and Barboy, A. (1994), “Academic departments efficiency via DEA”, Computers and Operations Research, Vol. 21 No. 5, pp. 543-56. Steward, T. (1997), Intellectual Capital. The Wealth of Organizations, Currency Doubleday, London. Sveiby, K.E. (1997), The New Organizational Wealth: Managing and Measuring Knowledge-Based Assets, Berrett-Koehler, San Francisco, CA. Teodorescu, D. (2000), “Correlates of faculty publication productivity: a cross-national analysis”, Higher Education, Vol. 39 No. 2, pp. 201-22. Titscher, S., et al. (Eds) (2000), Universita¨ten im Wettbewerb, Hampp Verlag, Mu¨nchen. Vakkuri, J. and Ma¨lkia¨, M. (1996), “The applicability of DEA method in performance. The case of Finnish universities and university departments”, working paper, University of Tampere Tampere, p. C 12. Wagner, K. (2002), “Evaluierung der Forschungsleistungen an der Technischen Universita¨t Wien. Analyse verschiedener Modelle zur Effizienzmessung”, doctoral thesis, University of Technology, Vienna (in German (parts available in English)). Wheelock, C. and Wilson, P.W. (1995), “Evaluating the efficiency of commercial banks: does our view of what banks do matter?”, Review of Federal Reserve of St. Louis, Vol. 77 No. 4, pp. 39-52.
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Thomas J. Housel Naval Postgraduate School, Monterey, California, USA, and
Sarah K. Nelson Intellectual Capital Ventures, LLC, Chicago, Illinois, USA Abstract Purpose – The purpose of this paper is to provide a review of an analytic methodology (knowledge valuation analysis, i.e. KVA), based on complexity and information theory, that is capable of quantifying value creation by corporate intellectual capital. It aims to use a real-world case to demonstrate this methodology within a consulting context. Design/methodology/approach – The fundamental assumptions and theoretical constructs underlying KVA are summarized. The history of the concept, a case application, limitations, and implications for the methodology are presented. Findings – Although well-known financial analytic tools were used to justify IT investment proposals, none provided a satisfying result because none offered an unambiguous way to tie IT performance to value creation. KVA provided a means to count the amount of corporate knowledge, in equivalent units, required to produce the outputs of client core processes. This enabled stakeholders to assign revenue streams to IT, develop IT ROIs, and decide with clarity where to invest. Practical implications – When stakeholders can assign revenue streams to sub-corporate processes, they have a new context for making IC investment decisions. “Cost centers” and decisions based on cost containment can be replaced. Concepts such as a knowledge market, the knowledge asset pricing model, k-betas, and a sub-corporate equities market can be developed and applied. Some of the limitations related to real options analysis can be resolved. Originality/value – This paper introduces an approach to measuring corporate intellectual capital that solves some long-standing IC valuation problems. Keywords Intellectual capital, Knowledge management, Measurement Paper type General review
Journal of Intellectual Capital Vol. 6 No. 4, 2005 pp. 544-557 q Emerald Group Publishing Limited 1469-1930 DOI 10.1108/14691930510628816
Introduction The fundamental building material and engine of wealth of the modern corporation is the creation and utilization of knowledge. The real challenge in the Information Age is to understand how to accelerate the conversion of knowledge into money through understanding how to measure knowledge assets (Kanevsky and Housel, 1998, p. 1). According to King and Zeithaml (2003, pp.1-2), current knowledge resource identification and measurement tools (such as patent or citation counts) are “crude and often inadequate.” Yet, knowledge resources are a source of competitive advantage, i.e. valuable, rare, inimitable, and lacking substitutes (Barney, 1991). A knowledge based theory of the firm requires knowledge to be “defined precisely enough to let us see which firm has the more significant knowledge and explain how that leads to competitive advantage” (Spender, 1996a, p. 49).
Sudarsanam et al. (2003, p. 1) define knowledge assets as the collection of intangible assets, as distinguished from physical or financial assets, that comprise the intellectual capital of the firm. We use the terms intellectual capital, intellectual capital assets, intellectual assets, IC assets, intangible assets, and knowledge assets interchangeably throughout this paper. Sudarsanam, Sorwar, and Marr also state (Marr, 2005: 56) that: Relative to the other components of a firm’s capital, such as physical and monetary capital, intellectual capital is more difficult to define, measure, manage, and value in the traditional sense. Yet, given the profound importance of such assets to a firm’s competitive advantage and value creation capabilities, serious attempts need to be, and increasingly are, made to establish clear definitions, measurement rules, and valuation principles.
The burgeoning body of knowledge related to measuring and managing intellectual capital testifies acutely to this need (Housel and Bell, 2001). In this paper, we introduce a set of definitions, measurement rules, and valuation principles that have guided our work for over a decade. We call them knowledge valuation analysis (KVA). KVA falls within the general parameters of the resource-based view (RBV) of the firm. However, since KVA is an analytic tautology rather than heuristical, it functions more like accounting in terms of the kinds of data it produces (i.e. data that can be described in common, universal units) and the way in which this data is gathered and analyzed (i.e. data that can be observed, counted, and utilized in traditional ways such as performance and profitability ratio analysis). Existing valuation models, including real options models, can be populated with KVA data, yielding useful results. Since the KVA tool is pragmatic and useable at the level of accounting and finance, we propose its direct application to a broad range of consulting activities and problems. Although the case we present demonstrates a single application within the telecom industry, KVA has been tested and refined for for-profit, not-for-profit, and government organizations in over 100 consulting engagements and in-house corporate settings and in equal numbers of academic research papers. We suggest it as a tool to enable organizations to quantify the performance of IC assets and link it with value creation. Current activities in IC asset valuation The valuation of intellectual capital assets has been the subject of extensive research and debate. Currently, IC valuation activities appear to be launched from three main trajectories: Accounting, finance, and the “qualitative metrics” camp. Each trajectory’s activities offer potentially valuable insights and a number of carefully reasoned valuation approaches. Accounting The accounting profession has traditionally used the term intangible assets to describe IC assets. Lev et al. (Marr, 2005, p. 42) provide the following accounting definition of intangible assets as: “ [T]he nonphysical value drivers in organizations that represent claims to future benefits.” Most regulatory accounting bodies include in their definition of IC assets the lack of physical substance, the non-monetary nature of the asset, and the prospective rather than historical nature of associated benefit streams. (Marr, 2005, p. 45) Since the objective of financial reporting is to provide useful information for making decisions based on the financial position and performance of the firm and since there is a
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high degree of uncertainty associated with future cash flows from intangible assets, the Financial Accounting Standards Board and the International Accounting Standards Board have ruled that intangible assets, especially those developed in-house, cannot be included on the balance sheet unless they meet the following criteria (Marr, 2005, p. 43): . they are identifiable; . they are controlled by the reporting firm; . it is probable that their future benefits will flow to the reporting firm; and . their cost can be reliably measured. Whether IC assets are recognized on the balance sheet or not, accounting regulatory authorities require that the costs related to developing them must be expensed immediately. This ensures that in some reporting periods the profitability and financial condition of the company will be understated and, in other periods, overstated. The result is a possible loss of relevance of accounting information (Marr, 2005, p. 43). When IC assets such as patents are acquired in a business combination, standard costing methods can easily be used to meet recognition and valuation rules. SFAS 141 and 142 allow for recognition of an acquired intangible asset at fair market value so long as it either can be identified separately from the acquired firm or has been created via contractual or legal rights. All non-identifiable IC assets are grouped under “goodwill”, which can no longer be amortized over an indefinite life and must undergo annual impairment tests for possible value depletion. Marr (2005, pp. 46-48), Lev and Zarowin (1999) and Høegh-Krohn and Knivsfla˚ (2000) support this “condition-based cost capitalization” approach (Marr, 2005, p. 50). Lev and Zarowin (1999) also propose condition-based cost capitalization for the historical costs of developing intangibles that have clearly defined development phases but have not yet achieved technical feasibility. Their method includes the carefully regulated restatement of historical information, where the firm records an asset on its balance sheet once benefits start to flow from it and also reverses prior year expenditures related to it (Marr, 2005, p. 50). To increase transparency and information flow, some enterprises have engaged in voluntary presentations of information about IC assets using methodologies such as book-to-market ratios, Tobin’s Q (stock market value of firm 4 replacement cost of assets), or a variety of qualitative measurement tools. However, firms in the USA and in Europe differ in the way information on IC is published, as do firms within industries and across reporting years. “[F]rom an investor’s perspective, there is a serious drawback to these reports or any other voluntary information on intellectual capital: the lack of harmonization (comparability) among firms, industries, or different years for which the data are published. This significantly reduces the usefulness of the information” (Marr, 2005, p. 49). The FASB project on intangibles is currently frozen, and the 2001 Securities Exchange Commission task force on intangibles states “the need for developing a disclosure framework for information on intangibles and other measures of operating performance” (Marr, 2005, p. 51). Clearly, the accounting profession is considering various intangible assets valuation approaches seriously, but must, by its conservative role and nature, be guarded in what it accepts. It is also clear that the current inability to attribute a benefit stream (i.e.
revenue, income) to intangibles at the corporate or sub-corporate level, the continued reliance on historical cost as a measure of value, and the lack of comparability in proposed valuation approach results have created a barrier to further advances within the profession. Finance As with the accounting definition already provided, the finance definition of intellectual assets indicates that: they have no immediate, measurable, or certain payoffs (income streams); and due to their embedded nature, are not susceptible to the development of a secondary market by which they could be valued. In addition, the finance definition states that intellectual assets embody the firm’s growth opportunities, “contributing to both their evolution over time and their realization in the future” (Marr, 2005, p. 56). So, firm value from a finance perspective can be viewed as: value of assets in place þ value of future growth opportunities from assets in place þ value of future growth opportunities from new assets. The second and third elements of this value proposition are “largely path dependent and derive from the firm’s accumulation of resources and capabilities from past investments” (Marr, 2005, pp. 57, 60). Sudarsanam et al. (2003) (in Marr, 2005, pp. 58-62) suggest that financial valuation models can be divided into two groups: static (historically based) and dynamic (prospectively based). They provide the following discussion of both groups. Static models develop an estimate of value as of a specified valuation date. The assets being valued are generally aggregated at the firm level, although intellectual property (IP) such as patents and brands are often valued alone. Static model valuation approaches include: . Lev’s (2001) Residual Income Model. Subtract the after-tax earnings attributable to financial and physical assets from the firm’s after-tax earnings to arrive at a residual, the knowledge earnings that can be capitalized at an appropriate discount rate. This model is a variant of the traditional financial valuation “Excess Earnings Approach.” . Brooking’s (1996) Technology Broker Model. Use an audit questionnaire to identify the firm’s intellectual asset categories. Then apply traditional valuation approaches (market, income, or cost) to each category. The market approach uses market comparables as a benchmark for asset value. The income approach estimates the income-producing capability of the asset. The cost approach estimates value based on the asset’s replacement cost. . Market- or value-based approach. Take the difference between the stock market value of the firm and the net market value of its assets. . Tobin’s Q: Take the difference between the market value of the firm and the replacement cost of its tangible assets. Dynamic valuation models include: . The Discounted Cash Flow Model. Estimate future asset cash flows and discount them using a market-determined discount rate. This model requires relatively stable, predictable cash flows and the ability to estimate an appropriate discount rate.
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Real Options Models. Use financial option pricing models to value intellectual assets, since intellectual assets are, in effect, real options created by the firm through such activities, investments, or acquisitions as: Investments in IT and human resources, customer relationship arrangements, intellectual property (IP), R&D, and practices and routines.
A review of the literature indicates that all financial valuation models have the same limitations in one form or another: . IC assets must be valued as an aggregate with no ability to separately value individual assets (other than certain types of IP); . differences in the national, industry, and firm accounting standards and policies that govern the recording of the IC assets create a lack of comparability of value estimates; . an inability to define either exactly how much IC assets contribute to firm value or the process by which they do so; . difficulty in estimating the replacement cost of IC assets, their future cash flows, or the risk (volatility) and uncertainty (probabilities) associated with these cash flows; . difficulty in capturing path dependencies and asset synergies in value estimates; and . lack of historical data to use for benchmarking and forecasting. Many or most of these problems could be addressed by a method to estimate sub-corporate cash flows, i.e. cash flows for IC assets such as people, processes, and information technology. Qualitative metrics There is a large universe of qualitative metric approaches: Kaplan and Norton’s Balanced Scorecard, Edvisson and Malone’s Skandia Reporting Model, Prusak and Davenport’s Knowledge Outputs model, and newer models such as those proposed by King and Zeithaml (2003) and Chen et al. (2004). However, since KVA fits within the accounting and finance disciplines, rather than the qualitative metric discipline, we will not provide a review of qualitative metrics approaches in this paper. Methodology The problem we sought to address was seemingly straightforward. “To value any asset, we need to specify an income stream clearly identified with that asset” (Marr, 2005, p. 58). In 1991, while at Pacific Bell, we were asked to make business cases for investments in IT, specifically expert systems. We needed to be able to assign a benefit stream (i.e. revenue) to IT in order to justify IT investment proposals because, without a benefit stream, the IT value equation had no numerator and valuation was impossible. We attempted to use the discounted cash flow model, NPV, IRR, activity-based costing models, and other approaches to develop a proxy for revenue so we could focus on value creation at a sub-corporate level. However, none of these provided a satisfying answer because there was no unambiguous, verifiable way to tie revenue to IT at the sub-corporate level. We wanted more than another approach to cost containment.
KVA theory was developed from the complexity theoretic concept of the fundamental unit of change, i.e. unit of complexity. The information bit was theoretically the best way to describe a unit of Kolmogorov complexity. However, to make the operationalization of the KVA more practical, we used a knowledge-based metaphor as a means to describe units of change in terms of the knowledge required to make the changes. We sought to meet a single goal: to provide a means to count the amount of corporate knowledge, in equivalent units, that is required to produce the outputs of the organization. Our underlying assumptions (represented in Figure 1) were that: humans and technology in organizations take inputs and change them into outputs through core processes; and by describing all process outputs in common units, i.e. the knowledge required to produce the outputs, it would be possible to assign revenue, as well as cost, to those units at any given point in time. Our notion of intellectual capital started with a practical need and was formulated around direct observation of IC asset performance within the core processes of the organization by describing process outputs in units of learning time. Over time, we came to define intellectual assets as one category of organizational assets, all of which can be quantified and valued in terms of a common unit of measure we called the Knowledge Unit (Km).
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A brief overview of KVA theory We have built KVA theory from a somewhat ad hoc language of description to a highly formalized methodology. The following is abstracted from the most current version of the theory (see Housel and Nelson, 2004). The amount of change caused by a process can be described in terms of the time required by an “average” learner to learn how to produce process outputs. These units of learning time we define as Km’s. The Ku unit is proportionate to an information bit which is proportionate to a unit of Kolmogorov complexity which is proportionate to a unit of change. It is the descriptive language for change within the knowledge metaphor. No measurement is possible without observation, and observation is meaningless without using a descriptive language to record it. Within the KVA theory, change measurement can be discussed in any descriptive language so long as the language
Figure 1.
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will provide reliable estimates of value stated in common (equivalent) units. For this reason, there have been many languages used within the KVA theory to describe units of change, e.g. tasks, process instructions, Haye knowledge points, Shannon bits, Jackson structural diagram decision points, units of knowledge. We have chosen to discuss units of change (complexity) in terms of the knowledge required to reproduce them, because the operational metaphor, “knowledge,” is easy to understand and rapidly apply to generate estimates of change. Although the term “bits” describes units of complexity (i.e. change) at the most granular level, it is currently impractical to count them. The knowledge metaphor, however, allows us to describe change in terms of the amount of knowledge required to make changes and to discuss intangible activity (i.e. activity that is not directly observable, as is the case with many IC activities) more satisfactorily. Knowledge units are less precise (rougher cut) than bits, but more practical to estimate. The outputs of all processes can be standardized by describing them in terms of the number of units of change (complexity) required to produce them, given the existing technology. Outputs, such as products and services, have value derived from their inherent characteristics. These characteristics have been predetermined by the products’ designers and are embodied in the corporate knowledge needed to produce the products. If the output will always be the same, given the same input, then we describe the output as predetermined. Another definition of a predetermined output is that, once it is produced, it does not vary within reasonably well-defined boundaries. Its inherent characteristics are fixed. Due to the growth of knowledge-based industries, the professional services, and “customized” manufacturing, there are a sizeable and growing number of core processes that produce outputs which belong to predetermined categories but whose inherent characteristics may differ (be customized) from each other within the category. For these processes, we vary our estimation techniques somewhat to properly measure output Km. Processes with predetermined outputs are more or less isomorphic with computer algorithms. Therefore, process changes are virtually identical to computing. This fundamental parallelism between the structural change of substances (inputs into outputs) and information processing allows us to describe the amount of knowledge required to produce process outputs and determine the value added by the process (Kanevsky and Housel, 1998). Knowledge is embedded in process assets such as IT, employees, training manuals, etc. and all processes can be described in terms of knowledge units. A process must execute once to produce a single unit of output, represented by a given number of knowledge units. Additional levels of detail in process descriptions provide additional levels of accuracy in the estimation of the number of knowledge units comprising those processes. Foundation for KVA methodology King and Zeithaml (2003) provide the following formal summary of the academic research undergirding the actual KVA methodology: . Organizational knowledge is enacted through the perspective of multiple “knowers” in a firm (Tsoukas, 1996; Glazer, 1998; Orlikowski, 2002). Therefore, it is not appropriate to attempt to measure knowledge from one individual’s viewpoint.
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Knowledge is acquired in two stages (e.g., Anderson, 1976, Singley and Anderson, 1989), the declarative and the procedural. The declarative stage involves conscious, general knowledge that can be verbalized. The procedural stage involves practice, growing recognition of patterns, improved abilities, and lower requirements for cognitive involvement (Newell and Simon, 1972; Simon, 1974; Anderson, 1995; Gobet and Simon, 1996). Procedural knowledge is rich, embedded, specific, and embodied in actions and skills (Singley and Anderson, 1989, p. 31). Organizational knowledge resources are predominantly procedural (Nelson and Winter, 1982). However, to measure organizational knowledge requires declarative knowledge. “Managers routinely are required to communicate and transform procedural knowledge into declarative knowledge as they negotiate organizational priorities and make strategic decisions. Although it is impossible to articulate all that one knows about organizational knowledge (Leonard and Sensiperm, 1998), we suggest that experienced top- and middle-level managers are particularly adept at recognizing and articulating organizational knowledge. Tapping their knowledge about the organization.. can provide a new and valuable way to measure organizational knowledge” (King and Zeithaml, 2003, pp. 2-3).
KVA makes extensive use of top and middle manager, as well as subject matter expert, knowledge about the organization. Components of KVA methodology There are at least three measures that can be used to estimate the amount of knowledge required to produce process outputs (Housel and Nelson, 2004). They each assume process efficiency maximization as a baseline, that is, the shortest learning time per average “learner,” the least number of process instructions, or the shortest sequence of binary questions required to obtain the predetermined outputs. We consult with the top and middle-level managers of the organization, as well as subject matter experts, to obtain estimates of these measures. Ideally, at least two estimation approaches should be used during any given process analysis to provide a reliability check, provided that both estimates reflect the same underlying construct, i.e. common units of change. . The time required to learn the process. Learning time can be described as the amount of time necessary for an average person (i.e. a common reference point, “learner”) to learn how to complete a process correctly. . The number of process instructions. The number of process instructions required to generate the process output successfully can serve as the proxy for the amount of change produced by a given process. Process instructions must be made roughly equivalent in terms of the amount of knowledge required to execute them. . The length of the sequence of binary questions (i.e. bits) required to complete the process. Computer code is a reasonable proxy for amount of change and thus can serve as a proxy for the amount of knowledge required to produce process outputs.
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Case study To illustrate the KVA methodology, we have selected a case study taken from a consulting engagement performed on behalf of SBC Telecom. We chose this case because it was rich in detail, had large-scale implications for SBC, and its success created several further opportunities for follow-on consulting. The full details of the case can be found in Cook and Housel (2005).
552 Statement of SBC case problem The President of SBC Telecom was faced with a critical deadline. His new Tier One subsidiary had to write at least three sales orders and provide service to the customers in three separate market areas on October 8, 2000 or face a $10.1 million fine from the FCC. Given the short time frame for making the company operational, management decided that it would be necessary to use the company’s billing, network provisioning, and network maintenance legacy systems. However, top management invited the management team to make the case for new information systems to support sales, marketing, finance-accounting, and/or corporate management. Top management did not want to use standard company procedures to justify such investments based on how much downsizing the IS would enable. Instead, they wanted to focus on how much value new systems would provide, and on growth accompanied by low marginal costs. They wanted to know where investments in information technology would pay off, what the optimal resource allocation would be for IT. Many of these benefits had already been projected by a variety of consulting firms and integrators. However, top management wanted a sanity check on their projections. The cultural context surrounding SBC decision processes included a low tolerance for failure, consistent with SBC history as a long product cycle, monopolistic company. In addition, SBC decision makers were deeply rooted in cost-accounting based approaches and methods. Based on our experience in the telecommunications industry, and with SBC in particular, we knew that if we did not provide the company decision makers with what they viewed as credible data within this highly constrained decision context, they simply would not use it. Conducting the KVA Our team met with subject matter experts and top management over a period of three months, although we only consumed a total of about two weeks of actual work time. We primarily used audio conferences and email to gather and verify KVA data. We developed learning time data estimates based on the relative amount of time it would take for one average learner to learn eight core processes (marketing, ordering, provisioning, maintenance, billing, customer care, corporate management, and sales) within a normalized 100 months. Since we could not observe the actual executions of each process, we assumed that they executed only once per person per sample period. This meant that we could use employee head count as our proxy for number of process executions. Using the product of the number of process Km generated per employee and the number of employees executing each process, we estimated the total Km generated per process. From this data and the total annual revenues for the firm, we were able to assign revenues to each process. The next step was to develop return on knowledge ratios (i.e. ROKs, our KVA productivity ratio) for each process. KVA results are shown in Table I. ROKs are shown in Table II.
6 12 36 20 7 11 4 4 100
Marketing Ordering Provisioning Maintenance Billing Customer care Management Sales Totals
30 75 60 60 80 70 60 70
Automation (percent)c 50 225 2,592 1,440 84 285 180 672 5,528
LT for IS 218 525 6,912 3,840 189 692 480 1,632 14,888
Total LT including IS 2,350,450 5,650,119 74,387,858 41,326,588 2,034,043 7,446,319 5,165,823 17,563,800 155,925,000
540,180 2,411,517 27,780,672 15,433,707 900,300 3,053,516 1,929,213 7,202,396 59,251,500
Revenue assigned to Revenue assigned non-IS ($) to IS ($)
2,700,000 2,875,000 12,583,721 10,162,791 4,025,000 4,775,000 6,425,000 20,000,000 63,400,000
Cost assigned to non-IS ($)
600,000 1,000,000 3,583,720 1,016,279 2,900,000 2,000,000 800,000 2,000,000 13,900,000
Cost assigned to IS ($)
Notes: aRLT is employee relative learning time within the framework of a normalized 100 months; bHC is employee head count, used as a proxy for number of executions of processes: c percent automation is the percent of IS contribution to processes over and above the human contribution
28 25 120 120 15 37 75 240 660
RLTa HCb
Process
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Table I. KVA data
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Marketing Ordering Provisioning Maintenance Billing Customer care Management Sales Aggregate
Table II. Returns on knowledge (ROKs)
ROK for non-ISa ( percent)
ROK for ISa ( percent)
87 197 591 413 51 156 80 88 246
90 241 775 1,519 31 153 241 360 426
Note: aROK is calculated as revenue/process 4 cost/process
Findings These results allowed us to concentrate in depth on estimating the value of using web-based IS to enhance the productivity of the current low-automation sales process. We used KVA to build a comparison between the current non-web-based sales process and a competitor’s existing web-based sales process. The ROK for the web-based sales process was over 432 percent higher than that of the non-web-based process. We were able to demonstrate that by implementing a sales automation tool (e.g., Siebel CRM system), SBC would increase the ROK of the sales process by at least 30 percent. We also were able to demonstrate that the ROKs on provisioning and maintenance (traditionally considered pure “cost centers” by management) were substantially higher than on other processes, in spite of their relatively high costs. KVA made it clear that some supporting IS provided better returns than others, and that higher returns on IS appeared to be associated with processes that had been intentionally optimized to take advantage of legacy IS (i.e. provisioning and maintenance, but not billing). This supported the notion that process design, rather than type of IS, might be the most crucial issue in predicting and maintaining the highest ROK on IS. All in all, during the three years following the initial KVA analyses, there was a 20-30 percent reduction in operating costs and a 20-30 percent improvement in revenues. “We have moved from a stand alone entity that competed out of the [SBC] region to one that has a nationwide footprint. This allows SBC to take care of our customers telecommunications needs end-to-end” (Tim Harden, October 3, 2002). SBC has implemented several other KVA initiatives since 2002. Limitations Since incipience, there have been a number of limitations to KVA theory and practice, all of which have been or are currently being addressed. They are: (1) We have been primarily focused on developing and implementing a practical, observation-based methodology for real-world use and only secondarily focused on standardizing KVA theory or migrating it into the languages of accounting and finance where it could be adopted more widely and easily.
(2) Our data-gathering methods have not been fully standardized. So, although much data has been collected, we do not yet have a database of comparable historical KVA information from which to begin to benchmark future work or provide broader-scale insights for current work. (3) Due to the non-observable nature of “meta” knowledge, “meta” processes, and management/creative staff processes, KVA theory, until recently, has simply added the cost of these components of value into the total cost of overhead. (4) Finally, we have needed to embed KVA in a solid, useable, flexible software product that would provide the analytic power and storage capacity to undertake large-scale, complex KVA research and practical applications. Latest developments and future possibilities KVA theory has been tested in a wide variety of practical and academic settings since its creation, including in collaboration with several leading management consulting firms such as Deloitte & Touche, KPMG, and Ernst & Young. We continue to refine and use it as an analytic tool for major IT projects and other organizational initiatives where intellectual capital is the focus of concern. Since early 2004, we have focused heavily on revisiting and addressing the limitations discussed above. By mid-2005, KVA will finally be embedded in software capable of unleashing its full potential. Once KVA becomes more universally utilized and larger pools of KVA data have been gathered across time and industries, it will supply valuable benchmarking information that is both consistent over time within the organization and comparable among organizations and industries. At that time, some of the IC asset transparency and comparability issues that plague accounting and finance can be addressed more successfully. Currently, we are in the process of exploring and publishing the implications of KVA theory for fundamental financial theory and for the practice of real options analysis. We have developed and introduced such concepts as an endogenous knowledge market, the knowledge asset pricing model, k-betas, and an exogenous sub-corporate equities market. We are also using KVA data and knowledge market theory to resolve some of the long-standing limitations faced by practitioners of real options analysis (Nelson and Housel, 2005). Finally, we hope that KVA theory will evoke new avenues of research and application within the consulting sector regarding the measurement and management of IC assets. Perhaps it may provide this sector with one additional key to further unlocking Information Age wealth creation on behalf of client companies world-wide. References Anderson, J.R. (1976), Memory, Language, and Thought, Erlbaum, Hillsdale, NJ. Anderson, J.R. (1995), Learning and Memory: An Integrated Approach, Wiley, New York, NY. Barney, J.B. (1991), “Firm resources and sustained competitive advantage”, Journal of Management, Vol. 17 No. 1, pp. 99-120. Brooking, A. (1996), Intellectual Capital: Core Assets for the Third Millenial Enterprise, Thompson Business Press, London. Chen, J., Zhu, Z. and Xie, H.Y. (2004), “Measuring intellectual capital: a new model and empirical study”, Journal of Intellectual Capital, Vol. 5 No. 1, pp. 195-212.
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Cook, G. and Housel, T. (2005), “Where to invest in information systems: a CRM case study”, paper presented at the HICS Conference, January. Glazer, R. (1998), “Measuring the knower: toward a theory of knowledge equity”, California Management Review, Vol. 40 No. 30, pp. 175-94. Gobet, F. and Simon, H.A. (1996), “Recall of random and distorted chess positions: implications for the theory of expertise”, Memory and Recognition, Vol. 24 No. 4, pp. 493-503. Høegh-Krohn, N.E. and Knivsfla˚, K.H. (2000), “Accounting for intangible assets in Scandinavia, the UK, the US and by the IASC: challenges and a solution”, International Journal of Accounting, Vol. 35 No. 2, pp. 243-65. Housel, T. and Bell, A. (2001), Measuring and Managing Knowledge, McGraw Hill/Irwin, New York, NY. Housel, T. and Nelson, S. (2004), Knowledge Valuation and Portfolio Management for Capabilities-Based Planning, Office of Force Transformation, Department of Defense, Washington, DC. Housel, T. and Kanevsky, V. (1995), “Reengineering business processes: a complexity theory approach to value added”, INFOR, Vol. 33 No. 4, p. 251. Kanevsky, V. and Housel, T. (1998), “The learning-knowledge-value cycle”, in von Krogh, G., Roos, J. and Kleine, D. (Eds), Knowing in Firms: Understanding, Managing and Measuring Knowledge, Sage, London. King, A.W. and Zeithaml, C. (2003), “Research notes and commentaries: measuring organizational knowledge: a conceptual and methodological framework”, Strategic Management Journal, Vol. 24 No. 8, pp. 763-72. Leonard, D. and Sensiperm, S. (1998), “The role of tacit knowledge in group innovation”, California Management Review., Vol. 40 No. 3, pp. 112-32. Lev, B. (2001), Intangibles: Management, Measurement, and Reporting, The Brookings Institution, Washington, DC. Lev, B. and Zarowin, P. (1999), “The boundaries of financial reporting and how to extend them”, Journal of Accounting Research., Vol. 37 No. 2, pp. 353-85. Marr, B. (Ed.) (2005), Perspectives on Intellectual Capital: Multidisciplinary Insights into Management, Measurement, and Reporting, Elsevier, Boston, MA. Nelson, R.R. and Winter, S.G. (1982), The Evolutionary Theory of Economic Change, Belknap Press of Harvard University Press, Cambridge, MA. Nelson, S. and Housel, T. (2005), “Knowledge market theory: methods and implications”, paper presented at the Real Options Conference, June. Newell, A. and Simon, H.A. (1972), Human Problem Solving, Prentice-Hall, Englewood Cliffs, NJ. Orlikowski, W.J. (2002), “Knowing in practice: enacting a collective capability in distributed organizing”, Organization Science, Vol. 13 No. 3, pp. 249-73. Simon, H.A. (1974), “How big is a chunk?”, Science, Vol. 183 No. 4124, pp. 482-8. Singley, M.K. and Anderson, J.R. (1989), The Transfer of Cognitive Skill, Harvard University Press, Cambridge. Spender, J.-C. (1996), “Making knowledge the basis of a dynamic theory of the firm”, Strategic Management Journal, Vol. 17, Winter special issue, pp. 45-62. Sudarsanam, S., Sorwar, G. and Marr, B. (2003), “Valuation of intellectual capital and real options models”, paper presented at the PMA Intellectual Capital Symposium. Tsoukas, H. (1996), “The firm as a distributed knowledge system: a constructionist approach”, Strategic Management Journal, Vol. 17, Winter special issue, pp. 11-26.
Further reading Lev, B., Can˜ibano, L. and Marr, B. (2005), “An accounting perspective on intellectual capital”, in Marr, B. (Ed.), Perspectives on Intellectual Capital: Multidisciplinary Insights into Management, Measurement, and Reporting, Elsevier, Boston, MA, pp. 42-55. Sudarsanam, S., Sorwar, G. and Marr, B. (2005), “A finance perspective on intellectual capital”, in Marr, B. (Ed.), Perspectives on Intellectual Capital: Multidisciplinary Insights into Management, Measurement, and Reporting, Elsevier, Boston, MA, pp. 56-68.
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The current issue and full text archive of this journal is available at www.emeraldinsight.com/1469-1930.htm
Strategic use of IC reporting in small and medium-sized IT companies A progress report from a Nordic project Eggert Claessen Tolvumidlun hf., Reykjavik, Iceland Abstract Purpose – The purpose of this paper is to report on how IT sector organizations in all five Nordic countries have worked together to start a project on using intellectual capital (IC) reporting to improve strategy formulation in SMEs in the IT sector. The project, called PIP (Putting IC into Practice), is partly funded by the Nordic Innovation Centre (www.nordicinnovation.net). Design/methodology/approach – The paper builds on the existing literature as well as the experience from the Nordic project. Findings – The objective of the project is to produce, implement and disseminate harmonized indicators for realising intangible values in companies. Results from the project include the identification of common indicators for intangible values and how they can be used as supportive evidence for IC reporting. By using these indicators with strategy maps and scorecards, the companies are provided with tools and information to improve their strategy formulation process and develop further their competitive advantage. Practical implications – The project aims to provide ways to put IC into practice as a tool for management in order to improve performance. Providing an open source framework for assisting the knowledge transformation process within companies is an important step is this respect. If successful, this will affect the management and reporting of IC. Originality/value – This paper reports on the practical application of the resource-based view of strategy, where intangible resources play a major role in the internal development path of a company. Even though the scope is limited to SMEs in information technology in the Nordic countries, the results are of interest, especially in terms of practical value to other types of companies and industries. Keywords Intellectual capital, Disclosure, Knowledge management, Small to medium-sized enterprises Paper type Case study
Journal of Intellectual Capital Vol. 6 No. 4, 2005 pp. 558-569 q Emerald Group Publishing Limited 1469-1930 DOI 10.1108/14691930510628825
Introduction The development in world business has changed the focus in management from looking only at tangible resources to looking at intangible assets and their relationship to future value creation. The problem for management has been that there appear to be gaps between what gets measured internally and what gets reported externally. This can be attributed to lack of reliability of measurement of intangible asset and a lack of understanding of how to present valid measures due to the characteristics of intangibles (Gray et al., 2004). Rylander et al. (2000) argue that the goal of disclosure should be to provide relevant, reliable, and timely information to those who need to know it so that they can make decisions concerning their relations with the company. Supporting the argument for successful implementation of measures and reporting is Thomas (2003), who suggests that corporate reports are more likely to generate
rewards in the capital markets if the reader can visualize a link between strategy and areas such as employees, the environment and corporate performance. There is a growing number of companies that have started to report their intangible assets or indicators for such in their annual reports (Guthrie and Petty, 2000). The companies may however be hesitant to disclose important figures for fear of giving away their competitive advantage but a lack of external disclosure standards and the lack of clarity in intellectual capital (IC) constructs for disclosure can also hinder measurement and reporting. There is also the question of the perceived importance of different intangible resources across companies or industries, which should call for more detailed studies of what is disclosed of IC and for what reasons. Such reasons are identified by Marr et al. (2003) and reveal that IC disclosure is not limited to external reporting. IC disclosure also has practical implications internally as it can help organizations in the strategy formulation, strategy execution assessment, assist in diversification and expansion decision and finally as a tool in determining compensation. This practical aspect of IC disclosure is the subject of a Nordic project initiated by the IT sector organizations in the five Nordic countries and the Nordic Innovation Centre. The objective of the project is to produce, implement and disseminate harmonized indicators for realising intangible values in the Nordic countries. It aims to provide new ways to put IC into practice as a tool for management to improve performance by providing an open source framework for assisting the knowledge transformation process. It is expected that the harmonization of indicators for IC will change the consulting practice when dealing with intangible resources within companies and how IT companies, in particular, identify and use their competitive advantage from a strategic perspective (PIP-Project, 2005). The motive and rationale of such a project can be questioned in terms of relevance and importance. The main argument is that IC disclosure is presumed to be relevant to companies internally and also to outside stakeholders. The IC report deals with intangibles, so only a limited part of it is discussing monetary value. This means that the reader has to form an opinion based on his understanding of the content of the report. This is why harmonization of contents and indicators is important. In order to create a common language for understanding the IC report in the same manner as traditional accounting practices have done for financial statements, it is necessary to handle the subject in the same manner. From a management perspective the question is also whether there is any relation between IC measures or indicators and the managerial steering models available and if this affects the strategy formulation process. In order to use IC as a resource in strategy formulation, it is necessary to understand what constitutes as a resource and how it can be used to leverage the competitive situation of the firm. The resource based perspective was identified by Penrose (1959) and continued with Wernerfelt (1984) and Grant (1991) but the intangible or knowledge aspect was addressed by Grant (1996) and further by Sveiby (2001), where the knowledge based theory of the firm looked at knowledge as an important resource in developing corporate strategy. Even though there is a vast body of available literature, Marr et al. (2003) state that there is little empirical testing of theories in the area of strategy development, diversification and expansion.
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IC reporting Traditional accounting information has been loosing relevance as the gap between market value and book value has been increasing. Nakamura (1999) has explained this as being partly because investors started to value the increasing level of investment in IC as potential sources of future profitability. IC reporting is intended to fill the gap in traditional accounts but as IC reports are relatively new, i.e. 15 years at most compared to over 300 years of traditional financial accounting, there is some controversy over the usability of IC reports. From an accounting perspective, the question is whether IC statements can be systematically read and analysed in a way that is comparable with the reading and analysis of financial statements. Mouritsen et al. (2003) offer a guarded “yes” as an answer, saying that the IC statement analysis method has much in common with the principles behind financial statement analysis but point out that the method is new and has only been tested by a few analysts. In order to explore the similarities between the financial statement and the IC statement, two parallel sets of questions have been prepared. For the financial statement the questions are: What are the company’s assets and liabilities? What has the company invested? What is the company’s return on investment? For the IC statement the questions are: How is the company’s knowledge resource comprised? What has the company done to strengthen its knowledge resources? What are the effects of the company’s knowledge work? (Danish Ministry of Science, Technology and Innovation, 2003) The accounting community has addressed these questions and tried to fit them into the traditional accounting framework. Lev et al. (2005) offer an overview of current practices for accounting for intangible assets. The basic approaches to accounting for intangibles is matching outlays with future revenues or expensing the outlays in the years incurred if they cannot be matched with future revenues. The current accounting standard for intangibles, IAS 38, lists the criteria as being able to identify the asset, the future economic benefits will flow to the enterprise and that the cost can be measured reliably. This could apply easily to intangible assets such as brand and patents but identifying, measuring and reporting internally generated IC causes serious problems for accounting. According to Mouritsen et al. (2005), shortcomings in corporate reporting have been addressed by various initiatives in Europe. Projects like E *Know Net, MERITUM and PRISM, dealt with the creation of a network of researchers and practitioners for research into the management and reporting of intangibles. On a Nordic level, the most noticeable projects have been the Danish government program, Nordika and Frame projects. These projects, like other mentioned, have been of a practical nature as they have produced a framework or guidelines for IC reporting. A common element of these guidelines is the need for not only finding measures for IC but also to use a narrative to explain how IC is used in the operation of the firm (Mouritsen et al., 2001a, b). The narrative is intended to provide the reader with enough information to determine the nature of a company’s business and its potential for value creation in the future. Mouritsen et al. (2001a, b) explain this as the knowledge narrative being a storyline explaining the capabilities of the company and how the firm will be able create value in the future. It is both a description of the company’s ability to create value and the value proposition itself. It also identifies the challenges that the company faces where management explains how it will put in place or develop measures to use
the company’s resources to realize the expectations for value creation. Indicators or numbers that in relation to the knowledge narrative explain the development of the company’s resources support any claims made in the narrative and is the only form of measure available in the IC report. The idea of using storytelling or narrative as way to get the corporate message across has been addressed by Denning (2001) where he asks the question whether stories really have a role to play in the business world. Stating that most executives operate with a particular mind-set, it is analysis that drives business thinking by cutting through the fog of myth, gossip, and speculation to get to the hard facts, undistorted by the hopes or fears of the analyst. The strength lies in the objectivity, which is at the same time also a weakness. But at a time when corporate survival requires disruptive change, leadership involves inspiring people to act in unfamiliar and often unwelcome ways. This is when the most logical arguments might not work, but effective storytelling could translate dry and abstract numbers into compelling pictures of a firm’s goals (Denning, 2004). This view is supported by Simmons (2001), saying that a story can do what facts cannot, just as knowledge can become wisdom, so do facts become a story. The story can influence the interpretation of the facts. The story also delivers the context in which the facts are evaluated. People are not rational which means that facts are not only the facts! This is why narrative forms could further different business goals. The two sets of questions addressed by traditional and IC accounting relate to the same management problems, but are not identical as the answers are based on different types of data. This makes it technically impossible to carry out a uniform financial analysis on both sets. The fact that detailed accounting standards have been established over time to specify the correct use and interpretation of figures and concepts, has created a common context for addressing financial issues that makes the IC statements seen as giving a less credible and less relevant company evaluation. Recent accounting scandals have proven that not all financial statement figures are as unambiguous and informative as could be expected (Chatzkel, 2003). This is why the role of IC statements is becoming greater as it completes the view of an organization and its possibilities for value creation in the future. The PIP project A Nordic project on IC disclosure and dissemination of IC practices called PIP, Putting IC into Practice, started formally on February 15, 2004. The project is scheduled to take 30 months and has the objective to produce, implement and disseminate harmonized indicators for realizing intangible values in the Nordic countries. It aims to provide new ways to put IC into practice as a tool for management to improve performance by providing an open source framework for assisting the knowledge transformation process. The project is focused on SMEs in the IT industry, as the majority of IT sector organization members are SMEs and the IT industry has a history of being IC dependent. The project is the collaborative effort of IT sector organizations in the Nordic countries, i.e. Denmark, Finland, Iceland, Norway and Sweden. It is partly financed by the Nordic Innovation Centre, which partly dictates how the project is organized. It has a steering committee with representatives from each of the member organization, a project leader and two project co-ordinators, who are responsible for the daily
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operations of the project. This management team is responsible for preparing the material used in the project, the organization of work meeting and represent the project externally. The project is divided into four phases and tasks. Table I gives an overview of the phases. The project’s participants consist of three generations of entrants. There are ten companies, two from each country starting as first generation companies, another ten added as second generation after eight months from start and finally the last ten as third generation enter the project in month 24. Phase one First generation companies started their work by identifying the IC indicators relevant to the business. The work was based on the reporting experience gained from the NORDIKA project and the proposed set of indicators from the Icelandic IC group (see www.stjornvisi.is). The participants had to decide on what indicators to use and how they were measured and also list new indicators if not already available. The first challenge was to articulate the IC in a structured manner. In order to initiate the process, the project leader and project co-ordinator visited the participants. In these meetings, there was a brainstorming session aimed at identifying the company’s vision/mission, management challenges and initiatives and finally indicators to measure status of these. These sessions varied between companies and depended mostly on what the company had already done in this respect. The level of participation within the companies was also very different as some involved all employees at their daily lunch whereas in others only the manager and chairman were involved. When working with participants, the management team noted that the level of participation affected the output in the way that the more people involved, the more time it took. Phase two In this phase the second generation companies were invited to the project. The task for all companies was to write the preliminary IC report using the Danish guidelines (Danish Ministry of Science, Technology and Innovation, 2003) for IC reporting. In Phase one
Phase two
Phase three
Phase four
Identify indicators
Implementing indicators in participating companies
Identifying proper management tools for participating companies
Presenting the results of the project final report
Decide on the meaning of indicators
External reporting of results from implementation
Measurement of input in new models by harmonized indicators
Further dissemination and post project continuum
Create liaison networks Revision based on results with accountants, consultants etc. Table I. Project overview
Prepare results for dissemination
Evaluation of contribution, i.e. to learning process and economic results
addition the participants studied the IC reports already published by Nordic IT companies for comparison. The Danish format for IC reporting is made up of two-parts consisting of a knowledge narrative on one hand and supporting indicators on the other. The narrative addresses the previously mentioned vision/mission, management challenges and initiatives. These are reported in the three accepted dimensions of IC i.e. human, structural and relational capital (Mouritsen et al., 2001a, b). The preliminary reports from the participating companies contained a very similar narrative. The first part of the report can be described as “creating value with values”. In this part the company stated its vision/mission and explained its business area. An example would be issues such as: . honesty and professionalism; . respect for life and personality; . empowerment of skills and knowledge; and . social responsibility
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Further to this, each company would explain its main area of business and its ambitions in this respect. The next part was management challenges. These would include issues like main goals and objectives. Such goals and objectives would be about: . profitability; . growth, both monetary and non-monetary; . market position, customer relations; and . new markets, products or customer groups. Finally there was a description of the company’s IC. This description would be supported by some of the indicators that the project supplied to the partners. Each company put forward a set of indicators that was either in the pre-described list or new. The management team compiled a set of the common indicators identified and used by all. These would be recommended to the companies as a starting point in IC reporting. In addition the companies had listed which indicators would be included in their formal IC report due for phase two and the reason for discarding indicators, if not used. The reasons quoted by the companies for not using indicators were mostly relevance or not having the possibility to measure. The IC indicators were then arranged into 15 categories along the three different dimensions of IC, i.e. human, structural and relational capital. Table II lists the categories. Human capital
Structural capital
Relational capital
Employees Staff turnover and recruiting Skills and competence Employee satisfaction and attitude Executive competency
Information systems Quality management Innovativeness Competence development Working conditions Governance
Customers Market and image Visibility of expertise Networks
Table II. Categories of IC indicators
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The total number of indicators identified was around 100. The experience from the first draft of the IC reports indicates that the participating companies did not use them all or measure them all in the first instance. All intended to measure these indicators before their next IC report, even though they would not be disclosed in the external IC report. The companies stated that having guidelines or a structured framework for IC disclosure was an important step in realizing intangible assets. The challenge was to identify the right causal relationships and track the right elements as measures loose validity over time and the nature of things being measured is maybe not correctly understood. The first learning point was to identify what indicators to use and what these indicators represented. As Grojer (2001) points out, there is currently no accepted and understood method for disclosure. There was also the issue of avoiding the mistakes outlined by Ittner and Larcker (2003) where they identify three major errors in using IC indicators. The first one, not to link measures to strategy. The second, not validating the links and make sense of the causal relationship between measures and meaning. The third, not setting the right performance targets. The second learning point was to look at the indicators that none or only some of the companies used. Unused indicators were considered irrelevant to some of the companies in the IT industry but had apparent value for others. If unused indicators had value to some of the companies, this opened the question if there was some added or hidden meaning that the companies did not realize. When further comparing the preliminary IC report that the companies prepared, there is a difference between firms in the narrative, both in terms of visualisation of components and usage of numbers. (Mouritsen et al., 2001a, b) ask the question about the validity in terms of being a true and accurate description of IC. This implies an impression of diversity when reading an IC report. Even though the reports did not have a set model, it is important to note that they had a formal structure. This can be compared to the traditional financial accounts where the published accounts have categories like fixed and current assets, but behind the numbers in these categories are a number of individual accounts that are summed up in this way. The participants agreed that this would strengthen the validity of the IC report. Phase three The participants met for a work meeting to review the indicators and started work on identifying relevant managerial steering models. This was done on the premise that you can manage what you can measure. Based on Argyris (1977), Argyris’ (1991) “double loop learning” and Ryle’s (1949) concept of “knowing how and knowing that” the cognitive perspective was the assumption that having better information about the company’s resources would improve management strategy formulation and decisions. Prior to the meeting, the management team had suggested to the participants that they would use a revized version of the Kaplan and Norton (2004) strategy map. This was done to adapt the dimensions of the strategy map to the IC reporting dimension, as Kaplan and Norton have chosen not to use the generally accepted definitions of IC (Marr and Adams, 2004). The critical success factors (CSF) of the company were then aligned with the company’s vision/mission. The reason for selecting CSF instead of key performance indicators is to counter the argument that strategy maps confuse the two dimensions,
importance and influence. Just because something takes place often does not mean that it matters. It also confuses real activities with the influence of these activities. The maps created by the companies listed only a few critical success factors, not more than 12 and usually fewer. When presented with large and complex causality maps, the participants unanimously voted them to be too complicated to be of practical use. The model for the map is shown in Figure 1. The companies agreed that, the simpler the model, the more it would help. The main thing was to identify the IC values and how they affect the company and how the future vision/mission of the company could be translated into a plan for execution. The map was then aligned with the harmonized set of indicators and others that were company specific. From this, the companies developed scorecards in a Kaplan and Norton (1992) fashion. They made an effort to work on the limitations of this method as identified by Norreklit (2003) in terms of identifying cause and effect relationships and using the scorecard as a strategy implementation tool. The companies were now at a stage where they had developed a model for deployment and could adapt the process to their companies, both in terms of affecting strategy formulation and reporting their IC. The participants stated that they often struggled with the communication of the vision of rapid growth or being involved with heavy development of their product and services, which means poor economic performance for a period that exceeds the normal accounting year. They wanted a method for communicating their intent and future vision. This is much in line with Hamel and Prahalad (1989) discussion of the need for companies to have ambitions to drive their strategy. They use the term “strategic intent“ to describe how companies that began with ambitions that were out of all proportion to their resources, but created an obsession with winning at all levels. This meant that the companies needed to envision a desired leadership position and establish the criterion the organization would use to chart its progress. The PIP project deals with these issues to a great extent in its process.
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Figure 1. A PIP strategy map framework
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As the knowledge narrative of the IC report is a story with structure and evidence, it is a question if it is the criterion to chart the possibilities of the company. The story visualizes intangible values and helps to identify strengths and weaknesses. This is a vital part of the strategy formulation process as information is the key to creating knowledge. Concurring with the participants view on how detailed the story needs to be, Denning (2001) makes the point that the more specific all the elements are, the less intelligible it becomes. This is why it is not necessary for the IC report to state exact numbers like the financial accounts do. The purpose, or use, is different. The story in the IC report has done its work, when the reader will already be thinking in his own context and situation how it could be different. The reader imagines a parallel story in his own mind where things would be different if their know-how and expertise were organized in a different way (Denning, 2001). The stories however need to be based on the same understanding of what the elements of the story are, or what they represent. Otherwise a common understanding or benchmarking is impossible. In terms of creating a competitive advantage (Porter, 1985), the story can provide the manager or strategist with the material needed to utilize his strategic capabilities to the fullest. Therefore the narrative becomes the springboard for change. Phase four At the end of the project a possible 30 companies will have completed the learning journey, as this is designed as learning by doing. The project is different, because the participating companies are doing the work instead of external advisors even though it can be argued that the project management team has an advisory role. Every effort is made to create a liaison with accountants, consultants, academics, etc., and the work is based on previous experience in Nordic projects and corresponding literature. It is also worth noting that the companies get some compensation for their work as the Nordic Innovation Centre is partly financing the project. This is new to the field of IC research in the Nordic countries, as in the Nordika and FRAME project, this was not the case. This form of support has proven to be a major influence or catalyst for the companies when deciding on whether to join the project or not. The final phase of the project has a planned post-project continuum where the IT sector organizations will promote the project framework to their members and to other industries. The success of the project depends largely on this issue. The first generation companies have mostly finished the first two phases of the project, i.e. identifying indicators, writing the first IC report. They have also started work on identifying managerial steering tools, which falls into phase three of the project. When interviewed, the participants have expressed great expectations to the project. They are very interested to find an entry level to make their intangible assets visible. All of them agreed that working with other companies in the industry provided them with an excellent opportunity to start some practical application of IC management. One of the companies had already managed to chart its value creation process and communicate effectively to stakeholders based on the work done in the project. Even though the participants had not yet implemented any special managerial steering models, the preliminary results from the IC reporting exercize showed that addressing IC in a structured manner provided them with a platform to manage these issues. As most of the companies are very small, i.e. eight-180 employees, they stated that having the chance to join a project like PIP very crucial to the process of managing IC within their companies.
Conclusion When considering the various models and methods available to companies it raises the question whether the PIP project can contribute to the management of SMEs. (Mouritsen et al., 2001a, b) state that based on the results from the implementation of the Danish guidelines there is a clear indication that the companies involved considered their ability to manage their intangible assets to be improved. They further state that when firms talk about IC statements, they are expressing their interests in controlling and managing the activities of the firm. There are three types of interventions desirable for management i.e. resources, activities and effects. These interventions form the basis of the knowledge management within a firm. The participants have stated that working with other companies in the same industry has provided them with the opportunity to learn more than just IC theory and IC management because of the many things that companies have in common. The chance to have a dialogue in work groups has provided possible solutions to other practical problems. The fact that the IT sector organizations are involved in the project creates a valuable link for the participants and the future of initiatives similar to PIP. One of the problems in the project has been the turbulent nature of the IT industry that has resulted in changes in the participating companies. This has led to the replacement of two of the first generation companies and major structural changes in others. This puts added pressure on the project management team and emphasizes the need for strong leadership in a project of this nature. By participating in the PIP project, the companies hope to improve their operations and value creation mechanisms. The question to be answered is whether these objectives can be met? Participants have already agreed that systematically identifying their IC has enabled them to better manage their companies. Even though the relationship between IC management and performance has been identified, it is not possible to establish any causality. There are also limitations to these conclusions due to the limited number of participating companies. It is suggested that these issues would be addressed in a longitudinal study of a larger scale. This would be a logical next step for further research.
References Argyris, C. (1977), “Double loop learning in organizations”, Harvard Business Review, Vol. 55 No. 5, p. 115. Argyris, C. (1991), “Teaching Smart People How to Learn”, Harvard Business Review, Vol. 69, May-June, pp. 99-109. Chatzkel, J. (2003), “The collapse of Enron and the role of intellectual capital”, Journal of Intellectual Capital, Vol. 4 No. 2, p. 127. Danish Ministry of Science, Technology and Innovation (2003), Intellectual Capital Statements – The New Guidelines, Danish Ministry of Science, Technology and Innovation, Copenhagen. Denning, S. (2001), The Springboard: How Storytelling Ignites Action in Knowledge-era Organizations, Elsevier, New York, NY. Denning, S. (2004), “Telling Tales”, Harvard Business Review, Vol. 82, May, pp. 122-9. Grant, R.M. (1991), “The resource-based theory of competitive advantage: implications for strategy formulation”, California Management Review, Vol. 33 No. 3, p. 114.
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Grant, R.M. (1996), “Toward a knowledge-based theory of the firm”, Strategic Management Journal, Vol. 17, Winter special issue, p. 109. Gray, D., Rastas, T. and Roos, G. (2004), “What intangible resources do companies value, measure and report? A combination of UK and Finnish research”, paper presented at the IC Congress, Helsinki. Grojer, J.-E. (2001), “Intangibles and accounting classifications *1: in search of a classification strategy”, Accounting, Organizations and Society, Vol. 26 Nos 7-8, pp. 695-713. Guthrie, J. and Petty, R. (2000), “Intellectual capital: Australian annual reporting practices”, Journal of Intellectual Capital, Vol. 1 No. 3, p. 241. Hamel, G. and Prahalad, C.K. (1989), “Strategic Intent”, Harvard Business Review, Vol. 67 No. 3, p. 63. Ittner, C.D. and Larcker, D.F. (2003), “Coming up short on nonfinancial performance measurement”, Harvard Business Review, Vol. 81, November, pp. 88-95. Kaplan, R.S. and Norton, D.P. (1992), “The Balanced Scorecard – measures that drive performance”, Harvard Business Review, Vol. 70, January-February, p. 71. Kaplan, R.S. and Norton, D.P. (2004), Strategy Maps: Converting Intangible Assets into Tangible Outcomes, Harvard Business School, Boston, MA. Lev, B., Canibano, L. and Marr, B. (2005), “An accounting perspective on intellectual capital”, in Marr, B. (Ed.), Perspectives on Intellectual Capital: Multidisciplinary Insights into Management, Measurement and Reporting, Elsevier, Boston, MA. Marr, B. and Adams, C. (2004), “The Balanced Scorecard and intangible assets: similar ideas, unaligned concepts”, Measuring Business Excellence, Vol. 18 No. 3, p. 18. Marr, B., Gray, D. and Neely, A. (2003), “Why do firms measure their intellectual capital?”, Journal of Intellectual Capital, Vol. 4 No. 4, p. 441. Mouritsen, J., Bukh, P.N. and Marr, B. (2005), “A reporting perspective on intellectual capital”, in Marr, B. (Ed.), Perspectives on Intellectual Capital: Multidisciplinary Insights into Management, Measurement and Reporting, Elsevier, Boston, MA. Mouritsen, J., Larsen, H.T. and Bukh, P.N. (2001), “Intellectual capital and the ”capable firm”: narrating, visualising and numbering for managing knowledge”, Accounting,Organizations and Society, Vol. 26 Nos 7-8, pp. 735-62. Mouritsen, J., Larsen, H.T., Bukh, P.N. and Johansen, M.N. (2001), “Reading an intellectual capital statement: describing and prescribing knowledge management strategies”, Journal of Intellectual Capital, Vol. 2 No. 4, p. 359. Mouritsen, J., Bukh, P.N., Johansen, M.N., Larsen, H.T., Nielsen, C., Haisler, J. and Stakemann, B. (2003), Analysing Intellectual Capital Statements, Danish Ministry of Science, Technology and Innovation, Copenhagen. Nakamura, L. (1999), “Intangibles: what put the new in the new economy?”, Business Review – Federal Reserve Bank of Philadelphia, July, p. 3. Norreklit, H. (2003), “The Balanced Scorecard: what is the score? A rhetorical analysis of the Balanced Scorecard”, Accounting, Organizations and Society, Vol. 28 No. 6, pp. 591-619. Penrose, E. (1959), The Theory of Growth of the Firm, Blackwell, Oxford. PIP-Project (2005), Project Description, available at: www.si.is/nhki Porter, M.E. (1985), Competitive Advantage, Free Press, New York, NY. Rylander, A., Jacobsen, J. and Roos, G. (2000), “Towards improved information disclosure on intellectual capital”, International Journal of Technology Management, Vol. 20 Nos 5-8, p. 715.
Ryle, G. (1949), The Concept of Mind, Hutcheson, London. Simmons, A. (2001), The Story Factor; Inspiration, Influence and Persuasion Through the Art of Storytelling, Basic Books, New York. Sveiby, K.-E. (2001), “A knowledge-based theory of the firm to guide in strategy formulation”, Journal of Intellectual Capital, Vol. 2 No. 4, p. 344. Thomas, A. (2003), “A tale of two reports”, European Business Forum, pp. 79-81. Wernerfelt, B. (1984), “A resource-based view of the firm”, Strategic Management Journal, Vol. 5 No. 2, p. 171.
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The IC Ratinge model by Intellectual Capital Sweden Kristine Jacobsen and Peder Hofman-Bang Intellectual Capital Sweden AB, Stockholm, Sweden, and
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Reidar Nordby Jr Norsk Tipping A/S, Hamar, Norway Abstract Purpose – The purpose of this paper is to introduce the IC Ratinge approach as a management consulting approach to measure intellectual capital and to report on the implementation and experience in one case study firm. Design/methodology/approach – The paper describes the IC Ratinge model in the context of the exiting literature in the field of IC measurement and uses a case study to demonstrate its practical application. Findings – Based on the presented case study as well as implementations in other organizations we find the IC Ratinge model a useful tool to facilitate the analysis and discussion about intellectual capital in organizations. Practical implications – The article gives a complementary view to the most commonly used score card methods and guidelines for intangibles on how intangibles can be measured. IC Ratinge focuses on the comparability between companies and industries as well as a simplification of how to interpret intangible measures. Originality/value – The original idea for the paper was to answer the question “Why do companies really need to measure and develop intangibles?”. The answer is “To improve company financial performance”. The IC Ratinge methodology is therefore based on the answers to two other questions: “Which parameters does an executive manager need to have insightful knowledge of, in order to make the right decisions for the future?” and “From where and whom should the executive manager receive this information?”. Keywords Intellectual capital, Modelling, Management technique Paper type Case study
Journal of Intellectual Capital Vol. 6 No. 4, 2005 pp. 570-587 q Emerald Group Publishing Limited 1469-1930 DOI 10.1108/14691930510628834
Introduction Traditionally, most methods used to analyze an organization are primarily based on financial figures. The system used for financial reporting dates back to the fifteenth century, when Luca Pacioli invented double entry Italian bookkeeping (Macve, 1996). Mr Pacioli’s system was later popularized during the industrial era, as it was widely appreciated for its clarity and auto-corrective features. Now, the information era is unarguably replacing the industrial era. Evidence of this is that an increasing share of company assets cannot be found in the balance sheet, like for instance patents, customer base, brand, etc. (Andriessen, 2004). Knowledge is the main source of competitive advantage; the management of a company is becoming more about managing people than it is about managing physical and monetary assets. Today’s companies show only a limited amount of their assets on their balance sheet relative to the value they produce and they often apply different strategies than more traditional companies.
In this paper we present the The IC Ratinge approach, which was developed by Intellectual Capital Sweden[1] to enable firms to manage their intangible assets better and to give companies a practical tool to use when discussing, analyzing and measuring intellectual capital (IC). The remainder of this paper is organized as follows. We first discuss what we understand as IC before we outline our IC Ratinge tool and present a case study of how it was applied in practice.
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571 What is IC? The IC field is awash with different terms, concepts and metaphors that can often be more confusing than enlightening (see, e.g. Marr, 2005). What is for instance the difference between intangible assets and IC? Do non-financial assets and immaterial assets mean the same? For the purpose of this paper we will use the following definition for IC: All factors critical to an organization’s future success that are not shown in the traditional balance sheet.
The IC model The Intellectual Capital model (see Figure 1) is originally based on ideas put forth by Sveiby (1997) indicating a division in internal, external and market assets, and the groundbreaking work done by Leif Edvinsson at Skandia in the beginning of the 1990s (Edvinsson and Malone, 1997). Most IC models today use this division, but the words and details might vary. The IC Ratinge model contains three main areas of IC; organizational structural capital, human capital and relational structural capital. These will be discussed in more detail below. Human capital The core of the IC model is the human capital. In the knowledge-based economy, this is becoming the most important intangible asset for most organizations. Key value drivers for human capital are employee knowledge, skills, abilities, innovativeness and experience. In today’s marketplace, companies are looking for knowledge workers, for people with specific capabilities that they can apply within the organization. The key then becomes to capture that knowledge in the company’s structures, so it is transferred from individuals, to groups, to the entire organization and becomes part of the organization’s “structural” capital. As we will later see, this includes company practices, methods, and processes that yield competitive advantage. The IC model further divides the human capital into two parts: the management and the employees. There are two reasons for this: (1) They have different roles. If you believe that optimizing your IC optimizes your future success, the role of the management must be to optimize the IC. The role of the employee is then to contribute to this IC. Research has for instance shown that 70 percent of the variation in the companies’ ability to retain key people can be traced to leadership and employee commitment. (2) Experience from working with the IC Ratinge has shown that the management is extremely central to a company’s success. It is therefore separated out so that it can be analyzed in more detail.
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Figure 1. The IC model
In the management box important factors like leadership quality, communication skills, strategic skills, etc. are considered. Has management fully developed its strategic as well as operational leadership skills? Does the management function as well internally as externally? Does the company have the right management in the light of the defined business recipe? In the employee box, value drivers like loyalty, motivation, competence and experience are evaluated. Do the company’s employees have the best conceivable expert knowledge to fulfill the defined business recipe? The highest rate of productivity? Are they willing to share and transfer their knowledge into structures. This is important since it is not the presence of knowledge itself that creates value, but when it is applied to the business. Another important issue to highlight is that many believe that IC is only a measurement of how smart the employees are. Our model shows that human capital is a significant part of IC, but that there are much more to IC than the human capital part. Structural capital In addition to the human capital, the model consists of two types of structural capital, the first being the organizational structural capital, or internal structural capital. Even if a company has the right human capital foundation, it will have difficulties sustaining business success without the right enabling structural capital. That is because without the knowledge transfer methodologies, processes and systems, the company is left with individual knowledge, not replicable, organization knowledge. It will also be difficult to succeed with external relationships without any supporting structures. The IC Ratinge model divides internal structural capital in two parts: (1) The intellectual properties made up of patents, licenses, trademarks etc. Some would say that this is the most refined part of the structural capital, as there could be a market for this and it can be bought and sold. This could provide the company with a temporary monopoly and give the company outstanding performance over a period of time. (2) The process capital is perhaps the most encompassing box of the model. It consists of all internal processes (recruiting process, marketing process etc.) models (project models etc.), IT systems and documentation. Here the model looks at factors like whether the company has the most efficient tools and methods? Are all the processes structured and documented? If there is structure and documentation, to what extent are the processes actually in use? In the IC model culture is also considered a part of the structural capital. Organizational cultural aspects include an evaluation of the rate of centralization/decentralization, how hierarchical the organization is, whether or not the culture is expressed or more tacit, and to what extent visions, values and strategies are communicated in the organization. One of the main issues in IC management is, as previously discussed, to convert elements of human capital into structural capital. This transformation is crucial as structural capital can be owned by the organization. Structural capital can also be leveraged to a greater extent and reduces the dependence on human capital.
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It is, however, essential to have strong, well-developed human capital, because the major contact point with customers is through people or human capital. So it is the interplay between human capital, structural capital, and customer capital that results in the most robust IC. Relational capital The third part is called the relational, or external structural capital. This consists of a company’s external relations: (1) Their network: suppliers, distributors, lobby organizations etc. When considering a company’s network it is important to look at issues like does the company have all the contacts needed for the organization? If so, are these networks being utilized in the best possible way? Does the network give access to competence, finances, media coverage, etc.? (2) The brand. Here the model describes a company’s brand and not trademarks, which is part of intellectual properties. The model covers areas like attitude, preference, reputation etc. Is the company well known? Does the target group have great confidence in the company? Does the market perceive the company as having a significant competitive advantage in their brand. (3) Last but not least, the customers. This is in essence where your money is made, and it is one of the most important sources of competitive advantage. How the customers perceive you therefore become very important: Do they see you as a strategic supplier? A partner? Are your customers image-building in the sense that other companies might look to them to see who they buy from. Are they loyal and in it for the long term? Do you have a close relationship with them? The more you know about your customers and the closer you are to them, the more difficult it will be for them to switch. These three main parts of the IC rating model together form what we call the “operational effectiveness”. If an organization has a very good operational effectiveness, it means that it is good at what it does, but it does not necessarily mean that it is doing the “right” thing! The three different categories of intangibles must therefore to viewed in a strategic context. Business recipe For most companies, the strategic context is expressed through their business idea and the strategy they choose to pursue in order to achieve that idea. Companies that have defined a vision and outlined the strategy for achieving this is in a much better position to determine what role their IC should play in achieving the vision. Different companies will define different roles for their IC. It is actually quite unusual to find two companies with the exact same roles for their ICs simply because no two companies have the exact same context. The set of roles that a company selects for its IC depends largely on the kind of firm it is, its vision for itself and the strategy it has chosen (Harrison and Sullivan, 2000). In our IC Ratinge model, business recipe primarily contains three distinct parts: (1) The vision/mission and business idea. A company’s vision represents the long-term goal of the company and a desired future state of the organization.
The vision is often developed by top management and says something about their view on the company’s future development. The business idea gives a more detailed formulation of the vision. The business idea focuses on the possibilities that exists in the company and is an expression of what differentiates the company from its competition. It also gives a more thorough description of what the company wants to achieve both short and long term with regards to for instance market needs, technology, customers and products. (2) The business strategy. The business strategy is a further operationalization of the business idea. What are the company’s plans and activities in order to achieve their vision and strategy? Are they differentiated from the competition? Competitive advantages achieved based on the business idea are also sometimes included. (3) The surrounding business conditions. Different factors in the company’s business environment that will have an impact on the company, like for instance the competitive situation, the number of players in the industry, technological and environmental issues, etc. The business recipe is in essence the potential for the entire IC. If your organization has a weak business recipe, your operational effectiveness will be largely irrelevant. If your business recipe is strong this is a good starting point for a successful future provided that the operational effectiveness is strong. The limits of classification This model is in essence a classification system and many find this type of model to be static and claim that such models seem to miss the essence of wealth creation, and that only a combination of these resources can create value. The value, they argue, lies in the intangible assets’ combined strength and not in their individual characteristics. Companies become unique and successful by combining various types of intangible resources and not by separating human capital from structural capital and customer capital from organizational capital (Roos et al., 2001). It is the synergy in the intangibles that creates uniqueness and wealth. We agree wholeheartedly with this argument, but also realize the practical limits of comprehension and have therefore chosen to divide them like this to be better able to analyze, measure and evaluate. The elements and parameters discussed and analyzed in each box are however of a dynamic nature and look at the interaction between the different types of intangibles. Finally it is worth noting that IC/intangible assets behave differently to physical and monetary assets. First of all they are not additive and monetary. It does not make sense to try to add up human capital with customers. It is also pretty obvious that human capital, unlike financial capital cannot be owned by the organization, it can only be contracted. The question then becomes: “How do you as an organization contract it?”, and “How do you get your best people to stay?”. Are they motivated by high salaries, interesting and challenging work, an exciting corporate culture, or superior structural capital? The IC Ratinge tool Based on the IC model discussed above a comprehensive measurement and management tool has been developed (Hofman-Bang and Westerlund, 1997).
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In addition to the classification used in the IC model, the IC Ratinge model looks at the company’s intangible assets from three different perspectives, namely effectiveness, risk and renewal. A lot of the criticism directed at traditional accounting and financial management and measurement has been for the fact that it looks at history to try to predict the future. The IC Ratinge therefore consider three forward looking perspectives (see Figure 2), and in addition to looking at the current effectiveness of the organization, the model looks at the efforts and abilities to renew and develop itself, and also at the risks that the current effectiveness declines: (1) Effectiveness looks at how well is the organization performing today and if the organization is using their intangibles in the most optimal way. (2) Risk: the tool considers the threats that can be seen against the current effectiveness. It also looks at the likelihood of these threats occurring. In human capital the model would for instance look at how likely it is that key employees leave the company. If this is high it might be considered a threat, but it is also coupled to the sensitivity. The risk is higher if the person that might leave has knowledge/experience that is of crucial importance to the company. (3) Renewal and development looks at the effort to renew and develop present effectiveness. Here the model looks at factors like innovation and product development, education and development of employees, etc. Together the three perspectives illustrate the prerequisites for future success – the company’s capabilities and potential. When combined with more traditional financial measurements, the IC ratinge can provide invaluable insights for owners and managers. Methodology The methodology used for the IC Ratinge includes the evaluation of more than 200 intangible factors contributing to a company’s performance. These factors are classified under the different parts of the IC model. The structural capital is the capital
Figure 2. The three perspectives
type with most parameters. The purpose is to find the company specific key success factors with regards to the chosen or desired strategic context. In other words, the rating analyses whether the company has the right intangible assets to achieve what they want to achieve and if they are using them in the most effective way. As previously discussed, it also looks at the efforts to renew these critical success factors and the risks associated with them. The main source of information is the company’s most knowledgeable internal and external stakeholders. Personal in-depth interviews are therefore conducted with employees and management (internal) and customers, partners, government bodies etc. (external). The questions are answered using an eight-point scale and the respondents are also encouraged to provide a short explanation of their grade. Depending on the complexity of the rating, a full rating takes approximately four to six weeks to complete. The rating result is then presented on three levels: (1) the executive level; (2) the operational level; and (3) the respondent level. These are explained below.
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The executive level This is an overall comprehensive summary showing the three perspectives effectiveness (i.e. a snapshot of how well your IC is performing today), the risk that the effectiveness decreases, and the renewal, i.e. how well your current initiatives are improving the effectiveness. The grading is inspired by Standard & Poor’s (www.standardandpoors.com) terminology, where “AAA” is the best grade, and “D” is the worst. The colored bars show the result of the rating. The higher the bar, the higher the rating. In the risk perspective, a lower red bar signifies higher risk than a high bar. Consequently, the higher the bars the better it is. Table I is an illustration of the executive level (see also Figure 3). For each of the rows, the first column shows the effectiveness rating, the second shows the renewal rating and the third column shows the risk rating. The IC Ratinge result for this company is good with a relatively high overall effectiveness, strong efforts at renewal Effectiveness
Renewal
Risk
AAA – Extremely high effectiveness AA – Very high effectiveness A – High effectiveness BBB – Relatively high effectiveness BB – Average effectiveness B – Relatively low effectiveness CCC – Low effectiveness CC – Very low effectiveness C – Extremely low effectiveness D – Absence of effectiveness
AAA – Extremely strong renewal AA – Very Strong renewal A – Strong renewal BBB – Relatively strong renewal
– Negligible risk of decline
BB – Average renewal B – Relatively low renewal CCC – Weak renewal CC – Very weak renewal C – Extremely weak renewal D – Lacking renewal
R – Moderate risk of decline RR – high risk of decline RRR – Very high risk of decline
Table I. The IC Ratinge scales/grades
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Figure 3. Example of executive level
and a moderate risk. Strong competition in the industry and large powerful players are the main reasons for the relatively low effectiveness of the business recipe. The company is, however, taking several steps to improve their business recipe, shown by the strong renewal efforts. The strongest area can be found within the human capital, where both management and employees show high effectiveness and very strong renewal efforts. The main reason for the slightly higher risk in management is the company’s dependence on single individuals in the leadership team. Another relatively weak area is their processes. This comprises the company’s internal processes in addition to the company culture and organizational characteristics. There is no rating result for the intellectual property as the company in the example has not defined any intellectual property for their business. This is a fairly common occurrence as the criteria for describing assets as intellectual property is quite strict. The operational level The operational level provides additional details. The IC Ratinge uses a presentation technique called a polar chart. Figure 4 is an illustration of such a chart. This is only an example and the parameters are taken from the customer box. Polar charts like this can be made using all of the factors considered in the rating and polar charts are made for all the different parts of IC. In this case a best competitor score has also been included. In order to create the polar charts the 1-8 scale of the grading done by respondents is converted to a 0-100 scale. The higher the number the better the score. This chart provides good inputs for more detailed discussions and can also form the basis for identifying important inputs to a business management system like for instance a balanced scorecard. The scores given by the respondents in the IC Ratinge can also be divided so that results from internal respondents can be illustrated and contrasted with the external respondents score on the same factors. The same can be done to highlight differences and similarities between employees and managers. The company illustrated in Figure 4 is very vulnerable with respect to customers taking their business elsewhere. They are not perceived as being good at interacting
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Figure 4. Result example operational level – customers
with their customers either, but at the same time they seem to enjoy extremely good relationships with their customers. They also have quite a few image-creating customers. The respondent level In order to understand the operational level fully, an additional level of detail is added. In the interviews, respondents are asked to give additional comments to their rating answers. These clarifying comments are anonymously categorized according to questions and categories and included in a written document. Applying the IC Ratinge model The companies realizing the benefits of using the IC Ratinge vary to a large extent both in size and industry affiliation. The clients range from large international companies to small and medium-sized firms and governmental agencies. Below are some of the reasons why companies choose to focus on intangible assets and using the IC Ratinge. First of all it gives the company a better understanding of non-financial assets and their importance in the company’s value creation. As we have discussed earlier in this chapter, intangible assets behave differently to financial and monetary assets, and should therefore be treated differently. By having an intangible perspective we are able to bring new insights into how businesses change and perform and how intangibles interact to create value. It also provides the people in the organization with a shared language and terminology. Experience have shown that this is a very important aspect of the process as it provides the organization with a structured and pedagogical way of discussing and understanding a concept that is often perceived as blurry and unclear. The
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important thing is, however, not the exact term used, but that a company uses the same language when discussing IC. Better internal management of IC is also a result of this process. The IC Ratinge will show areas where improvements are necessary and it provides an excellent analysis and starting point for an internal measurement system that can be used to track performance and improvements over time. It also helps translate a business strategy into actionable results. Additionally it will help management to look at the whole company and not just the financial parts and establish a link between inputs, processes, the build-up of intangible assets and company performance. Most importantly it helps the management make intelligent trade-off decisions with regards to investments. Companies never have unlimited funds to invest in the company and the results of an IC Ratinge will give clear indications where the investments will give the best return. The IC Ratinge also provides the organization with an opportunity to increase transparency internally because the entire organization becomes more aware of what is actually happening in the organization. It also highlights the more tacit processes and ways of working that all organizations have, but that is not often discussed. This is a good starting point for an external reporting framework. As we will see in the case study, Norsk Tipping actively use their IC Ratinge for external reporting. Their ambitious thinking in this area earned them the MAKE award[2] both in 2003 and 2004. Many companies are reluctant to publish this type of information, but it is clear that it improves information to stakeholders about the real value and future performance of the company. It can also enhance the company’s external reputation and market valuation. Other areas of application The IC Ratinge can also be useful when an organization wants to buy or sell a company. It can serve as an intangible due diligence and give a potential buyer invaluable insight into the company they wish to acquire. There could however in some cases be problematic for a company to gain the level of organizational access required prior to a merger or acquisition. The tool has also been used in more general organizational development since an IC Ratinge will pinpoint areas where the organization has a potential to develop and improve. It also allows a company to measure improvements consistently over time. Limitations of the IC Ratinge model The IC Ratinge methodology is a generic and standardized methodology to the extent that the same questions and process are used (with only limited possibility for tailoring) for all clients. One the one hand this makes it possible to use the data as benchmark and allows for comparison, but on the other hand organizations might feel that the methodology is not sufficiently looking at their particular circumstances. The result of an IC Ratinge can provide valuable input to a measurement system like for instance a balanced scorecard, but the result cannot be used directly as an operational management system. The IC Ratinge gives an analysis of the current effectiveness of the company in addition to an assessment of the renewal and development efforts and risks, but it is not a complete measurement system. The result from certain areas where improvements are needed can, however, be entered into such
systems. Goals and critical success factors can be developed for these factors and tracked over time. Another limitation is that the IC Ratinge system and methodology is not readily available for companies to use, but is carried out by IC Sweden or one of their partner organizations. This limits the spread of the tool, but a more objective assessment is assured by using an outside company to do the analysis. Most management tools and systems have a certain amount of subjectivity, and the IC Ratinge is no exception. The number of respondents in any project is not statistically significant, but it is our experience that by choosing the most knowledgeable respondents carefully the outcomes are valid. There is always uncertainty involved when people are asked to give their opinions and grade abstract factors, but the benefits far outweighs the limitations in this respect. Case: Norsk Tipping Managing, measuring and reporting IC for future success Norsk Tipping, Norway’s leading games company, is wholly-owned by the Norwegian state. The company’s main objectives are to provide the Norwegian people with responsible games and entertainment within social acceptable conditions, and at the same time ensure a secure and long-term profit for the beneficiary organizations. They have since 2000 worked actively to develop their IC, realizing its importance as a value driver in addition to more traditional financial figures. They have twice presented the result of their IC Ratinge in the company’s IC statements forming an integrated part of their annual report. They conducted their third IC Ratinge in the fall of 2004 and the result will be included in the 2004 annual report. Supplementing their annual report with this information, they have increased transparency. The company’s strong and weak areas are exposed and this provides stakeholders with a better understanding of the company’s potential and consequently increasing stakeholders’ trust. This is however only one of the ways they use their IC Ratinge result. It has also proved to be invaluable for their internal management and business development. Below you can read their IC journey. In today’s betting world, there is a convergence between markets, between technologies – even nations and continents. Internet cafe´s are becoming mini casinos, you will soon have internet on the television screen and markets are becoming more and more open. In this environment it is important to navigate properly into the future, in order to be successful. A better understanding of new media and its impact on consumer behavior is needed and you need to understand customization and how to create successful communication both with the masses and with the individual. Yesterday’s tools and ways of thinking are not necessarily the right thing for the future. With this background Norsk Tipping felt a need to investigate some new methods and ways of looking forward and for managing and measuring their capabilities to achieve success. Norsk Tippings IC journey In 1999 Norsk Tipping (www.norsk-tipping.no) hosted the world congress for lotteries in Oslo with close to 1,500 participants from all over the world. Among the keynote
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speakers were Mr Leif Edvinsson, who told the audience about the power of the IC and how organizations could better use their intangible resources. Shortly after this congress an IC Ratinge project was initiated. The purpose of the IC Ratinge was to understand the company’s intangible assets and values better. IC Sweden offered a rating of all the company’s intangible assets. As described earlier in the paper, in-depth interviews were conducted with all key personnel in the lottery and representatives from all sectors of the company. In addition, a considerable number of staff members were offered to answer a web questionnaire. Representatives from the board of directors were interviewed as well as representatives from NT’s beneficiaries, the ministry, banks, media, retailers, suppliers and lotteries from abroad. All in all this formed a good cross section of all Norsk Tippings’ external relations. As is normal with an IC Ratinge, the outcome was a mixture of positive and negative scores. Many of them were actually very positive like external relations, brands etc. But areas with improvement potentials were also identified like the organization’s ability to share knowledge internally. The score was also very low on risk-taking, and the organizational structure was regarded as somewhat hierarchical. In addition there was a desire internally to have more clear goals and continuous evaluations of personal efforts. The IC Ratinge provided Norsk Tipping with several specific areas to work with. First of all they had a company meeting with all employees where the rating result was presented. An outcome of the meeting was to work on the areas relating to knowledge sharing, increasing the risk-taking profile and to start developing a program for using balanced scorecards to track the performance improvement and to lower the perception of the organization as hierarchical. In several of the projects teams were deliberately created with employees from different departments that would complement each other with respect to knowledge and skills. The management involved more people than usual in their strategic discussions related to crucial issues in an attempt to understand the risk exposure associated with new business development. But it was also made clear that the company would never take risks with respect to the running operations. The lottery business also has a role to play in society and has to be a responsibly run organization. With regard to the perception of the organization as being hierarchical, they were actually able to create a better feeling of a flat organization, only through discussions on how they ideally wanted to work together and by having an open door policy. The result of this process was also reflected by the improved score in the second rating that was conducted in the autumn of 2002 (see Figure 5). Several other new projects were started throughout 2002. Early in the year, a “value commission” was appointed, consisting of seven to eight people, that were asked to look into the company’s core values and give the management some recommendations for a new set of values. Several meetings later, in spring 2002, they agreed that the values courage, interaction, engagement and performance were the four words that could express the desired “soul” of the company. It became apparent that not all employees would feel the same ownership to these values, but they corresponded very well with the most important value they felt they should have, and these values also reflected the wish for stretching more towards risk-taking and to interact better and share knowledge internally.
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Figure 5. Norsk Tipping Ratinge result 2002
A team was also established to look at the organizational structure and recommend a future model. By April 2002 they came back with what can be described as a courageous model. They were not quite sure how management would receive this proposal. But it was very positively received. It led to a process ending up with a fairly radical change of the organization, a good process that they all felt quite proud of. In addition to all this a major project preparing for an eventual take over of the total gaming machine market in Norway was initiated. The Government was considering monopolizing this part of the market as it had become out of control. A model was developed and presented to the Government and they in turn recommended this to the Norwegian parliament. The IC Ratinge resulted in a high level of activities and the knowledge and awareness of the strength and weaknesses in the organization was crucial in order to be able to handle everything. Later in the autumn 2002 the CEO had two separate company meetings explaining the core values, discussing the importance of sharing the same values, etc. After these meetings surveys were conducted on the company intranet asking staff members how they felt about these values and the importance put on this work. The response was overwhelming. More than 90 per cent said this was very or rather important to them. In the annual personal development talks between managers and employees’ roles, tasks, personal and professional development related to the strategies of the company, the operative plan for the department involved and core values are now a standard part of the discussions. In this context the IC rating is always used as a reference. Based on these conversations a scorecard is created with specific goals to be achieved until the next development talk. The second IC rating conducted in the fall of 2002 was done very much in the same manner as the first. The goal was to find out if any changes had taken place and to be able to make comparisons with the 2000 rating. The rating ended up showing many similarities to the former, but there were also some changes. The perception of hierarchy had been significantly lowered, but the risk taking had not changed as much as hoped. This was also the case with the sharing of knowledge.
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Figure 6. Comparison effectiveness 2000-2002
The awareness of the strength and weaknesses in itself were perceived as valuable, and the way important processes were linked with developing the company core values and focusing of improving weak areas, had given positive results. Figure 6 shows the differences in rating for effectiveness and renewal between 2000 and 2002. The renewal efforts have improved their score for renewal efforts within processes and management, but the renewal efforts for employees are not seen as being as strong as in 2000. There might be several reasons for this, and a more detailed analysis of the operational and respondent views was conducted to find the underlying reasons. The renewal of the brand is also seen as having gone down, but it is still very high, and when looking at the effectiveness (see Figure 7), we see that it was AAA in 2000 and that it is still AAA. This might indicate that the respondents do not see the need for a very high renewal effort within that category. Looking at the effectiveness we also see that there has been an improvement within processes but that the remaining elements of IC remain the same. The changes in risk can also be illustrated like this in order to see the changes, but in this case it was not included. Last year Norsk Tipping continued their work on implementing the new organization. They established an organization for the future gaming machines, and they have also initiated a couple of new exciting projects. When the company started to work with Leif Edvinsson in 2000 they did not realize that this would lead to such a turbo-charge” of their development. The CEO feels that they cannot pinpoint exactly what has been the reason for their success, but he is certain that the attitudes developed through the different processes and IC ratings have helped tremendously. It will however always require top management attention and priorities and it must not be an annual stunt, but should be and integral part of company life and ideally referred to in different settings throughout the year like in
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Figure 7. Comparison renewal 2000-2002
company meetings, internal magazine articles, in smaller group meetings, even in Christmas greetings. External reporting of IC We end the story of Norsk Tipping by showing how they also use their IC Ratinge result for external reporting (see Figure 8). The main reason for them to use the rating result externally have been to improve information to their stakeholders about their value and future performance abilities, but also to enhance their reputation and communicate their corporate identity. There have been a lot of discussions over the last few years about the increased irrelevance of financial statements and how these inadequately represent what is actually happening in companies. There does however seem to be difficulties in agreeing on a reporting system that complements the traditional balance sheet and profit and loss statement with non-financial information for increased transparency. The reasons for this can be summarized in three interconnected problems: (1) There is evidence that the non-financial assets of importance not only varies between industries, but also between companies within an industry. (2) Without the possibility to compare non-financial indicators between companies, the readers of annual reports will have to become experts in interpreting very detailed non-financial information. (3) If the general readers of annual reports need non-financial expert interpretation skills, the entire argument for non-financial reporting – i.e. increased transparency – will most likely fail.
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Figure 8. External reporting framework Norsk Tipping (2002)
Looking at the reported IC Ratinge result for Norsk Tipping we see that it illustrates the non-financial information on such a generic level that the model could work for any type of company. Since one of the purposes of the reporting is to clarify interpretation, the result is presented with a letter grading scale, similar to the one used by financial institutes (like Standard & Poor’s and Moody’s). By adopting the scale, an existing frame of reference is used, making it easy for any stakeholder to read non-financial reports. Banks, venture capitalists, stock exchanges, private investors, etc. are all interest parties that watch the development within this area with great interest. Their challenge has been to find assessment tools that will give them a fair view of a company’s operational risk, such as strategic path, internal structures, corporate culture, people and relations. A study done in 1998 among 275 portfolio managers of institutional investors in the USA showed that the most important non-financial measures were (Mavrinnac and Siesfeld, 1998): . execution of corporate strategy; . management credibility; . quality of corporate strategy; . innovativeness; and . ability to attract employees. A tool like IC Ratinge can provide the platform they lack in order to make operational assessments for investment and lending decisions, as well as listing companies on stock exchanges.
Notes 1. Intellectual Capital Sweden is a small management consulting firm based in Sweden. Since the start, ICAB:s main focus has been to develop measurement tools within the non-financial frameworks. The most widespread tool, IC Ratinge, has been used in over 250 major IC rating projects on four continents. In short, IC Ratinge can be described as the non-financial equivalent to the financial ratings conducted by the likes of Standard & Poor’s and Moody’s. Outside Sweden, IC Ratinge projects are conducted by license partners in Norway, Japan, Germany, Finland, South Korea, Australia, New Zealand, Hong Kong, Singapore, Taiwan, China, Malaysia, the UK and Italy. 2. The Most Admired Knowledge Enterprises (MAKE) research program was established by Teleos, in association with The KNOW Network, in 1998 to identify and recognize those organizations which are creating shareholder wealth (or in the case of public and non-profit organizations, increasing societal capital) by transforming new as well as existing enterprise knowledge into superior products/services/solutions. For more information see www. knowledgebusiness.com References Andriessen, D. (2004), The Making Sense of Intellectual Capital, Butterworth-Heinemann, New York, NY. Edvinsson, L. and Malone, M.S. (1997), Intellectual Capital – Realizing Your Company’s Value by Finding Its Hidden Roots, Harper Business, New York, NY. Harrison, S. and Sullivan, P.H. (2000), “Profiting from intellectual capital – learning from leading companies”, Journal of Intellectual Capital, Vol. 1 No. 3, pp. 35-46. Hofman-Bang, P. and Westerlund, P.-O. (1997), “Intellektuellt kapital – Va¨rderingsproblematik”, Msc thesis, Stockholm School of Economics, Stockholm. Macve, R.H. (1996), “Pacioli’s legacy”, in Lee, T.A., Bishop, A. and Parker, R.H. (Eds), Accounting History from the Renaissance to the Present: A Rememberance of Luca Pacioli, New Works in Accounting History, Richard P. Brief and Garland Publishing, New York, NY and London. Marr, B. (Ed.) (2005), Perspectives on Intellectual Capital – Interdisciplinary Insights into Management, Measurement and Reporting, Elsevier, Boston, MA. Mavrinnac, S. and Siesfeld, G.A. (1998), “Measures that matter: an exploratory investigation of investors’ information needs and value priorities”, in Neef, D., Siesfeld, A. and Cefola, J. (Eds), The Economic Impact of Knowledge, Butterworth Heineman, Boston, MA, pp. 273-93. Roos, G., Jacobsen, K. and Bainbridge, A. (2001), “Intellectual capital analysis as a strategic tool”, Strategy and Leadership Journal, Vol. 29 No. 3, pp. 21-6. Sveiby, K.E. (1997), The New Organizational Wealth: Managing and Measuring Knowledge-based Assets, Berret-Koehler Publishers, San Francisco, CA. Further reading Edvinsson, L., Hofman-Bang, P. and Jacobsen, K. (2005), “Intellectual capital in waiting – a strategic IC challenge”, Handbook of Business Strategy, Vol. 6 No. 1. Hofman-Bang, P. and Jacobsen, K. (2004), “IC Ratinge - the missing link to a complete chain of IC for performance measurement”, paper presented at the PMA 2004 (Performance Management Association) Conference, Edinburgh, July 28. Marr, B., Gray, D. and Neely, A. (2003), “Why do firms measure intellectual capital”, Journal of Intellectual Capital, Vol. 4 No. 4, pp. 441-64.
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The current issue and full text archive of this journal is available at www.emeraldinsight.com/1469-1930.htm
No longer “out of sight, out of mind” Intellectual capital approach in AssetEconomics LLP and Accenture Inc. R.J. Burgman and G. Roos AssetEconomics, LLP, New York, New York, USA
J.J. Ballow Shareholder Value, Accenture Inc., New York, New York, USA, and
R.J. Thomas Accenture Institute for High Performance Business, Accenture Inc., Wellesley, Massachusetts, USA Abstract Purpose – The purpose of this paper is to describe the logic and processes for identifying, measuring and managing intellectual capital resources along side traditional economic resources to achieve sustainable outcomes valued by investors. Design/methodology/approach – The approach is founded on classical finance theory and draws on and multi-attribute value theory, system dynamics and the strands of thought known as intellectual capital in order to produce a grounded framework that both produces reliable results as well as achieves acceptance in the financial community. Findings – The findings from this approach, the FVMT Methodology, provide a comprehensive management framework that is agnostic as to the form of resources being utilized and the activities that are involved in the transformation of one resource form into another on the way to achieving sustainable outcomes valued by investors. Research limitations/implications – Whilst the approach has proven to work well the limitations are that it requires both effort and access to market makers. Practical implications – The implications is that for the first time an approach is now available that provides the same rigor for managing the future value component of a firm’s share price as there already exist for managing the current value component of the firm’s share price. This means that managers can both reduce the volatility surrounding their share price as well as predict the value outcomes of a given set of actions. Originality/value – The authors believe that this is the first presentation of a methodology grounded in classical finance theory for the managing of the future value component of a firm’s share price. Keywords Return on investment, Shareholder value analysis, Intellectual capital Paper type Case study
Journal of Intellectual Capital Vol. 6 No. 4, 2005 pp. 588-614 q Emerald Group Publishing Limited 1469-1930 DOI 10.1108/14691930510628843
1. Introduction “Managing for shareholder value” is widely accepted as the capital market’s raison d’eˆtre for management for publicly listed firms. This assertion can be made even though there is continued debate over the basis for this norm in law, which is illustrated in the legal literature (Green, 1993; Bainbridge, 1993, 2002; Roe, 2001; Fairfax, 2002; Fisch, 2004). Notwithstanding, this norm is the basis for the regulation
initiatives introduced by the US Securities and Exchange Commission (Glassman, 2003) and permeates US management thinking (Welch, 2001). The financial management disciplines that have been developed around the shareholder value maximization norm have now extended to all other forms of enterprise. For example, demonstrably creating value, though not necessarily financial, has become a major management focus for Government agencies worldwide. This focus is illustrated in the USA through the President’s Management Agenda, announced in 2001 and managed through the United States Executive Office of the President (2002, 2004a, b), supported through the United States General Accounting Office (2000) and in Canada through the Treasury Board of Canada (2002) and in the UK through HM Treasury et al. (2001). “Managing for value” is in the management lexicon of the management of all types of enterprises. The question is then: “What does it mean to ‘manage for value’?”. This was the question posed by Accenture and AssetEconomics some years ago. The simultaneous interest and the fact that the two organizations approach the area from complementary directions lead to a cooperation aimed at bringing to the market a set of tools, complementary and wider reaching than the existing ones, that would assist managers in addressing this issue. 2. Evolution of the intellectual capital concept In the section below we briefly explore how the concept of intellectual capital has evolved in both organizations. 2.1. Evolution of the intellectual capital concept at AssetEconomics The principals of AssetEconomics have had a shareholder value management advisory practice for the last 15 years plus. The principals of the practice have used standard firm-level economic performance frameworks and metrics, such as Economic Value Addede (EVAe), and market-level shareholder value creation metrics, such as Market Value Addede (MVAe), as the basis for client strategic insight development[1]. During the late 1990s, at the time of the dot.com boom, it was increasingly clear that a few things were occurring: . The companies that represented the leading edge of the new economy, firms like Microsoft, Cisco Systems and Lucent Technologies, could be typed as being “knowledge-based” as distinct from manufacturing or natural resource-based. . Not surprisingly, these knowledge-based companies were “asset-lite” in the conventional accounting balance sheet sense. Clearly, these companies were leveraging other resources to create shareholder value. . Very substantial proportions of total enterprise value were represented by future expectations for earnings and not by current earnings performance. According to Stern Stewart economic performance ranking data, of the 22 companies that represented the “new economy” (Fortune Magazine, 1999) in 1999, Microsoft was the most successful shareholder value creating company of any in the US with a MVA (enterprise value less capital employed) of $629.5 billion. Indeed, the 22 companies, with a combined MVA of $2,275.6 billion, accounted for some 31.8 percent of the
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Top 100 (Tully, 1999) MVA of $7,297.2 billion and 24.2 percent of the MVA of the entire Russell 3000 ($9,419.3 billion). These phenomena meant that managing for shareholder value was much more about managing the enterprise to meet (and exceed) the growth expectations already built into the share price and, simultaneously, managing capital that was not represented as “assets” on the traditional balance sheet; in other words, managing what we now call intellectual capital. These phenomena also meant that the frameworks and tools that were part of the approaches being then used, such as EVA and its attendant tools, were becoming increasingly passe´ and inappropriate for the types of knowledge-based companies then becoming and now an important and permanent part of the economic fabric of developed economies. The management needs of companies that represented the new economy dictated that a new framework and tools be developed to assist in identifying, quantifying, managing and reporting on all the capital forms and value creating activities that are causally connected to creating value for stakeholders. The only way in which this would be achieved, we believed, would be to answer a simple set of generic questions on behalf of enterprise management: . How do I maximize the value of the enterprise to all stakeholders over time? . How am I being valued by stakeholders? . What can I do to be more valued by stakeholders? If one accepts the premise in the introduction to this article i.e. that “managing for shareholder value” is widely accepted as the capital market’s raison d’eˆtre for management for publicly-listed firms, then the utilization of the concepts of intellectual capital has no real managerial use until they are linked to share market and enterprise performance outcomes as cause and effect. The test of whether the management of intellectual capital matters only has currency if this can be shown. Accordingly, the approach AssetEconomics has adopted to developing its consulting framework, analytic tools and diagnostics has reflected the need to reconcile and integrate capital market, and financial and economic firm performance outcome intentions on one hand and the management of all capital, including intellectual capital on the other. 2.2 Evolution of the intellectual capital concept at Accenture Managing for shareholder value and understanding the drivers of value have been the focus of management since the early 1990s. For this we can refer to writers from McTaggart et al. (1994) to Collins (2001). The attention of these writers has been on understanding the drivers of value – what value is, what its measures are and what the strategies have been that deliver it. More recently, Accenture has focused on the notion of high performance growth and the belief that management can systematically manage for this outcome. As a result, Accenture has explored both the concept of high performance and the strategies that consistently deliver high performance. While in its initial research phase, two conclusions have become immediately obvious, first, that different business models or value logics are associated with high performance outcomes (there is no predominant “success” business model) and second, that the management of intellectual capital is an ever important ingredient in the strategic mix for any business model. In other words, the management of intellectual capital matters. The focus of Accenture, and to some extent AssetEconomics, has primarily been on the
financial market aspects of identifying high performance while that of AssetEconomics has been on identifying, measuring and managing intellectual capital in the context of identifying, measuring and managing all organizationally relevant value drivers. This relationship between Accenture and AssetEconomics, initially forged based on the complementarity of the technical backgrounds of the two organizations and AssetEconomic’s particular strategy consulting experience, has grown to focus on common-interest outcomes. An example of this has been the joint submission of Accenture and AssetEconomics to the American Institute of Certified Public Accountants (AICPA) on enhanced business reporting (see Ballow and Burgman, 2004). The Accenture and AssetEconomics research and writing that have come from Accenture’s high performance business initiative includes the work of Breene and Numes (2004, 2005), Breene and Thomas (2004), Ballow and Burgman (2004), Ballow, Thomas and Roos (2004), Ballow, Burgman, Roos and Molnar (2004), Ballow, McCarthy and Molnar (2004), Ballow et al. (2005), Molnar (2004a, b), Davenport and Harris (2004), Harris et al. (2005), Harris and Burgman (2005), and Linder (2005). 3. How do we define intellectual capital? Writers such as Roos, Edvinsson, Sveiby (see, e.g. Roos and Roos, 1997; Roos et al., 1997; Edvinsson and Malone, 1997; Edvinsson, 2002; Sveiby, 1997, 1998) have laid the foundation for what today is known as intellectual capital. This foundation has been built particularly in the area of reporting on intellectual capital. Representative of this thought stream is that it formed part of the foundation for the Measuring Intangibles to Understand and Improve Innovation Management (MERITUM)[2] project of the European Commission and summarized by Canibano et al. (2002) and Starovic and Marr (2002), and the later Policy Research into Innovation and Measurement Practice in the Intangible Economy (PRISM)[3] program managed (European Commission Information Society Technologies Programme, 2003a, b, c). These bodies of work, while substantially progressing the recognition of intellectual capital in formal enterprise reporting environments have not come to grips with the underlying issue of why intellectual capital matters. The assumption is that is does. This assumption is normally never tested empirically but accepted as an article of faith (see, e.g. Marr et al., 2003). However, by ignoring this question and its answer we argue that the intellectual capital discipline cannot progress any further as an instrument of enterprise management. Accordingly, what we, given our initial assertion in the introduction to this article, mean by intellectual capital is identified by its instrumental use: Intellectual capital, as an asset, represents all the stocks of what matters to the creation of enterprise value of an enterprise that is not represented on its traditional balance sheet as monetary or physical assets.
In order to understand this definition, we need to have an appreciation of how the accounting discipline records an asset. In the USA, the Financial Accounting Standards Board, or FASB (1984, 2001a, b) defines intangibles (intangible assets) as “assets (not including financial assets) that lack physical substance. (The term ‘intangible assets’ is used . . . to refer to intangible assets other than goodwill)”, and further, “intangible asset class” refers to “a group of assets that are similar, either by their nature or by their use in the operations of an entity”[4].
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A report written for the FASB by Upton (2001)[5] identifies assets using four “recognition criteria”: definition, measurement, relevance, and reliability[6]. For the purpose of Upton’s report, intangibles are defined using a similar expression to the FASB, as “non-current assets (not including financial instruments) that lack physical substance)”. This definition reflects the essential characteristics of an asset in that an asset should, apart from its physicality: represent future economic benefits, be a consequence of a past transaction or event, and be controlled by the entity. The report extends the consideration of intangibles by considering whether intangible assets are: separable from one another, and based on contractual/legal rights. These last conditions are important to us in that they form a test for whether what matters can be defined, measured and whether such measures are relevant, reliable and valid. What we can deduce from these criteria is not whether a resource matters, but whether it meets certain definitional criteria. An intangible asset, according to the FASB, will exist because the definition for its recognition is accepted. In Europe, for the International Accounting Standards Board (IASB), the definition of intangibles is proscribed by International Accounting Standard IAS 38 Intangible Assets (IASB, 2004). We believe IAS 38 provides a more robust intangibles definition than does the FASB. IAS 38 requires an enterprise to recognize an intangible asset if certain criteria are met. The Standard also specifies how to measure the carrying amount of intangible assets and requires certain disclosures regarding intangible assets. For the IASB, an intangible asset is an identifiable non-monetary asset without physical substance. An asset is a resource that is controlled by the enterprise as a result of past events (for example, purchase or self-creation) and from which future economic benefits (inflows of cash or other assets) are expected. Thus, the three critical attributes of an intangible asset are: identifiability, control (power to obtain benefits from the asset), and future economic benefits (such as revenues or reduced future costs)[7]. Intangibles can be acquired by separate purchase, as part of a business combination, by a government grant, by exchange of assets, and by self-creation (internal generation). IAS 38 requires an enterprise to recognize an intangible asset, whether purchased or self-created (at cost) if, and only if it is probable that the future economic benefits that are attributable to the asset will flow to the enterprise, and the cost of the asset can be measured reliably. This requirement applies whether an intangible asset is acquired externally or generated internally. The probability of future economic benefits must be based on reasonable and supportable assumptions about conditions that will exist over the life of the asset. The probability recognition criterion is always considered to be satisfied for intangible assets that are acquired separately or in a business combination. If an intangible item does not meet both the definition of and the criteria for recognition as an intangible asset, IAS 38 requires the expenditure on this item to be recognized as an expense when it is incurred. As we can see, the definitions of intangibles by the two major accounting standard setters are not completely aligned. The FASB’s definition is more conservative and is reflective of the rules-based approached that has typified financial reporting in the USA to date. The IFSB requirement is broader than the FASB’s in that the FASB requires an arm’s-length transaction for the intangible to be recognized. Self-generation is not contemplated. When we consider the fundamental difference between the
accounting definition of intangibles and the definition of intellectual capital, it is clear that the essential difference is tied to the physical form of the resource or asset and the measurability of its existence in both absolute and dynamic terms. But from an intellectual capital perspective, resources do not have to have physical form to be real although the requirements for measurement remain. A consideration of the FASB and IASB criteria for acknowledging an asset nonetheless clearly reflects a necessarily restricted and conservative view on what is managed by any enterprise. In defense of the accounting discipline, its intention is not a managerial one. It never has been. But it is a managerial world we live in as consultants. For management purposes, recognition of the existence of all the assets that are being managed, understanding how to manage them for what value creating purposes and keeping score on performance are all fundamental to achieving the best possible sustainable economic performance result over time and through that, the best possible enterprise valuation result. Our reconciliation of the managerial and accounting views of the world are provided in what AssetEconomics has described as its Resource Recognition Template. This matrix juxtaposes the recognition of the form of the resource or asset and its accounting recognizability. In doing so, we have resolved a continuing issue and source of confusion for those in accounting and management and that is that intellectual capital and intangibles are not necessarily the same thing. While it is true that many intellectual capital resources are also intangible they are not exclusively so. In particular, organizational intellectual capital tends to be tangible while relationship and human capital tend to be intangible but again, not exclusively. The AssetEconomics Resource Recognition Template, as developed during 2002 and published in Burgman and Roos (2004a), is shown as Table I. The components of intellectual capital that we define are described in Table I and include two general categories of capital – traditional economic capital of which there are two forms – monetary and physical, and intellectual capital of which there are three forms – relationship, organizational and human. These five forms of capital may be represented by resources that are either tangible or intangible. The template is important for two reasons – first, it provides a descriptor for any and all nominated resources that are important for an enterprise’s value creation activities and second, it begs questions of resource dependency and transformation particularly in instances when the populated Resource Recognition Template shows that the resources upon which the enterprise relies for value creation are both of an intellectual capital form and are intangible. The issue of vulnerability is an important one for enterprises that are reliant on intangible intellectual capital since the enterprise’s authority and control over and ownership of this capital is much more tenuous than it is for enterprises relying on tangible monetary and physical capital. In addition, intellectual capital is by its nature “behaviorally different” from traditional economic capital. Additions to intellectual capital resource stocks tend to be non-linearly accretive and can be, in fact, asymptotic increasing over ranges (say, with the development of relationships), resource stock depletions can be immediate and complete (say, with the loss of reputation) and resource use does not always lead to depletion (say, with applied learning in human capital). The identification of intellectual capital for the purposes we are describing here – the instrumental identification of intellectual capital where the intention of the capital
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Monetary Tangible U Cash U Investment U Receivables U Payables U Compensation and benefits including long-term incentive schemes (including option schemes)
Intangible U Credit ratings U Accruals U Balance sheet strength U Cash flow volatility
Table I. Resource recognition template with examples
Physical U Property, plant and equipment U Inventories (raw materials, WIP, finished goods) U Stranded assets U Physical work environment
U Plant location
Relational
Organizational
Human
U Documented systems U Documented processes U Patents U Brands U Mastheads U Access rights U Management contracts U Employee contracts U Employee development and training programs U Performance management systems U Customer lists U Customer contracts U Supplier contracts U Formal alliances U Stakeholder U Organizational support structure U Preferred status U Culture U Organizational reputation U Rights to tender, to design, to participate U Networks U Regulatory imposts
U Leadership U Problem solving ability U Work environment (interaction) U Recruitment and selection U Career paths U Rewards and recognition U Employee satisfaction U Employee retention U Employee relations U Knowledge (including tacit) U Functional skills U Experience
Source: AssetEconomics, Inc
is to create value for shareholders and other stakeholders – has conditioned our definition of what intellectual capital is, and what it is not. It has become clear over time that we should be working with a resource-based view of intellectual capital. The reason for this is that it has been easy to confuse enterprise intellectual capital resources with the enterprise attributes that are the result of managing all resources, including intellectual capital resources. For this reason, and in order to establish causality as best we can, we have used the logic of attributes as valued outcomes and
resources and activities that result in resource transformations as the drivers of valued outcomes. We thus establish cause (resources and activities in relation to resources) and effect (valued attributes) as a (business model) hypothesis to be tested. 4. Evolution of tools and approaches developed to understand and manage intellectual capital AssetEconomics with Accenture has developed frameworks and set of integrated tools that permit us to answer the questions posed earlier: . How do I maximize the value of the enterprise to all stakeholders over time? . How am I being valued by stakeholders? . What can I do to be more valued by stakeholders?
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The answers to these questions will tell what represents value and therefore what matters to the enterprise’s stakeholders and what should be managed, how, in order to improve value. We believe the AssetEconomics’ Managing for Future Value Managemente Methodology (FVMe Methodology) accomplishes this. The logic for the methodology is shown in Figure 1 and the Methodology Framework in Figure 2. The framework that represents the FVMe Methodology is fundamentally an integration of a number of integrated components coming from a number of management disciplines. Illustrative of these influences (to which we have added a number of “review” and “application” references) are the following: . Market and enterprise value – Miller and Modigliani (1961), Sharpe (1964), and Kester (1984). . TRS and economic profit models shareholder value – Stern (1970), Rappaport (1986), McTaggart et al. (1994), the Boston Consulting Group (2004), Stewart (1991), Madden (1999), Ottoson and Weissenrieder (2003) and Myers (1996, 2001), Ballow and Burgman (2004).
Figure 1. Future Value Management Methodologye – a conceptual view
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Figure 2. Future Value Management Methodologye Framework .
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Business model development (strategy) – Drucker (1993), Prahalad and Hammel (1990), and Flaherty (1999). Business model development (intellectual capital) – Roos and Roos (1997), Bontis (2001), Webster (2002), Roos (2005a) and Marr and Roos (2005). Business model development (system dynamic modeling), Doman et al. (1995), Morecroft (1997, 2000), Sterman (2000), Barlas et al. (2000) and Warren (2002). Value attributes (technical) – Stevens (1946, 1951, 1959, 1968), Fishburn (1970), Krantz et al. (1971), Keeney and Raiffa (1976), M’Pherson and Pike (2001) and Pike and Roos (2004). Value attributes (stakeholder focus) – Freeman (1984), Mitchell et al. (1997), Burgman and Roos (2004b) and Bryson (2003). Valued attributes (application) – Roos and Jacobsen (1999), Roos and Lo¨vingsson (1999), Fletcher et al. (2003), Pike et al. (2005) and Roos et al. (2005). Trade-off (decision-making) analysis – Saaty (1981, 2000), Barzilai (2005), Pike and Roos (2004) and Expert Choice (2005). Futures scenarios planning – Jantsch (1972), van der Heijden (1996, 2002), and Royal Dutch/Shell Group (2005)
The components of the methodology inform one another and although a number of the components represent well-known research tools that can stand alone to provide answers to different kinds of questions, it is their application and the “informative” role that each of these tools plays that is the key to understanding the resolution to our value determination questions. The heart of the FVMe engine is the business model that drives both the recognition of the valued attributes and the value ascription to the perceived attributes, and drives economic profit. Thus, attributes and economic profit are both outcome reflections of the management of the enterprise business model and, at therefore its core, the management of the same resources and (value creating transformation) activities in relation to those explicitly or implicitly valued resources. This is fundamental to understand since attribute performance and economic performance should be reflected in its enterprise
value. The question then will be: what is the stakeholder valuation response to better/worse performance on valued attributes and what is the stakeholder valuation response to better/worse performance on economic performance? In this framework, the expected strategic and operating futures for the enterprise are captured in the business model. The progenitor for this is the development of alternative futures for the enterprise – often through the development of scenarios that describe future states for the industry and the enterprise’s position within in. The techniques we apply are well-known and follow the prescriptions described by Jantsch (1972), and van der Heijden (1996, 2002) among others. The value determining attributes of the enterprise are captured from the enterprise’s stakeholders as a separate exercise. This is necessary as company managements often believe they know what attributes the market values and how and how much they value the assumed valued attributes and do not clearly distinguish between sell-side and buy-side analysts. However, the business model must reflect all of market relevant attributes - either as direct measures or as proxy measures. The conjoint value hierarchy (CVH)[8] model, which captures the relevant attributes of the enterprise however need not reflect only the attributes captured in the business model since exogenous attributes can be dealt with outside the business model (but should be at an absolute minimum). The term conjoint value hierarchy is literal. It is a hierarchy of attributes that are joined together (ultimately) in logical groupings that, at a penultimate level, represent the value expression of the enterprise (Fletcher et al., 2003; Pike and Roos, 2003). For listed companies value will be represented by their enterprise value – the market value of its equity and of the market value of its net interest bearing debt obligations (NIBDOs) – but value can be expressed in any relevant terms, for example, in societal terms – see Burgman and Roos (2004b). Thus, there is a fundamental interplay between the attribute expressions and their valuations as expressed in the conjoint value hierarchy, and the resources and activities developed within the business modeling process. The former represents the “outside in” view, while the latter represents the “inside out” view of what creates value. The business model also drives the economic profit model. Of course, both attributes and economic profits are outcomes but managers do not manage these outcomes, they manage towards these outcomes. What mangers manage are the resources and activities that achieve the desired outcomes which are seen and valued as the attributes of the enterprise. This understanding is fundamental. Once we understand the juxtaposition of the business model to enterprise performance expressed in terms of valued attributes and expressed in terms of economic performance, the logic of the framework becomes accessible. The data for both the conjoint value hierarchy and the economic profit model are normalized based on the interval zero (0) to one (1) where, for example, zero will mean the level of “uselessness” and one will mean the level of “best in class” for attributes, and zero will mean the level of “minimum acceptable” to retain investors and one will mean “exceeding expectations” for economic profit. Performance on each dimension affects the valued outcome of the enterprise. The final outcome representation of the FVMe framework is shown in Figure 3. What remains then is to represent the surface response function for any improvement or any diminution in performance in terms of attributes or economic performance to establish the movement in overall enterprise value, or “value for money”.
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Figure 3. Enterprise value “value for money” as a function of attribute and economic performance
5. Overview of the current approach to managing intellectual capital with an emphasis on actual case studies 5.1. TRS Mapping The important connection we have made in managing for shareholder value is between managing all capital, including intellectual capital, and the valued outcome for shareholders. Conventionally, this is defined as total return to shareholders (TRS)[9]. Accenture and AssetEconomics have developed a methodology that examines Enterprise Value (EV)[10], and decomposes this to establish the proportion of EV that is represented by future growth expectations and the proportion that is represented by current operations (Ballow and Burgman, 2004). This decomposition is reconciled to the TRS performance of the enterprise. TRS comprises dividends received plus the change in market value of the equity. Put differently, dividing the sum of dividends received and the delta in market value of equity from time period 0 to time period 1 by the original share price produces TRS. TRS offers the immediate advantage of providing comparable data on listed companies, and widely accepted definitions of results. In addition, in our experience, TRS is strongly correlated to other key indicators of operating performance. Methodologically however, our answer lies in an approach that focuses on all aspects of value, both current and future, and on all asset drivers – tangible and intangible – in a holistic manner. Accenture and AssetEconomics have developed a TRS methodology called “TRS Mapping”, that tightly aligns internal financial performance measures with TRS. This methodology aims to accomplish two goals: (1) Clarify and detail out 100 percent of an enterprise’s valuation by connecting TRS back to traditional financial statements – income statement and balance sheet. (2) Simplify the communication and translation of enterprise performance to both internal and external stakeholders. Simply stated, TRS Mapping has the objective of linking connections among differing TRS drivers. Traditional accounting calculations (ðdividends þ change in the market
value of equity)/earnings), for example, will not yield TRS. This calculation may provide an earnings multiple that approximates earning per share (EPS); however, it will only motivate an enterprise to generically increase earnings. Depending on how this action is implemented (reducing investment or cutting cost), the multiple may actually be lowered and hence, firm value would be reduced. These inadequacies are particularly true when dealing with intangible assets, which are largely formed by selling, general and administrative (SG&A) expenses – such as training and education, software development, research salaries and so on. With intangibles, no clear tradeoff exists between EPS and value creation. As budgeted amounts for SG&A expense increase, EPS decreases (without a commensurate increase in sales) given that SG&A expense adversely affects net operating profit after taxes (NOPAT). “Economic Profit” calculations that factor in a cost for using the capital on which earnings were generated helps bring us a step closer to economic results versus standard accounting principles when explaining earnings. “total economic profit” offer managers the ability to annualize, report and manage the tradeoff between current and future value with the key component being the de-capitalization of future value. Using this concept, TRS Mapping provides a flowchart of sound information starting from traditional accounting statements and moving up to total enterprise value and finally to TRS. In Figure 4 we see that from a standard income statement, we get (with some adjustments) net operating profit after tax (NOPAT), which then has a capital charge removed from it. The capital charge represents the cost of using the capital for which earnings were generated. The capital charge is total (net) capital multiplied by the firm’s weighted average cost of capital (WACC). The result of NOPAT less the capital charge yields a standard economic profit (EP) amount. Taking the EP amount just calculated and dividing that by the cost of capital and adding back to this result total capital, we now have an approximation of the market’s valuation of current operations into perpetuity. Stated alternatively, we call this “current value”.
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Figure 4. TRS Mapping Framework
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To facilitate these calculations, a “total economic profit” measurement concept can be used to measure, manage and promote company performance by linking it directly back to shareholder return. The TRS Mapping framework also clarifies a few other issues. Total EV, for example, is calculated by adding the market value of equity (no. of shares outstanding £ share price) to the market value of debt. TRS Mapping is our fundamental point of departure for understanding the drivers of value. It is this because the TRS Mapping will reveal the “size of the prize”. It breaks up the EV in a way that we know how much of EV comes from current operations, the current value, and how much from future growth expectations, the future value. In doing this, we begin to understand the basis of the management task in managing for value. Apart from this starting point to our methodology, TRS is also important for other reasons: First, TRS is the performance metric that is the most important to shareholders since it captures both dividend returns and share price appreciation. The metric is ubiquitously used by companies and in the major business periodical rankings such as the annual Fortune 500 (Fortune Magazine (2005)) ranking and performance surveys such as the annual Boston Consulting Group (2004) Value Creators Report. Second, in the USA, TRS performance is a metric that is required by the US Securities and Exchange Commission to be reported on in annual DEF-14 Proxy Statements as part of peer group performance reporting. Third, enterprise TRS performance relative to peers is used by many US companies as a component of long-term incentive compensation (though not all in a direct sense). Examples where there is a direct link between TRS and long-term executive compensation include Weyerhaeuser Company (WY), a US$22.7 billion sales revenue forest products company with an Enterprise Value of US$26.79 billion in 2004, and Verizon Communications (VZ), a US$ 71.28 billion sales revenue telecommunications company in 2004 with an Enterprise Value of US$132.80 billion in 2004. These three realities make TRS performance an important consideration for all publicly listed US companies. The Accenture/AssetEconomics TRS Mapping Methodologye takes the standard TRS metric and decomposes this into the contributions made by current performance and expected future growth performance after adjusting for share and options issues, share buy-backs and the like. In addition, growth expectations can be normalized for gross domestic product (GDP) growth. The result is an understanding of the “real” expectations for share price growth embedded in an enterprise’s share price. The TRS Mapping Analysis provides the monetary “bid” value ascribed to the enterprise and because of what is known about “investment styles”, the current value versus future value split, together with an understanding of the industry and enterprise’s competitive positioning begins to inform us about what to look for in terms of the attributes that will define that enterprise. Two steps can then run in parallel. These are to establish the valued attributes of the enterprise and to establish its business model. 5.2. Attribute analysis Attribute analysis can be accomplished a number of ways. The most rigorous of these employs multi attribute value theory[11] to identify the attributes that matter to all value determining stakeholders, including shareholders. The techniques employed in
this step yield the following – a comprehensive list of the independent attributes that matter to value creation, the rank order of their importance, the response function for each attribute (the shape of the response function to perceptions of greater or lesser performance on that attribute), the range of upside (value increment to performing better) and downside (value decrement to performing worse) and finally, the rank order of attributes in terms of performing better or worse on that attribute relative to other attributes. The base information for the attribute analysis is obtained by having stakeholders perform three tasks – pair-wise choice preferences among attributes (pair-wise choice analysis), response function selection for performance variation (better/worse) on the attribute (shape choice analysis) and an importance assessment of the impact of the attribute (deprival analysis). 5.3. Business modeling Business modeling, like attribute analysis, can be accomplished through a variety of techniques. The first business modeling task is to establish the predominant value logic of the business. In this we follow the work of Thompson (1967) and Stabell and Fjeldstad (1998). The first two authors established three value logics, or business models – the value chain, the value shop and the value network – to use the terminology of Stabell and Fjeldstad. According to these authors, the value chain exists to produce products or services, the value shop to solve a problem or exploit an opportunity and the network to mediate transactions. Value chains are represented by companies like Ford Motor and Dell Computer, value shops by companies like Pixar and Mirage, and networks by companies like Microsoft and American Airlines. We have extended these descriptions by splitting value network into two types – the “asset-lite” network, represented by a company like eBay, and the “asset-heavy” network, represented by a company like UPS. Deciding on the predominant value logic is important since the business model that the logic represents will tend to rely on certain capital forms, as outlined by Roos (2005a, b) and Marr and Roos (2005) as the basis for creating competitive advantage and ultimately shareholder value. The value chain has been the business model that has predominated large firms in developed economies since the industrial revolution until the last decade or so. Since the mid-1990s, companies that have been created around the shop and network logics have become more important and a permanent parts of the USA and other developed economies. In contrast to the value chain, which tends to leverage monetary and physical capital these business models primarily leverage intellectual capital resources for the creation of value. The shop value logic primarily relies on human capital while the network value logic primarily relies on relationship and organizational capital (see, e.g. Roos, 2005a, b; Marr and Roos, 2005). Accordingly, the predominant value logic will inform us as to the likely content of the business model. Business modeling tends to be straightforward in terms of identifying the resources that an enterprise is likely to need to function and compete. When we think of a commercial airline, or a quick service restaurant chain, or a management consulting firm, or an auto parts manufacturing company, if we are experienced we can immediately think of how it is likely to be organized and the kinds of resources (assets and capabilities)[12] that it will have to have. In addition, we can probably identify its
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principal business processes. What we are talking about here is establishing the resources (or stocks) and processes (or flows) that comprise the value generating activities of any enterprise. The business models we develop simply represent the stocks of things that we need to create value and flows or transformations from one resource form into another and ultimately into the stakeholder valued outcomes – and in the case of publicly listed enterprises, into cash flows. The resource transformations may be circuitous but they should be headed only in one direction, toward creating valued outcomes. The business models in their most rigorous form are developed using causal models and the techniques of system dynamic modeling (Sterman, 2000). System dynamic modeling can be likened to a more dynamic form of business process mapping. As with multi attribute value theory, system dynamic modeling provides a strong foundation for believability in the results generated. Perhaps the most useful benefit of using this approach is the appreciation that it provides understanding the importance of time delays in considering cause and effect, for example, in the delay between, say, front-office staff training and customer recognition that “things have changed” in terms of service. The techniques employed in this step yield the following – flow models of the mega processes that define the total operations of the enterprise, identification of the key stocks within each mega process, identification of the transformation rates between each stock within each process and identification of the activities that underpin the transformations. The base information for the attribute analysis is obtained by having enterprise management identify the mega processes, the stocks that represent the process end-to-end and finally, the rates of transformation of one resource form into another within each process. The identification of transformation rates is central for two reasons. First, the dynamic behavior and realism of the business model is determined by these rates. More importantly however, for many processes where intellectual capital stocks are involved, it will frequently be the first time that management has thought about the fundamental integration of intellectual capital stocks within their enterprise’s mega processes and about what results are to be expected and managed for from their (transformation) activities. Managing for value has been part of the management lexicon since the term was coined by McTaggart et al. (1994)[13]. The business modeling approach we have described here underpins enterprise outcomes both in terms of perceived performance on perceived attributes as well as of economic performance. Given the time delays between events and their observation in economic performance results, it can be seen that attribute performance is likely to be an indicator of future enterprise economic performance. Furthermore, it will be clear that to the extent that attribute-related performance can be perceived and believed, that the information will be immediately reflected in the enterprise’s share price – well before it is manifested in enterprise-level financial/economic performance. This result will, of course, apply to perceptions on information that will have positive through to negative valuation consequences. Table II presents the key attributes of the three value logics[14]. First, each business model has a different focus. The value chain focuses on transforming inputs to product or service outputs; the value shop focuses on solving a problem or exploiting an opportunity; and the value network focuses on mediating or causing transactions
Understood by recognizing that the flow of activities is not linear but iterative between activities and cyclical across the activity set resulting in a high degree of both sequential and reciprocal interdependence between activities often involving multiple disciplines and specialties in spiraling activity sets (where each cycle implements the solutions the outcome of the previous cycle)
(continued)
Understood by recognizing that a concurrent and layered set of activities is required to service efficiently a random need for mediation services between a large number of customers
Scale, capacity utilization Performs a simultaneous and layered performance of activities based on standards (standards are critical)
Value creation logic Understood by disaggregating the value creation process of the firm into discrete activities that contribute to the firm’s relative cost position and create a basis for differentiation - this activity disaggregation must be complete in the sense that it captures all the activities performed by the firm
Relative value - the value of the service is dependent on who else adopts it
Personnel utilization Performs the selection, combination and ordering of the application of resources and activities according to the requirements of “the” problem
Relative benefit – although client problems often involve more or less standardized solutions, the value creation process is organized to deal with unique cases
Scale, capacity utilization Performs a fixed set of activities that enable it to produce a standard product in large numbers
Relative cost – customer value is defined either by cost reductions that the product can provide in the customer’s activities or the performance improvements that the customer can gain by using the product
Deliverable
Problem solution (change from an existing to Service, service capacity and service a more desired state – human, site, system or opportunity knowledge)
Linking customers; enabling direct and indirect exchanges between customers separated by time and space
Mediating and facilitating technology
Competitive focus Technology
Product or service
Business focus
Intensive problem-solving technology (Re)solving customers problems; mobilizing resources and activities to resolve unique customer problems
Value network
Firm reputation (signals value), relative Firm platform and process standardization, certainty in solution to problem(s), quality of contract management, interconnectedness of relationships and/or image, ability to recruit, structure, service provisioning retain and develop high-quality personnel, strong information asymmetry with the client
Transforming inputs into products
Business problem
Value shop
Resources leveraged Firm scope, linkages, capacity to learn, interrelationships, degree of vertical integration and physical location(s)
Long-linked technology
Technology
Value chain
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Table II. Three value logics and their explication
Two purposes: grow the size and pervasiveness of the communities on either side of the mediation; and encourage exchanges
Synchronization of simultaneous, parallel activities
Network infrastructure development Service development Procurement Contract management Human resource management
Network promotion and contract management Service provisioning Network infrastructure operation
Value network
IDEO, McKinsey, Amgen, DreamWorks, Accenture, Intuit, PeopleSoft, CIENA, Apple Computer, Walt Disney, Paychex, Skadden Arps, IBM (?)
eBay, Orbitz, FedEx, Southwest Airlines, Verizon Wireless, Bank of New York, New York Times, AXA, Microsoft, Visa International, Yahoo!, Amazon, Avon, McDonald’s (USA)
Professional service firms (management Airline, parcel delivery, insurance, retail consultant, legal, architectural, consulting bank, telephony, internet auction and many engineering), health, resource exploration and pure e-commerce firms production (E&P), property development and design firms
Two purposes: build the reputation of the firm’s professionals (often the critical marketing resource); and manage relationships for referrals from customers and colleagues
Source: Developed by AssetEconomics, Inc. from Thompson (1967) and Stabell, and Fjelstad (1998)
Companies illustrating value logics
GM, Wal-Mart, Dell, Anheuser-Busch, Sun Microsystems, Sony, ConAgra Foods, Nokia, Starbucks, Best Buy, Bausch & Lomb, Terra Parma, McDonald’s (ex USA)
Two purposes: develop and refine the value chain by providing product specifications and volume estimates; and simulate required level of demand for the chain’s output to ensure stable operation and capacity utilization Manufacturing and retail firms
Role of marketing
Industry examples of value logics
Coordination of sequential activities
Business logic “method”
Co-performance of cyclical, spiraling and reciprocal activities
Procurement Human resource management Technology development Human resource management Corporate services (planning, finance, accounting, legal affairs, government affairs, TQM)
Problem-finding and acquisition Problem solving Choice of problem-solution Execution Control and evaluation
Common value creation focus
Table II.
Inbound logistics Operations Outbound logistics Sales and marketing Service
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Medium for transferring value
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between customers. Second, they have quite different focuses for their major business processes. For example, for IT, best practice management for the various business models shifts - for the value chain from production productivity to production agility, for the value shop from decision support to knowledge management, and for the value network from infrastructure support to customer insight (Computer Sciences Corporation, 1998). Finally, they tend to rely on different resources for creating competitive advantage and sustainable shareholder value. Whereas value chains rely on monetary and physical resources, value shops and networks rely more on intellectual capital resources. 5.4. Economic profit modeling The economic profit model is also linked to the business model. The purpose of this model is to reflect the consequences of actions being contemplated by management to improve the value of the enterprise. The consequences of management actions can be observed in the forecast economic profit of the enterprise as well as in its enterprise value (given some simplifying assumptions). 5.6. Justification for the tools and approaches used The raison d’eˆtre of the FVMe Methodology we have outlined is to provide a causal model linking value drivers (resources and activities established in the business model) to two outcomes – valued attributes and economic profit. This will allow managers to more efficiently and effectively manage all their resources for maximum value outcome. 6. A look into the future There are three consequences of identifying, measuring and managing intellectual capital. First, management is able comprehensively to manage the enterprise since it will have a complete understanding of all value drivers. The adage “if you can’t measure it, you can’t manage it” has a precursor that is often ignored. This is, “if you can’t see it, you can’t measure it”. The FVMe Methodology resolves the issue of seeing, enabling measurement to occur, and consequently, for management to occur. Second, management is able to decide what it will communicate to its stakeholders. The issue of reporting and disclosure is one that today continually besets management as events occur that impact its present and potential future operations. The FVMe Methodology provides the basis for determining what matters, why it matters and how much it matters. Communication to stakeholders should be a consistently simpler choice although there are clearly other issues of providing information that is useful to competitors, litigation arising from shareholder reliance on such information for investing purposes and so on. In the future, we believe that the framework and methodology for managing intellectual capital that we have outlined here will be refined and that as this approach is employed, that managements will be able to both be more effective and efficient in resource allocation, planning, performance measurement and in communicating their understanding of the enterprise’s value creation processes. In relation to this last benefit Lang and Lundholm (1996), and Evans (2004), provide evidence that firms with more informative disclosure policies have a larger analyst following, more accurate analyst earnings forecasts, less dispersion among individual analyst forecasts and less
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volatility in forecast revisions. The results enhance our understanding of the role of analysts in capital markets. Further, they suggest that potential benefits to disclosure include increased investor following, reduced estimation risk and reduced information asymmetry, each of which have been shown to reduce a firm’s cost of capital in theoretical research. Enhanced business reporting, focusing on operations and value drivers, has received support in recent years – see, for example, the Canadian Institute of Chartered Accountants (2002) and Ballow and Burgman (2004). In addition, there have been a number of pilots and position papers developed and pilots completed on the reporting of intellectual capital. These have been reviewed by Pike et al. (2001), Starovic and Marr (2002), Bukh (2002), Gray et al. (2004) and Roos et al. (2004). We believe that there are four challenges that the intellectual capital discipline will have to overcome if it is to move forward. First, empirical research has to become the mainstay of intellectual capital academic research. The discipline cannot keep working with taxonomies and definitions and keep ignoring the issues of causal meaningfulness. The assertions that the discipline has made about the importance of intellectual capital have to be supported by strong cause and effect oriented research if there is to be a significant adoption of its tenets and prescriptions. Very little serious empirical academic or other research has been evidenced at this stage. Second, the discipline has to develop a theory of management in relation to intellectual capital resources and activities such that intelligent consultative discussions can take place with the managements of enterprises that primarily rely on the management of intellectual capital resources to create value. This will mean taking the concepts, tools and diagnostics that have been developed for value chain management thinking and providing counter concepts, tools and diagnostics for value shop and value network management thinking. Little of this exists in any formal way at this stage although this will be partly remedied through the work presented in Roos et al. (2005). Third, the discipline has to ensure that intellectual capital reporting and performance disclosure becomes part of formal prescribed enterprise reporting. It should not simply be an elective or an aspect of enhanced business reporting. In particular in the USA the rules-based compliance nature of public enterprise reporting means that nothing will be done that does not have to be done. Fourth, and tied to three above, the adoption of intellectual capital identification, quantification, management and reporting has to be achieved in the USA. The reason for this is simple. The USA is the home of global capital and the management of its stock markets and regulation of its companies have consequences well beyond its shores. Most of the major companies in Europe, for example, are foreign registrants in the USA. What happens in the USA directly affects all these companies. The initiatives being undertaken in small European countries by small companies in relation to the formal management of intellectual capital and reporting on intellectual capital are essentially irrelevant on the larger stage. The danger is that the recognition of the intellectual capital discipline will remain a northern European curiosity if it is not adopted by major US and other major global companies. And to be adopted by major US companies, the causal impacts on enterprise economic performance and the share price will have to be empirically shown.
We believe that the intellectual capital discipline holds an enormous amount of promise for the better management of all enterprises. However, for it to grow, the discipline cannot shrink from the challenges it has in front of it. Notes 1. Economic Value Addede, EVAe, Market Value Addede and MVAe are the trademarks of Stern Stewart & Co, New York, New York. 2. Initiated in 1998 and funded by the Targeted Socio-Economic Research (TSER) program of the European Commission, the aim of the MERITUM project is to investigate possibilities to measure and report intangibles. Nine universities and research institutes in six European countries are participating in the project (Denmark, Finland, France, Norway, Spain and Sweden). The general aim of this European project has been to: provide insight into the process of transforming intangibles into increased wealth – how are they managed and accounted for and how do they contribute to growth and employment?; and develop guidelines for the measurement and disclosure of intangibles. 3. In October 2001, the European Commission began funding a two-year program of economic research, bringing together leading experts from the business, academic and policy communities to focus on intangibles and how they relate to policy-making, reporting and measurement, skills development and management. In all, the PRISM project has produced over 60 research papers and 15 case studies. The acronym PRISM is taken from the focus of the project on policy-making, reporting and measurement, intangibles, skills development and management. 4. Refer to the FASB’s web site page on this issue – www.fasb.org/project/intangibles.shtml, and in particular to FASB (2001b, p. 105). 5. For the original, refer to Upton, 2001, specially pp. 60-73). 6. From Para. 63 of FASB (1984). 7. Examples of possible intangible assets include: computer software, patents, copyrights, motion picture films, customer lists, mortgage servicing rights, licenses, import quotas, franchises, customer and supplier relationships, and marketing rights. 8. Developed by ICS Ltd and used with their permission. 9. Total return to shareholders is the percentage return represented by dividends paid assumed to be reinvested in the same stock on receipt, and the share price appreciation of the stock measured as a percentage gain (or loss) against a base investment ordinarily indexed at 100, set at a point in time. TRS is often measured in three-year, five-year or seven-year arrests. 10. Enterprise value is equal to the addition of the market value of equity (number of shares on issue multiplied by the share price) and the net interest bearing debt obligations of the enterprise. 11. Multi attribute value theory is also known as multi attribute utility theory. 12. An asset is defines as something the enterprise has while a capability is something it can do. 13. In 1964, Peter Drucker wrote his text, Managing for Results (Drucker, 1993) and in 1986, Alfred Rappaport wrote his ground-breaking book Creating Shareholder Value (Rappaport , 1984). In 1994, Jim McTaggart of Marakon Associates first used the term “value based management” in his book, The Value Imperative: Managing for Superior Shareholder Returns (McTaggart, 1994). 14. For the value logic comparisons and the IT illustration, we are indebted to the Computer Sciences Corporation (1998).
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References Bainbridge, S.M. (1993), “In defense of the shareholder wealth maximization norm”, Washington and Lee Law Review, Vol. 50, Fall, pp. 1423-47. Bainbridge, S.M. (2002), “The bishops and the corporate stakeholder debate”, Villanova Journal of Law and Investment Management, Vol. 4 No. 1, pp. 3-27. Ballow, J.J. and Burgman, R.J. (2004), Enhanced Business Reporting: A Formal Joint Proposal to the AICPA from Accenture Inc. and AssetEconomics, LLP, Accenture Inc. and AssetEconomics, LLP, New York, NY. Ballow, J.J., Burgoz, S.G. and Noren, E.R. (2005), Enhanced Business Reporting: A New Framework for Public Company Disclosure, Accenture, Wellesley, MA, Research Note, March, 8 pp. Ballow, J.J., McCarthy, B.F. and Molnar, M.J. (2004), New Concepts in Value-based Management: TRS Mapping and Total Economic Profit, Accenture, Wellesley, MA, Research Note, May 10, 7 pp. Ballow, J.J., Thomas, R.J. and Roos, G. (2004), “Future value: the $7 trillion challenge”, Outlook, No. 1, available at: www.accenture.com/Outlook Ballow, J.J., Burgman, R.J., Roos, G. and Molnar, M.J. (2004), A New Paradigm for Managing Shareholder Value, Accenture, Wellesley, MA, Research Report, July, 24 pp.. Barlas, Y., Cirak, K. and Duman, E. (2000), “Dynamic simulation for strategic insurance management”, System Dynamic Review, Vol. 16 No. 1, pp. 43-58. Barzilai, J. (2005), “Measurement and preference function modeling”, International Transactions in Operational Research, Vol. 12 No. 2, pp. 173-83. Bontis, N. (2001), “Assessing knowledge assets: a review of the models used to measure intellectual capital”, International Journal of Management Reviews, Vol. 3 No. 1, pp. 41-60. Boston Consulting Group (2004), Winners in the Age of Titans: Creating Value in Banking 2004, Boston Consulting Group, Boston, MA. Breene, T. and Numes, P.F. (2004), “Is bigger always better?”, Outlook, No. 3, pp. 18-25. Breene, T. and Numes, P.F. (2005), “Balance, alignment and renewal: understanding competitive essence”, Outlook, No. 1, pp. 37-45. Breene, T. and Thomas, R.J. (2004), “In search of performance anatomy”, Outlook, No. 3, pp. 26-35. Bryson, J.M. (2003), “What to do when stakeholders matter: a guide to stakeholder identification and analysis techniques”, paper presented to the London School of Economics and Political Science, London, February 10, 40 pp.. Bukh, P.N. (2002), “The relevance of intellectual capital disclosure: a paradox?”, Accounting, Auditing & Accounting Journal, Vol. 16 No. 1, pp. 49-56. Burgman, R. and Roos, G. (2004a), “The new economy - a new paradigm for managing for shareholder value”, paper presented at the International IC Congress Interpretation and Communication of Intellectual Capital, September 2-3, Hanken Business School, Helsinki, 21 pp. Burgman, R.J. and Roos, G. (2004b), “Measuring, managing and delivering value performance in the public sector”, International Journal of Learning and Intellectual Capital, Vol. 1 No. 2, pp. 132-49. Canadian Institute of Chartered Accountants (2002), Management’s Discussion and Analysis: Guidance on Preparation and Disclosure, CICA, Toronto, November, 82 pp. Canibano, L., Sanchez, M.P., Garcia-Ayuso, M. and Chaminade, C. (2002), Guidelines for Managing and Reporting in Intangibles (Intellectual Capital Report), Measuring Intangibles to Understand and Improve Innovation Management (MERITUM), Madrid, January, 168 pp.
Collins, J. (2001), Good to Great: Why Some Companies Make the Leap . . . and Others Don’t, Harper Business, London. Computer Sciences Corporation (1998), “Chains, shops and networks: the role IS in new models of value creation: foundation strategic innovation report”, available at: www. cscresearchservices.com/foundation/library/value/RP01.asp Davenport, T.H. and Harris, J.G. (2004), Introduction to the Information Environment for Intangible Asset Management, Accenture, Wellesley, MA, Research Note, June 15, 4 pp. Doman, A., Glucksman, M., Mass, N. and Sasportes, M. (1995), “The dynamics of managing a life insurance company”, System Dynamics Review, Vol. 11 No. 3, pp. 219-32. Drucker, P.F. (1993), Managing for Results, HarperBusiness, New York, NY, (originally published in 1964). Edvinsson, L. (2002), Corporate Longitude: Discover Your True Position in the Knowledge Economy, Financial Times/Pearson Education, London. Edvinsson, L. and Malone, M.S. (1997), Intellectual Capital: Realizing Your Company’s True Value by Finding Its Hidden Brainpower, HarperCollins Publishers, New York, NY. European Commission Information Society Technologies Programme (2003), “Interim research findings, policy research into innovation and measurement practice in the intangible economy (PRISM)”, The PRISM Project, edited draft, January, 24 pp. European Commission Information Society Technologies Programme (2003b), “Report of research findings and policy recommendations, Policy Research into Innovation and Measurement Practice in the Intangible Economy (PRISM)”, The PRISM Project, preliminary draft, based on the final reports of the PRISM Consortium, May, 53 pp. European Commission Information Society Technologies Programme (2003c), The PRISM Report, Policy Research into Innovation and Measurement Practice in the Intangible Economy (PRISM), Report Series No. 2, The PRISM Project, Brussels, October, 60 pp.. Evans, M. (2004), Board Characteristics, Firm Ownership and Voluntary Disclosure, Fuqua School of Business, Duke University, Durham, NC, October, 27 pp. Expert Choice (2005), “Analytic hierarchy process”, available at: www.expertchoice.com/ customerservice/ahp.htm Fairfax, L.M. (2002), “Doing well while doing good: reassessing the scope of directors’ fiduciary obligations in for-profit corporations with non-shareholder beneficiaries”, Washington and Lee Law Review, Vol. 59, Spring, pp. 409-75. Financial Accounting Standards Board (FASB) (1984), “Recognition and measurement in financial statements of business enterprises”, FASB Concept Statement No. 5, December, available at: www.pwccomperio.com/CONTENTS/ENGLISH/EXTERNAL/US/ FASB_OP/CON5.HTM Financial Accounting Standards Board (FASB) (2001a), Statement of Financial Accounting Standards No. 141: Business Combinations No. 221-B, FASB, Norwalk, CT, June, 125 pp.. Financial Accounting Standards Board (FASB) (2001b), Statement of Financial Accounting Standards No. 142: Goodwill and Other Intangible Assets No. 221-C, FASB, Norwalk, CT, June, 118 pp. Fisch, J. (2004), “Measuring efficiency in corporate law: the role of shareholder primacy”, Law and Economics Workshop, University of California, Berkeley, CA, December 15, Paper 5, 37 pp. Fishburn, P.C. (1970), Utility Theory for Decision-making, John Wiley & Sons, New York, NY. Flaherty, J.E. (1999), Peter Drucker: Shaping the Managerial Mind, Jossey-Bass, San Francisco, CA.
Intellectual capital approach
609
JIC 6,4
610
Fletcher, A., Gutherie, J., Steane, P., Roos, G. and Pike, S. (2003), “Mapping stakeholder perceptions for a third sector organization”, Journal of Intellectual Capital, Vol. 4 No. 4, pp. 505-27. Fortune Magazine (1999), “Fortune 1999 500”, April 26, pp. 89-100 and pp. F-1 to F-56. Fortune Magazine (2005), “Fortune 2005 500”, Vol. 151 8, April 18, pp. 232-236 and F-1-F-69. Freeman, R.E. (1984), Strategic Management: A Stakeholder Approach, Pitman Publishing, Boston, MA. Glassman, C.A. (2003), “Improving corporate disclosure – improving shareholder value”, speech by SEC Commissioner, US Securities and Exchange Commission, April 10, available at: www.sec.gov/news/speech/spch041003cag.htm Gray, D., Roos, G. and Rastas, T. (2004), “What intangible resources do companies value, measure and report? A synthesis of UK and Finnish research”, International Journal of Learning and Intellectual Capital, Vol. 1 No. 3, pp. 242-61. Green, R.M. (1993), “Stakeholders: changing metaphors of corporate governance”, Washington and Lee Law Review, Vol. 50, Fall, pp. 1409-21. Harris, J.G. and Burgman, R.J. (2005), Chains, Shops, and Networks: The Logic of Organizational Value, Accenture, Wellesley, MA, Research Note No. 2, May, 8 pp. Harris, J.G., Burgman, R.J. and Davenport, T.H. (2005), New Frontiers of Operational Reporting, Accenture, Wellesley, MA, Research Note, February, 6 pp. HM Treasury, National Audit Office, Audit Commission and Office for National Statistics (2001), Choosing the Right Fabric: A Framework for Performance Information, HM Treasury, National Audit Office, Audit Commission and Office for National Statistics, London, March, 40 pp. International Accounting Standards Board (IASB) (2004), “IAS 38 intangible assets, IAS 38-124, Deloitte, IAS Plus, effective 1 July 1999 and revised effective 31 March 2004”, available at: www.iasplus.com/standard/ias38.htm Jantsch, E. (1972), Technological Planning and Social Futures, Cassell/Associated Business Programmes, London. Keeney, R.L. and Raiffa, H. (1976), Decisions with Multiple Objectives: Preferences and Trade-offs, John Wiley & Sons, New York, NY. Kester, W.C. (1984), “Today’s options for tomorrow’s growth”, Harvard Business Review, Vol. 62 No. 2, March-April, pp. 153-60. Krantz, D.H., Luce, R.D., Suppers, P. and Tversky, A. (1971), Foundations of Measurement, Vol. 1: Additive and Polynomial Expressions, Academic Press, New York, NY. Lang, M.H. and Lundholm, R.J. (1996), “Corporate disclosure policy and analysts behavior”, The Accounting Review, Vol. 71 4, October, pp. 467-92. Linder, J.C. (2005), Visible Options, Accenture, Wellesley, MA, Research Note, February, 5 pp. McTaggart, J., Kontes, P. and Mankins, M. (1994), The Value Imperative: Managing for Superior Shareholder Returns, The Free Press, New York, NY. Madden, B.J. (1999), Valuation – CFROI Valuation: A Total System Approach to Valuing the Firm, Butterworth-Heinemann, Woburn, MA. Marr, B. and Roos, G. (2005), “A strategy perspective on intellectual capital”, in Marr, B. (Ed.), Perspectives on Intellectual Capital: Multidisciplinary Insights into Management, Measurement and Reporting, Butterworth-Heinemann, London, pp. 28-41. Marr, B., Gray, D. and Neely, A. (2003), “Why do firms measure intellectual capital”, Journal of Intellectual Capital, Vol. 4 No. 4, pp. 441-64.
Miller, M.H. and Modigliani, F. (1961), “Dividend policy, growth and the valuation of shares”, Journal of Business, Vol. 34 No. 4, October, pp. 411-33. Mitchell, R.K., Agle, B.R. and Wood, D.J. (1997), “Toward a theory of stakeholder identification and salience: defining the principle of who and what really counts”, Academy of Management Review, Vol. 22 No. 4, October, pp. 853-86. Molnar, M.J. (2004a), Executive Views on Intangible Assets: Insights from the Accenture/Economist Intelligence Unit Survey, Accenture, Wellesley, MA, Research Note, April 13, 4 pp. Molnar, M.J. (2004b), What the Market Says About Future Value, Accenture, Wellesley, MA, Research Note, May 15, 5 pp. Morecroft, J. (1997), “The rise and fall of people express: a dynamic resource-based view”, paper presented at the 15th International Conference of the System Dynamics Society, Istanbul, August. Morecroft, J. (2000), “Visualising and simulating competitive advantage: a dynamic resource-based view of strategy”, working paper, WP/0036, London Business School System Dynamics Group, London. M’Pherson, P. and Pike, S. (2001), “Accounting, empirical measurement and intellectual capital”, Journal of Intellectual Capital, Vol. 2 No. 3, pp. 246-60. Myers, R. (1996), “Metric wars”, CFO Magazine, Vol. 12 No. 10, pp. 41-50. Myers, R. (2001), “Measure for measure”, eCFO, Summer, available at: www.cfo.com Ottoson, E. and Weissenrieder, F. (2003), “Linking capital allocation to individual capital expenditure decisions”, in Black, A. (Ed.), Questions of Value: Master the Latest Developments in Value-based Management, Investment & Regulation, FT Prentice-Hall, London. Pike, S. and Roos, G. (2003), “Mathematics and modern business management”, Journal of Intellectual Capital, Vol. 5 No. 2, pp. 243-56. Pike, S. and Roos, G. (2004), “Theoretical foundations of intellectual capital measurement and valuation”, paper presented at The International Intellectual Capital Conference, NCCU, Taipei, October 11-12. Pike, S., Roos, G. and Marr, B. (2005), “Strategic management of intangible assets and value drivers in R&D”, R&D Management, Vol. 35 No. 2, pp. 111-24. Pike, S., Rylander, A. and Roos, G. (2001), “Intellectual capital management and disclosure”, in Bontis, N. and Choo, C.W. (Eds), The Strategic Management of Intellectual Capital and Organizational Knowledge: A Selection of Readings, Oxford University Press, New York, NY, pp. 657-72. Prahalad, C.K. and Hammel, G. (1990), “The core competence of the corporation”, Harvard Business Review, Vol. 68 3, May-June, pp. 79-91. Rappaport, A. (1986), Creating Shareholder Value: The New Standard for Business Performance, The Free Press, New York, NY. Roe, M.J. (2001), “The shareholder wealth maximization norm and industrial organization”, Harvard Law School Public Law Working Paper No. 019, April 2, 18 pp. Roos, G. (2005a), “Intellectual capital and strategy: a primer for today’s manager”, in Coate, P. (Ed.), Handbook of Business Strategy, Emerald Group, Bradford, pp. 123-32. Roos, G. (2005b), “An epistemology perspective on intellectual capital”, in Marr, B. (Ed.), Perspectives on Intellectual Capital: Multidisciplinary Insights into Management, Measurement and Reporting, Butterworth-Heinemann, London.
Intellectual capital approach
611
JIC 6,4
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Roos, G. and Jacobsen, K. (1999), “Management in a complex stakeholder organization”, Monsah Mt Eliza Business Review, July, pp. 82-93. Roos, G. and Lo¨vingsson, F. (1999), “El oroceso CI en el ‘nuevo mondo de las telecomunicacione’”, in Gu¨ell, A.M. (Ed.), Homo Faber, Homo Sapiens – La Gestio´n del Capital Intelectual, Ediciones del Bronce, Barcelona, pp. 141-69. Roos, G. and Roos, J. (1997), “Measuring your company’s intellectual performance”, Long Range Planning, Vol. 30 Nos 3, speical issue on intellectual capital, pp. 413-26. Roos, G., Pike, S. and Fernstro¨m, L. (2004), “Valuation and reporting of intangibles – state of the art in 2004”, International Journal of Learning and Intellectual Capital, Vol. 1 No. 3, pp. 21-48. Roos, G., Pike, S. and Fernstro¨m, L. (2005), Managing Intellectual Capital in Practice, Elsevier, New York, NY. Roos, J., Roos, G., Drangonetti, N.C. and Edvinsson, L. (1997), Intellectual Capital: Navigating the New Business Landscape, Macmillan Press, London. Royal Dutch/Shell Group (2005), “Introduction to Shell global scenarios to 2025, presented by van der Veer, Jeroen, Royal Dutch/Shell Group of Companies”, available at: www.shell.com/ Saaty, T.L. (1981), The Analytic Hierarchy Process, McGraw-Hill Book Company, New York, NY. Saaty, T.L. (2000), The Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, AHP Series, Vol. VI, RWS Publications, Pittsburgh, PA. Sharpe, W.F. (1964), “Capital asset prices: a theory of market equilibrium under conditions of risk”, Journal of Finance, Vol. 19 No. 3, pp. 425-42. Stabell, C.B. and Fjeldstad, O.D. (1998), “Configuring value for competitive advantage: on chains, shops and networks”, Strategic Management Journal, Vol. 19, pp. 413-37. Starovic, D. and Marr, B. (2002), Understanding Corporate Value: Managing and Reporting Intellectual Capital, Chartered Institute of Management Accountants (CIMA), London, 28 pp. Sterman, J.D. (2000), Business Dynamics: Systems Thinking and Modeling for a Complex World, Irwin/McGraw-Hill, New York, NY. Stern, J. (1970), “Maximizing earnings per share”, Financial Analysts Journal, Vol. 26 No. 5, pp. 107-12. Stevens, S.S. (1946), “On the theory of scales of measurement”, Science, Vol. 103, pp. 677-80. Stevens, S.S. (1951), “Mathematics, measurement, and psychophysics”, in Stevens, S.S. (Ed.), Handbook of Experimental Psychology, John Wiley & Sons, New York, NY, pp. 1-49. Stevens, S.S. (1959), “Measurement”, in Churchman, C.W. (Ed.), Measurement: Definitions and Theories, John Wiley & Sons, New York, NY, pp. 18-36. Stevens, S.S. (1968), “Measurement, statistics, and the schemapiric view”, Science, Vol. 161, pp. 849-56. Stewart, B.G. III (1991), The Quest for Value, HarperCollins Publishers, New York, NY. Sveiby, K.-E. (1997), The New Organizational Wealth: Managing and Measuring Knowledge-based Assets, Berrett-Koehler Publishers, San Francisco, CA. Sveiby, K.-E. (1998), “Measuring intangibles and intellectual capital – an emerging first standard”, August 5, available at: www.sveiby.com. Thompson, J.D. (1967), Organizations in Action, McGraw-Hill Book Company, New York, NY.
Treasury Board of Canada (2002), Results for Canadians: A Managerial Framework for the Government of Canada, President of the Treasury Board of Canada, Treasury Board of Canada Secretariat, Toronto, March 30, available at: www.tbs-sct.gc.ca Tully, S. (2005), “America’s wealth creators”, Fortune, November 22, pp. 275-84. United States Executive Office of the President (2002), The President’s Management Agenda, Office of Management and Budget, Washington, DC, 64 pp. United States Executive Office of the President (2004a), The President’s Management Agenda Report: The Federal Government Is Results-oriented, Office of Management and Budget, Washington, DC, 19 pp. United States Executive Office of the President (2004b), Progress Implementing the President’s Management Agenda, Office of Management and Budget, Washington, DC, available at: www.whitehouse.gov/ United States General Accounting Office (2000), Executive Guide: Creating Value Through World-class Financial Management, GAO/AIMD-00-134, United States General Accounting Office Accounting and Information Management Division, Washington, DC, April, 60 pp. Upton, W.S. (2001), Business and Financial Reporting: Challenges from the New Economy, Special Report No. 219-A, Financial Accounting Standards Board of the Financial Accounting Association, Norwalk, CT, April, 135 pp. van der Heijden, K. (1996), Scenarios – The Art of Strategic Conversation, John Wiley & Sons, Chichester. van der Heijden, K. (2002), The Sixth Sense: Accelerating Organizational Learning with Scenarios, John Wiley & Sons, New York, NY. Warren, K. (2002), Competitive Strategy Dynamics, John Wiley & Sons, Chichester. Webster, E. (2002), “Intangible and intellectual capital: a review of the literature”, Melbourne Institute Working Paper No. 10/02, Melbourne Institute of Applied Economic and Social Research, Melbourne, June, 48 pp.. Welch, J.F. (2001), Jack: Straight from the Gut, Warner Books, New York, NY.
Further reading Cohan, M.D., Burkhart, R., Dosi, G., Egidi, M., Marengo, L., Warglein, M. and Winter, S. (1995), “Routines and other recurring action patterns of organizations: contemporary research issues”, Working Paper 95-07-065, Santa Fee Institute, Santa Fee, NM, 53 pp. Hoffman, W. (2005), “Nachhaltige Schaffung von Unternehmenswert und tempora¨rer Wettbewerbsvorteil”, Finanz Betrieh, No. 5, pp. 323-32. Itami, H. (1987), Mobilizing Invisible Assets, Harvard Business Press, Cambridge, MA. Libermann, M.B. and Montgomery, D.B. (1988), “First-mover advantages”, Strategic Management Journal, Vol. 9, Summer, pp. 41-58. Peteraf, M.A. (1993), “The cornerstones of competitive advantage: a resource-based view”, Strategic Management Journal, Vol. 14 No. 3, pp. 179-91. Pike, S. and Roos, G. (2004), “Measurement issues in intellectual capital – a review”, paper presented at The International Intellectual Capital Conference, NCCU, Taipei, October 11-12. Prahalad, C.K. and Bettis, R.A. (1986), “The dominant logic: the new linkage between firm diversity and performance”, Strategic Management Journal, Vol. 7 No. 6, pp. 485-550.
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Roos, G. (2003), “The management and measurement of intangible resources”, Institute of Chartered Financial Analysts of India Reader, October, pp. 45-53. Santos, S.P., Belton, V. and Howick, S. (2001), “Adding value to performance measurement by using system dynamics and multicriteria analysis”, Research Paper No. 2001/19, Strathclyde Business School, Glasgow, 20 pp. Schoemaker, P.J.H. (1990), “Strategy, complexity and economic rent”, Management Science, Vol. 36 No. 10, pp. 1178-92. Verizon Communications (2005), Proxy Statement Pursuant to Section 14(a) of the Securities Exchange Act of 1934, Verizon Communications, Washington, DC, March 21, 31 pp. Vignaux, G.A. (2005), “Multi-attribute decision problems”, March 7, 8 pp., available at: www.mcs. vuw.ac.nz/courses/ OPRE251/2004T1/Lecture-Notes/multi.pdf Weyerhaeuser Company (2005), Notice of 2005 Annual Meeting of Shareholders and Proxy Statement, Weyerhaeuser Company, Federal Way, WA, March 15, 35 pp.
Note from the publisher Outstanding Doctoral Research Awards As part of Emerald Group Publishing’s commitment to supporting excellence in research, we are pleased to announce that the 1st Annual Outstanding Doctoral Research Awards have been decided. Details about the winners are shown below. 2005 was the first year in which the awards were presented and, due to the success of the initiative, the programme is to be continued in future years. The idea for the awards, which are jointly sponsored by Emerald Group Publishing and the European Foundation for Management Development (EFMD), came about through exploring how we can encourage, celebrate and reward excellence in international management research. Each winner has received e1,500 and a number have had the opportunity to meet and discuss their research with a relevant journal editor. Increased knowledge-sharing opportunities and the exchange and development of ideas that extend beyond the peer review of the journals have resulted from this process. The awards have specifically encouraged research and publication by new academics: evidence of how their research has impacted upon future study or practice was taken into account when making the award selections and we feel confident that the winners will go on to have further success in their research work. The winners for 2005 are as follows: . Category: Business-to-Business Marketing Management Winner: Victoria Little, University of Auckland, New Zealand Understanding customer value: an action research-based study of contemporary marketing practice. . Category: Enterprise Applications of Internet Technology Winner: Mamata Jenamani, Indian Institute of Technology Design benchmarking, user behaviour analysis and link-structure personalization in commercial web sites. . Category: Human Resource Management Winner: Leanne Cutcher, University of Sydney, Australia Banking on the customer: customer relations, employment relations and worker identity in the Australian retail banking industry. . Category: Information Science Winner: Theresa Anderson, University of Technology, Sydney, Australia Understandings of relevance and topic as they evolve in the scholarly research process. . Category: Interdisciplinary Accounting Research Winner: Christian Nielsen, Copenhagen Business School, Denmark Essays on business reporting: production and consumption of strategic information in the market for information. . Category: International Service Management Winner: Tracey Dagger, University of Western Australia Perceived service quality: proximal antecedents and outcomes in the context of a high involvement, high contact, ongoing service.
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Category: Leadership and Organizational Development Winner: Richard Adams, Cranfield University, UK Perceptions of innovations: exploring and developing innovation classification. Category: Management and Governance Winner: Anna Dempster, Judge Institute of Management, University of Cambridge, UK Strategic use of announcement options. Category: Operations and Supply Chain Management Winner: Bin Jiang, DePaul University, USA Empirical evidence of outsourcing effects on firm’s performance and value in the short term. Category: Organizational Change and Development Winner: Sally Riad, Victoria University of Wellington, New Zealand Managing merger integration: a social constructionist perspective. Category: Public Sector Management Winner: John Mullins, National University of Ireland, Cork Perceptions of leadership in the public library: a transnational study.
Submissions for the 2nd Annual Emerald/EFMD Outstanding Doctoral Research Awards are now being received and we would encourage you to recommend the awards to doctoral candidates who you believe to have undertaken excellent research. The deadline by which we require all applications is 1 March 2006. For further details about the subject categories, eligibility and submission requirements, please visit the web site: www.emeraldinsight.com/info/researchers/funding/doctoralawards/ 2006awards.html
