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Introduction

The business-tobusiness customer in the service innovation process

A major impediment to designing and assimilating a business-to-business service innovation is understanding and adjusting to the change in role played by the client in a service dominant offering, as opposed to the role for one of product dominance. The term client is used to highlight a dual role played in service consumption, compared to a singular role of the customer for a product offering. Previously, Martin and Horne (1992) discussed a significant definitional problem: what is a service? Using Neubauer’s four attributes (Moeller, 1986) as a foundation and seeking to avoid a definitional jungle the definition used was of offerings that are tangible and concrete as products and those that are intangible and abstract as services. This is consistent with the observation of Berry (1980) that for services “benefits are realized not from holding or using a physical object, but rather from the delivery of some effort or the performance of a deed; services are consumed but not possessed.” Indeed, we found that use of the term “offerings” was a much more acceptable way of handling the problem. Fundamentally any firm can have a product offering without a customer, the product is merely placed in inventory or, in the case of the consumer market, on the store shelf. In most cases, a service offering is not produced until the client makes the purchase. This is particularly common for the businessto-business client. The complexity is highlighted by the client playing two roles: that of the customer and that of a co-producer of the offering. In other words, clients not only receive and consume the service offering, but also serve as participants in its innovation, production, and delivery. This basic concept is best described by Norman (1984, p. 21):

Claude R. Martin David A. Horne and Anne Marie Schultz

The authors Claude R. Martin is the Winkelman Professor of Marketing in the School of Business, at the University of Michigan, Michigan, USA. David A. Horne is Associate Professor of Marketing at the California State University at Long Beach, California, USA. Anne Marie Schultz is an Information Technology Management Consultant at the University of Michigan, Michigan, USA. Keywords Consultants, Customer, Innovation, Participation, Service Abstract This paper addresses a major impediment to business-tobusiness service innovation. The focus is on the role played by the client in a service dominant offering, compared to product dominant offerings. Part of this concerns the concept of customer input uncertainty includng the diversity of customer demand and the customer’s disposition to participate in the innovation process. The paper concludes by tracking and innovation process in a consultation between a major global consulting firm and one of its clients.

…the client plays an interesting complex role in the service organization, since he not only receives and consumes the service but also serves as a component in its production and delivery.

The complexity of this role and its management is further highlighted by the various ways a client can participate in the production function. These include specification of the service, where the client participates at varying levels in specifying the nature and level of the service offering; pure co-production, where the client does some or all of the physical or intellectual production of the

European Journal of Innovation Management Volume 2 · Number 2 · 1999 · pp. 55–62 © MCB University Press · ISSN 1460-1060

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The business-to-business customer in the service innovation process

European Journal of Innovation Management

Claude R. Martin, David A. Horne and Anne Marie Schultz

Volume 2 · Number 2 · 1999 · 55–62

offering, including in some cases, substituting for employees; quality control, where the client participates not only in the origination, but in the on-going production of the service offering and its quality level; and/or marketing, where the client participates in the selling of the service to other clients. In this paper we focus on the innovation process for business-to-business services and the concept of customer participation in that process. We propose that customer involvement is a key construct in differentiating a service innovation from that for products. Specifically we address the following: (1) The role of the customer in the innovation process, including as a co-producer of the service. (2) The disposition of the customer to participate in the innovation process and strategies to enhance that participation. (3) Different interdependence patterns consisting of (a) the division of service work between employees and customers, and (b) customization versus standardization. (4) Client participation risk assessment. (5) Where the service quality dimensions (SERVQUAL model) of tangibles, responsiveness, empathy, assurance, and reliability fit in the innovation process for services (Parasuraman et al., 1985, 1988, 1991). (6) Accounting for the service production model (SERVUCTION) in service innovation, focusing on both the “on-stage” servers and inanimate environment, but also on the “backstage” of the customer (Parasuraman et al., 1985, 1988,1991).

product innovation literature the implication is clear that the customers’ role may be critical. Previously a significant difference was found between successes and failures in service innovations with greater customer participation in successful offerings compared to those that were unsuccessful (Martin and Horne, 1993). However, a troubling finding was the relatively low average scores for direct customer participation for all innovations, whether successful or not. It was expected, on the basis of in-depth interviews conducted prior to the study, that mean scores would be considerably higher. The speculation at the time was that perhaps the pressure to shorten the development cycle led to the use of surrogate measures of actual customer participation, such as simulated test markets, in-house or employee testing, or a single focus group’s reaction. Here we offer an understanding that mixing findings for consumer services with those from business-to-business services was probably the main contributory reason for our expectation not being met. A careful review of the in-depth interviews reveals that in the previous research the concentration was on business-to-business service innovations in that phase of the research, but no such concentration or attempt to differentiate the nature of the innovation was undertaken in the latter stages.

The concept of customer input uncertainty Customer-induced input uncertainty is the service organization’s incomplete information about what, where, when, and how customer input is going to be processed to produce desired outcomes. This uncertainty reflects incomplete information about the customer’s supply of knowledge, material, skill, place, time, relationship, and desired outcome concerning service innovations. Service design that relies heavily on reciprocal information and material exchange between the customer and service provider is most vulnerable to uncertainties of customer input.

Previous research In the product innovation literature, Feldman and Page (1984) found a high involvement of the customer in the evaluation phases of the innovation process; Cooper and Kleinschmidt (1988) found testing with customers in 2/3 of the 252 product innovation projects; Maidique and Zirger (1984) report that continual, informal, in-depth contact with leading customers throughout the development process is a factor in success; Pinto and Slevin (1988) identify “active client consultation” and “client commitment” as two major factors critical to success; and Von Hipple (1984) advocates both concept generation and testing with major clients. From the

Defined scope The initial step is to define the scope of the innovation process in terms of the client’s goals and the work the service provider will perform to achieve those goals. In most cases, these are project-oriented innovation goals. 56

The business-to-business customer in the service innovation process

European Journal of Innovation Management

Claude R. Martin, David A. Horne and Anne Marie Schultz

Volume 2 · Number 2 · 1999 · 55–62

Thus, the definition of the scope must provide a breakdown of phases, activities, and tasks (with accompanying tools and techniques) to achieve a business-to-business innovation goal. Some of these can then be customized to include or exclude detail specific to the particular engagement. Obviously, refinement of these methodologies can represent significant intellectual capital to a service innovator. Project engagement management skills and techniques are necessary to manage resources, scope, tasks, schedules, and finances. In many cases, clients lack these skills and knowledge in their own organization. Obviously, all of these are sophisticated techniques that require extensive training and experience to achieve service provider delivery. However, the challenge is much greater for client personnel to both learn and innovate at the same time during the project engagement.

With such a wide range of unique customer demands, many such service providers can have very little specific information before a project begins resulting in high input uncertainty. Customer disposition to participate Customer disposition to participate refers to the extent the customer tends to play an active role in supplying labor or information inputs to the service production process. The disposition to participate is driven primarily by customer motivation, which stems from two sources: customers find doing it for themselves intrinsically attractive; and customers may feel that their active involvement is necessary to guarantee quality. According to Larsson and Bowen (1989), the more actions the customers tend to contribute the higher the input uncertainty because the organization has incomplete information before the service encounter about what the customer actually will do. The design of the service operation can be viewed in terms of different interdependence patterns consisting of division of service work between employees and customers and customization versus standardization of service actions and interdependencies.

Defined roles The next step is to have specific roles defined for both service provider personnel and client staff. The designation of each has a slightly different purpose. Service provider personnel roles are defined so the client understands the skill set and activities of each provider member for which the client is paying. The client personnel roles are defined to inform the client project engagement leader of the skills and experience levels needed in order for the project to be successful.

Seamlessness and conflicts An important concept that has emerged in the service management field is that of seamlessness (which is defined as the uninterrupted flow of services from innovation to production to delivery to post-consumption problem resolution). Seamlessness leads to higher quality services, lower customer acquisition costs, and higher customer retention rates which flow to profitability. Larson and Bowen (1989) identify a seamless flow of services from innovation forward as involving “considerable contact between the service provider and customers; customers frequently participate in service production tasks performed at the organization-customer interface” (Bateson, 1995, p. 101). The reason why co-production by employees and customers is the key to seamlessness is clear: Service providers need customer inputs so that the strategies they employ align with customer’s legal, financial, and cultural demands. Professional service organizations can only act according to these demands. However, in many service provider’s innovation processes there is a loss of seamlessness because customers have marketing, organizational, and human resource cultures of

Analysis of customer input uncertainty Larsson and Bowen (1989) report that input uncertainty will vary with two environmental variables: (1) Diversity of customer demand. (2) Customer disposition to participate. Diversity of customer demand Diversity of customer demand refers to the uniqueness of customers’ demands, including both the uniqueness of the customer to be serviced and uniqueness of the desired outcome. Particularly in the case of professional services, such as management consulting, every innovation project is necessarily customized in terms of size, scope, activities, and deliverables to meet the specific business goals and constraints of each client. It is because of the diversity of customer demand that each innovation process is not subject to generic contract. 57

The business-to-business customer in the service innovation process

European Journal of Innovation Management

Claude R. Martin, David A. Horne and Anne Marie Schultz

Volume 2 · Number 2 · 1999 · 55–62

their own that could be seamlessly bundled but, the provider organization’s personality and skill set prevents it from bundling its culture with those of its customers. The marketing, organizational, and human resource cultures of customers need to be core inputs into service provider’s innovation process. This means the service provider must come to understand those inputs because overcoming input uncertainty leads to seamlessness in innovation and the subsequent delivery of that innovation to the marketplace. Once input uncertainty is overcome by way of seamlessly bundling together the cultures of customers and organizations, desired outcomes are achievable. Larsson and Bowen (1989) suggested that this bundling process increases the quality of services. They suggest that the more the customer is disposed to participate the greater the amount of work that can be shifted to the customer. Table I is a list of sources of innovation uncertainty for service provider organizations and corresponding service concepts that can resolve these uncertainties by tapping into the marketing, organizational, and human resource cultures of customers. The list is applicable to most industries and was developed by Larsson and Bowen (1989, Table I) to demonstrate the different ways that customer inputs add value to the highly customized work of services companies.

But what do we mean by productivity in a service environment and particularly concerning customer productivity? Typical ways of measuring the productivity are, for example, the number of vehicles coming off an auto assembly line in a certain time period or the costs per unit sold by a retailer. Such measures do not work in service firms, because they have nothing to do with the quality of the service output. However as Lovelock (1991, p. 373) points out, productivity measurements are most often related to internal efficiency measures of the performance of employees and do not deal with client performance. In the service-profit chain model (Heskett et al., 1994) there is consideration of employee productivity as an input to external service value, but nothing dealing with client productivity. Previously, we addressed this issue and suggested that guidelines for accomplishing increased client productivity might come from the more widely developed theory and research on increasing employee productivity. Essentially we suggested focusing on the client rather than the employee to reformulate these guidelines (Martin and Horne 1992, p. 29). Within that exercise we suggested using clients instead of employees as the focal point for productivity improvement techniques such as behavior modification, understanding and accepting change, fitting the service to client values, and client training.

Analysis of customer output

Tracking an innovation process in a consulting environment

Not only is there the issue of customer willingness to participate in the production function, but allied with this is the measurement of the output from that participation. Here we address the construct of customer productivity.

We were privileged to be allowed by a major global consulting firm to sit through the total innovation process they undertook for a business client. Here we report on our monitoring of the various outcomes from stages in that process and the problems, opportunities, and solutions that resulted. It should be emphasized this process could also be applicable to other types of service providers, such as financial institutions, system design firms, investment consultants, etc.

Table I

Sources of uncertainty for organizations

Corresponding service concepts that can resolve these uncertainties

Incomplete information about: What to process Where to process it When to process it How to process it Into what Distributed where Distributed when

Input uncertainty stemming from customers: Customers’ supply of output Customers’ supply of place Customers’ supply of time Customers’ supply of labor Customers’ desired outcome Customers’ desired place outcome Customers’ desired time outcome

The project Because of confidentiality, the client and the service provider (consulting firm) cannot be specifically identified. However, the intent of the project was to innovate system requirements for a new application that would replace one that was outdated. Specifically, this engagement was established to help the 58

The business-to-business customer in the service innovation process

European Journal of Innovation Management

Claude R. Martin, David A. Horne and Anne Marie Schultz

Volume 2 · Number 2 · 1999 · 55–62

client solve a complex and unique business problem, meeting the high diversity of demand characteristics. This was clearly a project-oriented innovation effort. This was the strong source of motivation for the client organization to actively participate by providing the needed business-specific knowledge throughout the process, thus ensuring the overall quality of the innovation. Both high diversity of demand and a high customer disposition to participate characterize a reciprocal service innovation and design. However, this led to high input uncertainty for this engagement.

of the client team members and the progress of the project. In the early phases of the project, much of the work was collaborative between service provider analysts, client system analysts, and client business system users. Facilitated work sessions were used to draw out system requirements and to design innovative new business processes. As the tasks became more complicated and more detail began to emerge, there was often confusion and frustration in these work sessions about how detailed discussions needed to be or when the appropriate time was to discuss certain issues. While client personnel were experienced in both using and maintaining the current system, none of them had ever been exposed to the innovation approaches that the service provider was using in the work sessions. This became apparent by the end of the first two weeks of the project when information gathering and current situation analysis had been completed. Team members were confused by both the individual tasks assigned to them and how those tasks fitted into the “big picture.” Frustrations and concerns were high; productivity was low. This translates directly into a lack of knowledge and skills of the methodology (and tools and techniques that support it) employed by the service provider. Ironically, the methodology employed by this particular service provider is very typical of the approach we have found in use by other major consulting and systems integration firms. The client, however, had never been exposed to any structured application innovation methods. An assumption (made in the absence of complete information) about the knowledge and experience of the client personnel on the project was judged to be incorrect.

Risk assessment Initially a risk analysis was performed by the service provider (the consulting firm) as input to the development of the scope of the project. This risk assessment included a customerinput uncertainty analysis in order to determine the service provider’s business risk for the engagement. The elements in this pre-project client participation risk assessment included: • Expected customer attitude. • Customer participation. • Percentage of team made up of customer personnel. • Percentage of project leadership made up of customer personnel. • Customer team members’ location relative to service provider team members’ location. • Customer project leadership. • Customer personnel. This risk assessment analyzed areas where incomplete information from customer uncertainty could affect the success of the project. Quantitative factors determined the level of risk associated with the project. Highrisk customer participation factors reflected selections of difficult customer attitudes, customer impacting of project success, high percentage of team comprised of customers, and customer and service provider not being located in the same facility.

Customer innovation corrective actions The first attempt to resolve the gaps in the knowledge and understanding of the client personnel involved two corrective actions. First, service provider analysts led overviews for only the client systems analysts. Material directly from the service provider’s training class for their own personnel was given to the client, along with samples of documents and diagrams they were expected to create. The ‘”big picture” was stressed in these sessions to alleviate some of the ambiguity about the timing of tasks and when issues would be addressed during the different phases of the innovation project.

Customer innovation participation problems The project experienced problems with client innovation participation. The service provider established that the engagement was at risk because of high client input uncertainty, but only contractual changes were made to address that risk. This input uncertainty manifested itself and affected the contribution 59

The business-to-business customer in the service innovation process

European Journal of Innovation Management

Claude R. Martin, David A. Horne and Anne Marie Schultz

Volume 2 · Number 2 · 1999 · 55–62

The second corrective action was also for the benefit of only the client system analysts. Each work session that involved service provider analysts, client analysts, and client business/user specialists was preceded by a pre-work planning session that was to discuss the details behind the goals and techniques for that day’s work session. Only the service provider analysts and client analysts attended this pre-work planning session. It was intended to help the client analysts understand the detailed techniques (facilitation approaches, diagramming techniques, data and process modeling techniques, etc.) and help facilitate the sessions. The hope was that the client analysts would be able to strengthen the very weak bridge between the service provider analysts (who knew the desired output) the client business specialists (who knew the business specific information).

the model was correct because they did not have the sufficient background to translate the model’s diagram back into the business rules that describe the relationships between their data. This pain and confusion went on for over three weeks. This hampered the project schedule by extending the time line for this task and taking away resources from other tasks. Too much training was required to improve client innovation process participation in this activity at the late stage of the project. To resolve the impasse, service provider personnel were trained by the client analysts in a data modeling technique with which they were familiar. Fortunately, learning a second data modeling technique is a lot like learning a second foreign language. Because the service provider professionals were already predisposed to formal data modeling, it was overall less effort for them to be trained than for the client to be trained. The downside to this approach was that the technique employed by the client analysts was at a lower level of detail than was called for at this stage of the project. This added more time to the schedule to accommodate the detail. Eventually, an innovative data model was in place that could be used as an input to subsequent project activities.

Continuing and new customer innovation participation problems Up to this point, the client innovation participation problems had affected the project in two ways: team member morale was low and the quality of the initial work products was acceptable, but not strong. However, it was anticipated that there would be opportunities later in the development process to refine these outputs to the level of certainty and quality required to design a system. The project was still on track with respect to schedule, scope, and cost to the client. Confidence began to dwindle as the team entered the most challenging portion of the project. The successful transition from the client’s old system to the new system would rely on the ability to collaboratively design a new data model that corrected all of the deficiencies of the existing system. The team employed the same approach to this new activity as they had on the previous ones: client analyst “big picture” training, client analyst “pre-work session planning,” and joint work sessions to perform the actual work. Facilitated innovative data modeling in itself is a very difficult task when you have trained professionals working together. The challenge proved to be too much for the under-trained joint service provider/client team. Innovation sessions experienced several false starts. A service provider data analyst was replaced at the client’s request because of his (perceived) inability to facilitate the sessions correctly. The client team could not confirm if

Service quality impacts The failure to address the client’s lack of necessary skills in the innovation process ultimately reflects on the client’s total service experience. Impacts included schedule delays, scope reductions, and a lack of confidence in both the techniques employed by the service provider and its personnel in delivering them. In this case, the client’s expectations for professional innovation tools and techniques were not met. In our opinion, as observers, if the client were to be asked to evaluate the service with respect to client training and education on five service quality categories (Parasuraman et al., 1985, 1988, 1991), the response would likely be as shown in Table II.

Analytical prescriptions Instead of offering analytical prescriptions for the specific and flawed innovation process above, we instead are opting to use those observations as a foundation for offering a series of more general prescriptions for business-to-business innovations. Hopefully this will prove more useful to the reader. Our focus in designing, planning, and delivering of 60

The business-to-business customer in the service innovation process

European Journal of Innovation Management

Claude R. Martin, David A. Horne and Anne Marie Schultz

Volume 2 · Number 2 · 1999 · 55–62

Table II

Service category

Assessment

Comment

Tangibles

Inadequate

Responsiveness

Insufficient

Empathy

Poor

Assurance

Damaged

Reliability

Suspect

Tangibles should have included more client-oriented reference and training materials describing the innovation methodology and approaches. Specific tasks should have been identified, planned, and executed to address the training gaps This client required special needs in education and training that were recognized too late in the process. Training that was performed was “too little, too late” There was lack of both willingness and ability on the part of certain service provider personnel to recognize the gap and to deliver the additional training which is above and beyond the “normal” template for service innovation The customer’s trust and confidence in the service innovation process was damaged. This assurance deteriorated as the project progressed Overall, the reliability of the service provider’s experience was damaged. While the overall delivery promise may have been kept, the experience of the client staff to achieve the desired results was far more difficult than the client was promised and expected

client training and education is to improve the role of the client in the innovation process and to reduce impacts of client input uncertainty.

review of the risk factors and the development of the risk mitigation plan. Identify “client training” as a risk mitigation approach.

A generic risk assessment will not uncover all the client specific details related to the expected inputs and performance requirements for a specific innovation project and client. In the case described above, the service provider calls for documenting assumptions that are made with respect to the project approach and the roles and responsibilities of the client. It would be wise and necessary to document the assumptions made in the absence of complete information that exists in reciprocal service delivery. However, capturing assumptions is not enough. Assumptions must be linked to “moments of truth” in the project plan and tracked accordingly. We would advocate planning contingencies if an assumption is wrong.

Assessing and monitoring input uncertainty Based on our observation of the service provider/client innovation process above, we predict that the initial area for a mistake is with respect to risk assessment. A risk item discussed earlier is the degree to which customer personnel are considered experienced and trained. It is noted that customer disposition to participate can be constrained by insufficient ability (in knowledge, strength, skills, and time); and by role clarity (understanding their role in the service innovation process). Prescription 1: Modify risk assessments to include a specific dimension on client background and exposure to structured innovation methods and project management techniques. Review the results of the assessment with the client.

Prescription 3: Link service provider assumptions about client participation to specific “moments of truth” in the plan where the assumption will be tested. Track the assumptions against the plan and develop contingency plans for bad assumptions.

Identifying risks without an accompanying risk mitigation plan does not accomplish the desired risk reduction. Risks identified for client anticipation in the innovation process require actionable plans to minimize the likelihood of occurrence and the level of impact. The only way to ensure that the plans are actionable and realistic is to gain the client’s consensus on the risk factors and accompanying plans.

Designing and delivering client-oriented training The variables diversity of demand and disposition to participate are customer characteristics external to the organization, constituting constraints and contingencies to which the design of the service innovation must adapt. This adaptation can be both

Prescription 2: Require risk mitigation plans for all high-risk items in client participation in the innovation process. Involve the client in the

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The business-to-business customer in the service innovation process

European Journal of Innovation Management

Claude R. Martin, David A. Horne and Anne Marie Schultz

Volume 2 · Number 2 · 1999 · 55–62

References

anticipatory in start-up and reactive as the service operations evolve over time. The training delivered during the course of the project described above was reactionary, in response to specific problems as they occurred on the project. As a result, not all of the training truly required for the client was delivered.

Bateson, J.E.G. (1995), Managing Services Marketing, 3rd ed., The Dryden Press, New York, NY. Berry, L.L. (1980), “Services marketing is different”, Business, May-June. Cooper, R.G. and Kleinschmidt, E.J. (1988), “Resource allocation in the new product process”, Industrial Marketing Management, Vol. 17 No. 3, pp. 249-262.

Prescription 4: Based on the client team composition, the activities to be performed in the innovation process, and their experience with the activities, determine the types of training required as a part of the process start-up.

Feldman, L.P. and Page, A.L. (1984), “Principles versus practice in new product planning”, The Journal of Product Innovation Management, January, pp. 43-55. Heskett, J.L., Jones, T.O., Loveman, G.V., Sasser Jr, W.E. and Schlesinger, L.A. (1994), “Putting the service profit chain to work”, Harvard Business Review, MarchApril, pp. 165-74.

Once the required training has been identified, it is necessary to determine the most appropriate delivery schedule for the training. This will depend on the depth and type of training required. Examples may include pre-innovation-process training and/or intermittent training. Either way, formal training, if required, should be planned for as part of the innovation process schedule. It is necessary for this training to be delivered to the right people, at the right time, at the right level of detail to maximize the client productivity in the process.

Larsson, R. and Bowen, D.E. (1989), “Organization and customer: managing design and coordination of services”, Academy of Management Review, Vol. 14 No. 2, pp. 213-33. Lovelock, C. (1991), Services Marketing, 2nd ed., Prentice-Hall, Englewood Cliffs, NJ. Maidique, M.A. and Zirger, B.J. (1984), “A study of success and failure in product innovation: the case of the US electronics industry”, IEEE Transaction on Engineering Management, EM-31, November, pp. 192-203 Martin, C. and Horne, D.A. (1992), “Restructuring toward a service orientation”, International Journal of Service Industry Management, Vol. 3 No. 1, pp. 25-38.

Prescription 5: Schedule time, effort, and resources for training as part of the formal project schedule.

Martin, C. and Horne, D.A. (1993), “Service Innovation: successful vs. unsuccessful firms”, International Journal of Service Industry Management, Vol. 4 No. 1, pp. 49-65.

The ability to recognize potential risks for input uncertainty and translate those risks into potential training requirements requires service providers proposing and designing engagements involving innovation to look at both sides of the equation. Historically, the focus of many firms is to design projects that accommodate what they want to deliver in the way of innovation versus a client’s specific needs.

Moeller, A. (1986), “Nuggets from the chief executive roundtable”, in Venkatesan, M., Schmalensee, D.M. and Marshall, C. (Eds), Creativity in Services Marketing: What’s New, What Works, What’s Developing, American Marketing Association, Chicago, IL, pp. 1-3. Normann, R. (1984), Service Management: Strategy and Leadership in Service Businesses, John Wiley & Sons, New York, NY. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1985), “A conceptual model of service quality and it’s implications for future research”, Journal of Marketing, Vol. 49, Fall, pp. 41-50.

Summary Input uncertainty has the potential to interfere with the success of reciprocal business-tobusiness service innovation. However, in spite of this uncertainty, service firms can attempt to mitigate the risks associated with input uncertainty. Firms that consciously perform risk assessments with respect to customer participation can put plans in place to reduce those risks. In the cases where the risk has to do with customer knowledge and skill for participation in the process, client training and education in the techniques to be used can reduce input uncertainty and improve client productivity in that process.

Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1988), “SERVQUAL: a multiple-item scale for measuring consumer perceptions of service quality”, Journal of Retailing, Vol. 64 No. 1, pp. 12-40. Parasuraman, A., Zeithaml, V.A. and Berry, L.L. (1991), “Refinement and reassessment of the SERVQUAL scale”, Journal of Retailing, Vol. 67, Winter, pp. 420-50. Pinto, J.K. and Slevin, D.P. (1988), “Critical success factors across the project life cycle”, Project Management Journal, June, pp. 67-75. Von Hipple, E. (1984), Novel Product Concepts From Lead Users: Segmenting Users by Experience, Report No. 84-109, Marketing Science Institute, Cambridge, MA.

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Introduction

Career route preferences of design engineers: an empirical research

Innovation and creativity are fundamental to successful companies’ competitive advantage (Tanner, 1998). Jain and Triandis (1990) state that the kind of people who are most likely to succeed in an R&D organization are those who are curious, independent, intellectual, analytical, introverted and who enjoy scientific and mathematical activities. Such people tend to be complex, flexible, self sufficient, task-oriented and tolerant of ambiguity. They also display high autonomy and change requirements and low deference needs. Scientists and engineers need special treatment irrespective of traditional management and control approaches. This is proved by two central themes in the career paths of scientists and engineers: the difficulty of retaining them within the organization (McGovern, 1995) and, at the same time, their problematic transition into management (Roberts and Biddle, 1994; Johnson and Sargeant, 1998). This transition is described as difficult both for those who make it and for those who do not. The relative complexity of these two aspects is, probably, the reason why human resources management in R&D has not been fully developed and has focussed mainly on selection, personnel administration and job guarantees (Pelz and Andrews, 1966; Ritti, 1971). However, times are changing and the organization has led to new management concepts such as business process reengineering, cross-functional integration, concurrent engineering, team-based working and cellular manufacturing. Nowadays, effective human resource management strategies in R&D organizations are specifically targeted at fostering innovation capabilities and creativity along four directions (Gupta and Singhal, 1993): human resource planning, performance appraisal, reward systems and career management. In terms of human resource planning, innovative companies create and staff new product venture teams with the right skill-mix of individuals. Recruiting, therefore, tends to be increasingly based on competency analysis and long-term staff development planning. When dealing with performance appraisal, innovative companies expect their employees

Alberto Petroni

The author Alberto Petroni is Assistant Professor in the Department of Engineering, University of Parma, Italy. Keywords Career, Designers, Engineers, Food industry, Packaging industry Abstract Based on a preliminary field research of career development systems for technical professionals, combined with a survey of 442 design engineers in the food processing and packaging machinery industries, the aim of this paper is to investigate the correlation between demographic variables, career values, success orientation and career route preferences. The purpose of this study is to add elements of discussion to the long-lasting debate about the evaluation of alternative modes of career development for technical professionals.

The author wishes to acknowledge the collaboration of the personnel of the firms that were involved in the research.

European Journal of Innovation Management Volume 2 · Number 2 · 1999 · pp. 63–70 © MCB University Press · ISSN 1460-1060

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Career route preferences of design engineers: an empirical research

European Journal of Innovation Management

Alberto Petroni

Volume 2 · Number 2 · 1999 · 63–70

to take risks and pursue innovations to create profitable ventures. Managing employee careers means accomplishing two objectives: enhancing individual career growth and maximizing their contributions to the organization. So, training should be aimed at building both technical and managerial skill bases, cross-functional transfer should be promoted and entrepreneurial behavior should be encouraged. Finally, as far as reward systems are concerned, innovative companies provide employees with various kinds of freedom to boost creativity, and they honor achievers with financial rewards, promotions and other forms of recognition. Over the last decade, a considerable amount of research has been devoted to issues related to reward mechanisms for R&D professionals (Frohman, 1978; Goldberg and Shenav, 1984; Allen and Katz, 1986; Miller, 1986; Baylin, 1991; Allen and Katz, 1992; McCormick, 1995; Allen and Katz, 1995; Debackere et al., 1997). The vast majority of these contributions, in addressing the investigation of career management systems via case studies or research surveys, have emphasized the importance of developing alternative modes of career development which are able to emphasize and valorize the individual-specific job expectations and career route preferences. Accordingly, various approaches to managing technical professionals have been proposed. The most famous (and perhaps the most controversial) is the so-called “dual ladder system” (Realin, 1987; Shephard, 1988; Smith and Szabo, 1977; Tampoe, 1993; Von Glinow, 1988; Wolff, 1982; Hesketh et al., 1992). As pinpointed by Allen and Katz (1992), the shortcomings of the dual ladder system can be traced back to: • the fact that technical and managerial careers have a different attractiveness for organization members, which is mainly due to cultural reasons, i.e. society as a whole seems to attribute lower prestige to a technical career than to a managerial advancement; • the fact that even when technical positions are put on the same level as managerial positions in terms of prestige, salary and status, the former lacks the vital ingredient of power; • the generalized practice where technical promotions sometimes tend to become a

“loyalty” prize instead of true career advancement. In order to choose the most appropriate career management systems, the antecedents of career development for technical professionals should be explored. This paper aims at addressing this basic issue through the direct investigation of the relationship between possible career routes, success orientation, job expectations and demographic factors of engineers staffed in product design and engineering departments of food processing and packaging machinery firms. The reason for limiting the analysis to this specific industry is motivated by: • the relative importance of these manufacturers for the regional economy of the Emilia-Romagna district; and • the increasing managerial emphasis on the retention of engineers in technical engineering departments. Furthermore, the area investigated is particularly interesting since it requires a continuous re-shaping of skills and competencies. This is mainly due to the evolution that the sector has undergone and which may be summarized as follows: • The computer-induced revolution of the way industrial designers work, speeding-up product development through the adoption of new techniques (CAD/CAM, rapid prototyping and machining tools). • An increasing involvement of designers in product development from the outset, rather than the firms’ rethink approach or need to seek a market niche. • The need for increasing design and marketing bi-directional communication resulting in increased sensitivity to the marketplace and the end user. Now, more firms incorporate engineering into design from the beginning, starting with the analysis of the consumer’s basic needs. • The search for improved product functionality which is tending to blur the distinction between standard, modified and special products and, in the process, to the generalized shrinking of design time and assembly costs.

The research Methodology The results presented in this paper are derived from a broad study which is organized in two 64

Career route preferences of design engineers: an empirical research

European Journal of Innovation Management

Alberto Petroni

Volume 2 · Number 2 · 1999 · 63–70

phases: a preliminary field research and a questionnaire survey. The former was carried out by interviewing the CEOs of six companies manufacturing systems for food preparation and packaging lines. The interviews concerned the prevailing human resource management practices adopted for technical professionals. This was done with two different types of objectives in mind: to get a better understanding of the antecedents of career development for design engineers in the specific context investigated and to build a framework for a structured research hypothesis for the investigation that followed (data selection for the questionnaire survey). The population for the questionnaire survey consisted of 11 companies: five large divisions of a worldwide engineering group active in the design, production and marketing of machines and complete lines for the food and tobacco industry and a host of smaller firms manufacturing bottling equipment and food processing machinery. The 1996 economic profile of the sample is shown in Table I. Preliminary meetings were scheduled with the members of technical staff in order to inform them about the general purpose of the study and provide some methodological explanations. A questionnaire was distributed to 642 participants and 442 were returned, which yields a total response rate of 69 per cent. The crucial criterion for selecting the participants was the belonging to a technical department with some product development-related responsibilities (design, engineering, etc.).

preferred orientation towards technical or commercial success. As far as career values are concerned, a total of 20 items (each rated on a seven-point Likert scale) were used to investigate which of the following categories could describe the individual profile: • Independence (three items defined this profile): this profile tends to be associated with the desire of developing a career in which respondents can decide when, what on and how hard to work. Autonomy is of primary importance and it may even be sacrificed for a promotion or other rewards. • Managerial competence (three items defined this profile): this profile indicates the attitude towards integrating and coordinating other employees’ activities together with the willingness to be totally accountable and responsible for the results of a process or function. · Functional competence (three items defined this profile): the primary aim of people having a marked functional capability is to exercise their technical skills. The respondents’ sense of identity is strictly related to interest in a job/task that enables him/her to enhance these capabilities. • Security (two items defined this profile): security is a twofold construct. On the one hand, it concerns security with respect to the organization and on the other hand it implies stability understood as the possibility of working in the same geographical area. • Dedication (three items defined this profile): this profile concerns achievement and respect for the specific individual and social values (i.e. environmental friendliness of products). Respondents with a clear dedication profile will chase opportunities which make it possible for them to continue working on projects in line with their deep-seated beliefs. • Variety (three items defined this profile): this profile is typical of respondents who look for job novelty, variety and tasks ensuring challenge and emotional commitment. • Identification (three items defined this profile): this profile provides a measure of the degree of overlapping between the identity of the respondent and that of the organization.

Research variables The set of independent variables considered in the questionnaire can be grouped into four classes: demographic characteristics, success orientation, career values and career orientations. The first part of the questionnaire considered demographic variables such as education, seniority and age as factors explaining career development of technical professionals. Previous research had pinpointed the relevance of demographic variables in this type of study (Allen and Katz, 1992). Among the variables considered, a modified version of Allen and Katz’s success orientation scales (1986) was included in order to get a clear indication of the respondent’s 65

Career route preferences of design engineers: an empirical research

European Journal of Innovation Management

Alberto Petroni

Volume 2 · Number 2 · 1999 · 63–70

Table I Profile of the sample of the firms

Employees Percentage staffed in Turnover of export/ engineering/ (US$ million) turnover Employees design

Companies Product

A1

A2 A3 A4 A5 A6 A7 A8 A9 A10

A11

Rows distribution systems, electronic in-line feeders, electronic high speed horizontal pillow-pack machines, robotic handling and collating units, feeders for continuous and intermittent motion cart Turn key plants for drink in any style container (glass, PET, cans) Machines for food processing, preserving, packaging and packing Machinery for weighing, packaging and packing Turn key projects for industrial bakeries Rinsers, unscramblers, fillers, complete traditional bottling lines, complete aseptic filling lines Carton fillers, confectionery packaging machines, horizontal pillowpack wrappers Yeast packaging lines, various wrappers and overwrappers, labeling machines Horizontal pouch F.F.S. machines and pouch cartoning systems Automatic can pressure/leak testing machines, high speed checkweighers of aerosol lines·gassing room to fill inflammable propellant gas Bagging machines for liquids, powders and granular products

78.125 187.500 65.625 60 218.750

98.8 95 75 75 80

446 1400 402 up to 500 900

90 250 76 N/A 211

50

70

240

60

112.500

59

600

131

Over 350 15

97 75

Over 1,500 45

N/A 10

56 8

80 30

185 15

39 4

Major findings

As for career orientations, respondents were asked (on a seven-point Likert scale) to express a preference for one of the following five alternative career routes: (1) Managerial route: this route considers advancement in the managerial track to a higher level within or outside the engineering department. (2) Horizontal technical career route: this route mainly concerns a switch towards novel and different technical specialties (not necessarily correlated with the original area of competence), irrespective of promotion. (3) Cross-functional route: this route involves a switch to other less technically-oriented roles, irrespective of promotion. (4) Vertical technical career route: this route indicates an evolution towards higher levels of specialization within the original area of competence, irrespective of promotion. (5) Project responsibility career route: this route is preferred by those dynamic individuals who are looking for challenges and excitement within the execution of product engineering projects, irrespective of promotion.

Demographic characteristics Of the 442 respondents, 15 per cent hold a university degree (n = 66), and only 4 per cent of the total population are female (n = 16). The average age of the respondent is 35.6 years, with a standard deviation of ten years. Of the total population 58 per cent (n = 256) have been employed at least once (22 per cent more than once) at other companies before joining their current employer. The average period already spent with the company is 12.5 years with a standard deviation of 6.14 years. A total of 22 per cent of all respondents (n = 98) participate with relatively constant frequency (at least five days per year) in training programs carried out either by consultants or external (public) educational institutions. Present positions Of all respondents, 15 per cent (n = 66) currently hold a managerial position within the company and an almost equal percentage (14 per cent, n = 61) hold a functional technical position without either project (either temporary or stable) responsibilities or managerial tasks. The remaining 71 per cent hold a position along the technical ladder and are steadily assigned to a project team (n = 313). 66

Career route preferences of design engineers: an empirical research

European Journal of Innovation Management

Alberto Petroni

Volume 2 · Number 2 · 1999 · 63–70

Of the respondents, 40 per cent (n = 177) describe their current job as pertaining mainly to strict product design activities (i.e. prototyping), 44 per cent (n = 194) state that his/her job is related to engineering and product manufacturability aspects, 10 per cent (n = 45) mainly act as the interface between and in support of either manufacturing or commercial departments, while the remaining 6 per cent (n = 26) have been categorized as “other” (most common answers: project management roles relating to economic and financial appraisal, data storage and retrieval, costing and budgeting).

managers at nine different organizations, also found that the majority of the respondents chose “the opportunity to engage in those challenging and exciting research activities and projects, irrespective of promotion” rather than either a pure managerial or technical route (even though marked age-dependent differences in responses were found). The results of this study, on the other hand, clearly emphasize a generalized preference for the managerial ladder and the project track. A possible explanation for the partial discrepancy in the results is to be found in the different research domains; Katz and Allen’s work was based on scientists working in R&D labs rather than design engineers.

Career preferences Out of the 442 respondents, one of the five career route preferences unequivocally emerges in only 260 cases (59 per cent). The remaining 182 respondents are characterized by tied scores which, for the purpose of the research, are omitted. As partially expected, the project responsibility career route together with the managerial route are almost equally rated as the most preferable career patterns by the relative majority of participants. The lowest preferences are the cross-functional route and the vertical technical career route, while the horizontal technical career route ranked in an intermediate position. More specifically, managerial preference is expressed by 35 per cent (n = 91) of the responses and 33 per cent (n = 85) indicated a clear preference for a project-based career. A total of 18 per cent (n = 47) are attracted by the possibility of tackling novel and different technical specialties (not necessarily correlated to the original area of competence): 11 per cent (n = 28) of the respondents consider themselves comfortable with the idea of augmenting their level of specialization within the original area of competence while the crossfunctional route collected only 3 per cent (n = 9) of the preferences. In relative terms, a high proportion of younger engineers favor the technical ladder progression more strongly than older ones who see the project-oriented career route as the most likely form of reward. As expected, those engineers holding a managerial position do not agree with this latter statement at any time. These results partially overlap with those provided by earlier studies. Allen and Katz (1986), while investigating technical career preferences of 2,157 engineers, scientists and

Significant correlation found Success orientation, demographic characteristics and career preferences First a factor analysis was performed which included success orientation indications and demographic characteristics. The findings are precisely in line with those provided by Allen and Katz (1992). A comparison between individuals holding a university degree and those who do not, based on the factor scores related to success orientation, reveals a clear statistical difference between the technical/ commercial orientation of the two groups considered. Individuals holding a university degree are on average more technically oriented (mean of factor score: 0.31) than their colleagues (mean of factor score: –0.09). What is to some extent surprising is that the former are, on average, also more commercially oriented (mean of factor score: 0.36) than the latter (mean of factor score: –0.11). The difference between the two groups is highly significant in both cases (p = 0.0002 for technical orientation and p = 0.0001 for commercial orientation). In a successive analysis, success orientation and educational background were further introduced as control variables in ANOVA analyses with the five career preferences (managerial ladder, horizontal technical career, cross-functional career, vertical technical career and project career) as dependent variables. Two-factor ANOVAs using success orientation (technical versus commercial) and educational background (university degree or not) as independent variables revealed, as expected, a statistically significant interaction effect with respect to the managerial career 67

Career route preferences of design engineers: an empirical research

European Journal of Innovation Management

Alberto Petroni

Volume 2 · Number 2 · 1999 · 63–70

route preference (p = 0.048; two-tailed). In other words, those engineers oriented towards management are more concerned with organizational matters and tend to prefer projects with high potential for external (customers and commercial staff) relationships. As far as the managerial career preference is concerned, no statistically significant main effect was detected, neither for the educational background independent variable nor for the success orientation independent variable. With regard to the preference for a horizontal technical route, a statistically significant main effect for the respondents’ success orientation (p = 0.002; two-tailed) was observed. Respondents with a technical orientation clearly consider a horizontal technical career route more valuable as compared to their colleagues with a stronger commercial orientation. As for the cross-functional career preference, no significant main effect is associated either with the educational background independent variable or with the success orientation independent variable. Furthermore, when dealing with the vertical technical career route preference, evidence of a statistically significant main effect for the respondents’ educational background (p = 0.003; two-tailed) emerged. Those individuals holding a university degree consider a vertical technical career more desirable, in comparison to their non graduate colleagues. A statistically significant main effect has also been found for the respondents’ success orientation (p = 0.001; two-tailed), which means that respondents with a strong technical orientation clearly consider a technical career more attractive than their colleagues with a stronger commercial orientation. This evidence could be expected from the findings of Allen and Katz’s previous research (1992). Finally, as far as the project career preference is concerned, the analysis was unable to detect any statistically significant main effects, either for the educational background independent variable or for the success orientation independent variable.

with the management career preference (r = 0.71; p < 0.001, one-tailed). The managerial route preference is also positively and significantly correlated with both identification (r = 0.41; p < 0.001, one-tailed) and variety (r = 0.40; p < 0.001, one-tailed) characteristics, while it is negatively and significantly correlated with functional competence (r = –0.41; p < 0.001, one-tailed). The association between functional competence features and vertical technical career route preference is also positive and statistically significantly (r = 0.45; p < 0.001, one-tailed). Furthermore, the preference for the vertical technical career route is positively and statistically significant correlated with autonomy (r = 0.46; p < 0.001, one-tailed) and identification (r = 0.39; p < 0.01, one-tailed). Project career preference is strongly and positively correlated with variety (r = 0.46; p < 0.001, one-tailed). Finally, a positive and significant association between the horizontal technical career route preference and variety (r = 0.37; p < 0.001, one-tailed) has been found. With respect to cross-functional career preference, no statistically significant correlation was detected. These results are shown in Figure 1. Briefly summing up the major insights gained from the analysis, one can say that job variety is certainly the most commonly pursued element. It is a feature desired by those engineers aspiring to managerial advancements or, more simply, looking for challenging project responsibilities or, again,

Figure 1 Correlation between career routes and values MANAGERIAL COMPETENCE

Managerial route IDENTIFICATION

FUNCTIONAL COMPETENCE

Vertical technical route

VARIETY AUTONOMY

Career preferences and career values Then, the search for correlation between the five career management systems preferences and career values was addressed. As expected, managerial competence is strongly correlated

Projectoriented route

Horizontal technical route

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Career route preferences of design engineers: an empirical research

European Journal of Innovation Management

Alberto Petroni

Volume 2 · Number 2 · 1999 · 63–70

In other words, this study supports the hypothesis that a formalized career ladder system may be inopportune since, in some cases, it represents a limited approach towards managing technical professionals which perhaps should give way to hybrid career routes.

willing to turn to different technical specialties. Identification with the firm tends to increase as technical specialties are explored in depth (vertical technical route) but, at the same time it is a vital ingredient for managerial career-oriented engineers who, show a greater preference for working on projects of importance to the organization and on those they see as having a potential for advancement. Finally it is worth noting that autonomy is of primary importance for those engineers who endeavor to move towards higher levels of specialization within the original area of competence.

References Allen, T.J. and Katz, R. (1986), “The dual ladder: motivational solution or managerial delusion?”, R&D Management, Vol. 16 No. 2, pp. 185-97. Allen, T.J. and Katz, R. (1992), “Age, education and technical ladder”, IEEE Transactions on Engineering Management, Vol. 39 No. 3, pp. 237-45. Allen, T.J. and Katz, R. (1995), “The project-oriented engineer: a dilemma for human resource management”, R&D Management, Vol. 25 No. 2, pp. 129-40.

Conclusions In this paper, some further insights into the career route preferences of technical professionals involved in product design and engineering activities have been provided. A major finding is that it appears that there are distinct classes of design engineers, with respect to career orientation. Some desire a managerial career, some desire a technical ladder career and a substantial number of them opt for interesting projects over the two traditional paths. A second set of results points out that measuring the job expectations (career values) of design engineers may help explain a good deal of the variance in the different career systems preferences they express. The empirical research has, in fact, emphasized that a significant correlation exist between what an individual expects from his/her job and the career route that he/she will preferably follow. Generally speaking, expectations are the portrait of an individual’s personality which, as a consequence, is found to play a significant role in the development of career preferences and orientations. The managerial implications of this study are mainly related to the fact that it can add further elements to the long-lasting debate about the most efficient system for managing technical professionals. Given the results presented in the preceding sections, one can confidently state that a dual ladder system moves from an excessively schematic and mechanistic conception: designing a two or three track career development framework may oversimplify and distort those roles which, as seen, should be developed on the basis of a plurality of patterns.

Bailyn L. (1991), “The hybrid career: an exploratory study of career routes in R&D”, Journal of Engineering and Technology Management, Vol. 8 No. 1, pp. 1-14. Debackere, K., Buyens, D. and Vandenbossche, T. (1997), “Strategic career development for R&D professionals: lessons from field research”, Technovation, Vol. 17 No. 2, pp. 53-62. Frohman, A.L. (1978), “Mismatch problems in managing professionals”, Research Management, Vol. 21, September, pp. 20-5. Goldberg, A.I. and Shenav, Y.A. (1984), “R&D career paths: their relation to work goals and productivity”, IEEE Transactions on Engineering Management, Vol. 31, pp. 111-17. Gupta, A.K. and Singhal, A. (1993), “Managing human resources for innovation and creativity”, Research Technology Management, Vol. 36 No. 3, pp. 41-8. Hesketh, B., Gardner, D. and Lissner, D. (1992), “Technical and managerial career paths: an unresolved dilemma”, International Journal of Career Management, Vol. 4 No. 3, pp. 9-16. Jain, R.K. and Triandis, H.C. (1990), Management of Research and Development Organizations. Managing the Unmanageable, John Wiley & Sons, New York, NY. Johnson, D. and Sargeant, A. (1998), “Motives for transition: an exploratory study of engineering managers”, Human Resource Management Journal, Vol. 8 No. 3, pp. 41-53. McCormick, K. (1995), “Career paths, technological obsolescence and skill formation: R&D staff in Britain and Japan”, R&D Management, Vol. 25 No. 2, pp. 197-211. McGovern, P. (1995), “To retain or not to retain? Multinational firms and technical labour”, Human Resource Management Journal, Vol. 5 No. 4, pp. 7-23. Miller, D.B. (1986), Managing Professionals in Research and Development, Jossey-Bass, San Francisco, CA.

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Career route preferences of design engineers: an empirical research

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Alberto Petroni

Volume 2 · Number 2 · 1999 · 63–70

Pelz, D.C. and Andrews, F.M. (1966), Scientists in 0rganizations Productive Climates for R&D, Wiley and Sons, New York, NY.

Smith, J.J. and Szabo, T.T. (1977), “The dual ladder: importance of flexibility, job content and individual temperament”, Research Management, Vol. 20, pp. 20-3.

Realin, J.A. (1987), “Two track plans for one-track careers”, Personnel Journal, Vol. 66 No. 1, pp. 96-101.

Tampoe, M. (1993), “Motivating knowledge workers – the challenge for the 1990s”, Long Range Planning, Vol. 26 No. 3, pp. 49-55.

Ritti, R.R. (1971), The Engineer in the Industrial Corporation, Columbia University Press, New York, NY.

Tanner, D. (1998), Total Creativity in Business & Industry: Road Map to Building a More Innovative Organization, John Wiley & Sons, New York, NY.

Roberts, K. and Biddle, J. (1994), “The transition into management by scientists and engineers: a misallocation or efficient use of human resources?”, Human Resource Management, Vol. 33 No. 4, pp. 561-79.

Von Glinow, M.A. (1988), The New Professionals: Managing Today’s High-Tech Employees, Ballinger Press, Cambridge, MA.

Shephard, H.A. (1988), “The dual hierarchy in research”, in Katz, R. (Ed.), Managing Professionals in Innovative Organizations, Harper Business, New York, NY.

Wolff, M.F. (1982), “Revamping the dual ladder at General mills”, in Thushman, M.L. and Moore, W.L. (Eds), Readings in the Management of Innovation, Pitma, Boston, MA.

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1. Introduction

Quality differentiation for competitive advantage: a contingency approach

Accelerated global competitiveness, reduced product life cycles, rapid technological advancements, and dynamic customer requirements have drastically altered the nature of industrial competition. Price (cost) is no longer the sole criteria for creating a sustainable competitive advantage (Hill, 1995). Firms must develop and deploy competitive strategies driven by market requirements. Product, process, and service quality improvement has been adopted by many firms as a key strategic initiative for achieving world-class performance levels (Adam, 1992). However, sustainable worldclass performance will not occur if there is a mis-alignment between a firm’s competitive strategies and actual market requirements. There is general agreement among researchers that firms that align their competitive strategies with the requirements of their environments outperform those firms who fail to achieve such alignments (Venkatraman and Prescott, 1990). Furthermore, despite the importance of industry life cycle (ILC) to the choice of competitive strategies (Hofer, 1975; Porter, 1980), little empirical research has centered on this critical contingency. The few studies that have focused on ILC, have not examined the strategy-performance linkages across all stages of the ILC (Anderson and Zeithaml, 1984; Hambrick et al., 1982; Miller and Dess, 1993). Moreover, these studies have primarily relied on PIMS-based indices for operationalization of competitive strategies and multiple regression as the method of analysis: an approach that does not permit a holistic view of competitive strategies (Venkatraman and Prescott, 1990). The purpose of this paper is to examine alignment between each of the four primary stages of the ILC and the competitive strategy of quality differentiation. The framework of this paper is based on the results of a larger study of 101 small manufacturing firms located in the mid-west. The larger study focused on the performance consequences of aligning competitive strategies with each of the four fundamental ILC stages: intro duction, growth, maturity, and decline. This paper reports findings relating to the implementation of quality differentiation across all four ILC stages. The findings provide a foundation for the further development of ILC and strategic quality management theory. In

Reginald M. Beal and Archie Lockamy III

The authors Reginald M. Beal is Assistant Professor of Strategic Management and Archie Lockamy III is Professor of Operations Management, both in the School of Business and Industry, Florida Agricultural and Mechanical University, Tallahassee, Florida, USA. Keywords Quality, Competitive advantage, Alignment Abstract This study examines the performance consequences of aligning the competitive strategy of quality differentiation with each of the four fundamental industry life cycle stages: introduction, growth, maturity, and decline. Reports results from a questionnaire survey of CEOs in a mid-western state in the USA. The findings suggest that small manufacturing firms can achieve a sustainable competitive advantage vis-à-vis quality differentiation across three stages of industry evolution: introduction, growth, and maturity.

European Journal of Innovation Management Volume 2 · Number 2 · 1999 · pp. 71–81 © MCB University Press · ISSN 1460-1060

71

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

addition, the results and interpretations have implications for managers in their determining of the appropriateness of deploying quality differentiation to achieve a sustainable competitive advantage.

workers’ attention on continuous quality improvement. Additionally, Zhao et al. (1995) conducted a study, which compared the frequency of the use of quality information to evaluate business performance between India, China, Mexico, Canada, Germany, and the USA. The study revealed that the USA had the highest percentage of companies using quality information to evaluate business performance less than annually, or not at all. Takeuchi and Quelch (1983) were among the first researchers to articulate the need for firms to adopt a “customer-driven” approach to quality designed to avoid mis-alignments between a firm’s product or service offerings and the quality requirements of a targeted market or market segment. Thus, more research is needed to assist managers in developing and maintaining a strategic quality alignment between the firm and the marketplace. Over two decades ago, Hofer (1975) suggested that ILC was the most fundamental variable in determining an appropriate competitive strategy. Hofer also advanced several propositions on the effects of ILC on strategy-performance relationships. Hambrick and Lei (1985) further demonstrated empirically the importance of the ILC as a key environmental contingency. ILC is important to the choice of competitive strategy because the nature and intensity of competition and thus the key success factors vary considerably across different stages of the ILC. Whereas in the introductory stage, competition primarily revolves around technological innovation; in the maturity stage, intense price competition sets in (Porter, 1980). Shifts in the nature and intensity of competitive rivalry require major changes in competitive strategies during different stages of the ILC (Hofer, 1975; Porter, 1980). Superior performance may thus be realized by aligning competitive strategies with the requirements imposed by the unique characteristics of each stage of the ILC. A large body of theoretical research on the impact of ILC on choice of competitive strategies has been assembled over the years. This body of research offers somewhat conflicting suggestions on the appropriateness of a variety of competitive strategies during different stages of ILC (Anderson and Zeithaml, 1984; Hambrick et al., 1982; Hofer, 1975; MacMillan et al., 1982).

1.2 Organization This paper is organized as follows. The introduction provided the motivation for and purpose of the paper. A review of the literature regarding quality differentiation and hypotheses-based examination of the literature are provided in Section 2. Section 3 describes the research methodology used for the study. The results of the data analysis are discussed in Section 4. Finally, conclusions and implications are presented in Section 5.

2. Literature review As Japanese firms began to show how a quality differentiation strategy could result in a competitive advantage, some researchers began to explore the relationship between quality improvement and competitive success. In addition, researchers began to define strategically significant product and service quality measurement determinants for assessing the long-run impact of qualityfocused initiatives on firm performance. For example, Garvin (1983) illustrated how improvements in field failures and product defect rates reduced cost and increased productivity in the US and Japanese room air conditioning industries. He later defined eight “dimensions” or measurement areas of product quality from which a firm can select to provide a basis for developing a competitive advantage (Garvin, 1984). Similarly, Parasuraman et al. (1985) outlined ten measurement areas for the assessment of service quality from a strategic perspective. Although the above-cited literature makes a significant contribution to the understanding of quality differentiation, additional studies suggest that more research is needed to assist managers in its strategic use. A comparison of US and Japanese electronic industries conducted by Daniel et al. (1994) revealed that fewer US managers received management control information needed to support quality strategies than their Japanese counterparts. According to the study, by receiving goal setting and feedback information regarding quality performance, Japanese managers are better able to focus 72

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

However, despite the widespread belief about the role of ILC in choice of competitive strategies, there is a paucity of empirical research that examines strategy-performance linkages under different stages of ILC.

respectively (Porter, 1980). Encouraged by the prospects of attractive market opportunities, embryonic companies and firms competing in other industries enter the rapidly growing industry (Grant, 1991; Macdonald, 1985). Multiple product technologies compete for attainment of the most acceptable or the “dominant design”. While the rate of process innovation is not as rapid as that of product technological development (Utterback, 1994), innovations in process technology enable some firms to compete more effectively as rivalry among firms will tend to increase (Porter, 1980). Although multiple functional demands exist in conditions of growth, one demand – meeting rapidly growing customer demand – will tend to dominate others (Porter, 1980). The organizational challenge is to select from several alternative strategies a strategy that satisfies a specific customer need, based on the firm’s preferences, strengths, and distinctive competencies (Porter, 1980). Porter (1980) further suggests that firms will address the situation by positioning themselves in niches that correspond to different functional demands and yet match their skills and competencies and realize superior performance by adopting this strategic approach. As an industry moves through the growth stage, more informative data on the performance, reliability, and dependability of the competing products is disseminated either by word of mouth or firms’ comparative promotional pieces (Porter, 1980). Customer learning may lead to demands for the elimination or reduction of product defects, better product performance, and product warranties (Porter, 1980). Thus, quality-conscious buyers provide a viable market niche for firms producing and marketing products superior in quality to those of their competitors. Therefore: H2: In the growth stage, increasing emphasis on the strategy of quality differentiation will lead to higher levels of firm performance.

2.1 Introductory (embryonic) stagecompetitive strategy alignment Product innovation lays the foundation for the embryonic stage of industry development (Porter, 1980; Onkvisit and Shaw, 1989; Wasson, 1974) as newly formed or existing firms introduce new products in response to perceived opportunities created by “technological innovations, emergence of new customer needs, or other economic and social changes” (Porter, 1980, p. 215). This initial stage of industry development is characterized by two primary functional demands: innovation in product features and building customer demand (Porter, 1980; Utterback, 1994). Neither functional demand is more determinate of how firms should respond strategically than the other. Although product innovation creates an embryonic industry, the growth and survival of the industry requires customer acceptance and purchase of the new product offerings. While intrigued by an embryonic industry’s products, some customer innovators are often confused by conflicting claims and different features of the competing products (Porter, 1980). These first-time buyers are seeking products that are innovative, but product reliability and dependability are of primary importance to them (Wasson, 1974). It is not uncommon for the products of some firms in an embryonic industry to contain numerous or serious defects (Porter, 1980; Utterback, 1994). Thus, firms offering relatively higher quality products may be well patronized by quality conscious buyers dissatisfied with their purchases of defective, new products. Consequently: H1: In the introductory stage, increasing emphasis on the strategy of quality differentiation will lead to higher levels of firm performance.

2.3 Maturity stage-competitive strategy alignment In the maturity stage of industry evolution, the level of sales is higher than in the growth stage but sales growth has tailed off, threats are more evident, and competition has intensified as firms compete for fewer opportunities (Porter, 1980). Customers’ greater

2.2 Growth stage-competitive strategy alignment The growth stage of industry development is characterized by accelerating growth in demand for the industry’s products as many new first-time buyers combined with repeat buyers enter and re-enter the market, 73

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

familiarity with industry’s products and the knowledge they have gained during the industry evolution enable them to exercise increased bargaining power by demanding lower prices (Grant, 1991). Moreover, firms confronted with few growth opportunities, more intense competition for market share, and fewer viable strategic options, shift their focus towards competing on the basis of price, thus driving down profit margins (Wasson, 1974). In such an environment, key sources of competitive advantage are high levels of operational efficiency, application of technological advances that lower manufacturing, distribution, and administrative costs. Furthermore, firms will seek cost advantages through economies of scale in manufacturing, distribution, and purchasing (Grant, 1991; Porter, 1980). Porter (1980) and Grant (1991), among others, argue that competition in the maturity stage shifts not only to price but also to customer service and quality as well, because knowledgeable customers demand highly reliable, dependable, products and support services. For example, in the maturing personal computer market, several manufacturers feel compelled to increase the level of support services such as operating “hot lines” where customers can call in and receive immediate help on technical problems, in the copier industry firms compete through provision of training services, maintenance contracts, and speed of repair services (Grant, 1991). A further example, may be found in the automobile industry where the Japanese manufacturers have captured a significant share of the US market by offering high quality, low-priced automobiles. However, strong emphasis on service or quality in the absence of a simultaneous, strong emphasis on low cost will, most likely, not yield high levels of firm performance as service and quality differentiators are forced by competitive pressures to keep their prices at low levels. This line of reasoning suggests the following hypothesis: H3: In the maturity stage, increasing emphasis on quality differentiation will not lead to higher levels of performance.

coupled with rapid decline while others decline slowly and predictably over a decade or more allowing firms to survive profitably and eventually exit without being battered by fierce price wars. However, on the whole, the decline stage is characterized by a number of years of declining unit sales, price competition, significant reductions in the number of different product types, and reductions in advertising/promotional and R&D expenditures. During the decline stage many firms may exit the industry as profit margins are eroded (Porter, 1980). Survival while maintaining profitability becomes paramount. When confronted by declining sales, fierce price competition, and resultant declining profit margins, a firm must have a low cost structure, if it is to be profitable. Thus, the strategy of low-cost leadership may be the only viable strategy alternative. Anderson and Zeithaml (1984), for example, found that decline stage firms that reduced their investments in plant/equipment while emphasizing efficiency were profitable. It is highly unlikely that quality differentiators can charge sufficiently higher prices that offset declining demand for their products in the midst of increased price pressures brought to bear on the industry by customers and competitors. The aforementioned arguments and empirical findings suggest the following hypothesis: H4: In the decline stage, increasing emphasis on quality differentiation will not lead to higher levels of performance.

3. Research methodology 3.1 Sample We chose to study small manufacturing firms competing in single businesses. Although there is no generally accepted definition of a small business, the most widely used definition is that offered by the Small Business Administration (SBA). The SBA defines a small business as a firm that is independently owned and operated and not dominant in its market. The SBA also uses revenues or number of employees in classifying businesses according to size. In the case of manufacturing companies, those with less than 500 employees are considered small. Questionnaires were mailed to the CEOs of 500 small (as defined by the SBA) manufacturing firms randomly selected from a directory of manufacturers in a mid-western state. The questionnaire was anonymous as a

2.4 Decline stage-competitive strategy alignment As Porter (1980) points out, the decline stage of the ILC may vary substantially across different industries. For example, some industries experience fierce price competition 74

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

firm could not be identified unless the CEO elected to do so. Only five of the 101 CEOs who returned completed questionnaires chose to identify themselves and their firms. Twenty-seven of the questionnaires were returned as undeliverables. Thus, taking the undeliverables into consideration, the response rate was 21.4 percent. The participating organizations range in size from 4 to 480 employees (median employment level of 55; mean of 94; and standard deviation [SD] of 11). Their revenue ranged from $250,000 to $219,000,000 (median = $5,000,000; mean = $15,814,200; and SD = $34,664,900). The median age of the firms was 40 years (mean = 45.86; SD = 29.5). The sample represented a wide range of industries. It included 13 firms in consumer durables, eight firms in consumer non-durables, 33 firms in capital goods, 28 firms in industrial sub-components, 11 firms in industrial supplies, and eight firms in raw materials and semi-finished goods. The survey instrument used in our study was pilot-tested using five small mid-western manufacturing companies. Modifications were made to the questions wherever necessary to increase the clarity of the survey instrument. None of the firms that participated in the pre-test were included in the sample. The potential for common method bias, but more specifically, potential for single source bias exists for this study as the CEOs of small firms were the primary source of information on several constructs – competitive strategy, environmental conditions, scanning, and firm performance. However, as Phillips (1981) points out, several factors including “situational factors, individual differences, or implicit theories can contribute to variation or covariation in ratings” across several different but related constructs. Thus, without fully understanding how all these factors affect ratings, it cannot be concluded that ratings by a single source are “unequivocally biased” (Phillips, 1981).

strategies as suggested by Miller (1988) and Mintzberg (1988). Respondents were asked to indicate the extent to which their firms emphasized each of the 23 competitive methods in the past three years. Data were recorded using fivepoint scales that ranged from (1) no emphasis to (5) major and constant emphasis. Self-typing of business-level strategy by key informants (e.g. CEOs) has been challenged (e.g. Venkatraman and Grant, 1986). Findings of recent empirical studies examining the self-typing of business-level strategy by CEOs (Shortell and Zajac, 1990; James and Hatten, 1995) suggest that the methodology is valid. Exploratory factor analysis (principal component factor analysis with varimax rotation) was used to construct the competitive strategy indices. The factor analysis produced a five-factor solution accounting for 64.5 percent of the variance. Only one of the five factors extracted – quality differentiation – corresponding to competitive strategy dimensions is relevant to this study. Each of the five factors had eigenvalues greater than 1. Composite measures (indices) representing each competitive strategy dimension were constructed as an average of the scores on the variables with highest loadings on each factor. Following is a description of the quality differentiation index and its component variables and the reliability (i.e. Cronbach alpha) of the index. Quality differentiation (alpha = 0.78). Emphasis on superiority in reliability and durability is the hallmark of quality differentiators (QD). The competitive methods that had high loadings on this factor are: (1) strict product quality control techniques; (2) benchmarking best manufacturing processes in the industry; (3) benchmarking best manufacturing processes in any industry; (4) immediate resolution of customer problems; and (5) product improvements based on detailed assessment of gaps in meeting customer expectations.

3.2 Measurement of competitive strategy Twenty-three items are used to measure the five competitive strategy dimensions. Of these items, 12 are based on operationalization by Dess and Davis (1984) and Miller (1988) of Porter’s (1980) generic competitive strategies. These items are complemented with a set of 11 additional items to represent a multidimensional view of differentiation based

3.3 Measurement of ILC stages Researchers have suggested numerous characteristics that help distinguish among the four stages of the ILC (Grant, 1991; Onkvisit and Shaw, 1989; Porter, 1980). The following measures were used for identification of the ILC stage: (1) growth in the industry’s sales during the past five years; 75

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

(2) level of demand for the industry’s products; (3) stage of development of the industry’s products; (4) level of diffusion of information about the industry’s products; (5) plant capacity of the industry’s firms over the past five years; (6) current price levels of the industry’s products; (7) growth in the different types of distribution channels for the industry’s products over the past three years; and (8) level of the industry’s advertising expenditures over the past three years.

statements are available, they may be inaccurate because they are usually unaudited (Sapienza et al., 1988). On the other hand, CEOs or owners of small firms are inclined to provide subjective evaluation of their firms’ performance (Sapienza et al., 1988). Our study thus relies on perceptual measures of organizational performance. In particular the approach to measuring financial performance by Naman and Slevin (1993) is adopted. Respondents were asked to indicate on five-point scales ranging from (1) very unimportant to (5) very important the degree of importance they attached to each of six financial performance indicators. We included measures of profitability (ROS, ROI, and ROA) and growth (growth of sales and growth of profits) and total amount of profits. The latter measure was included because to many CEOs who derive the majority of their income from their businesses, the actual amount of profit is an important indicator of the financial performance of their firms. The respondents were further asked to indicate the extent of their satisfaction with their firms’ performance along each of the six performance indicators. The five-point scales used for this measurement range from (1) very dissatisfied to (5) very satisfied. The six satisfaction scores were then multiplied by their respective importance ratings. The resulting six scales were averaged to construct a composite measure of firm performance. This composite measure reflects an aggregate view of performance based on the level of CEOs’ satisfaction with their firms’ performance along each of the six financial performance indicators weighted by their respective importance to each firm.

Each author independently assigned an ILC stage to each of the 101 firms based on our individual analyses of the CEOs’ responses. In our first assignments there was agreement on 90 percent of the classifications. We resolved the discrepancies in assigning an ILC stage for the remaining 10 percent of the cases by discussing each individual case. We used the rate of growth of industry sales as the pivotal variable in determining life cycle stages. However, we relied on responses to the other seven variables in determining the appropriate stage of the life cycle. Fifty-four firms were assigned to the maturity stage, 20 firms to the introductory stage, 17 firms to the growth stage, and seven firms were assigned to the decline stage of the ILC. Four firms could not be assigned due to missing data. 3.4 Measurement of firm performance Although firm performance plays a key role in strategy research, there is considerable debate on the appropriateness of various approaches to the conceptualization and measurement of organizational performance (Venkatraman and Ramanujam, 1986). The complexity and multi-dimensionality of performance are perhaps the major factors contributing to the debate. Despite such debate, there is general agreement among organization scholars that objective measures of performance are preferable to those based on manager’s perceptions. However, objective data on the performance of small firms is usually not available because most small firms are privately held and the owners are neither required by law to publish their firms’ financial results nor are they usually willing to reveal such information voluntarily to outsiders (Dess and Robinson, 1984). Furthermore, when financial

3.5 Control variable A widely held belief in strategic management and organizational economics is that industry conditions may significantly impact firms’ competitive actions and performance. Thus, several researchers, among them Dess et al. (1990), assert that empirical strategic management studies should control for possible industry effects. In this study, survey respondents classified their firms as operating in one of six different types of industries. The six industry categories were included as dummy variables in the regression analyses used to test the competitive strategy-ILC stage alignment hypotheses. 76

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

3.6 Method of analysis In this study, the impact of the fit between competitive strategy and environment (i.e. ILC stage) is conceptualized as moderation. ILC is posited as affecting the form of the relationship between competitive strategy and firm performance. That is, firm performance is determined by the joint effect of competitive strategy and ILC stage. The moderation perspective is represented by the equation: y = a0 + a1x + a2z + a3xz (1)

For the decline stage, the deviation of intercept (δ4) and the deviation of the slope (δ14) are computed (Padhazur, 1982) δ04 = – δ01 – δ02 – δ03; δ14 = – δ11 – δ12 – δ13. Effect coding captures the direct effects on firm performance of the ILC stages and the joint relationship of ILC stage and competitive strategy. However since the regression analysis produces an estimate of the deviation from the slope rather than an estimate of the actual slope of a particular stage, the slopes (coefficients) of the competitive strategy in each life cycle stage must be computed in order to test the hypotheses. The direction and statistical significance of the slope of competitive strategy-performance relationship in a particular life cycle stage is an appropriate test of the moderation hypothesis. This slope is not estimated by the regression analysis but is computed as follows: β11 = b1 + δ11 = the competitive strategyperformance slope in the introductory stage; β12 = b1 + δ12 = the competitive strategyperformance slope in the growth stage; β13 = b1 + δ13 = the competitive strategyperformance slope in the maturity stage; β14 = b1 + δ14 = the competitive strategyperformance slope in the decline stage.

where, y = firm performance; x = competitive strategy dimension (e.g. quality differentiation); z = ILC. In testing the hypotheses, equation (1) had to be modified to account for ILC stage as a categorical variable consisting of four categories – introductory stage, growth stage, maturity stage, and decline stage. Effect coding was used to code the categorical variable. Effect coding captures the effect of the categorical variable on the dependent variable. Following is the modified equation: x = b0 + b1x + δ01z1 + δ02z2 + δ03z3 + (2) δ11xz1 + δ12xz2 + δ13xz3 + e where, y = firm performance; x = competitive strategy dimension; z1 z2 z3 __ __ __ 1 0 0 (firms competing in the introductory stage) 0 1 1 (firms competing in the growth stage) 0 0 1 (firms competing in the maturity stage) –1 –1 –1 (firms competing in the decline stage) δ01 = deviation of the intercept in the introductory stage from b0; δ02 = deviation of the intercept in the growth stage from b0; δ03 = deviation of the intercept in the maturity stage from b0; δ11 = deviation of the slope in the introductory stage from b1; δ12 = deviation of the slope in the growth stage from b1; δ13 = deviation of the slope in the maturity stage from b1.

In addition, standard errors of the aforementioned slopes are needed to compute the t-statistics used in determining the statistical significance of the slopes. The standard errors are computed using a method developed by Soofi (1994). Effect coding allowed us to test in a single pass multiple hypotheses involving a particular competitive strategy. Thus, equations (1) and (2) can be considered a general set of equations capable of testing whether or not a particular competitive strategy aligns with one or more stages of the ILC.

4. Results Table I shows the estimated models relating to tests of the hypotheses. The rows display the intercept, regression coefficient for the competitive strategy variable, the coefficients of the indicator variables relating to ILC stages (introductory, growth, and maturity stages), 77

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

the coefficients relating to the control variables (e.g. consumer durables), and the cross products of the ILC stage indicator variables and the quality differentiation strategy variable. Recall from the discussion of regression equation (2) that: the regression coefficient for each ILC stage is an estimate of the deviation from the average (or overall) intercept; and the regression coefficients for the cross products (i.e. interaction terms) are estimates of the deviation from the slope of the competitive strategy-performance relationship in each ILC stage from the average (or overall) slope. That is, the aforementioned regression coefficients are estimates of deviations from the slopes not estimates of the slopes, themselves. Thus, the results shown in Table I are not direct tests of the hypotheses. They provide additional insight regarding the dynamic relationships between ILC stages, competitive strategy, and

firm performance. The slopes or direct tests of the hypotheses are computed as delineated in the Methods section and are shown in Table II along with the cross product deviations that were also displayed in Table I. Table II shows the effects of competitive strategy – quality differentiation – on firm performance in each of the four stages of the ILC. Each row of Table II shows the effects of quality differentiation on firm performance in each stage of the ILC (indicated as “Slope”), and the differences (deviations) of the effect of the competitive strategy on firm performance in each life cycle stage from the average of the effect of the competitive strategy across all the four stages (indicated as “Dev.”). A positive and statistically significant slope indicates that increasing emphasis on quality differentiation in the specified ILC stage produces higher levels of performance in that stage. Displaying the deviations (Dev.) obtained from the regression analyses provides additional insight regarding the dynamic relationships between ILC stages, the competitive strategy of quality differentiation, and firm performance. The first hypothesis (H1) posits that in the introductory stage of industry development increasing emphasis on the competitive strategy of quality differentiation will produce higher levels of performance. Table II shows support for this hypothesis as the slope of the competitive strategy-performance relationship was both positive and highly significant (4.73***). H2 predicting that firms that strongly emphasize quality differentiation in the growth stage achieve high levels of performance is also supported (slope = 3.95***). Apparently, quality differentiation successfully copes with both distinctive environmental demands of an embryonic industry and those of a growth industry. Results in Table II do not support H3 which predicts that firms strongly pursuing quality differentiation in mature industries will not achieve high levels of performance. The slope of the competitive strategy-performance relationship is positive and highly significant (1.47***). This result suggests that in the maturity stage of the ILC, quality differentiation is an effective rather than ineffective competitive strategy as predicted. Apparently, quality differentiation aligns with the environmental conditions of three different ILC stages – introduction, growth, and maturity.

Table I Results of multiple regression analysis for H1-H5

Variable Intercept Quality differentiation strategy Introductory stage Growth stage Maturity stage Consumer durables industry Consumer non-durables industry Capital goods industry Industrial subcomponents industry Industrial supplies industry Quality differentiation-introductory stage interaction Quality differentiation-growth stage interaction Quality differentiation-maturity stage interaction R2 F Df

Statistic 4.88 (4.79) 2.67** (1.28) –5.21 (6.62) –1.68 (6.22) 2.82 (5.18) 2.17** (1.07) –0.99 (1.24) –1.68 (6.22) –5.43*** (0.85) 1.40* (1.03) 2.06 (1.84) 1.28 (1.57) –1.20 (1.37) 0.56 8.60*** 12, 82

Note: * = p < 0.10; ** = p < 0.05; *** = p < 0.01 78

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

Table II Computed slopes and deviation from the slopes of the competitive strategy-performance relationship for each ILC stage

Industry life cycle stage Competitive strategy Quality differentiation

Introductory

Growth

Maturity

Decline

Slope: 4.7*** (1.87) Dev: 2.06 (1.08)

Slope: 3.95*** (1.25) Dev: 1.28 (1.57)

Slope: 1.47** (0.75) Dev: –1.20 (1.37)

Slope: 0.52 (0.37) Dev: –1.24 (2.44)

Note: * = p < 0.10; ** = p <0.05; *** = p < 0.01 established (Onkvisit and Shaw, 1989; Wasson, 1974), apparently demand by discriminating customer pioneers for quality as well as innovative products in the earliest stage of industry development creates a viable niche for firms producing and marketing quality products. Oftentimes, initial product offerings of a new industry are plagued by numerous or significant defects (Utterback, 1994). Substantial competitive advantage can accrue to those firms that utilize strengths and distinctive competencies in process/product quality control and in assessing and satisfying customer concerns with product quality. This study’s findings are consistent with these arguments. Quality differentiators may be uniquely positioned to take advantage of the rising growth in demand for a new industry’s products as the nascent industry enters and moves through the growth stage. Usually, the availability and dissemination of information about the performance characteristics of products offered by various industry competitors increases in the growth stage (Porter, 1980). Thus, current and potential customers are knowledgeable about product quality. This creates viable opportunities for small manufacturing firms pursuing a competitive strategy of quality differentiation. The study’s findings of the effectiveness of quality differentiation in the growth stage support these suppositions. Surprisingly, increasing emphasis on quality differentiation increases performance for firms competing in the maturity stage. It was argued that firms seeking to differentiate themselves in some way from their competitors would not achieve high levels of performance because of a dominant competitive shift in the maturity stage toward price competition. Results of the study do not

H4 predicts that firms competing in the decline stage of the ILC that strongly emphasize quality differentiation will not achieve high levels of performance. This hypothesis is supported as shown in Table II (i.e. the competitive strategy-performance slope is positive (0.52) but not significant). The lack of statistical significance indicates that the positive relationship is probably attributable to chance. Therefore, the alternative hypothesis that increasing emphasis on quality differentiation in the decline stage leads to higher performance levels is not supported, indicating support of H4.

4. Conclusions and implications The objective of this paper was to examine alignment between each of the four primary stages of the ILC and the competitive strategy of quality differentiation in a sample of small manufacturing firms. Survey, factor analysis, and regression analysis techniques were employed. Quality differentiation appears to be a strategic orientation that can be effectively emphasized by small manufacturing firms competing in the introductory, growth, or maturity stages but not for those competing in the decline stage. Surprisingly, strong emphasis on quality differentiation appears to have the greatest impact on firm performance in the introduction and not in the growth stage as expected. Collectively, the findings imply that small manufacturing firms that develop strengths or distinctive competencies in producing/marketing high quality products in the embryonic stage of their industry’s development can continue to successfully pursue that strategy as the industry moves through the subsequent stages of growth and maturity. Although new product innovation is the foundation on which an industry is 79

Quality differentiation for competitive advantage

European Journal of Innovation Management

Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

appear to support this argument as quality differentiation was found to have a positive and significant effect on firm performance. Thus, it appears that in the maturity stage firms can successfully differentiate themselves from their competitors on the basis of product quality. As hypothesized, it does not appear that strong pursuit of quality differentiation in the decline stage will produce outstanding performance. The study’s findings suggest that a competitive strategy of quality differentiation does not fit the environmental conditions of the final stage of industry development. In summary, small manufacturing firms can apparently achieve competitive advantage sustainable across three stages of industry evolution – introduction, growth, and maturity – by strongly pursuing a competitive strategy of quality differentiation. If and when an industry declines, small manufacturing firms that remain in the industry cannot achieve high levels of performance by differentiating themselves from their competitors on the basis of product quality.

Hambrick, D.C. and Lei, D. (1985), “Toward an empirical prioritization of contingency variables for business strategy”, Academy of Management Journal, Vol. 28, pp. 763-88. Hambrick, D., MacMillan, I. and Day, D. (1982), “Strategic attributes and performance in the BCG matrix – a PIMS-based analysis of industrial product businesses”, Academy of Management Journal, Vol. 25, pp. 510-31. Hill, T. (1995), Manufacturing Strategy, Irwin Professional Publishing, New York, NY. Hofer, C.W. (1975), “Toward a contingency theory of business strategy”, Academy of Management Journal, Vol. 18, pp. 784-810. James, W.L. and Hatten, K.J. (1995), “Further evidence on the validity of the self typing paragraph approach: Miles and Snow strategic archetypes in banking”, Strategic Management Journal, Vol. 16, pp. 161-8. Macdonald, R.J. (1985), “Strategic alternatives in emerging industries”, Journal of Product Innovation Management, Vol. 3, pp. 158-69. MacMillian, I., Hambrick, D. and Day, D. (1982), “The product portfolio and profitability – a PIMS-based analysis of industrial product businesses”, Academy of Management Journal, Vol. 25, pp. 733-55. Miller, D. (1988), “Relating Porter’s business strategies to environment and structure: analysis and performance implications”, Academy of Management Journal, Vol. 31, pp. 280-308.

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Reginald M. Beal and Archie Lockamy III

Volume 2 · Number 2 · 1999 · 71–81

Soofi, E.S. (1994), “A simple formula for computation of standard errors of coefficients in regression with indicator variables”, Technical note, University of Wisconsin-Milwaukee.

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As Charles Darwin visited the Galapagos Islands, 600 miles off Ecuador, he began examining the animal specimens he had collected from the different islands. The only difference in them was the size and development of their beaks. Some were short and stout while others were longer and more slender. Darwin noted that the diversities in the finch’s tool – its beak – had permitted various species of finches to adapt to different environments. The finch was aggressive and fewer competitors existed. Some variations of the finch developed longer beaks and assumed a role probably dominated by woodpeckers in other areas. Survival in nature is all about having the opportunity to compete and then acquiring the tools for conquest of that environment. Toolmaking has been one of humans’ most powerful weapons. Homo habilis, over 2.5 million years ago, was the first human-like creature to manufacture and use tools. These stone tools were crude but they produced powerful selective advantages. The same is true for today’s technology.

Gaining competitiveness through innovation D. Keith Denton

The author D. Keith Denton is a Professor of Management at Southwest Missouri State University, Springfield, Missouri, USA. Keywords Competitiveness, Creativity, Improvement, Innovation, Risk Abstract Innovation has always been at the centerpiece of competitiveness. Experimentation, exploration and a drive to maximize resources is as essential for companies as it is for nations and our whole species. Many of the lessons for how to best innovate can be drawn from nature herself. The Cambrian explosion provides a good blueprint for how innovations occur. It shows us that true innovation often occurs in sudden dynamic shifts. It is not one of continual or gradual improvements but rather “lumpy” improvements. It is these sudden competitive changing innovations that open up and close out vast areas of commerce. Unfortunately, we never know where these competitive changing innovations will occur, so it is best to be ever vigilant and explore not only main lines of inquiry but also by-products. Often, it is these by-products that turn out to be the competitive shifting innovations.

The Red Queen Great Britain, during Victorian times, was able to dominate the world because it was able to manufacture and use relevant tools and technology better than anyone on the face of the planet. But Britain should have listened to the Red Queen’s advice in Lewis Carroll’s Through the Looking Glass where she says that to stay in place you have to run very, very hard and to get anywhere you have to run even harder. Victorian England forgot the Red Queen’s wisdom and Britain lost its dominance of the world. Victorians said the sun never set on their empire. England did dominate 25 percent of the land surface. They ruled more than 20 million square miles of territory and a quarter of the globe’s population (Tuchman, 1979, p. 63) and it produced 27.9 percent of all the world’s goods. Today the British share of world productivity has slipped from almost 23 percent to 3 percent (Kennedy, 1987). So what happened? From 1790 to 1815 British exports skyrocketed. They came up with startling technological innovations. Asians had already figured out how to turn a scrubby bush into cotton, but the British discovered how to produce it at bargain-basement prices. They also figured out how to mass-produce pig iron – a substance of worldwide demand. British

European Journal of Innovation Management Volume 2 · Number 2 · 1999 · pp. 82–85 © MCB University Press · ISSN 1460-1060

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Gaining competitiveness through innovation

European Journal of Innovation Management

D. Keith Denton

Volume 2 · Number 2 · 1999 · 82–85

ships controlled the sea lanes and they developed markets for merchandise everywhere from India to South America. From 1796 to 1812 France’s Napoleon humbled nearly every nation he encountered… Spain, Holland, Prussia, Austria and even Egypt but somehow could not bring the pesky British (he called them a “nation of shopkeepers”) to their knees. One of the main reasons was that England kept making profits and reinvesting them in industrial innovation and military resistance to Napoleon. Napoleon may have been a great general but he was a lousy manager because he failed to understand the importance of business innovation. Management workers and bosses within his empire were mired in obsolete technologies and eventually could not afford his armies. He overlooked the fact that military might depends not just on guns and strategic brilliance but on industrial innovation and marketing smarts. The next 59 years were good ones for Britain, in part because of technology. The first of these technologies was the spread of mechanization (beyond the mere cotton mills) and the second was steam. The British mastered both, including how to use steam engines to make goods that artisans had taken great pain to produce by hand. One machine-operating British worker could turn out as much cloth as 20 old-fashioned competitors. During Queen Victoria’s era (1837-1901) productivity per person in Britain rose 2.5 times! Wages rose an astonishing 80 percent in real dollars from 1850 to 1900. The world could not wait to get its hands on inexpensive, high tech British goods. By 1860 the English – with 2 percent of the world’s population – were turning out 25 percent of the world’s wares and over 40 percent of the items from modern industrial plants. Transactions everywhere were financed by British banks and insured by British insurance companies. They knew their prosperity depended on the fact that they were ahead of other countries in commercial utilization of technology and banned export of high tech fabric-making machines – but they grew fat with prosperity. They also forgot the fact that: (1) every technological breakthrough grows old; (2) new inventions arrive to replace it; and (3) the country that dominates these new technologies rules the world (Mass, 1981).

Oddly, technologies that made steam look old-fashioned were developed in Britain, but self-satisfied British industrialists seldom tried to turn them into tempting new products.

By-products and the British collapse British soldiers on Indian soil had only half the life expectancy of compatriots back home. Malaria swatted down British troops in India like flies. Illness was a serious obstacle to British colonization. White men who traveled a few miles inland in Africa invariably became sick and died because of malaria. There was hope the bark of a Peruvian plant could be used to produce a derivative called quinine. It seemed to be the magic bullet against malarial fever, but British botanists had almost no luck in cultivating enough cinchona plants to make even the smallest amount of quinine. Meanwhile, the British had learned how to extract a vapor from coal and use this gas for lighting, but the process produced a useless byproduct – coal tar. Disposing of the stuff was a messy business. At London’s Royal College of Chemistry, a German professor suggested to an assistant that he should see if he could somehow create artificial quinine from the goo. The assistant, William Perkin, tried but missed. Instead of quinine, he ended up with a liquid whose color was the shade of mauve. When he tried the solution as a cloth dye, it worked! He realized he had a hot property, dropped his assistantship, borrowed money from his father and opened up a small factory outside London. Before long, even Queen Victoria was wearing gowns tinted with Perkin’s mauve. Despite Perkin’s rapid rise to millionaire status, most British industrialists ignored his discovery – that is, everyone except the Germans. They worked frantically to find out what other by-products they could extract from coal tar. In 1863 one German researcher came up with a rich shade of green. When Empress Eugenie wore it to the Paris Opera, it became the fashion rage. After Perkin retired at age 36, the British dye industry shriveled in his absence but the German dye business became the first step in a revolutionary technology. Their experiments with coal dye became the foundation of the chemical industry (Burke, 1978). The exploration of this by-product would have far-flung implications. One example is the use of chemical fertilizers with which German 83

Gaining competitiveness through innovation

European Journal of Innovation Management

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Volume 2 · Number 2 · 1999 · 82–85

farmers soon were able to produce more food per acre than any other of the Great Powers. England invented, then discarded, while Germany scooped up the British by-product.

have to continually try to develop useful applications from what many would consider useless by-products. Experimentation, exploration and a drive to maximize resource utilization and consumer appeal. To stay in place, you have to run. To get anywhere, you have to run very, very fast!

Run – very, very hard English steam engines would soon look oldfashioned. The greatest physicists of the age – Britishers Michael Faraday and James Clerk Maxwell – were experimenting with electricity but British industrialists did not look at what practical uses they could find for the pair’s discoveries. Those who did were Americans and Germans. The first electric-generating plant in Britain to sell power to the ordinary householder was built by Thomas Edison. Britain’s Faraday had earlier discovered the principle of the alternating-current transformer but had not bothered to find a practical value. It took an American scientist working at Westinghouse to produce a practical product 66 years later. Faraday’s experiment also inspired a Massachusetts painter to figure out a use for electrical power-communications. In 1835 the portrait artist Samuel Finley Breese Morse built the first telegraph. In 1873 Britain went through a “Great Depression” that lasted for 20 years. Steam technology was on the decline. Other countries were producing inexpensive fabrics. There was a demand for new products, those being turned out by Germany’s chemical industry and electrical devices, with great consumer appeal, made by Americans and Germans. German exports tripled from 1890 to 1913. By 1913 German companies Sieman and AEG dominated the European electrical industry. German chemical giants Bayer and Hoechst would produce 90 percent of the world’s industrial dyes. On the American side, Andrew Carnegie in 1902 produced more steel than all the factories of England combined. Competitiveness comes from innovative minds. Germany maintained the best school system and by the 1890s had 2.5 times as many university students per unit of population as England. Germany was on the move and this eventually led to World War I. Britain won but lost her prosperity. The British worker became the lowest, not the highest, paid. Her factories became the most inefficient when they used to be the most efficient. The lesson is clear. To remain on top you have to produce the consumer products people want. You have to find and convert new innovations into producible goods. You

Nature’s lessons about technology The earliest signs of life go back 3.45 billion years ago. That is just 300 million years after the earth’s crust cooled enough to support liquid water and here is the amazing part. These early life forms were well-formed cells or at least they look like that to the experts. The fossils appear to have cell membranes that separate their internal working from the external environment. The cells surely were the product of some still earlier evolution of interacting molecules complex enough to be considered alive. They could metabolize, reproduce and evolve. By 800 million years ago, multicellular organisms appeared. Then again something truly remarkable occurred. About 550 million years ago is known as the Cambrian explosion. It is called the explosion because it generated almost all the major divisions of plants and animals. Only the vertebrates, which is our own niche, arose later. For the next 100 million years, things happened which seem to challenge common sense. When we look at organisms, humans tend to group them hierarchically from specific to general. The Linnean chart does this and goes from species (which are capable of breeding with each other) to genera (groups of several species) to families of these species to still larger orders and classes and to still larger phyla and finally to kingdoms. Logic would lead us to believe therefore that the first multicellular creatures would be very similar. Only later would we expect them to diversify from the bottom up. First we might expect to see one species then see related groups of species or genera and then see these evolve to eventually still broader groups of families and so on. Darwin would have thought this since he proposed that all evolution occurred by very gradual accumulations of useful adaptations and variations. Using this logic, early multicellular creatures should have diverged gradually from one another, but this did not happen. It seems that in the Cambrian explosion, our chart filled in from the top down, not bottom up! There was a sudden explosion of widely different biological body plans. It 84

Gaining competitiveness through innovation

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Volume 2 · Number 2 · 1999 · 82–85

was the broader phyla, not the more closely related species, that came first. This process though is not the normal way things occur. The worst extinction of life in this planet occurred about 245 million years ago. This was long before the birth or death of the dinosaur. This extinction event killed about 96 percent of all species. On the rebound, many new species evolved with life filling in this time from the bottom up, with new families, new orders and one new class, but no new phyla. Life had resorted to TQM’s continual improvement and adaptation rather than the more radical Cambrian explosion which produced a wide range of possible ways of living and competing. Technological innovation, like the Cambrian explosion, also fills in from the top. Striking variations arise early and then diminish to minor or continual improvements. Each major innovation can be greatly improved on by making design variations. Later on, when most of the variations have been explored, nature and us begin worrying about the details. Specialization and extinction of biological and technological life go together. For 100 million years, in the Cambrian period, diversity increased to a point where a steady state existed. There were still small and large extinctions of species, larger classifications of species and whole families. Meteors or new technologies can wipe out a way of existing but they can also occur in more subtle ways. The very struggle to survive, biologically or technologically, depends on being extremely sensitive to read your landscape. Major technological innovation is similar to the branching radiations of the Cambrian explosion. Soon after multicelled creatures were invented, many fundamentally different life forms suddenly appeared. Major innovations in body plans were followed by gradually finer and finer adjustments. Some innovations drive others extinct. The marketplace changes the landscape we all live on and creates selective pressures for some technology and not for others. New technological innovations are rapidly followed by dramatic improvements and then gradual improvements in the technology. The bicycle, when invented, had little wheels, then big wheels, two seats and so forth. Extinction followed and only the most fit design prevailed. Stuart Kauffman notes it seems to be a natural law that after increasingly long periods of no improvement, sudden improvements often occur. These improvements then typically reach a plateau

and all improvement ceases (Kauffman, 1995, p. 204). Such logic that shows that the amount of cost reduction or innovation achieved with each step slows exponentially while the rate of finding such improvements also slows exponentially. This does not bode well for long term “stretch” goals. Companies like General Electric that have used these goals with some success to make dramatic increases in productivity may find their innovations and improvements plateauing. It is a lesson we can learn from the Cambrian explosion.

Conclusion Humans are not much different than their cave-dwelling ancestors. It is doubtful that they are much smarter. We owe our improved lifestyle to our inventions and accumulated knowledge. The hunter is ingrained into our biology, it is only our civilization that separates us from the jungle. It is the hunting and gathering economy which sustained human life for many thousand years. Homo sapiens, in fact, they have lived something like ten times as long as created civilizations, perhaps 20 times if we count Neanderthals as part of our family (Roberts, 1990, p. 47). Then about 10,000 years ago, something truly extraordinary occurred; we invented agriculture. In a hunting and gathering society, thousands of acres are needed to support a family. A primitive agricultural society only needs about 20 acres. It produced a huge explosion in population. Then and as now our future is in our hands and the hand that holds the latest technology is increasingly in control of the future. Continual innovation is essential to life and competitive organizations.

References Burke, J. (1978), Connections, Little, Brown & Co., Boston, MA, pp. 204-20. Kauffman, S. (1995), At Home in the Universe, Oxford University Press, New York, NY, p. 204. Kennedy, P. (1987), “The (relative) decline of America”, Atlantic Monthly, August, pp. 29-38. Mass, N.J. and Senge, P.M. (1981), “Reindustrialization: aiming at the right targets”, Technology Review, August/September, pp. 56-65. Roberts, J.M. (1990), History of the World, Penguin, London, p. 47. Tuchman, B.W. (1979), A Distant Mirror: The Calamitous 14th Century, Ballantine Books, New York, NY.

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