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Tourism Management 25 (2004) 777–788 www.elsevier.com/locate/tourman
Tourism destination competitiveness: a quantitative approach Michael J. Enright, James Newton School of Business, University of Hong Kong, Pokfulam Road, Hong Kong Received 12 November 2003; accepted 5 May 2004
Abstract Recently, researchers have suggested an approach to tourism destination competitiveness that goes beyond conventional destination attributes to include, in addition, generic business factors of competitiveness. Despite its apparent promise, there appears to have been little applied research building on this combined approach. This paper is designed to address this gap. Factors pertaining to the competitiveness of both the destination’s attractions and its tourism industry were used to construct an instrument that was used to survey tourism practitioners in Hong Kong. Respondents were asked to rate the factors for both importance and relative competitiveness, in a method consistent with importance performance analysis (IPA). The results were analysed and discussed by reference to the IPA Grid. The paper concludes that the study has developed a promising research methodology that offers a quantitative, theoretically informed empirical analysis that will be able to provide a basis for managerial and policy decisions in the tourism industry. r 2004 Elsevier Ltd. All rights reserved. Keywords: Tourism competitiveness; Industry and destination factors; Importance and relative competitiveness; Quantitative measures
1. Introduction The success of tourism destinations in world markets is influenced by their relative competitiveness. Tourism destination competitiveness is becoming an area of growing interest amongst tourism researchers (see particularly Crouch & Ritchie, 1999; Pearce, 1997). The contention is that destination competitiveness has ‘‘y tremendous ramifications for the tourism industry and is therefore of considerable interest to practitioners and policy makers.’’ (Ritchie & Crouch, 2000, p. 6). Dwyer, Forsyth, & Rao (2000, p. 10) reinforce this view, stating that it is ‘‘yuseful for the industry and government to understand where a country’s competitive position is weakest and strongesty’’ and hence that it is important to know how and why competitiveness is changing. Corresponding author. Tel.: +852-2859-1012; fax: +852-28585614. E-mail addresses:
[email protected] (M.J. Enright),
[email protected] (J. Newton).
0261-5177/$ - see front matter r 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.tourman.2004.06.008
Crouch and Ritchie’s approach to destination competitiveness extends previous studies that focused on destination image or attractiveness (see Chon, Weaver, & Kim, 1991; Hu & Ritchie, 1993). Such studies are part of a long tradition of destination image research (Gallarza, Saura, & Garcı´ a, 2002) and, in keeping with that tradition, have concentrated on those attributes that are seen to attract visitors, such as climate, scenery, and accommodation. Whilst tourism services in general are recognised as being important elements of destination image or product (Murphy, Pritchard, & Smith, 2000) it is less common in destination image research to pay explicit attention to the firms that supply the services and to the factors that may affect the competitiveness of these firms. Buhalis (2000) recognises the importance of suppliers and the multiplicity of the individually produced products and services that help make up the overall tourism product, but is more concerned with the difficulties this raises for marketing issues than for destination competitiveness. Building on the prior conceptualisations of Crouch and Ritchie (see also Ritchie & Crouch, 2001), this
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paper argues that a proper understanding of destination competitiveness requires, in addition to destination or tourism-specific factors, the inclusion of such factors that affect the competitiveness of firms and other organisations involved in producing the tourism ‘‘product’’. In other words, a destination is competitive if it can attract and satisfy potential tourists and this competitiveness is determined both by tourism-specific factors and by a much wider range of factors that influence the tourism service providers. The objective of this study, therefore, was to advance this broader approach to destination competitiveness by adding generic factors of competitiveness, that are applicable to any industry, to the mainstream factors of destination attractiveness, and to operationalise this combination in such a way as to obtain quantitative measures of competitiveness. In doing so, the study attempts to marry the concepts of two literatures in order to generate a broader, and hence more comprehensive, model of tourism destination competitiveness.
2. Towards a comprehensive model of tourism destination competitiveness (TDC) In developing their conceptual model of TDC, Crouch and Ritchie (1999) build on Michael Porter’s (1990) well-known framework of the ‘‘diamond of national competitiveness’’. Porter’s framework postulates that success in international competition in a given industry depends on the relative strength of an economy in a set of business-related features or ‘‘drivers’’ of competitiveness, namely ‘‘factor conditions’’; ‘‘demand conditions’’; ‘‘related and supporting industries’’, and ‘‘firm strategy, structure, and rivalry’’. De Holan and Phillips (1997, p. 781) also explicitly recommend the inclusion of Porter’s framework, particularly when examining tourism in developing countries. They contend that for ‘‘ycountries like Cuba, the existence of world-class ‘‘sun and sand’’ provides a basis for competitiveness in tourism, but it does not guarantee development or success in the tourism industry. Other factor conditions such as human resources, infrastructure and capital, and the other three determinants that make up the diamond stand as potential barriers to development.’’ Porter’s framework, or variations thereof, has been used in a number of studies of industries and of individual economies (Crocombe, Enright, & Porter, 1991, and Enright, France`s, & Scott-Saavedra, 1996, for example). In more recent conceptual developments, Enright, Scott, and Dodwell (1997) proposed an alternative framework that divides the drivers of competitiveness into six categories, namely ‘‘inputs’’, ‘‘industrial and consumer demand’’, ‘‘inter-firm competition and cooperation’’, ‘‘industrial and
regional clustering’’, ‘‘internal organisation and strategy of firms’’, and ‘‘institutions, social structures and agendas’’. Crouch and Ritchie (1999, p. 146) have incorporated concepts of such generic models to derive a model that postulates that TDC is determined by four major components: ‘‘core resources and attractors’’, ‘‘supporting factors and resources’’, ‘‘destination management’’, and ‘‘qualifying determinants’’. The ‘‘core resources and attractors’’ include the primary elements of destination appeal. It is these ‘‘that are the fundamental reasons that prospective visitors choose one destination over another’’. The factors included within this component of the model are physiography, culture and history, market ties, activities, special events and the tourism superstructure. Physiography includes landscape and climate, market ties includes linkages with the residents of tourism originating regions, and the tourism superstructure is comprised primarily of accommodation facilities, food services, transportation facilities and major attractions. With the exception of market ties, therefore, these factors are consistent with mainstream destination attractiveness studies (see Kim, 1998 for a comprehensive review). The other components of the model, however, extend the determinants of TDC by adding a wider range of factors that help link the destination ‘‘attractors’’ with the factors more usually found in studies of generic business competitiveness. The ‘‘supporting factors and resources’’ are factors that provide the foundation for building a successful tourism industry and include, in particular, the extent and condition of a destination’s general infrastructure, a range of facilitating resources such as educational establishments, together with factors influencing the destination’s accessibility. The third component, ‘‘destination management’’, focuses on activities that can influence the other components, first by enhancing the appeal of the core resources and attractors, secondly by strengthening the quality and effectiveness of the supporting factors and lastly by adapting to constraints imposed by the fourth component, the ‘‘qualifying determinants’’. Whilst the most researched aspect of management is destination marketing, the authors argue that a much wider set of management activities should be considered, including services, organisation and the maintenance of the key tourism resources and attractors. The final component, the ‘‘qualifying determinants’’, includes factors that can modify, possibly in a negative sense, the influence of the other three components. Hence, these can possibly limit a destination’s capability to attract and satisfy potential tourists and hence affect its competitiveness. This component includes critically important variables, such as location, overall costs, and safety, which are beyond the control of the tourism sector but which play a major role in destination competitiveness. As Crouch and
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Ritchie (1999, p. 150) state, ‘‘if tourists are gravely concerned about crimeynatural disasters, the quality of medical servicesyother competitive strengths may amount to very little in the minds of potential tourists’’. In summary, by adding these more generic businessrelated factors, captured within the supporting factors, destination management and qualifying determinants, to the tourism-specific factors captured in the core resources and attractors, Crouch and Ritchie’s approach differs from, and advances, other studies that focus primarily on models of the tourist product or destination image (see in addition Schroeder, 1996; Formica, 2002). The use of both tourism-specific and generic determinants also differs from work on tourism competitiveness that used Porter’s basic framework, but paid only limited attention to more tourism-specific elements (such as Go, Pine, and Yu, 1994). The study has the potential, therefore, to offer a more comprehensive assessment of the factors that influence a destination’s capability to attract and to satisfy its tourism customers.
3. The present study Given the relatively recent conceptualisation of such an approach to tourism destination competitiveness it is not surprising that the framework has yet to be tested empirically. This study attempts to fill that gap by operationalising the conceptual approach in order to generate measures of competitiveness across this much broader spectrum, and to consider the usefulness of the approach for tourism practitioners and policy makers. It was therefore necessary to develop a methodology to generate data suggested by the combined frameworks from which to draw implications for the competitiveness of tourism destinations. Given the novelty of this approach, the initial study was restricted to a single destination, an approach that is not uncommon in the emerging literature on the destination product (Murphy et al., 2000) and on the traditional literature on destination attractiveness (Kim, 1998). Whilst the results from a single destination would not be expected to generate a definitive global statement regarding TDC, the study serves as an initial test of the combined approach, helps demonstrate the value of a broader framework and aims to provide a template for further refinement and research into the broader determinants of TDC. Hong Kong was chosen for this first study for a number of reasons. Tourism is an important part of Hong Kong’s economy. The Hong Kong Tourism Board estimates that roughly 5% of Hong Kong’s GDP and 10% of Hong Kong’s employment is directly linked to tourism (Hong Kong Tourism Board, 2001). Hong Kong also has been among the leading destina-
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tions for international tourism in the East Asia-Pacific region. However, events in the 1990s have heightened concerns about the competitiveness of the region and Hong Kong’s competitiveness versus other destinations in the region. Tourism arrivals in the East Asia-Pacific grew faster than in any other region in the world from 1960 to 1990. However, between 1990 and 1998 arrivals in the region grew slower than in Africa, the Middle East, and South Asia. Questions about the changing competitiveness of the region were brought into sharper focus during the Asian Financial Crisis, which saw arrivals and revenues from tourism fall in most of the major economies of the region. The impact also was felt in Hong Kong, which saw tourism arrivals fall 11.1% in 1997 and another 8.0% in 1998. Thereafter, however, growth resumed with an increase of 18.3% in 1999 and 16% in 2000. On the revenue side, Hong Kong’s tourism receipts fell 14.7% in 1997 and another 23.4% in 1998. The trend then stabilised (with 1.8% growth) in 1999 and growth of 9.6% was recorded in 2000. Despite this, Hong Kong went from the leader in international tourism receipts in the Asia-Pacific region in 1996 to second (behind the Chinese Mainland) in 1997, where it remained in 2000 (World Tourism Organization (WTO), 1999, 2002). Given the importance of tourism to Hong Kong’s economy and the recent variations in demand, the destination’s tourism competitiveness has become a major economic issue.
4. Methodology In order to generate the desired empirical data, a survey instrument was constructed itemising the factors that were postulated to influence TDC. This was done by generating a set of tourism specific items based, in the first instance, on the ‘‘core resources and attractors’’, and a set of generic business factors. As the business factors are more developed in the competitiveness literature than the tourism literature, greater reliance was placed on the former in developing the specific items in this set. In developing the set of tourism-specific items, it was recognised that no universal set of items exists, even within the abundant literature on tourism destination attractiveness or image. Kim’s (1998, p. 343) summary of previous research into destination attractiveness indicates clearly the variety of items adopted by researchers in the field, although some items are common to many approaches. The tourism ‘‘attractors’’ derived directly from Crouch and Ritchie’s core resources and attractors, and shown in Fig. 1, were equally consistent with such approaches. Hong Kong, however, is best classified as an urban destination, following Abe’s (1996) taxonomy. Consequently, given
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780 Core Resources & Attractors (Crouch & Ritchie, 1999) Physiography
Items derived directly from Core Resources and Attractors Visual appeal Climate
Culture & history
Different culture Notable history
Market ties
Items derived from Core Resources and Attractors but classified within Business-related Factors
Items added from specifically urban tourism studies (Jansen-Verbeke, 1986; Law, 1993) Interesting architecture Well-known landmarks
Local way of life
Ethnic ties Visiting friends and relatives (VFR) Business ties
Activities
Nightlife Music & performances Museums & galleries Dedicated tourism attractions
Special events Tourism superstructure
1
Special events Interesting festivals Cuisine
High quality accommodation Transportation facilities
Shopping1
Subsequently classified at a business factor
Fig. 1. Construction of tourism-specific factors of competitiveness: ‘‘Attractors’’.
the particular nature of the destination selected for this initial study, more detailed items were added from prior studies of specifically urban destinations (Jansen-Verbeke, 1986; Law, 1993) as Fig. 1 also shows. Crouch and Ritchie (1999, p. 148) recognise that, in the construction of their model, there may be ambiguities in classifying some items. This was particularly so with the ‘‘tourism superstructure’’ where the authors note that such items as accommodation and transportation could equally fall within the ‘‘supporting factors’’. Following a review of the pilot instrument with practitioners and tourism experts in the region, these two items were classified within the business factors in the instrument. ‘‘Market ties’’ were also considered better classified as business factors, since market demand is a major element of the literature on business competitiveness. Crouch and Ritchie describe ‘‘market ties’’ as ethnic ties, visiting friends and relatives, and business ties. Consequently these were translated for this study as ‘China market potential’, ‘other Asia-Pacific market potential’, and ‘long haul market potential’ given the close links with the Chinese Mainland, the Chinese diaspora throughout the region and Hong Kong’s global family and business linkages. Similarly, shopping, which was also added from Jansen–Verbeke’s work on urban tourism, was also treated as a business factor, being covered by the item ‘good retail sector’ which was derived from the competitiveness literature. The practitioners in the region also placed great stress, as do Crouch and Ritchie, on the importance of ‘safety’ in determining the competitiveness of a destination and in this item being of especial importance to tourism. As a result, ‘safety’, which appears in the conceptual model as a Qualifying Determinant was classified within the set of tourism specific items. The six items to be included within the set of generic competitiveness factors were added to a further set of 31
Major Drivers
Inputs
Industrial & Consumer Demand
Inter-firm Competition & Cooperation Industrial & Regional Clustering Internal Organization & Strategy of Firms Institutions, Social Structures and Agendas
Additional Drivers Tourism Business Superstructure Market Ties
Items Derived from Drivers of Competitiveness (Porter, 1990; Enright, Scott, and Dodwell, 1997; Enright, 2000) Internal transportation facilities, Communication facilities, Staff skills, Access to information, Local managerial skills, Banking & financial system, Geographic location, Level of technology, Staff costs, Other infrastructure, Property related costs, Other costs China market potential, Long haul market potential, Other Asia Pacific market potential, Local market demand Good firm cooperation, Tough local competition Support from related industries, Presence of international firms Strategies of international firms, Strategies of local firms Political stability, Free port status, Government policy, Cleanliness of government2, Overall economic condition, Transparency in policy making, Investment incentives, Tax regime, Education & training institutions, Regulatory framework, Strong currency, Community institutions Items derived from Figure 1 International access, Good retail sector3, High quality accommodation China market potential, Other Asia-Pacific market potential, Long haul market potential
2 A 3
term that is a widely understood euphemism in the Asia Pacific region for “lack of corruption”. “Good Retail Sector” was considered to cover “Shopping”.
Fig. 2. Generic business factors of competitiveness: ‘‘Business-related factors’’.
‘‘business-related’’ items derived from the generic competitiveness frameworks of Porter (1990), Enright et al. (1997) and Enright (2000). Fig. 2 shows the items selected for the study, classified according to Enright et al. (1997) framework. The three ‘‘market ties’’ items were added to the category of ‘‘industrial and consumer demand’’ and the further category of ‘‘tourism business superstructure’’ was added. The two sets of items were listed separately on the final instrument to aid responses and the instrument was again reviewed with industry
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practitioners and researchers with a view to ensuring face validity (DeVellis, 1991). Given the addition of the generic business-related factors, the instrument thus differs markedly from the mainstream studies of destination image or attractiveness (such as Chen, 2001). This raises the question of whether the most common target groups of respondents, namely tourists, are the appropriate respondents for this study. Tourists are well placed to evaluate the normal components of destination attractiveness, including the services that they consume. However, they are less likely to know about, and hence be able to evaluate, those factors that underlie and influence the competitive production of those services, especially because of their status as visitors. Since one of the goals of operationalising the combined framework was to gauge the relative importance of tourism attractors and business features, it was necessary to survey individuals who could respond to questions on both the tourism attractors and the business features. It is, naturally, a common feature of research in the generic management field, including competitiveness research, for the survey population to be managers and other industry practitioners, as this is the population seen to be the most knowledgeable about management and competitiveness. Discussions with tourism industry practitioners indicated that industry participants (in this case, managing directors or the most senior person in the organisation otherwise) generally are aware of the overall conditions in both the business features and the tourism attractors, as they see what works and does not work on a daily basis. In addition, most were knowledgeable about the state of the industry in the major competing locations as well. This knowledge generally came through their own experience in other cities in the region, other operations of their own companies in the region, participation in local and regional industry associations, or competitive intelligence. Thus tourism industry practitioners were viewed as the appropriate population to respond to the questions on both sets of determinants. This, however, raised the issue of whether the evaluation of the tourism attractors by practitioners would be consistent with an evaluation gathered from the tourists themselves. However, it is not uncommon for destination factors to be evaluated by practitioners, suggesting that their views do constitute accurate measures of the attractors (see Evans & Chon, 1989; Faulkner, Oppermann, & Fredline, 1999). Gearing, Swart, and Var (1974, p. 2) in particular argued the case for using respondents who were widely experienced in dealing with tourists, rather than the tourists themselves. They suggested that such ‘‘experts’’ would be able to speak for the tourists, given their experience and that each expert opinion would be representative of a large group of tourists. Whilst a motivation for this approach lay in the cost savings compared to a large-
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scale survey, they also suggested that there might be ‘‘ydifferences between the opinions expressed by tourists and their actual behaviour. For instance, a respondent might very well express a greater interest in archaeological museums or a lesser interest in luxury accommodation than his behaviour reveals’’. On the other hand respondents who are used to dealing with tourists have been observing actual behaviour and are therefore equipped to comment on what factors evoke which responses from tourists and hence may produce a more accurate picture of preferences. Formica (2002) discusses the view that studies including both experts’ and tourists’ evaluations could have the highest degree of accuracy, but points out that the literature contains only a single case where this has been done. An advantage of combining business competitiveness research with tourism research was that methodological approaches found in the tourism literature helped address a number of shortcomings in the competitiveness literature. The use of a large sample survey, rather than a limited number of interviews (such as used in Porter, 1990) with all the inherent difficulties of that methodology is one advance. Secondly, most studies of competitiveness seem to assess a location’s competitiveness in a given industry by generating lists of pluses and minuses, or advantages and disadvantages against a set of criteria without any means of prioritising these criteria. This is the greatest shortcoming of Porter’s framework, one which has made the framework, and others like it, much more useful for ex post rationalisation, than ex ante prediction. The problem is remedied by the adoption of a two-stage approach, which explores both the importance of each factor and the destination’s relative competitiveness on each factor. The approach was consistent with a number of earlier studies of destination image and attractiveness known as importance performance analysis (IPA) (see Chon et al., 1991; Opperman, 1996; Go & Zhang, 1997; Uysal, Chen, & Williams, 2000; Qu, Li, & Chu, 2000; Joppe, Martin, & Waalen, 2001; Oh, 2001). The technique was largely modelled on concepts developed in the marketing literature (see Martilla & James, 1977; Ennew, Reed, & Binks, 1993) and is less common in studies of competitiveness at the industry or national level, although Leong and Tan (1992) is something of an exception. On a conceptual level, most studies of competitiveness, including Porter (1990), assess the competitiveness of an industry without an appropriate context. Competitiveness cannot be assessed in a vacuum. A given location is competitive or uncompetitive in an industry, not in the abstract, but against relevant competing locations (Enright et al., 1997). Specific tourism destinations are not competitive or uncompetitive in the abstract, but versus competing destinations and it is important to establish which destinations comprise the
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competitive set (Kozak & Rimmington, 1999). Consequently, the instrument began by asking respondents to identify Hong Kong’s three main competing locations, confining the competitors to similar types of destination, namely other cities in the Asia Pacific region. For the tourism attractors and the business-related factors, respondents were first asked to assess the importance of each factor in contributing to competitiveness in urban tourism in the Asia-Pacific region on a 5-point Likert scale with 1=very important, 2=unimportant, 3=neutral, 4=important, and 5=very unimportant. In the second stage, respondents were asked to compare Hong Kong with the relevant competitors and assess Hong Kong’s relative competitiveness for each of the factors. The initial question regarding Hong Kong’s main competing locations served to establish in the mind of each respondent, the main competitive set. The objective was not to establish whether Hong Kong was more competitive in a given factor than another specific destination, but rather whether, when compared to the relevant competitive set of its main rivals, Hong Kong was more or less competitive in the given factors. The respondents were thus asked to rate Hong Kong’s relative competitiveness or position versus relevant competitors for each factor on a 5-point Likert scale with 1=much worse, 2=worse, 3=the same, 4=better, and 5=much better. The survey instrument was distributed by fax to practitioners in the travel industry in Hong Kong as identified by their membership in the Hong Kong Tourist Association, (HKTA) which includes a wide cross-section of the service businesses active in tourism. The instrument was addressed to the most senior manager identified in the membership guide and the responses showed that respondents were all at managerial grade. Two mailings in the first quarter of 2000 yielded 183 responses from the 1,116 companies contacted, representing a response rate of 16.4 percent. Of the 183 respondents, 49 were in the hotel industry, 36 were in retailing, with the balance in travel agencies, tour operators, airlines and other similar areas. Around 12% (22 respondents) were engaged in more than one major line of business. Over 43% (79 respondents) identified themselves as part of a multinational firm. Of these, the leading parent nationalities were: the United States (18%), Hong Kong (15%), Japan (10%), and the United Kingdom (10%).
5. Results and analysis To assess reliability and validity the resulting data were subjected to internal consistency measures (Murphy et al., 2000; Cronbach, 1951) and inter-rater reliability (Dooley, 1990). When the importance measures of the entire 52 items on the scale were assessed,
internal consistency, as measured by Cronbach’s Alpha, was found to be 0.94, indicating a high degree of reliability (Kline, 2000). Item reliability was also confirmed through testing inter-rater reliability by subjecting the results of the first and second mailings to an independent t-test to assess whether significant differences existed between the two groups of respondents. In only one case out of the total of 52 items, ‘‘access to information’’, was a difference detected at the pp0:5 level, again suggesting a high degree of reliability. Construct validity was also assessed through a technique using Cronbach’s Alpha. Whilst construct validity is often assessed by correlations between a current study instrument and an established instrument drawn from the literature (DeVellis, 1991), the uniqueness of the present scale renders the usual assessment impossible. Hence, validity was determined by computing Cronbach’s Alpha following deletion of each item sequentially from the data set (Cronbach, 1951). The results for the 52 resulting sets showed that Alpha remained consistently at 0.94 indicating that all the items contributed to the high value of Alpha and hence that construct validity was acceptable The survey responses indicate, first, that the leading competitors to Hong Kong in urban tourism in the Asia-Pacific region are Singapore, Bangkok, Tokyo, and Shanghai (see Table 1). Given its comparable role as an economic centre, a gateway for part of the region, and similar size, it was not surprising that Singapore would emerge as Hong Kong’s principal competitor. The high ranking of Bangkok might indicate that Hong Kong has some attributes that attract leisure travellers, for whom Bangkok is more of a competitor. The ranking of Tokyo would indicate that the Japanese capital has a mixture of commercial and cultural attributes that rivals Hong Kong. Shanghai is Hong Kong’s main tourism rival within China, perhaps reflecting the cities’ roles as leading commercial centres in China as well as cultural similarities.
Table 1 Hong Kong’s major competitor tourism destinations City
Ranking
Singapore Bangkok Tokyo Shanghai Beijing Taipei Kuala Lumpur Sydney Manila Jakarta
1 2 3 4 5 6 7 8 9 10
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5.1. Importance of ‘‘attractors’’ and ‘‘business-related factors The ‘‘attractors’’ are ranked in Table 2 in terms of their relative importance in determining, in general, the competitiveness of a city destination in Asia-Pacific. The table shows the mean scores for each factor, out of the maximum of 5. All the listed factors were considered to be relevant (all mean scores are above the ‘‘neutral’’ 3), which indicates that the frameworks of tourism competitiveness on which they were based are broadly consistent with the views of respondents. The most important attractors, according to respondents, are safety, cuisine, dedicated tourism attractions, visual appeal, and well-known landmarks. Although different culture ranked seventh, some specific attributes of culture, such as local history, museums and galleries, and music and performance, ranked at the bottom. Given the emphasis that many government tourism agencies, including those in Hong Kong, place on such attributes, the low ranking is interesting. One possible explanation is that this is consistent with Gearing et al. (1974) argument that there may be differences between what tourists say and what they do, and that it is surveys of tourists that has stimulated this emphasis. Climate ranked only twelfth, perhaps because climate in city destinations is considered less important than in resort destinations. In order to check that the differences in the rankings are not due simply to sampling error, particularly given a response rate of under 20%, Table 2 also shows the standard error (Kline, 2000) at the 95% confidence level for each result. The standard error ranged between 70.4 and 70.6, which would marginally change the rank Table 2 Attractors ranked by importance mean scores (N=183) Attractors
Safety Cuisine Dedicated tourism attractions Visual appeal Well-known landmarks Nightlife Different culture Special events Interesting festivals Local way of life Interesting architecture Climate Notable history Museums and galleries Music and performances Average a
Importance Rank
Mean
S.D.
S.Ea.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
4.64 4.36 4.33 4.20 4.12 4.06 3.98 3.96 3.75 3.73 3.72 3.71 3.59 3.42 3.29
0.55 0.63 0.73 0.67 0.65 0.67 0.74 0.72 0.83 0.87 0.74 0.80 0.76 0.77 0.79
0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.05 0.06 0.06 0.05 0.06 0.06 0.06 0.06
3.92
S.E. is the standard error of the mean at the 95% confidence level.
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order of the three items ranked at 10, 11 and 12. However, as will be discussed shortly when the results are analysed by means of the importance/performance (IPA) grid, such changes affect neither this analysis nor the final conclusions of the study. This check also confirmed that all importance scores remained above the neutral score of 3, even allowing for sampling error. The results for the importance of business-related factors in determining urban tourism competitiveness in the Asia-Pacific are shown in Table 3. Political stability ranked first, consistent with the ranking of safety amongst the tourism-specific factors. Forms of accessibility featured prominently, with international access and internal transportation facilities ranking second and third, as would be expected. However, less expected were the rankings ascribed to costs, which ranked Table 3 Business-related factors ranked by importance mean scores (N=183) Business-related factors
Political stability International access Internal transportation facilities Free port status Government policy Cleanliness of government Communication facilities Good retail sector Staff skills Overall economic condition Access to information China market potential Local managerial skills Transparency in policy making Investment incentives Banking and financial system Geographic location High quality accommodation Support from related industries Tax regime Long haul market potential Presence of international firms Other Asia Pacific market potential Education and training institutions Regulatory framework Level of technology Good firm cooperation Staff costs Other infrastructure Property-related costs Strategies of international firms Other costs Strong currency Strategies of local firms Community institutions Tough local competition Local market demand Average a
Importance Rank
Mean
S.D.
S.Ea.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
4.66 4.54 4.44 4.44 4.42 4.38 4.34 4.34 4.32 4.32 4.24 4.22 4.19 4.17 4.16 4.16 4.15 4.11 4.07 4.06 4.06 4.03 4.01 3.99 3.98 3.97 3.96 3.95 3.91 3.90 3.90 3.79 3.78 3.73 3.73 3.66 3.60
0.54 0.59 0.60 0.70 0.67 0.72 0.64 0.62 0.54 0.71 0.70 0.69 0.64 0.78 0.74 0.79 0.72 0.66 0.71 0.77 0.71 0.83 0.65 0.74 0.71 0.75 0.69 0.77 0.60 0.79 0.78 0.76 0.93 0.76 0.74 0.77 0.79
0.04 0.04 0.04 0.05 0.05 0.05 0.05 0.05 0.05 0.04 0.05 0.05 0.05 0.06 0.05 0.06 0.05 0.05 0.05 0.05 0.06 0.06 0.05 0.06 0.05 0.06 0.05 0.06 0.04 0.06 0.06 0.06 0.07 0.05 0.06 0.06 0.06
4.10
S.E. is the standard error of the mean at the 95% confidence level.
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relatively low. The three factors of staff costs, propertyrelated costs, and other costs were ranked, respectively, 28, 31, and 30th out of the 37 factors. Again, as with climate, this could be because of the focus on city tourism, where costs are seen as less of a determinant of competitiveness than in other types of tourism destinations. The least important factor, ranked 37th, was local market demand. In other words, respondents did not think that local demand for tourism related activities was a major contributor to the competitiveness of a city destination. Again, as with the attractors, all scores lay above the neutral 3, and remained so after allowing for sampling error. The range of scores is also interesting with the business-related factors ranging between a high of 4.66 to a low of 3.59 and the attractors from 4.65 to 3.28. In both cases this suggests that respondents differentiate between the different factors and, also that many of the business-related factors were considered as important if not more important than the attractors. This helps to reinforce the argument that tourism competitiveness should be set within a broader context of generic competitiveness and provides support for this combined approach.
Table 4 Attractors ranked by relative competitiveness mean scores (N=183) Attractors
Cuisine Safety Nightlife Visual appeal Climate Well-known landmarks Different culture Local way of life Special events Interesting architecture Interesting festivals Dedicated tourism attractions Notable history Music and performances Museums and galleries Average a
Competitiveness Rank
Mean
S.D.
S.E.a
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
4.34 4.04 3.82 3.73 3.46 3.38 3.38 3.36 3.35 3.29 3.28 3.18 3.15 2.99 2.69
0.74 0.83 0.89 0.75 0.78 0.89 0.84 0.84 0.79 0.88 0.86 0.94 0.87 0.78 0.80
0.06 0.06 0.07 0.06 0.06 0.06 0.07 0.06 0.06 0.07 0.06 0.07 0.06 0.06 0.06
3.43
S.E. is the standard error of the mean at the 95% confidence level.
which resulted in depreciations in the currencies of many competing destinations.
5.2. Hong Kong’s relative competitiveness: attractors and business factors
5.3. The IPA grid
While the first stage generated a ranking of what is important for a destination in general, it left open the question of how a specific destination is performing versus relevant competitors. The second stage explored this issue and the results for the tourism-specific factors are shown in Table 4. Here the mean scores ranged from a high of 4.34 to a low of 2.69, indicating a wide variation in Hong Kong’s comparative performance or positioning. According to the results, Hong Kong’s main strengths lie in cuisine, safety, nightlife, visual appeal, and climate. Its greatest weaknesses are in museums and galleries, music and performances, and notable history. The results for Hong Kong’s relative performance in the business-related factors are shown in Table 5. Consistent with the earlier results, the spread between the highest and lowest ranked factors is substantial. The highest rating for competitiveness was identified as China market potential with a mean score of 4.18 and the lowest rating was ascribed to staff costs with a mean score of 2.31. Hong Kong was seen to have substantial advantages in terms of international access, internal transportation facilities, communication facilities, and in its free port status. The factors where Hong Kong was seen to have disadvantages were staff costs, propertyrelated costs, and other costs. This is as would be expected, given Hong Kong’s reputation as a high cost centre and the impact of the Asian economic crisis,
A standard approach adopted by IPA is to combine measures of importance and performance into a two dimensional grid so as to ease data interpretation and elicit suggestions for action. The overall mean scores of importance and performance are then used to create four quadrants within the plot (Oh, 2001). Substituting Hong Kong’s relative competitiveness for the concept of performance generates the result shown in Figs. 3 and 4. Fig. 3 shows the position of the attractors in the four quadrants, and Fig. 4 shows the same for the businessrelated factors. Given that IPA grids of this nature are used to generate prescriptions for action, it is important to check whether sampling error would change the results. Tables 2–5 show the standard error at the 95% confidence level for each importance and relative competitiveness result. Whilst the standard error does impact some of the rankings, causing a shift in the relative position of the factors within each table, the shifts do not, in any single case, change the Quadrant that each item falls within. As prescriptions are based on an item’s location within a Quadrant, rather than its ranking in respectively, importance or relative competitiveness, the conclusions based on these results remain unaffected by sampling error. This test therefore offers reassurance that the relatively small number of responses would not adversely affect the IPA analysis. The analysis would only be affected if the position of a result
ARTICLE IN PRESS M.J. Enright, J. Newton / Tourism Management 25 (2004) 777–788 Table 5 Business-related factors ranked by relative competitiveness mean scores (N=183)
785
5
IV
I
15
Competitiveness
China market potential International access Banking and financial system Internal transportation facilities Communication facilities Free port status Access to information Geographic location Political stability Cleanliness of government Tax regime High-quality accommodation Transparency in policy making Presence of international firms Other infrastructure Level of technology Regulatory framework Strategies of international firms Other Asia Pacific market potential Overall economic condition Local managerial skills Good retail sector Strong currency Government policy Strategies of local firms Community institutions Investment incentives Long haul market potential Staff skills Good firm cooperation Education and training institutions Local market demand Support from related industries Tough local competition Property-related costs Other costs Staff costs Average a
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37
Mean 4.18 4.16 4.16 4.16 4.13 4.12 4.05 4.03 3.90 3.88 3.86 3.82 3.74 3.74 3.73 3.66 3.59 3.58 3.58 3.54 3.54 3.53 3.49 3.48 3.45 3.43 3.42 3.36 3.30 3.30 3.27 3.23 3.20 3.16 2.44 2.42 2.32
S.D. 0.66 0.72 0.67 0.74 0.72 0.75 0.73 0.68 0.79 0.85 0.75 0.84 0.77 0.73 0.70 0.82 0.70 0.71 0.71 0.84 0.73 0.81 0.98 0.94 0.75 0.74 0.91 0.76 0.85 0.73 0.88 0.82 0.80 0.77 0.95 0.80 0.85
S.Ea 0.05 0.05 0.05 0.05 0.05 0.06 0.05 0.05 0.06 0.06 0.06 0.06 0.05 0.06 0.05 0.06 0.05 0.05 0.05 0.06 0.05 0.06 0.07 0.07 0.06 0.05 0.07 0.06 0.06 0.05 0.07 0.06 0.06 0.06 0.07 0.06 0.06
3.57
S.E. is the standard error of the mean at the 95% confidence level.
in its quadrant would be altered by a larger number of responses. As the position was not affected by the standard error, this suggests that it would not be altered by a larger number of responses. The Quadrants can be used to generate suggestions for managers in both public and private sectors by differentiating between them. Quadrant I, which includes the high importance and high relative competitiveness factors, identifies the attributes that the destination should strive to maintain or ‘‘keep up the good work’’ (Martilla & James, 1977, p. 78). Quadrant II includes factors that are low in importance but high in relative competitiveness, and thus identifies areas where there may be ‘‘wasted effort’’, given the low importance.
12
14
Importance
Rank
1 3 7 13
4
11
8 2 6 4 5 9 10
III
II
3 2
3
4
5
Relative Competitiveness Key: 1. 2. 3. 4. 5. 6. 7. 8.
Visual appeal Interesting architecture Well-known landmarks Climate Notable history Local way of life Different culture Interesting festivals
9. 10. 11. 12. 13. 14. 15.
Museums & galleries Music & performance Nightlife Cuisine Special events Dedicated tourism attractions Safety
Fig. 3. Importance and relative competitiveness of tourism attractors.
5
IV
I 30
10
Importance
Business-related factors
4
8
28 1226
2 27 3 4 14 17 1 13
31
32 29 11 6 22 16 18 23 37 35 21 3315 25 5 24 36 34 20 19
7 9
III
II
3 2
3
4
5
Relative Competitiveness Key: 1. Geographic location 2. International access 3. Internal transportation facilities 4. Communication facilties 5. Other infrastructure 6. High quality accommodation 7. Property related costs 8. Staff costs 9. Other costs 10. Staff skills 11. Local managerial skills 12. Good retail sector 13. Banking & financial system 14. Access to information 15. Level of technology 16. Long haul market potential 17. China market potential 18. Other Asia-Pacific market potential 19. Local market demand
20. 21. 22. 23. 24. 25. 26. 27. 28. 29. 30. 31. 32. 33. 34. 35. 36. 37.
Tough local competition Good firm cooperation Support from related industries Presence of international firms Strategies of local firms Strategies of international firms Overall economic condition Free port status Government policy Investment incentives Political stability Cleanliness of government Transparency in policy making Regulatory framework Community institutions Education & training institutions Strong currency Tax regime
Fig. 4. Importance and relative competitiveness of business-related factors.
Quadrant III identifies areas of low priority, including factors in which the destination is not particularly competitive but which are low in importance. Quadrant
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IV, which includes factors that are high in importance but where there is low relative competitiveness, identifies critical areas for improvement where decision makers are recommended to ‘‘concentrate here’’ (Martilla & James, 1977, p. 78). Hence, Fig. 3’s Quadrant I shows that Hong Kong rates well versus the competition in some factors, such as cuisine and safety, that are also very important in determining destination competitiveness for urban tourism in the region. This knowledge is much more valuable to industry participants and policy makers than knowing only that Hong Kong rates well on these factors. Similarly, from Quadrant III, knowledge that Hong Kong does not rate as well for museums and galleries, but that this is not nearly as important as the other attractors, is far more valuable to participants and policy makers than knowledge of Hong Kong’s relative competitiveness rating alone. Fig. 4 allows a similar interpretation for the businessrelated factors. As Quadrant I shows, Hong Kong rated well in terms of international access and internal transportation facilities, factors that were deemed to be very important in influencing destination competitiveness. Quadrant IV indicates that Hong Kong rated below average in staff skills and government policy, both of which rated highly in importance. Quadrant II shows that Hong Kong rated highly in tax regime and other infrastructure, but these were rated below average in importance. Quadrant III shows that Hong Kong has a relatively weak position with respect to costs, but according to the respondents, costs are relatively unimportant as determinants of city destination competitiveness. This should be viewed as an interesting result, given concerns in Hong Kong about cost competitiveness relative to other destinations in the region. The results indicate that Hong Kong should make sure it maintains its strong position in safety and cuisine, and in international access and internal transport facilities, and suggest that the features should be at the heart of Hong Kong’s tourism promotion efforts. In addition, Hong Kong should focus on improving the features of dedicated tourism attractions and wellknown landmarks, and government policy and staff skills, given their high importance but relatively low competitiveness. On the other hand, Hong Kong should not expend too much effort on features such as music and performances and museums and galleries, and property, staff and other costs, despite their relatively low competitiveness, given their low importance.
6. Discussion and conclusions The present study makes a number of contributions to the tourism literature and to the literature on competitiveness. The study fills an important gap in the
literature by developing a methodology that has operationalised the concept of destination tourism competitiveness in a manner that is useful for researchers, industry participants, and policy makers. In particular it has demonstrated the value of including business-related factors as well as the more conventional destination image or attractiveness factors in studies of tourism competitiveness. In applying this methodology, as a first step, to Hong Kong, the study also has shown the practical importance of the identification of relevant competitors and understanding the relative importance of tourism attractors and business-related factors in determining tourism destination competitiveness. It has reinforced the value of the two-stage process that assesses the importance of the determinants of competitiveness as well as their competitiveness relative to those main competing destinations. The use of the IPA Grid offers a method of analysis that is common in tourism destination research but not in the generic competitiveness literature and hence provides a contribution to the latter. The Grid also demonstrates how the resulting analysis may be presented in a readily accessible manner so as to inform practical decisions for action. However, this is not to suggest that, at this stage of development of the methodology, the current results provide an unambiguous guide to action. Further research will be required in a number of directions, as it would be unwise to rely on a single, initial study if practical changes were contemplated. As an example, it would be valuable to investigate further the low importance ascribed to museums and galleries as determinants of tourism competitiveness, given the, often significant, investments in such facilities. Alternative sources of information, such as the percentage of total tourists visiting such facilities, where available, could be used to check this finding against recorded behaviour. This would also allow the methodology to be further refined should the additional results either contradict the findings derived from practitioner respondents or, alternatively provide support for the argument that practitioners provide an accurate view of tourist behaviour. A further caveat should be added when considering factors that fall into the IPA quadrants that denote low importance, and again the example of museums and galleries is instructive. Once again there should be a note of caution, given the conclusion that these factors could represent areas of wasted effort. It is possible that these are necessary factors for the overall competitiveness product in that tourists may not actually use them, but if they were not present, then it might generate dissatisfaction. It would be instructive if future research could address this issue. Despite these caveats, the overall results provide strong support for the combined approach to tourism
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destination competitiveness suggested by Crouch and Ritchie. A far better picture of a destination’s competitiveness in the tourism industry emerges when one combines an analysis of ‘‘traditional’’ tourism attractors with business-related factors based on general models of competitiveness. Some of the business-related factors, in fact, are viewed by industry participants as far more important than some of the tourism attractors. In addition, the approach goes beyond simple list making, often found in generic competitiveness studies, to identify a specific set of competitors in urban tourism in the Asia-Pacific and to suggest a ranking of the importance of various attractors and business-related factors for urban tourism in the region and perhaps more generally. In summary, therefore, this study has developed a methodology that has operationalised the broader approach to tourism competitiveness suggested in the literature. It has provided a quantitative understanding of the industry and location that can be replicated in other jurisdictions. Finally it has provided a quantitative, theoretically informed empirical analysis that offers a basis for strategy development and policy formulation in the tourism industry.
Acknowledgements The authors would like to acknowledge the support of the Hong Kong Institute for Economics and Business Strategy (HKIEBS) at the University of Hong Kong.
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