Drivers of Destination Competitiveness in Tourism

Abstract

Debates about competitiveness and productivity are practically unexplored with respect to tourism. This article posits a productivity-related measure—total tourism contribution to GDP per employee in tourism—in order to examine destination competiveness. Comprehensive results based on a destination competitiveness model are obtained by analyzing tourism-specific and wider economy-based competitiveness factors. These are represented by six destination competitiveness factors measured by 55 indicators for 139 destinations over the period 2007–2011. Study findings demonstrate that tourism-specific factors, such as Tourism Infrastructure and Destination Management, are the major competitiveness drivers in developing countries, while destination competitiveness in developed countries depends on the tourism-specific factor of Destination Management as well as on wider economic conditions such as General Infrastructure, Macro-Environment, and Business Environment. The study offers a novel approach in the operationalization and estimation of a theoretically grounded and empirically validated tourism competitiveness model and discusses the implications for tourism policy.

Keywords

destination competitiveness welfare competitiveness factors and indicators developed and developing countries

Introduction

Achieving destination competitiveness, with its promise of prosperity for residents, is a central feature of tourism policy debate. Interest in destination competitiveness has stimulated a plethora of research studies. These include the development of comprehensive frameworks (Ritchie and Crouch 2003; Dwyer and Kim 2003; WEF 2015); studies of single destinations (d’Hauteserre 2000; Enright and Newton 2004; Omerzel Gomezelj and Mihalič 2008; Dwyer et al. 2013; Dwyer et al. 2014; Dwyer et al. 2014); comparisons of destinations (Enright and Newton 2005; Kozak 2003); and studies that focus on specific attributes of competitiveness such as price competitiveness, environmental competitiveness, and business performance (Buhalis 2000; Mihalič 2000; Dwyer, Forsyth, and Rao 2000; Barros et al. 2011; Assaf and Dwyer 2013).

Despite a voluminous literature identifying and measuring “determinants” of destination competitiveness (Ritchie and Crouch 2003; Dwyer and Kim 2003), there is no consensus regarding just what destination competitiveness means. Regardless of the specific nature of study, research in this area fails to identify precisely the destination competitiveness concept given its complex association with economic, noneconomic, objective, and subjective features. It must be appreciated that failure to clearly identify destination competitiveness precludes ascertainment of an “acceptable” list of determinants for any particular destination. Let us consider two widely accepted definitions of destination competitiveness. Ritchie and Crouch (2003, 2) propose that “what makes a tourism destination truly competitive is its ability to increase tourism expenditure, to increasingly attract visitors, while providing them with satisfying, memorable experiences, and to do so in a profitable way, while enhancing the well-being of destination residents and preserving the natural capital of the destination for future generations.” Meanwhile, Dwyer, Forsyth, and Rao (2000, 9) state that “tourism competitiveness is a general concept that encompasses price differentials coupled with exchange rate movements, productivity levels of various components of the tourist industry, and qualitative factors affecting the attractiveness or otherwise of a destination.” These definitions of destination competitiveness, which are typical of those advanced by tourism researchers (Buhalis 2000; Hassan 2000), imply that destination competitiveness is associated with specific measurable features such as growth of tourism expenditure and price differentials and more qualitative features such as “destination attractiveness” and enhancing the well-being of destination residents.

Despite its mention by Dwyer, Forsyth, and Rao (2000), the role of productivity in achieving any form of competitiveness, strongly emphasized by economists, has been ignored in the destination competitiveness literature with respect to tourism business (Blake, Sinclair, and Soria 2006). Productivity is measured by the value of goods and services produced per unit of the nation’s human, capital, and natural resources (Porter and Ketels 2003), that is, the volume of outputs produced per volume of inputs used. As Porter states, “the only meaningful concept of competitiveness at the national level is productivity” (Porter 1990, 40). For Krugman, “Competitiveness is a poetic way of saying productivity” (Krugman 1994, 18). While these comments are regarded by some critics as overreaching, there is widespread agreement that competitiveness relates to “sustained superior performance” of a country relative to its rivals in a global environment in creating welfare, where welfare is determined by its absolute level of productivity (Aiginger 2006; Kohler 2006; Atkinson 2013). In economics literature on national competitiveness, productivity is identified as the central driver of cross-country differences in prosperity (Porter et al. 2008).

In this article, consistent with the economics literature on national competitiveness, we maintain that the main purpose of destination competitiveness is to create welfare and socioeconomic prosperity for the country or destination residents, a goal that recognizes the importance of productivity measures in cross-country comparisons. Our approach is thus based on an output-focused approach to competitiveness. This approach is consistent with policy documents such as the OECD’s Growth Agenda (OECD 2005) and the European Commission’s 2020 strategy (EC 2010). More specifically, we follow Porter et al. (2008) and Delgado et al. (2012) in operationalizing competitiveness as an input/output evaluation assessment.

This article specifically aims, first, to operationalize a measure of destination competitiveness consistent with recent economic analyses; second, to test the influence on destination competitiveness of a set of indicators that are hypothesized to drive tourism competitiveness at the destination level; and third, to discuss the relevance of policy findings to enhance destination productivity and thus destination competitiveness.

The structure of the article is as follows: The next section operationalizes destination competitiveness. The third section employs a data set of 55 destination competitiveness indicators across 139 countries covering the period 2007–2011. This data set is premised on previous studies and various secondary data. To simplify the analysis, we categorize these indicators into six major destination competitiveness factors: Business Environment, Macro-Environment, General Infrastructure, Endowed Resources, Tourism Infrastructure, and Destination Management. The fourth section presents the major findings of the study. The article concludes with a discussion on policy relevance of the study, study limitations, and some issues for further research.

Destination Competitiveness

For the purpose of this article, we define destination competitiveness as the total tourism contribution to GDP per tourism employee. This measure aligns with the broader notion of national competitiveness measurement used in Delgado et al. (2012). It recognizes that in the long term, the income and living standards of a nation or an economy are ultimately determined by the volume of goods and services produced by its residents over a given period, typically expressed in terms of real gross domestic product (GDP) per employed worker. The more GDP (or output) is generated per employed person, the higher the national average income and the higher the material living standards of a nation. We propose a tourism equivalent that represents tourism (log of)¹ output or tourism total contribution to GDP in absolute terms generated per an employee in tourism.

The average total tourism contribution per tourism employee in 139 countries for the period 2007–2011 was US$27,755. The total tourism contribution per tourism employee in developed countries (40,538) was 1.5 times above average, while the value for developing countries (US$7,367) was only one quarter of the average (see Table 1).

Table 1.

Descriptive Statistics of Productivity.

Destination Productivity	Mean/Standard Deviation	All Countries (U.S. Dollars)	Developed Countries (U.S. Dollars)	Developing Countries (U.S. Dollars)
Tourism contribution to GDP per tourism employee	Mean	29,775.99	40,538.18	7,367.75
	Median	15,067.74	29,131.06	2,877.48
	Minimum	115.74	1,780.20	115.74
	Maximum	179,587.80	179,587.80	109,233.00
Natural logarithm of tourism contribution to GDP per tourism employee	Mean	9,526	10,215	8,091
	Median	9,620	10,279	7,964
	Minimum	4,751	7,485	4,751
	Maximum	12,098	12,098	11,601

Source: Authors’ calculations.

Indicators of Destination Competitiveness

Developing destination competitiveness indicators involved a five-step process.

The destination competitiveness literature highlights a substantial number of indicators or drivers of destination competitiveness (Ritchie and Crouch 2003; Dwyer and Kim 2003; WEF 2015). The present challenge was to select a set of indicators and determine their impact on competitiveness measures. A total of 105 potential indicators were identified in this literature.

Surveying various data sources, both public and private, we sought operational measures for each of the 105 potential indicators of destination competitiveness. For many of the identified indicators, we were unable to find proper data (e.g., for location, climate, value of natural resources), while for others we could find more than one proxy (e.g., quality of air and ground transportation). In the end, we employed 135 indicators for 139 countries for the period 2007–2011. Data sources included UNWTO e-library (UNWTO 2008, 2009, 2010, 2011, 2012), WTTC database (WTTC 2011), World Economic Forum (WEF) Tourism and Travel Competitiveness Reports (WEF 2008a, 2009a, 2011a, 2012a, 2013), WEF Global Competitiveness Reports (WEF 2008b, 2009b, 2011b, 2012b), UNDP statistics (UNDP 2008, 2009, 2010, 2011, 2012), Lonely Planet (LP 2012), International living database (IL 2008, 2009, 2010, 2011, 2012), and Future Brand (FB 2010, 2011, 2012).

Data reduction was then undertaken. Dealing with 135 indicators would make the analysis and its interpretation far too intractable. To this end, we applied a data reduction method and conducted separate factor solution analyses using Quartimax rotation, maximizing the variance of the squared loadings within the variables. We excluded indicators with poor factor loadings (the threshold was represented by factor loading conditions lower than 0.500). Following this procedure, 72 indicators remained.

Thereafter, we included the 72 indicator variables into a multiple solution factor model. We used factor analysis with Varimax rotation, which maximizes the variance of the squared loadings within factors. The final result involved six destination competitiveness factor solutions and 55 indicators (Dwyer et al. 2014). The six factors are Macro-environment, Business Environment, General Infrastructure, Endowed Resources, Tourism Infrastructure, and Destination Management. The indicators of destination competitiveness within those factors are presented in Table 2.

Finally, we normalized all competitiveness factors using standard normal distribution to ascertain the factors of destination competitiveness that have the greatest impact on destination productivity. Hence, each factor has a mean of zero and a standard deviation of one.

Table 2.

Destination Competitiveness Factors and Indicators.

Factors	Indicators
Economy-Based Factors
Macro- Environment	Reliance on professional management, GCR; Willingness to delegate authority, GCR; Extent of staff training, TTCI; Purchasing power parity (PPP), TTCI; Ethical behavior of firms, GCR; Quality of education system, TTCI; Capacity for innovation, GCR; Cost of living, IL; Judicial independence, GCR; Public trust in politicians, GCR; GDP per capita, GCR; Nature of competitive advantage, GCR; Quality of natural environment, TTCI; Quality of scientific institutions, GCR; CO² emissions, TTCI
General Infrastructure	Access to improved drinking water, TTCI; Access to improved sanitations, TTCI; Physician density, TTCI; Quality of health care, IL; Internet users, TTCI; Quality of electricity supply, GCR; Broadband Internet subscription, GCR; Road density; TTCI; Risk and safety, IL; ATM accepting visa cards, TTCI; Presence of major car rental companies, TTCI; Life expectancy, TTCI; Intensity of local competition, GCR; Quality of domestic transportation, TTCI
Business Environment	FDI and technology transfer, GCR; Business impact of rules on FDI, TTCI; Venture capital availability, GCR; Extent of business internet use, TTCI; Firm level of technology absorption, GCR; Availability of the latest technology, GCR; Local supplier quality, GCR; Country credit rating, GCR

Tourism-Based Factors
Endowed Resources	Variety of cultural sights, LP; Number of world heritage cultural sights, TTCI; Number of world heritage natural sights, TTCI
Tourism Infrastructure	Variety of Activities, LP; Variety of shopping, LP; Variety of restaurants, LP; Number of international fairs and exhibitions, TTCI; Variety of entertainment, LP; Number of hotel rooms, UNWTO; Number of tour operators, UNWTO; Number of food and beverage establishments, UNWTO
Destination Management	Sustainability of TT, TTCI; Effectiveness and branding of marketing to attract tourists, TTCI; Government prioritization of TT, TTCI; Quality of air transport infrastructure, TTCI; International air transport network, TTCI; Attitude of the local population towards foreign visitors, TTCI; Protected areas, TTCI;TT government expenditure, TTCI

Sources: UNWTO e-library (UNWTO 2008, 2009, 2010, 2011, 2012), World Economic Forum (WEF) Tourism and Travel Competitiveness Reports (WEF 2008a; WEF 2009a; WEF 2011a; WEF 2012a), WEF Global Competitiveness Reports (WEF 2008b; WEF 2009b; WEF 2011b; WEF 2012b), Lonely Planet (LP 2012) and International living database (IL 2008, 2009, 2010, 2011, 2012).

Note: TTCI = Tourism and Travel Competitiveness Index; GCR = Global Competitiveness Report; LP = Lonely Planet; UNWTO = United Nations World Tourism Organization; IL = International living database; FDI = foreign direct investment.

For enhanced understanding of the entire process of operationalization of the constructs, the framework for analysis is displayed in Figure 1.

Figure 1.

Research framework.

At the bottom of Figure 1, we list the indicators of destination competitiveness. A necessary condition for the selection of each indicator is that each has been previously validated as a source of destination competitiveness (Dwyer et al. 2014). The middle box shows the group of six destination competitiveness factors. Three factors may be considered to be tourism-related (destination management, endowed resources, tourism infrastructure), while the others (general infrastructure, macroeconomic environment, and business environment) are more general. The top box shows the fundamental purpose of achieving destination competitiveness—welfare of residents. The box immediately below comprises a destination competitiveness measure of tourism’s economic contribution to welfare and socioeconomic prosperity. The measure constructed for this purpose is “tourism total contribution to GDP per tourism employee,” the tourism equivalent of the national productivity indicator used in a recent study on national competitiveness (Delgado et al. 2012).

Methodology

The model presented in Figure 1 could be tested with either structural equation modeling or multiple regression analysis. Given panel data that enabled us to capture the dynamic change over the time period, following Kline’s advice (Kline 2010, 10) that “there is no ‘embarrassment’ in using a simpler statistical technique over a more complicated one, especially if the simpler technique is sufficient to test your hypotheses,” we used multiple regression analysis to relate destination competitiveness to its drivers. Taking into account the panel structure of our data, we estimate the following model:

y_{it} = α + x ’_{it} β_{it} + CONTROLS ’_{it} γ + ε_{it}

where i stands for country and t stands for time, y_it is the (log of) tourism total contribution to GDP per tourism employee, x′ are normalized competitiveness factors (Macro-Environment, Business Environment, General Infrastructure, Endowed Resources, Tourism Infrastructure, Destination Management). CONTROLS′ are control variables (size, year dummies), and ε is the residual.

We estimated equation (1) using the pooled ordinary least square estimator (OLS), the fixed effect estimator (FE), and the random effect estimator (RE). The Hausman test shows² that in all specifications,³ the fixed effect estimator is the most appropriate to be used.

Results

As the purpose of this article is to relate destination competitiveness to its underlying factors and indicators, we ran a set of regression models, first on the whole sample (Table 3) and, second, on the subsample of developed and developing countries (Table 4).

Table 3.

Results of the Fixed Effect Estimator (FE) on the Entire Sample.

Destination Competitiveness Factors and Control Variables		FE	FE	FE
Destination Competitiveness Factors and Control Variables		1	2	3
Business Environment	Coefficient	0.091	0.090	0.180
	Standard Error	0.044**	0.043**	0.039***
Macro-Environment	Coefficient	0.438	0.428	0.233
	Standard Error	0.066***	0.064***	0.050***
General Infrastructure	Coefficient	0.088	0.097	0.001
	Standard Error	0.050*	0.048**	0.015
Endowed Resources	Coefficient	0.051	0.017	0.001
	Standard Error	0.016***	0.013	0.019
Tourism Infrastructure	Coefficient	0.237	0.051	−0.129
	Standard Error	0.244	0.225	0.147
Destination Management	Coefficient	0.056	0.054	0.004
	Standard Error	0.019***	0.018***	0.016
Population (size)	Coefficient	−	0.006	0.003
	Standard Error	−	0.001***	0.001***
Year dummies		No	No	Yes
Constant	Coefficient	9.595	9.248	9.308
	Standard Error	0.017***	0.082***	0.035***
Observations		433	433	433
R-squared (pseudo R-squared for median regression)		0.31	0.35	0.52

Source: own calculations.

Note: Standard errors are based on the Huber/White/sandwich VCE (variance–covariance estimator) estimator. *Significant at 10%; **significant at 5%; ***significant at 1%.

Table 4.

Results of Fixed Effect Estimator (FE) for the Developed and Developing Countries.

Destination Competitiveness Factors and Control Variables		FE			FE			FE
		Developed Countries	Developing Countries	Difference	Developed Countries	Developing Countries	Difference	Developed Countries	Developing Countries	Difference
		1	2	3	4	5	6	7	8	9
Business Environment	Coefficient	0.106	−0.129	−0.235	0.106	−0.104	−0.21	0,177	0.05	−0.127
	Standard Error	0.042**	0.197	0.197	0.042**	0.199	0.15	0.039***	0.179	0.135
Macro- Environment	Coefficient	0.405	0.814	0.409	0.402	0.828	0.426	0.243	0.574	0.331
	Standard Error	0.060***	0.409*	0.404	0.051***	0.415*	0.28	0.050***	0.315*	0.252
General Infrastructure	Coefficient	0.194	0.061	−0.133	0.192	0.076	−0.116	0.039	0.033	−0.006
	Standard Error	0.039***	0.046	0.060**	0.042***	0.045	0.067*	0.044	0.062	0.064
Endowed Resources	Coefficient	0.036	−0.131	−0.167	0.025	−0.17	−0.195	0.01	−0.14	−0.15
	Standard Error	0.015**	0.089	0.088*	0.024	0.90*	0.109*	0.023	0.149	0.099
Tourism Infrastructure	Coefficient	−0.01	5.42	5.43	−0.056	4.552	4.608	−0.131	2.803	2.934
	Standard Error	0.107	0.720***	0.711***	0.172	0.850***	1.039***	0.156	1.013**	0.956***
Destination Management	Coefficient	0.029	0.144	0.115	0.028	0.142	0.114	−0.008	0.077	0.085
	Standard Error	0.011**	0.073*	0.072	0.014**	0.073*	0.029***	0.015	0.003*	0.026***
Population (size)	Coefficient	/			0.003			0.002
	Standard Error	/			0.002*			0.001
Year Dummies		No			No			Yes
Constant	Coefficient	9.957			9.739			9.635
	Standard Error	0.067***			0.141***			0.128***
Observations		433			433			433
R-squared		0.43			0.44			0.55

Source: own calculations.

Note: Standard errors are based on Huber/White/sandwich VCE (variance–covariance estimator) estimator. *Significant at 10%; **significant at 5%; ***significant at 1%.

In Table 3, we report results of the equation (1) that are based on the fixed effect estimator (FE) for different specifications depending on the controls used. No control variable is used in column 1. In column 2, population size was the control, while in column 3 the controls were size and year. All results in Table 3 are based on the Huber/White/sandwich VCE (variance–covariance estimator) estimator.⁴

Our results indicate that the Business Environment has a positive and statistically significant impact on destination competitiveness in all specifications. The value of the coefficient varies from 0.09 in specification where size is used as the control variable, to 0.180 in specification when we use both size and year dummies as controls.⁵ The Macro-Environment also has a positive and statistically significant impact on destination competitiveness varying from 0.23 to 0.44. In addition, General Infrastructure has a positive and statistically significant, but rather lower (between 0.01 and 0.1) impact in almost all specifications (2 out of 3). On the other hand, Endowed Resources have a significant impact on competitiveness only in one model specification and, even in that case, the value of the coefficient is relatively small (0.05). We were unable to find a significant impact of Tourism Infrastructure on competitiveness, while Destination Management slightly and positively affects destination competitiveness.

Overall results showed that the Macro- and Business Environments, General Infrastructure, and Destination Management have a positive and significant impact on destination competitiveness in almost all model specifications tested.

Previous research argued that national competitiveness is associated with a country’s level of economic development (Kester and Croche 2011). To address this issue, we ran the regression analysis on sub-samples of developed and developing countries in addition to the whole sample. Countries were grouped according to the World Bank income level classification. Based on Gross National Income (GNI) per capita, economies are divided into four groups: low income of US$1,035 or less; lower middle income of US$1,036–4,085; upper middle income of US$4,086–12,615; and high income of US$12,616 or more (World Bank 2014). For present purposes, we label low income and lower middle income as “developing” countries and upper middle income and high income countries as “developed” countries.

Results are presented in Table 4 and are based on the FE⁶ estimator using different specifications. In columns 1–3, we do not have any controls, in columns 4–6 we control for population size only, while in columns 7–9 we control for population size and years. In order to test the difference of coefficients between developed and developing countries, we estimated developed and developing countries jointly, since this allows us to test the difference of corresponding coefficients directly from the output.

The results in Table 4 highlight some interesting differences between developed and developing countries with respect to destination competitiveness⁷ and the factors driving it.

Although the Business Environment has a positive effect on competitiveness in developed countries, it has no effect on competitiveness in developing countries. However, the difference is not statistically significant. The same holds for the Macro-Environment. On the other hand, the effect of the General Infrastructure on competitiveness is different in developing and developed counties in most specifications (2 out of 3). In the case of those two specifications, the coefficient is positive but statistically smaller in developing counties compared to developed countries. Endowed Resources have a positive effect on competitiveness in developed countries and a negative effect in developing countries. In both cases, only in one specification is this effect statistically significant. However, the difference in coefficients of Endowed Resources between developed and developing countries is statistically significant in most specifications. The converse is true for Tourism Infrastructure. While Tourism Infrastructure does not influence competitiveness in developed countries, it positively affects competitiveness in developing countries. The difference is statistically significant in all specifications. Finally, the effect of Destination Management on competitiveness is generally positive and statistically significant for both developing and developed countries. The difference is statistically significant in most specifications, while Destination Management more positively affects competitiveness in developing countries than in developed countries.

Overall, results show that the main drivers of competitiveness in developed countries are: Macro- Environment, Business Environment, and General Infrastructure. Interestingly, these factors are all economy-based rather than tourism-specific. The main driver of competitiveness in developing countries is Tourism Infrastructure. Destination Management is an important driver of competitiveness in both developed and developing countries, but the magnitude of its impact is higher in developing countries. The impact of Endowed Resources on destination competitiveness is insignificant and inconclusive in most of the specifications.

Discussion

The study approach and results are interesting on a number of levels. Results show that several of the variables that have been identified by researchers as factors of destination competitiveness have a different force in developed and developing countries.

Employing the total tourism contribution to GDP per tourism employee as the destination competitiveness measure, the present study shows that competitiveness is sensitive to changes in several key factors that researchers have identified in the past. It transpired that Macro-Environment, Business Environment, General Infrastructure, and Destination Management each have a positive and significant impact on destination productivity in developed countries. These results point to the significant importance of overall national economy stability for tourism development. A strong and healthy economy is an important driver of tourism competitiveness in developed countries. Tourism is not a sole and self-sustained system, but functions within the broader system of a national economy that highly impacts upon destination competitiveness. Developed countries’ destination competitiveness is driven by stable macro-economic conditions. As displayed in Table 2, indicators falling under this heading include purchasing power parity (PPP), the cost of living, GDP per capita, the nature of competitive advantage, the quality of natural environment, and the quality of scientific institutions. As elements of what management theorists refer to as the “remote environment,” changing these variables is beyond the control of tourism operators. The importance of the macro-environment does, however, flag the importance of good economic management within developed destinations, and that tourism operations need stable remote environments to flourish. Destination competitiveness in developed countries is also sensitive to the business environment. Comprising indicators such as the volume of FDI and technology transfer, the business impact of rules on FDI, venture capital availability, the extent of business internet use, a firm level of technology absorption, the availability of the latest technology, and local supplier quality, business environment falls under the control of operators in a destination. The findings reinforce the importance of detailed investigations into the role that tourism operators and “firms’ micro climate” play in overall destination competitiveness. The results also show that an important driver of destination competitiveness in developed countries is the quality of the general infrastructure (the quality of domestic transportation and health system, Internet use, road density, etc.).

Destination management is recognized as an important driver of competitiveness in both developed and developing countries, yet the magnitude of its impact is much higher in developing countries. This confirms the importance placed on destination management as a key driver of destination competitiveness (Crouch and Ritchie 1999; Dwyer and Kim 2003), although its role has been relatively neglected in empirical studies to date. Sustainability, marketing effectiveness, government prioritization of tourism, attitudes of local population toward visitors, government expenditure on tourism, international air transport network, and the quality as well as management of protected areas have proven to be very important drivers of destination competitiveness. Our results provide empirical evidence that destinations can and should build their success stories not only on tangible resources but also on intangible resources, such as destination management, which are knowledge-based.

In developing countries, the most important driver of destination competitiveness, alongside destination management, is tourism infrastructure. Results highlight the importance of attracting investors and large investor participation in tourism development in developing countries to improve destination competitiveness.

Results also show a nonsignificant and inconclusive relation between endowed resources and tourism competitiveness in developed and developing countries. We believe this is the consequence of a limited variety and quality of secondary data available for developing indicators for estimating the values of natural and cultural resources.

While many of the 55 indicators employed in this study are the same as those employed in the TTCI (WEF 2015), this study goes beyond the TTCI in several respects. In the TTCI, the weightings accorded to tourism-specific and other indicators are equal. The findings of this study, however, suggest that unequal weights may be appropriate regarding tourism-related and wider economy-related indicators and for developing versus developed countries. Further research needs to be undertaken on this issue, but given our findings, the assumption of equal weights of destination competitiveness indicators needs more critical attention than it has received thus far. This study also employed a wider body of sources than the TTCI based on theoretical frameworks of competitiveness proposed in tourism research literature. The wider the body of sources used to measure indicators, the greater the potential to link drivers of destination competitiveness with productivity outcomes. At present, there is only a limited variety and quality of secondary data available to develop indicators for estimating the values of natural and cultural resources. As Gooroochurn and Sugiyarto (2005, 41) emphasized, “One area of competitiveness measuring in which more effort is required is the environment indicators.” Crouch (2011) offers the same opinion regarding the limited role played by environmental indicators in established destination competitiveness frameworks. The message is that, globally, the tourism industry must make a greater effort to develop and test the various indicators of destination competitiveness hitherto proposed only at a theoretical level.

Conclusions

This article addresses several issues that have been relatively ignored in the destination competitiveness literature. One issue concerns the ultimate objective of destination competitiveness. On this front, there is widespread consensus that the end goal of achieving destination competitiveness is to improve resident welfare. This view is consistent with research on national competitiveness, which posits the same ultimate goal for policy making to achieve this. Unfortunately, the destination competitiveness literature has generally failed to address the implications of this claim or to test the associations between different nominated determinants and the fundamental goal. To our knowledge, no detailed analyses have been undertaken by tourism researchers as to the precise meaning of this fundamental goal or to discuss the sensitivity of this outcome to changes in the various “hard” and “soft” determinants of destination competitiveness. Unless we have a clear concept of destination competitiveness, we cannot determine the extent to which any activities or attributes bring a destination closer to or further away from some “ideal” level of socioeconomic performance. Nor can we estimate the effect on destination competitiveness of possible trade-offs between different types of determinants.

Consistent with the recent work of Porter and others (Delgado et al. 2012) emphasizing the importance of productivity measures in estimating national competitiveness, this article posits a productivity-related measure associated with destination competitiveness—the total tourism contribution to GDP per employed worker in tourism. While admittedly a narrower notion than destination competitiveness, the use of the productivity measure in this study serves, theoretically, to recognize the importance of productivity as essential to destination competitiveness and, empirically, to demonstrate its links to the standard indicators of destination competitiveness proposed by tourism researchers.

The limitations in regarding the GDP level or growth as an indicator of resident well-being are well known (Diener and Suh 1997). There is consensus in literature that we need to address competitiveness in a broader sense, not only as an economic output per unit of input, but also to acknowledge social distribution and environmental protection of resources (Glatzer 2012). Despite this recognition, years of research have yet to produce empirical measures that can be implemented to measure competitiveness holistically in terms of social welfare, a complex notion combining economic performance, social progress and environmental performance. While some tourism researchers understand that destination competitiveness is an intermediate goal towards the more fundamental aim of socioeconomic prosperity, or quality of life of residents (Ritchie and Crouch 2003; Dwyer and Kim 2003), little or no attempt has been made to determine the links between the two concepts. The United Nations Development Programme has recently attempted to measure social progress through the creation of the Human Development Index that includes economic performance, education, and life expectancy pillars (UNDP 2014). Other recent attempts at the national level are the creation of the Happy Planet Index (2013) (Abdallah et al. 2012), which scores countries based on their efficiency, how many long and happy lives each produces per unit of environmental output; and the Good Country Index, which measures what each country on earth contributes to the common good of humanity (Good Country Index 2015). There is substantial potential for tourism research on this neglected topic. Ideally, such measures will be incorporated progressively into analysis of destination competitiveness.

To our knowledge, this is the first attempt to examine the drivers of destination competitiveness distinguishing between developed and developing countries. The results from a regression analysis of 139 countries identified several of the main drivers of destination competitiveness. In particular, destination competitiveness in developed countries is sensitive to changes in the business environment, macro-environment, and general infrastructure. These are all not tourism specific in scope. This is an interesting finding that demands further investigation by researchers as to the actual and potential role these variables play in influencing destination competitiveness, given its close relation to national competitiveness. The relative importance of tourism-specific and more general (nontourism) determinants in different country contexts remains an important issue for public policy discussion. Tourism infrastructure has been shown to be a main driver of competitiveness in developing countries. Destination management is driving the competitiveness in both groups of countries, yet with a higher impact in developing countries. These are important findings in destination competitiveness research literature and they provide a solid basis for further operationalization of the destination competitiveness concept that will hopefully eliminate some of the misunderstandings currently present in the destination competitiveness literature.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Notes

Author Biographies

Ljubica Knežević Cvelbar is an Associate Professor, Faculty of Economics, University of Ljubljana. Her research interests include sustainable tourism development, sustainable tourism behaviour and tourism economics.

Larry Dwyer is a Professor, Faculty of Economics, University of Ljubljana, and Board Chair, International Academy for the Study of Tourism.

Matjaž Koman is an Assistant Professor, Faculty of Economics, University of Ljubljana. His research interests include mamanerial eeconomics and econometrics.

Tanja Mihalič is a Professor of Tourism at the Faculty of Economics, University of Ljubljana and the head of its Tourism Institute. Her research interests include tourism economics, environmental economics, policy and sustainable tourism development. She is a member of the UN WTO World Committee on Tourism Ethics and member of the executive body of the International Association of Tourism Economics and work in editorial boards in many quality tourism journals.

References

Abdallah

Michaelson

Shah

Stoll

Marks

2012. The Happy Planet Index: 2012 Report. A Global Index of Sustainable Well-Being. London: The New Economics Foundation.

Aghion

Dewatripoint

Harrison

Legros

Forthcoming. “Industrial Policy and Competition.” American Economic Journal: Macroeconomics. https://www.aeaweb.org/forthcoming/output/accepted_MAC.php.

Aiginger

2006. “Competitiveness: From a Dangerous Obsession to a Welfare Creating Ability with Positive Externalities.” Journal of Industry, Competition and Trade 6 (2): 161–77.

Assaf

Dwyer

2013. “Benchmarking International Tourism Destinations.” Tourism Economics 19 (6): 1233–47.

Atkinson

R. D.

2013. “Competitiveness, Innovation and Productivity: Clearing up the Confusion.” http://www2.itif.org/2013-competitiveness-innovation-productivity-clearing-up-confusion.pdf (accessed September 18, 2015).

Barros

C. P.

Botti

Peypoch

Robinot

Bernardind

Assaf

A. G.

2011. “Performance of French Destinations: Tourism Attraction Perspectives.” Tourism Management 32 (1): 141–46.

Blake

Sinclair

M. T.

Soria

J. A. C.

2006. “Tourism Productivity: Evidence from the United Kingdom.” Annals of Tourism Research 33 (4): 1099–1120.

Buhalis

2000. “Marketing the Competitive Destination of the Future.” Tourism Management 21 (1): 97–116.

Crouch

G.I

Ritchie

B. J. R.

1999. “Tourism, Competitiveness, and Societal Prosperity.” Journal of Bussines Research 44:137–152.

10.

Crouch

G. I.

2011. “Destination Competitiveness: An Analysis of Determinant Attributes.” Journal of Travel Research 50 (1): 27–45.

11.

Delgado

Ketels

C. C.

Porter

M. E.

Stern

2012. “The Determinants of National Competitiveness.” NBER Working Paper Series No. 18249.

12.

d’Hauteserre

A. M.

2000. “Lessons in Managed Destination Competitiveness: The Case of Foxwoods Casino Resort.” Tourism Management 21 (1): 23–32.

13.

Diener

Suh

1997. “Measuring Quality of Life: Economic, Social and Subjective Indicators.” Social Indicators Research 40 (1-2): 189–216.

14.

Dwyer

Forsyth

Rao

2000. “The Price Competitiveness of Travel and Tourism: A Comparison of 19 Destinations.” Tourism Management 21 (1): 9–22.

15.

Dwyer

Kim

2003. “Destination Competitiveness: Determinants and Indicators.” Current Issues in Tourism 6 (5): 369–413.

16.

Dwyer

Knežević Cvelbar

Edwards

Mihalič

2013. “Tourism Firms’ Strategic Flexibility: The Case of Slovenia.” International Journal of Tourism Research 16 (4): 377–87.

17.

Dwyer

Knežević Cvelbar

Mihalič

Koman

2014. “Integrated Destination Competitiveness Model: Testing Its Validity and Data Accessibility.” Tourism Analysis 19 (1): 1–17.

18.

Dwyer

Armenski

Knežević Cvelbar

Dragićević

Mihalič

2014. “Achieving Destination Competitiveness: An Importance–Performance Analysis of Serbia.” Current Issus in Tourism. Published online August 11. doi:10.1080/13683500.2014.944487.

19.

EC (European Commission). 2010. EUROPE 2020. A European Strategy for Smart, Sustainable and Inclusive Growth. Brussels: European Commission.

20.

Enright

M. J.

Newton

2004. “Tourism Destination Competitiveness: A Quantitative Approach.” Tourism Management 25 (6): 777–88.

21.

Enright

M. J.

Newton

2005. “Determinants of Tourism Destination Competitiveness in Asia Pacific: Comprehensiveness and Universality.” Journal of Travel Research 43 (4): 339–50.

22.

FB (Future Brand). 2010. Country Brand Index. Future Brand.

23.

FB (Future Brand). 2011. Country Brand Index. Future Brand.

24.

FB (Future Brand). 2012. Country Brand Index. Future Brand.

25.

Glatzer

2012. “Cross-national Comparisons of Quality of Life in Developed Nations, Including the Impact of Globalization.” In The Handbook of Social Indicators and Quality of Life Research, edited by Land

K. C.

Michalos

A. C.

Sirgy

, 381–98. Rotterdam: Springer.

26.

Good Country Index. 2015. “The Good Country Index: A New Way of Looking at the World.” http://www.goodcountry.org/contact (accessed February 24, 2015).

27.

Gooroochurn

Sugiyarto

2005. “Competitiveness Indicators in the Travel and Tourism Industry.” Tourism Economics 11 (1): 25–43.

28.

Happy Planet Index. 2013 “Happy Planet Index 2012 Report: A Global Index of Sustainable Well-Being.” http://b.3cdn.net/nefoundation/d8879619b64bae461f_opm6ixqee.pdf (accessed September 18, 2015).

29.

Hassan

2000. “Determinants of Market Competitiveness in an Environmentally Sustainable Tourism Industry.” Journal of Travel Research 38 (3): 239–45.

30.

IL (International Living). 2008. Annual Global Index. International Living.

31.

IL (International Living). 2009. Annual Global Index. International Living.

32.

IL (International Living). 2010. Annual Global Index. International Living.

33.

IL (International Living). 2011. Annual Global Index. International Living.

34.

IL (International Living). 2012. Annual Global Index. International Living.

35.

Kester

Croche

2011. “Tourism Development in Advanced and Emerging Economies: What Does the Travel &Tourism Competitiveness Index Tell Us?” In Travel and Tourism Competitiveness Report, edited by Blanke

Chiesa

, 45–52. Geneva: World Economic Forum.

36.

Kline

R. B.

2010. Principles and Practice of Structural Equating Modeling. New York: Guildford.

37.

Kohler

2006. “The ‘Lisbon Goal’ of the EU: Rhetoric or Substance?” Journal of Industry, Competition and Trade 6 (2): 85–113.

38.

Kozak

2003. “Measuring Competitive Destination Performance: A Study of Spain and Turkey.” Journal of Travel and Tourism Marketing 13 (3): 83–110.

39.

Krugman

1994. “Competitiveness: A Dangerous Obsession.” Foreign Affairs March/April:28–44.

40.

LP (Lonely Planet). 2012. “Lonely Planet Data Base.” Lonely Planet.

41.

Mihalič

2000. “Environmental Management of a Tourist Destination: A Factor of Tourism Competitiveness.” Tourism Management 21 (1): 65–78.

42.

OECD (Organisation for Economic Co-operation and Development). 2005. Economic Policy Reforms 2005: Going for Growth. Paris: OECD Publishing.

43.

Omerzel Gomezelj

Mihalič

2008. “Destination Competitiveness—Applying Different Models, the Case of Slovenia.” Tourism Management 29 (2): 294–307.

44.

Porter

1990. The Competitive Advantage of Nations. New York: Free Press.

45.

Porter

M. E.

Delgado

Ketels

Stern

S. S.

2008. “Moving to a New Global Competitiveness Index.” In The Global Competitiveness Report 2008–2009, edited by Porter

M. E.

Schwab

, 43–64. Geneva: World Economic Forum.

46.

Porter

Kettels

2003. “UK Competitiveness: Moving to the Next Stage.” DTI Economics Paper No. 3, London: Department of Trade and Industry.

47.

Ritchie

J. R. B.

Crouch

G. I.

2003. The Competitive Destination : A Sustainable Tourism Perspective. Wallingford: Cabi Publishing.

48.

UNDP (United Nations Development Programme). 2008. Human Development Index. United Nations Development Programme.

49.

UNDP (United Nations Development Programme). 2009. Human Development Index. United Nations Development Programme.

50.

UNDP (United Nations Development Programme). 2010. Human Development Index. United Nations Development Programme.

51.

UNDP (United Nations Development Programme). 2011. Human Development Index. United Nations Development Programme.

52.

UNDP (United Nations Development Programme). 2012. Human Development Index. United Nations Development Programme.

53.

UNDP (United Nations Development Programme). 2014. Human Development Report 2014. Sustaining Human Progress: Reducing Vulnerabilities and Building Resilience. New York: United Nations Development Programme.

54.

UNWTO (World Tourism Organization). 2008. Compendium of Tourism Statistics. Madrid: United Nations World Tourism Organisation.

55.

UNWTO (World Tourism Organization). 2009. Compendium of Tourism Statistics. Madrid: United Nations World Tourism Organisation.

56.

UNWTO (World Tourism Organization). 2010. Compendium of Tourism Statistics. Madrid: United Nations World Tourism Organisation.

57.

UNWTO (World Tourism Organization). 2011. Compendium of Tourism Statistics. Madrid: United Nations World Tourism Organisation.

58.

UNWTO (World Tourism Organization). 2012. Compendium of Tourism Statistics. Madrid: United Nations World Tourism Organisation.

59.

WEF (World Economic Forum). 2008a. The Travel & Tourism Competitiveness Report. Geneva: World Economic Forum.

60.

WEF. (World Economic Forum). 2008b. The Global Competitiveness Report. Geneva: World Economic Forum.

61.

WEF (World Economic Forum). 2009a. The Travel & Tourism Competitiveness Report. Geneva: World Economic Forum.

62.

WEF (World Economic Forum). 2009b. The Global Competitiveness Report. Geneva: World Economic Forum.

63.

WEF (World Economic Forum). 2011a. The Travel & Tourism Competitiveness Report. Geneva: World Economic Forum.

64.

WEF (World Economic Forum). 2011b. The Global Competitiveness Report. Geneva: World Economic Forum.

65.

WEF (World Economic Forum). 2012a. The Travel & Tourism Competitiveness Report. Geneva: World Economic Forum.

66.

WEF (World Economic Forum). 2012b. The Global Competitiveness Report. Geneva: World Economic Forum.

67.

WEF (World Economic Forum). 2013. The Travel & Tourism Competitiveness Report. Geneva: World Economic Forum.

68.

WEF (World Economic Forum). 2015. The Travel & Tourism Competitiveness Report. Growth through Shocks. Geneva: World Economic Forum.

69.

Wiggins

Poi

2013. “Testing for Panel-Level Heteroskedasticity and Autocorrelation.” http://www.stata.com/support/faqs/statistics/panel-level-heteroskedasticity-and-autocorrelation/ (accessed September 18, 2015).

70.

Wooldridge

J. M.

2002. Econometric Analysis of Cross Section and Panel Data. Cambridge: MIT Press.

71.

World Bank. 2014. “How Does the World Bank Classify Countries? ” https://datahelpdesk.worldbank.org/knowledgebase/articles/378834-how-does-the-world-bank-classify-countries (accessed September 18, 2015).

72.

WTTC (World Travel & Tourism Council). 2011. Economic Data Search Tool. London: World Travel & Tourism Council.