Identifying Destination Health: Developing the Concepts of Market Indifference and Destination Dependence / Market Irrelevance

Abstract

This article develops a diagnostic tool to identify destinations “at risk” as a result of markets’ interest or lack thereof in a place. “At risk” is used in a similar manner to the medical profession’s use of the term “patients at risk” to identify those patients at risk of deterioration in order to trigger early intervention. Two situations are identified: market indifference, where markets as a whole show little interest in a destination, and destination dependence/market irrelevance, where destinations are overly reliant on markets that in the larger scheme of outbound travel show little interest in the destination. The study analyzed 162 destinations using UNWTO data. Almost 80 are affected potentially by one of these conditions. Most at-risk destinations are either small island countries, micro states, or countries with an underdeveloped tourism sector. Interestingly, no differences were found in the contribution tourism makes to GDP between seemingly at risk and not at risk places.

Keywords

tourism flows destination dependence market irrelevance market indifference

Introduction

Traditionally, “healthy” destinations were believed to be those that appealed to a large number of markets in order to spread the risk of one nonperforming market adversely affecting arrivals (Dupeyras and MacCallum 2013; Rodolfo, Domingo, and Agner 2010). A policy document written for the Organization for Economic Cooperation and Development, for example, notes “countries with a wide range of source markets and a focus on growth markets would receive higher scores [in destination well-being] than countries with a narrow market dependency focus” (Dupeyras and MacCallum 2013, p. 17). Appealing to a large number of markets has also been used as a metric to measure destination competitiveness (Águas, Vega, and Reis 2010; Croes 2010; Dupeyras and MacCallum 2013; Dwyer and Kim 2003; Enright and Newton 2004; ETC 2014; Hingten et al. 2015; Perles-Ribes, Ramón-Rodríguez, and Sevilla-Jiménez 2014). Conversely, destinations that rely on one or a small number of markets are often considered to be potentially at risk should arrivals from one or more markets fall (De Keyser and Vanhove 1994; Seaton 1996; Sirse and Mihalic 1999). This situation is especially prescient in these days of volatile global market conditions (Jalilian and Reyes 2014; WEF 2017).

But, this aphorism may not be entirely valid, for a review of UN World Tourism Organization (UNWTO) arrival figures documented in this article indicates that most destinations rely on a small number of core markets to generate the vast majority of their arrivals. Indeed, as will be argued, the need to draw visitors from a large number of markets to achieve a certain arrivals’ share threshold may indicate weakness and not strength, while reliance on a small number of markets is not ipso facto an indicator of an unhealthy destination, providing certain conditions are met. Instead, destination well-being, as viewed from the perspective of the number of markets destinations attract can best be assessed through a more systematic and holistic examination of the relationship between the importance a destination places on a market as a source of visitors and the concomitant importance the market places on the destination.

This article develops a global diagnostic tool to assess two scenarios where destinations may be “at risk” of losing their competitiveness. At risk here is used as an adjective in the same manner that the medical profession uses the term to identify hospital inpatients who are at risk and therefore may require early intervention to prevent further deterioration (Rees and Mann 2004; Worcestershire, n.d.). Here it is used in the context of enabling destinations to assess their market position and thus trigger interventionist marketing activities to abate a potential decline in their attractiveness. And just as the medical profession uses a “patient at risk score” to measure how at risk a patient is (Rees and Mann 2004, p. 698), this article also adopted a quantitative “destination at risk” score.

Destinations may be at risk from market indifference, where markets as a whole show little concentrated interest in visiting, or destination dependence/market irrelevance, where the destination is overly dependent on a source market and yet that market regards the destination as being largely irrelevant within the broader context of its overall outbound travel flows. In a worst-case scenario, a destination can be considered to be extremely at risk if it suffers from both market indifference and destination dependence/market irrelevance. Both concepts are developed and tested through an analysis of UNWTO data for 162 destinations. Countries or territories are the unit of analysis for both destinations and markets.

The article is organized as follows. To begin, a conceptual discussion of the two relationship scenarios is presented to argue the case for their importance in assessing destinations at risk. Both scenarios are new to the tourism literature and as such have no theoretical basis for their development. The method used to build the database is discussed next followed by two analytical phases: market indifference and destination dependence/market irrelevance. Discussion of the results follows, with conceptual and operational implications identified. The article concludes with suggestions for further research.

Destinations at Risk

Being at risk is conceptually different from risk. While risk can be used as a noun or verb to reflect exposure to danger, at risk is used as an adjective to express the possibility of danger or harm. At risk is used commonly in the tourism literature to describe situations where destinations or individuals may be subjected to loss or damage should certain events occur. Lane (1994) was one of the first to use the term to describe how the countryside could be at risk from unmanaged or ill-managed tourism because of the fragility of the natural environment, and large-scale economic development that upsets the local economy or powerful stakeholders. Dwyer et al. (2009) write about how destinations can be at risk if they fail to diversify their economies, while Pechlaner, Smeral, and Matzier (2002) write about the need for nearby, but less competitive, destinations to follow the lead of the more powerful destination or be at risk of losing consumers. Bosworth and Farrell (2011) write about how tourism development policies focusing on urban areas leave rural regions at risk of being marginalized. Others have written about how tourists can be at risk from crime (Baker, Page, and Meyer 2002) or the spread of disease if not properly immunized (Steffen 2005).

In the context of this study, being “at risk” relates to a potential deterioration on a nation’s competitive position as a tourism destination. A vast amount of research has been published examining tourism destination competitiveness at a country-level unit of analysis. These studies range from conceptual works identifying possible indicators for examination (Crouch 2011; Dwyer and Kim 2003; Kozak and Rimmington 1999; Seaton 1996), academic studies usually focusing on one or a small number of destinations that have developed and tested models (Águas, Vega, and Reis 2010; De Keyser and Vanhove 1994; Enright and Newton 2004), through to texts examining this subject (Ritchie and Crouch 2003) and finally some commercial studies such as the annual Travel and Tourism Competitiveness Report produced by the World Economic Forum (WEF 2017). Between 50 and more than 250 separate metrics have been developed, depending on the complexity of the model (Croes 2010). Yet, for the most part, these indicators have focused predominantly on supply-side factors, while largely excluding demand-side considerations. Demand-side considerations tended to be placed within the rather axiomatic context of the need to match products to target segments (Ritchie and Crouch 2003; Crouch 2011), which lies at the heart of basic marketing theory. As an example, Dwyer and Kim (2003) discuss how destinations with a more diversified product portfolio can attract a greater array of markets, and in doing so be more competitive.

Other market-oriented comments tend to be brief and rather simplistic, primarily because the focus on these studies is on the competitiveness of a destination, rather than the attractiveness of individual markets. De Keyser and Vanhove (1994), Seaton (1996), and Sirse and Mihalic (1999), for example, caution about the risk of market dependence, that is, a reliance on one or a small number of markets. McKercher (1995) writes about the need for a balanced portfolio of markets. Other organizations, like the OECD (Dupeyras and MacCallum 2013) and the UNWTO, indicate the benefits of market diversification in spreading the risk of one nonperforming market having a high impact on a destination (Rodolfo, Domingo, and Agner 2010). Smeral and Witt (2002) analyzed the life cycle stage of markets and indicated that destinations should focus on performing markets while avoiding nonperforming ones. Croes (2010) also comments on the emphasis of being able to attract more markets, but then cautions that this may result in a misleading impression that a destination has broad appeal, when in reality it may not be seen to have any special point of differentiation that makes it appealing to a smaller number of robust markets.

The following sections expand on the concepts of market indifference and destination dependence/market irrelevance.

In Search of the “Optimal” Number of Core Markets

Market indifference occurs when source markets, collectively, show little interest in visiting a destination. Most destinations, though, rely on a small number of markets to generate the vast majority of visitors, with fully half of the 162 destination countries/territories monitored by the UNWTO receiving at least 50% of arrivals from one or two source markets, and three quarters from four or fewer markets. Moreover, more than half the destinations analyzed in this article attracted at least 70% of their total arrivals from five or fewer markets while 80% relied on 10 or fewer markets to generate this level of arrivals.

The reasons for reliance on a small number of markets are manifold, but the decaying effect of distance on demand is, arguably, most critical. Tourists are rational consumers and if their needs can be met close to home, then they have no reason to incur the extra costs, effort, and cultural uncertainty of traveling to faraway places. McKercher, Chan, and Lam’s (2008) study of global tourist movements revealed that 56% of international tourism flows are between source markets and destinations that share a land border, whereas 80% of all tourism activity occurs to nations within 1,000 km of a source economy. Other factors such as the lingering impact of colonial ties (McKercher and L’Espoir DaCosta 2007) as well as deep ethnic, religious, and cultural ties (Dwyer and Kim 2003) also influence persistence of such patterns. These patterns vary little over time (Lee et al. 2012), with observed minor differences attributed to changes in the general economic conditions (Croes, Ridderstaat, and Rivera 2018; Wong, Fong, and Law 2016). Indeed, Lorde, Li, and Airey (2016) attribute such stability to habit persistence, as much as loyalty.

Chen and his colleagues (Chen and Chen 2012; Jang and Chen 2008; Chen, Jang, and Peng 2011) developed and tested a model to identify the optimal number of markets destinations need to pursue to achieve different objectives. Their model was based on the application of the financial portfolio approach theory. Portfolio theory suggests that investors seek the most efficient returns by either minimizing the risk for a given level of expected return or maximizing the expected return for a given level of instability (Chen and Chen 2012). Zhang, Botti, and Petit (2016) used the directional distance function in mean-variance spaces to calculate the optimal portfolio share of a destination in France while Botti, Goncalves, and Ratsimbanierana (2012) applied the mean-variance shortage function approach in helping the French destination management organization to minimize the inbound tourism instability. Chen and his colleagues tested the model using different risk/reward scenarios for the United States (Chen and Chen 2012), Japan (Chen, Jang, and Peng 2011), and Taiwan (Jang and Chen 2008). These studies revealed that the optimal number of target markets ranged from a low of one or two for high-risk/high-return scenarios through to between two and five for a medium-return/medium-risk scenario and up to six for a lowest-risk/best-reward scenario.

Table 1 summarizes these and other studies that also sought to identify optimal market numbers. The methods used varied from simple counts needed to generate a large majority of visitors through to more sophisticated scenario models identified above. However, the results are quite consistent. On average, somewhere between three and six source markets seems to be the ideal number depending on the location of the destination in question. Proximity to land neighbors with a large, affluent population may reduce this number, while isolation, as in the case of Australia, or being situated in an emerging economic zone, as in the case of Asian destinations, may result in a higher figure.

Table 1.

Optimal and/or Practical Number of Tourism Markets.

Destination	Number of Markets	Source
Australia	Up to 15	Valadkhani and O’Mahony (2018)
Croatia	4 to 7	Ivanovoc, Bogdan, and Bareas 2018
Czech Republic	5	Malachovský and Királová (2015)
Europe	3 to 5 per country	ETC (2014)
Japan	4	Andonian et al. (2016)
Japan	1, 5, or 6 depending on scenario	Chen, Jang, and Peng (2011)
Macao	4	Loi (2004)
Malaysia	12	Tang and Tan (2013) Lean and Smyth (2008)
Philippines	3 to 5	Rodolfo, Domingo, and Agner (2010)
Portugal	3 to 5	Águas, Veiga, and Reis (2000)
Slovakia	5	Malachovský and Királová (2015)
Southern Europe	3 per country	ETC (2014)
Southern Europe	2, 4, or 9 depending on scenario	Águas, Veiga, and Reis (2010)
Taiwan	1 to 4 depending on scenario	Jang and Chen (2008)
Turkey	Up to 10	Ozcan and Erdogan (2017)
USA	2 to 4 depending on scenario	Chen and Chen (2012)

Pike and Mason (2011) suggest the need to attract a large number of markets to reach arrivals’ thresholds is a signal of market failure, whereby the destination has not achieved top of mind status in its core markets. Dupeyras and MacCallum (2013) complement this view by indicating it is an indicator of the failure to create a clear brand position. King (2017) offers a worst-case scenario where the place is seen as a me-too brand, which may reflect uncertainty on what experience is on offer, leading to a lack of interest. Another school of thought suggests the reluctance of core markets to visit is a function of an increase perceived risk, leading to a decline in repeat arrivals that will have to be replaced by targeting new markets that may not be familiar with the destination (Çetinsöz and Ege 2013; Chew and Jahari 2014; Kozak and Rimmington 1999). Either scenario presents major challenges for Destination Marketing Organizations (DMO). They have limited resources and for pragmatic reasons must focus their activities on selected core markets that provide the best returns while investing less in other markets (Mazanec, Wöber, and Zins 2010). Market failure may require a complete and costly rethink of the marketing strategy, including rebranding and repositioning. Resolving loss of consumer confidence may be an even more challenging task for it may involve convincing existing markets to return, while enticing new markets that might be nervous about a destination to visit.

Dependency—A Share Approach

Destination dependence/market irrelevance occurs when destinations rely on a small number of markets that show little interest in the destination. Dependence may also be a sign of an unhealthy relationship (Sirse and Mihalic 1999). Concerns about destination dependence are pervasive in much of the tourism literature (Weaver 2017), especially in small island destinations (Croes 2010; Hingten et al. 2015; Hoti, McAleer, and Shareef 2005; Podhorodecka 2017), in eastern European countries such as Slovenia (Sirse and Mihalic 1999; Gomezelj and Mihalic 2008) and Croatia (Hendija 1999) in the aftermath of the collapse of the Soviet Union; among Latin American destinations, including Mexico (ILO, n.d.) and Cuba (Hingten et al. 2015), and in some Asian (Rodolfo, Domingo, and Agner 2010), and African (UNECA 2013) destinations.

Again, the causes are manifest (Croes 2010; Dwyer and Kim 2003; Hoti, McAleer, and Shareef 2005), but they can generally be attributed to peripherality and the associated higher travel time and cost commitments that limit demand (Chaperon and Bramwell 2013). Shanaman (2015) indicates that this issue is especially prescient for destinations that are reliant on long-haul markets. As an example, Hingten et al. (2015) cite Cuba’s reliance on the Canadian and European markets as a potential sign of weakness, should either or both regions face financial difficulties. Ivars-i-Baidal, Rodrigues-Sanchez, and Vera-Robollo (2013), writing about Benidorm, Spain, observed how a combination of economic recession and unfavorable exchange rates led to a 30% decline in the dominant British market in two years. The UNWTO (2003) further noted that the downturn in international tourism in the early 2000s was caused by the combination of economic and political uncertainty in the aftermath of the 9/11 terrorist attacks that made people travel for shorter periods of time and staying closer to home.

However, as noted earlier, reliance on one or a small number of markets is not axiomatically a sign of an unhealthy relationship, especially if the origin and destination share a land border or if each is a substantive origin and destination in its own right (Águas, Veiga, and Reis 2010). Instead, we argue that the relative relationship between origin and destination is a better indicator. This relationship can be quantified by calculating the ratio between inbound and outbound share. Ritchie and Crouch (2003) argue, ideally, both market and destination must be comparable, in order to achieve optimal efficiency, even though they did not specify a quantitative relationship.

While arrival share is a well-documented indicator used in many competitiveness studies (Croes 2011; Dupeyras and MacCallum 2013; ETC 2014; Hingten et al. 2015; Perles-Ribes, Ramón-Rodríguez, and Sevilla-Jiménez 2014), departure share is rarely if ever considered. Yet, adding a departure share component provides a much more robust indicator of where the destination ranks in the collective minds of the market. The China-Mongolia pair is used as an example to indicate how the use of both arrival and departure share can provide deeper insights. In 2016, Mongolia attracted roughly 186,000 tourists from China. This figure accounts for about 39.5% of all arrivals, and yet, represented only about 0.14% of departures from China, yielding a destination dependence/market irrelevance score of 286.88. This score indicates that Mongolia is proportionately far more reliant on the China market, while Chinese tourists as a whole have little interest in visiting here.

Destination dependence/market irrelevance occurs when this relationship is out of balance. Of course, like most things in tourism, few absolutes apply, for the ratio is a function of both the denominator as indicated by the size of the outbound market and numerator as reflected by the capacity of the destination’s tourism sector. As a result, the interpretation of any destination dependence/market irrelevance ratio must be made with caution to avoid Type I errors. A high score should be expected when residents of a populous outbound market visit a small destination with limited bed capacity, while scores for the same source market visiting a large and well-developed destination should be lower. One would, therefore, expect, a higher score for Americans traveling to a small Caribbean island, than travel by Americans to Canada, for example.

The ratio score may be indicative of a potential problem, especially if like is compared to like. A relationship that reveals roughly equivalent arrival and departure shares is an indicator of balance. One where the arrival share is less than the departure share may indicate future growth opportunities. If, on the other hand, the arrival share is much higher than the respective departure share, then it may signify overreliance on a market that may care little about the destination.

Destinations that record a high destination dependency/market irrelevance score are potentially at risk if conditions change in the source market that result in a reduction or change in outbound tourism flows, for even a small decline in arrivals to destinations with high destination dependency/market irrelevance can be devastating. As an example, between 2008 and 2009, UNWTO figures indicate that outbound travel from the United States fell by about 2.5% as a result of the Global Financial Crisis, but arrivals to the Barbados fell by three times as much, or by about 8%. This situation was observed also in the aftermath of the 9/11 terrorist attacks, the outbreak of SARS, and associated economic uncertainty in the early 2000s (UNWTO 2003) as well as in other places in the aftermath of the 2008 global financial crisis (Papatheodorou, Rosselló, and Xiao 2010; Song and Lin 2010). In a similar manner, adverse fluctuations in exchange rates in key source markets has also had an effect on departures (de Vita 2014). People still travel in both scenarios, but they travel less frequently, for shorter periods of time, and tend to stay closer to home (UNWTO 2003).

Method

This study adopted the empirical-analytical approach that is data driven (Hultgren and Coomer 1989). Secondary data are the main source of data in this article. Descriptive data analysis was the major tool in calculating the data set, for example, calculation of the mean scores of different regions as indicators of market indifference. Ratios of arrival share and departure share were computed using inbound and outbound data. This statistical information was identified, calculated, and analyzed for discussions to ground the proposed concepts of market indifference and destination dependence/market irrelevance.

Arrival and departure data were derived from official UNWTO statistics for 2016, the most current year available. If 2016 data were not available, data from the most recent year back to no earlier than 2012 were used. Destinations were included where detailed country/territory-specific arrival data were available. Although the UNWTO documents tourism activity in 222 economies, reliable arrival data were available for only 162 territories, countries, or economies (Please note that not all places monitored by the UNWTO are independent countries. Some, like Hong Kong and Macau, are Special Administrative Regions of China that were allowed to retain membership in the World Trade Organization and remain as separate customs territories with control over who can and cannot enter. For ease of discussion, the term destination will be used to avoid confusion). They represent the sample for this study and accounted for 1.26 billion arrivals or almost all recorded for the year. As indicated in Table 2, destinations are located in all geographic regions. Further, the coverage is comprehensive, including destinations that attracted few visitors (i.e., Kiribati with 5,700 arrivals and Niue with 7,100), through to the top four receiving countries of Spain, France, the USA, and China that each attracted more than 75 million arrivals. The sample also includes micro-states with limited accommodation capacity (such as San Marino, Liechtenstein, and Timor-Leste) through to highly developed European and North American destinations with large capacities.

Table 2.

Destination Region.

Region	Count
Africa	18
Atlantic / Mediterranean island	3
Australia / New Zealand	2
Canada / USA	2
Caribbean island	21
Central America	8
Asia	18
Eastern Europe	15
Former Soviet Republic	10
Indian Ocean island	6
Middle East	7
Pacific island	18
South America	12
Western Europe	22

A database was created that listed total arrivals to each destination, arrivals from each of up to the top five source markets per destination, and the number of markets required to reach 50% and 70% of arrivals, respectively. In some cases, fewer than five source markets are reported in official statistics. For example, 87% of all arrivals to Andorra originate in either Spain or France. Likewise, almost 84% of all arrivals to Bermuda come from the United States, Canada, and the United Kingdom. A total of 142 source markets, representing 798 origin–destination pairs were identified in this exercise. Departure data from source markets was then documented, where those data were available. This information was gleaned primarily from UNWTO data, using “tourist” departures (overnight departures) where available or all departures (day and overnight) where overnight data were not reported. A search of National Tourism Organization statistical databases was undertaken to identify outbound travel where no data were reported to UNWTO. Reliable departure information was identified from 94 source markets that represented 92% of origin–destination pairs. No data were available for 48 source markets, primarily among African or small island nations. These missing cases represented only 8% of all origin–destination pairs. Arrival shares were reported as they appeared in the UNWTO reports.

Departure shares were calculated in one of the two ways to control for the distortionary impact travel to land neighbors may have on outbound figures. “All departures” were used for island source markets (such as Australia and Japan) and also for calculating scores for destinations that shared a land border or that were within 500 km of the source market’s gateway. In all other cases, a modified outbound volume figure was calculated by deducting travel to land neighbors from the total outbound figure. The use of this modified figure provides a more reliable indicator of the share of medium- to long-haul travel attributed to a source market. For example, using the United States as an example, the calculation of a modified outbound score by subtracting departures to Canada and Mexico reduces the volume of American outbound from 73.5 million to 33.4 million person-trips. An even more drastic reduction was noted in the case of France where excluding travel to land neighbors reduced the outbound travel volume from 29.6 million to 5.8 million departures. The ratio between arrival and departure shares was then calculated by dividing the arrival share by the respective market’s relevant departure share.

Arrival share is as reported by the destination. These data were augmented by additional information gained primarily from UNWTO sources, such as mode of transport used to reach the destination, trip purpose, annual occupancy rates, the number of beds available, and mean length of stay. (Please note, these data present aggregate figures for the destination and are not controlled for land neighbors as are departure data.) The contribution tourism made to the destination’s GDP was retrieved from World Travel and Tourism Council (WTTC) reports and again supplemented by official national data in cases not covered by the WTTC. Finally, destination competitiveness scores and ranks derived from the Travel and Tourism Competitiveness Report (WEF 2017) were included. This report provides information on only 110 of the 162 destinations included in the study and therefore must be treated cautiously.

Findings

Table 3 documents the frequency that individual source markets appear as a top five market globally, as well as by destination region. The United States of America appears as a key source market in more than half of all destinations analyzed, followed by the United Kingdom, Germany, France, and China, which appear in one-quarter or more of cases. In total, these five markets were identified 290 times and alone account for 36% of the almost 800 top five total origin–destination pairs. When other markets that appeared in at least 10% of cases are included (Canada through to Australia), these 10 source markets account for 50.2% of all top five source market appearances. The United States is an especially vital market for the Americas and Oceania, appearing among 85% of destinations, while Germany and the United Kingdom are dominant European markets appearing in almost 80% and 60% of destinations, respectively. China is a dominant Asian market while Australia is one of the most influential source markets for the Oceania region. Brazil and Argentina are important regional sources of tourists in Central and South America, while Korea, South Africa, and New Zealand are key markets within their respective regions.

Table 3.

Most Popular Source Markets (among Top Five Source Markets).

	Destination Region, n (%)
Source Market	Americas(n = 43)	Europe(n = 46)	Asia(n = 33)	Oceania(n = 16)	Africa(n = 24)	All
United States of America	36 (83.7)	15 (32.7)	15 (45.5)	14 (87.5)	9 (37.5)	89 (54.9)
United Kingdom	17 (39.5)	27 (58.7)	6 (18.2)	5 (31.3)	8 (33.3)	63 (38.9)
Germany	5 (11.6)	35 (76.1)	3 (9.1)	1 (6.3)	8 (33.3)	52 (32.1)
France	14 (32.5)	16 (34.7)	5 (15.2)	2 (12.5)	10 (41.7)	47 (29.0)
China	2 (4.6)	2 (4.3)	23 (70.0)	8 (50.0)	4 (16.7)	39 (24.1)
Canada	24 (55.8)	1 (2.1)	2 (6.1)	1 (6.3)	0	28 (17.3)
Italy	5 (11.6)	15 (32.7)	1 (3.0)	0	2 (8.3)	23 (14.2)
Japan	1 (2.3)	0	12 (36.4)	10 (62.5)	0	23 (14.2)
Russian Federation	0	13 (28.2)	8 (24.2)	1 (6.3)	1 (4.1)	23 (14.2)
Australia	0	0	5 (15.2)	13 (81.3)	1 (4.1)	19 (11.7)
Other important regional markets
Brazil	14 (32.5)					16 (9.9)
Korea, Republic of			13 (39.4)	2 (12.5)		16 (9.9)
New Zealand				12 (75.0)		13 (8.0)
Netherlands		8 (17.4)				12 (7.4)
Argentina	10 (23.2)					10 (6.2)
Poland		9 (20.0)				10 (6.2)
South Africa		1 (2.1)			9 (37.5)	10 (6.2)
Turkey			5 (15.2)			10 (6.2)

This finding illustrates how most destinations are competing for a share of the same market, which can be problematic if a perception exists that the product is largely undifferentiated. This situation has been observed by both Daye (2010) in the Caribbean and Berno and Douglas (1998) in the South Pacific, where the generic “Caribbean” and “Polynesian” brands are more ubiquitous than national brands, especially among lesser known destinations that offer generic sun, sand, and sea holidays. This blending of images and associations makes it difficult for destinations to stand out.

Market Indifference

The literature suggests the optimal number of markets for a destination averages somewhere between three and six, depending on the nature of the destination and its location. An analysis of arrivals’ share for the 162 destinations studied here reveals that the mean number of markets required to achieve 50% of arrivals is 3.2, with a standard deviation of 2.1, while the mean number of markets required to reach 70% of arrivals is 6.4, with a standard deviation of 4.8. These results correspond closely to the “optimal” number of markets identified in Table 1, and as such, it can be argued the number of markets required to achieve a 50% and 70% arrival threshold is a reasonable proxy for the optimal number of markets. Águas, Vega, and Reis (2010) suggest one standard deviation from the mean as a sign of vulnerability. This threshold will be applied in this study, again to avoid Type I errors.

Table 4 identifies the mean number of markets and the standard deviation for 50% and 70% of arrivals in five regions: the Americas, Europe, Asia, Oceania, and Africa. Applying the one standard deviation threshold at a regional level reveals 34 destinations could be suffering from a degree of market indifference, with fully 20 failing to meet the norms for both the 50% and 70% thresholds. Thus, a market indifference score of one standard deviation from the mean is used to identify destinations at risk, while a score of two or more standard deviations from the mean identifies places that are very at risk.

Table 4.

Indicators of Market Indifference.

50% Arrival Threshold	70% Arrival Threshold
Americas (mean 2.1 markets, SD = 1.5)	Americas (mean 4.6 markets, SD = 3.9)
Moderate market indifference (4 or 5 markets)	Moderate market indifference (9–12 markets)
Argentina (4 markets) Brazil (4) Cuba (5) Ecuador (4) Nicaragua (4) Panama (4) Venezuela (4)	Bolivia (9) Brazil (9) Dominican Republic (10) Ecuador (9) Panama (10)
Large market indifference (≥6 markets)	Large market indifference (≥13 markets)
Colombia (8)	Colombia (19) Cuba (13)

Europe (mean 4.4 markets, SD = 2.1)	Europe (mean 9.2 markets, SD = 5.4)
Moderate market indifference (7 or 8 markets)	Moderate market indifference (15–19 markets)
Bosnia and Herzegovina (7) Czech Republic (7) Germany (8) Finland (7) Romania (7) United Kingdom (7)	Bosnia and Herzegovina (15) Germany (15) Serbia (15)
Large market indifference (≥9 markets)	Large market indifference (>19 markets)
Serbia (9) Turkey (11)	Turkey (35)

Asia (mean 2.8 markets, SD = 1.7)	Asia (mean 5.7 markets, SD = 3.6)
Moderate market indifference (5–7 markets)	Moderate market indifference (10–12 markets)
Lebanon (7) Maldives (5) Nepal (6) Singapore (5) Sri Lanka (5) Thailand (6)	Indonesia (10) Lebanon (12) Maldives (12) Nepal (11) Thailand (12)

Oceania (mean 2.1, SD = 0.9)	Oceania (mean 4.2 markets, SD = 2.2)
Moderate market indifference (3 markets)	Moderate market indifference ( 7 or 8 markets)
Kiribati (3) Micronesia (3) Solomon Islands (3)	Kiribati (7)
Large market indifference (≥4 markets)	Large market indifference (≥9 markets)
Australia (5)	Australia (10)

Africa (mean 3.4 markets, SD = 2.2)	Africa (mean 6.7 markets, SD = 4.4)
Moderate market indifference (6 or 7 markets )	Moderate market indifference (12–16 markets)
Burkina Faso (7) Mali (7) Nigeria (6) Seychelles (6) Sierra Leone (6)	Burkina Faso (14) Mali (14) Sierra Leone (12) Tanzania (16)
Large market indifference (≥8 markets)	Large market indifference (>16 markets)
Egypt (8) Tanzania (8)	Egypt (17)

Destinations registering potentially high market indifference scores could be found in all regions, with emerging Eastern European and to a lesser degree Latin American destinations appearing frequently, along with emerging African and Pacific Island destinations. Some anomalies appear on this list that may reflect conditions other than market indifference. For example, Australia is an anomalous case in the Oceania region. It is the largest, most populous, and most popular destination in this region, attracting some 8.2 million of the 14.1 million visitors (or almost 60% of all arrivals) to Oceania. It is also the largest single source market for the region that alone serves to lower the mean number of markets regional markets need to reach the threshold. Likewise, Germany may also represent another anomalous case, due primarily to the fact it shares land borders with nine countries and is within 500 km of another seven countries. The United Kingdom may also fall into this category. All three can be excluded from further analysis.

Market-indifferent destinations share three common features. The first is their heavy reliance on air travel as a means of access, with statistically significant differences noted in the share of visitors that arrive by air between these and non–market-indifferent destinations (t = 3.457, p = .001). On average, close to three-quarters of arrivals (74.1%) to these destinations came by air, compared to little more than half (51.2%) to other destinations. Destinations that appeared in both volume categories recorded an even higher 80% share of arrivals by air.

Additionally, those places that registered the largest number of markets to reach the 70% threshold have undergone extended periods of political turmoil. Such destinations include Egypt, Turkey, Thailand, Nepal, the Maldives, Brazil, and Colombia. Egypt, for example, has seen a 50% decline in arrivals since 2012. Russia, Germany, and the United Kingdom, its three largest markets in 2012, now rank 24th, 1st, and 7th, respectively. A similar situation has been observed in the case of Turkey, where arrivals from Western European and Russian markets have fallen by more than seven million since 2012, while smaller source markets have shown an increase in visitation but not enough to offset this decline.

Third, market volatility, leading to drastic declines in departures from key outbound markets is also a trigger. In particular, the collapse of the ruble and the resulting decline in the outbound Russia market, has had significant consequences on Asian, Middle Eastern, and Eastern European destinations. They have been unable to attract new markets or draw more visitors from existing markets to replace the Russian market.

Destination Dependence / Market Irrelevance

Destination dependence/market irrelevance occurs where a destination is overly reliant on a source market, and yet that source market sees it as being largely irrelevant within the context of total outbound travel. The destination dependence/market irrelevance score was calculated by dividing inbound share by outbound share. This study applied two conditions when analyzing results to avoid Type I errors. First, since the focus is on arrivals from major markets, only those markets that generated 20% or more of arrivals were included. Second, a conservative approach was adopted when analyzing the destination dependence/market irrelevance score. Here, a ratio score of at least 20 was used as the threshold to identify an at-risk relationship, while a score of greater than 100 identifies a place very at risk.

Table 5 lists 49 destinations with 57 destination–market pairs where evidence of destination dependence/market irrelevance exists. A total of 32 cases were identified with scores of between 20 and 99, with an additional 25 cases with scores of 100 or more. The first two columns identify the destination and its chief characteristic. The third and fourth columns show the source market, with its rank in terms of arrivals in parentheses and the share of inbound arrivals attributed to that market. The fifth column shows the share of outbound tourism from the market. Outbound shares based on all departures are highlighted in the table with the superscript letter a. In all other cases, a modified outbound volume figure was calculated by deducting travel to land neighbors from the total outbound figure. The sixth column shows the dependence/irrelevance ratio. The last column shows the relationship between the source market and the destination.

Table 5.

Ratio of Arrival Share versus Departure Share.

Destination	Type of Destination	Market (and Rank)	Inbound Share	Outbound Share^a	Ratio Inbound/Outbound Share	Relationship
American Samoa	Small island	USA (2)	21.70	0.01	16,649.2	Former or current colony
Anguilla	Small island	USA (1)	63.74	0.15	421.3	USA to Caribbean
Antigua and Barbuda	Small island	UK (2)	28.87	0.13	222.2	Former or current colony
Antigua and Barbuda	Small island	USA (1)	41.00	0.32	126.0	USA to Caribbean
Armenia	Central Asia	Russian Federation (1)	63.49	2.53^a	25.1	Near neighbor
Aruba and Curacao	Small island	USA (1)	44.36	2.05	21.6	USA to Caribbean
Bahamas	Small island	USA (1)	78.22	3.47	22.5	USA to Caribbean
Barbados	Small island	UK (1)	33.82	0.36	93.2	Former or current colony
Barbados	Small island	USA (2)	23.44	0.44	52.8	USA to Caribbean
Belarus	Former Soviet republic	Russian Federation (1)	78.77	0.54^a	145.8	Land neighbor
Belize	Central / South America	USA (1)	55.75	0.64	86.5	USA to Central America
Bermuda	Small island	USA (1)	64.41	0.47	136.8	USA to Caribbean
British Virgin Islands	Small island	USA (1)	59.64	0.65	91.2	USA to Caribbean
Brunei	Small nation	Malaysia (1)	24.85	0.05^a	50.7	Land neighbor
Cabo Verde	Small island	UK (1)	22.04	0.22	98.5	UK to Atlantic island
Cayman Islands	Small island	USA (1)	77.98	0.90	86.6	USA to Caribbean
Comoros	Small island	France (1)	47.59	0.22	216.0	Former or current colony
Dominica	Small island	USA (1)	24.80	0.06	426.1	USA to Caribbean
El Salvador	Central / South America	USA (2)	31.55	1.36	23.3	USA to Central America
French Guiana	Central / South America	France (1)	65.94	2.18	30.2	Former or current colony
French Polynesia	Small island	USA (1)	34.21	0.19	173.8	USA to Pacific island
French Polynesia	Small island	France (2)	20.31	0.67	30.2	Former or current colony
Grenada	Small island	USA (1)	33.13	0.13	247.2	USA to Caribbean
Haiti	Small island	USA (1)	75.80	1.06	71.7	USA to Caribbean
Honduras	Central / South America	USA (1)	31.35	0.83	37.9	USA to Central America
Iceland	Atlantic	Germany (1)	23.49	0.80	29.2	Germany to Atlantic island
Kiribati	Small island	USA (1)	21.57	0.004	28,259.7	USA to Pacific island
Liechtenstein	Small nation	Germany (2)	24.19	0.02	853.1^a	Near neighbor
Liechtenstein	Small nation	Switzerland (1)	27.95	0.12	226.6^a	Land neighbor
Maldives	Small island	China (1)	25.22	0.63	40.0	China to Indian Ocean island
Mali	Africa	France (1)	27.54	0.82	33.5	Former or current colony
Malta	Small island	UK (2)	28.48	0.095	30.0	Former or current colony
Micronesia	Small island	USA (1)	23.16	0.02	1129.8	USA to Pacific island
Moldova	Small nation	Romania (1)	24.64	0.19^a	132.9	Land neighbor
Monaco	Small nation	France (1)	22.46	0.28^a	79.3	Land neighbor
Mongolia	Asia	China (1)	39.45	0.14^a	286.9	Land neighbor
Myanmar	Asia	China (1)	52.9	1.14^a	22.7	land neighbor
New Caledonia	Small island	France (1)	31.75	0.63	50.0	Former or current colony
Niue	Small island	New Zealand (1)	78.27	0.21^a	368.9	Former or current colony (protectorate)
Northern Marianas Islands	Small island	China (1)	43.37	0.44	97.0	China to Pacific island
Northern Marianas Islands	Small island	Korea (2)	39.35	0.93^a	42.2	Korea to Pacific island
Pakistan	Asia	UK (1)	31.68	0.52	61.0	Former or current colony
Palau	Small island	China (1)	46.95	0.13	372.9	China to Pacific island
Palau	Small island	Japan (2)	21.12	0.17^a	124.0	Japan to Pacific island
Papua New Guinea	Asia	Australia (1)	44.57	0.89^a	50.2	Former or current colony
Saint Lucia	Small island	USA (1)	43.89	0.46	95.9	USA to Caribbean
Saint Maarten	Small island	USA (1)	50.49	0.80	63.2	USA to Caribbean
Saint Vincent and the Grenadines	Small island	UK (2)	21.20	0.03	749.1	Former or current colony
Saint Vincent and the Grenadines	Small island	USA (1)	29.01	0.07	423.5	USA to Caribbean
Solomon Islands	Small island	Australia (1)	41.13	0.09^a	428.0	Australia to Pacific island
Suriname	Central / South America	Netherlands (1)	39.94	0.88	45.3	Former or current colony
Timor-Leste	Small island	Indonesia (1)	27.17	0.22^a	122.3	Land neighbor
Tonga	Small island	Australia (2)	20.18	0.12^a	168.0	Australia to Pacific island
Tonga	Small island	New Zealand (1)	43.72	0.99^a	44.2	Former or current colony (protectorate)
Trinidad and Tobago	Small island	USA (1)	42.58	0.52	81.4	USA to Caribbean
Turks and Caicos	Small island	USA (1)	80.65	1.10	73.5	USA to Caribbean
Vanuatu	Small island	Australia (1)	52.32	0.50^a	104.4	Australia to Pacific island

Based on all departures from source market. All other outbound share figures based on modified departures that exclude travel to land neighbors.

Small island destinations represent two-thirds of cases where a destination dependence/market irrelevance situation may be evident and 21 of the 25 cases where the score was at least 100. They along with small, enclave microstates in Europe and Asia, a number of Central and South American nations, and some former Soviet Republics complete this set. The most extreme cases are found in the undeveloped Pacific Island destinations of Kiribati, American Samoa, and Micronesia, where the ratio exceeds 1,000. The United States is the primary market for 20 of these destinations, including 15 Caribbean destinations. Current or former colonial relationships are also evident in at least 15 cases, especially involving outbound from France and the United Kingdom, and to a lesser extent from Australia and New Zealand to former protectorates or colonies in the South Pacific. It is also noteworthy that 15 of the 20 Caribbean destinations examined scored high destination dependence/market irrelevance ratios. The only exceptions are Cuba, the Dominican Republic, Guadeloupe, Jamaica, and Martinique.

Comparing Market-Indifferent, Destination-Dependent/Market-Irrelevant and Seemingly Healthy Destinations

Table 6 compares key features across three types of destinations identified in this article: those where market indifference is evident; those demonstrating a high degree of destination dependence/market irrelevance, and those that demonstrated neither condition. Based on this analysis, almost half of the 162 destinations examined in this study can be considered as having potentially unhealthy relationships with their source markets, either due to market indifference or an over-reliance on a market. Moreover, five destinations, Micronesia, Kiribati, the Maldives, Mali, and the Solomon Islands, showed signs of both market indifference and destination dependence/market irrelevance.

Table 6.

Comparison among Destinations That Showed “Market Indifference,” “Destination Dependence/Market Irrelevance,” and Neither Condition.

	Market Indifference(n = 31)	Destination Dependence / Market Irrelevance (n = 49)	Seemingly Not at Risk (n = 87)	All	Test Scores
Count, n (%)	31 (19.1)	49 (30.2)	87 (53.7)	162 (included double counts)
All arrivals (mean in millions)	4.7	5.2	12.9	7.6	F = 9.702, p <.001
Number of markets needed to reach 50% of arrivals (mean)	5.6	2.1	2.7	3.1	F = 35.957, p <.001
Number of markets needed to reach 70% of arrivals (mean)	10.3	3.0	5.1	5.5	F = 17.960, p <.001
Competitiveness	n = 23(11 missing, or 32.3%)	n = 11(39 missing or 78.0%)	n = 58(26 missing or 30.9%)
Overall competitiveness rank (mean)	67.3	73.7	47.3	55.5	F = 4.421, p = .015
Overall competitiveness score (mean)	3.8	3.7	4.2	3.9	F = 4.794, p = .011
Business environment (mean)	4.2	4.6	4.8	4.6	F = 7.301, p = .001
HR and labor market (mean)	4.4	4.7	4.9	4.8	F = 8.746, p <.001
Price competitiveness (mean)	5.0	4.5	4.7	4.7	F = 2.233, p = .211
Tourism services infrastructure (mean)	4.1	4.3	4.7	4.5	F = 3.000, p = .055
Other indicators
Tourism as a % of GDP (mean)	6.7(n = 27)	11.1(n = 33)	8.9(n = 67)	9.0(n = 127)	F = 0.876, p = .423
Ave length of stay (nights)	4.7(n = 18)	6.7(n = 19)	3.4(n = 45)	4.5(n = 82)	F = 5.490, p = .006
Trip purpose for leisure (% mean)	70.0(n = 24)	59.1(n = 33)	54.2(n = 58)	58.9(n = 115)	F = 3.525, p = .033
% arriving by air (mean)	68.5(n = 25)	64.0(n = 33)	52.2(n = 57)	59.2(n = 115)	F = 2.693, p = .073
% arriving by water (mean)	5.5(n = 25)	22.9(n = 33)	5.4(n = 57)	10.5(n = 115)	F = 12.415, p <.001
% arriving by land (mean)	26.0(n = 25)	13.0(n = 33)	43.3(n = 57)	30.3(n = 115)	F = 9.049, p <.001

Destinations that showed a degree of market indifference were typically found in the less developed regions of as Africa, Central and South America, and in parts of Eastern Europe. As a group, they generated the smallest mean number of arrivals. Those places that showed a high level of destination dependence/market irrelevance tended to be smaller island economies that were overly reliant on the US market, or former British and French colonies that still relied heavily on these source markets. Conversely, the 87 destinations that had seemingly healthy relationship with markets were disproportionately located in developed western or Asian economies. As a cohort, these economies generated an average of between two and almost three times as many arrivals as members of the other two groups. Yet, no significant differences were observed in the total share of GDP (direct, indirect, and induced) attributed to tourism.

Travel patterns did vary significantly by group, though. A large minority of tourists traveling to countries that had seemingly healthy relationship with markets arrived by land, while almost two-thirds of arrivals to members of the other groups arrived by air. Notably, as well, almost one-quarter of arrivals to destination-dependent/market-irrelevant destinations came by ship, indicated their reliance on the cruise sector. Cruises may generate many visitors, but the jury is still out as to the extent of economic benefits. Differences were noted in trip purpose and length of stay with market-indifferent destinations attracting a higher share of pleasure tourists. The mean length of stay for destination-dependent/market-irrelevant destinations was about one week, while it was only about half that for healthy places.

Other differences were noted in their competitiveness. The most striking feature is that almost 80% of the destination-dependent/market-irrelevant destinations were not included in the Travel and Tourism Competitiveness Report 2017 (WEF 2017), suggesting many may be seen as being unimportant tourism places. While about one-third of market-indifferent and seemingly healthy destinations were not included in the report, fully 39 destination-dependent/market-irrelevant destinations were omitted. Omissions included 13 Caribbean and 13 Pacific Island destinations. Comparison of competitiveness measures between groups, therefore, must be seen as indicative and not definitive, as a result of the large number of omissions. Unsurprisingly, healthy destinations were seen to be more competitive (lower rank and higher score) than members of other cohorts, while the destination dependence/market irrelevance group was least competitive. Market-indifferent destinations scored poorly on the business environment and labor market conditions. However, no differences were noted in either price competitiveness or tourism services infrastructure.

Conclusion

This article analyzed the relationship between outbound and inbound tourism to identify potential “at risk” destinations where a seemingly unhealthy relationship with markets exists. Two concepts were developed: market indifference and destination dependence/market irrelevance. The first, market indifference, occurs when markets, collectively, show little interest in a destination and instead, the destination has to draw from a large number of markets to achieve certain thresholds of arrivals. The second, destination dependence/market irrelevance, occurs when destinations are overly reliant on a market and yet the market as a whole shows little interest in it. A total of 162 destinations were analyzed using data derived primarily from UNWTO sources, with the country or territory as the unit of analysis.

This study established quantitative criteria to identify two situations where market imbalance exists that could signify destinations at risk, and therefore call for intervention to rectify, or at least begin to arrest the situation. Market indifference of between one and up to two standard deviations from the regional norm signifies destinations at moderate risk, while a score of more than two standard deviations represented places that were very at risk. Likewise, a destination dependence/market indifference score of between 20 and 99 may signify a destination at risk, while a score of 100 or more may signal major difficulties for a destination unless it can begin to diversify its markets. The few cases where both market indifference and destination dependence/market irrelevance are evident signifies a place at major risk.

The appeal of these diagnostic tools lies in the ready availability of data, mathematical parsimoniousness of the calculations required, and ease of interpretation of results. The outbound and arrival data are readily available from the UNWTO e-library that its over-200 member economies can access. These data can be supplemented by data collected by regional associations, such as the South Pacific Tourism Organisation, the Caribbean Tourism Organization, the Pacific Asia Tourism Association and the European Union, and other regional organizations, as well as in-house arrival data collected by individual economies. As demonstrated in this article, the use of ratios and simple descriptive statistical tests provides powerful results that can be interpreted easily.

By adopting this test, appropriate intervention can be implemented in a timely manner to reduce further risk or improve the situations. Moreover, the models provide an indication of the types of interventions needed to rectify the situation. The market indifference score signals that destinations do not have a clear position in their core markets and thus need efforts to reenergize these markets. A high destination dependence/market irrelevance score signals that destinations do not have broad market appeal, and thus need to focus their marketing efforts either by geographic or psychographic segment in order to reduce the score.

The study findings also challenge the long held assumptions that appealing to a diverse array of markets is a signal of strength while reliance on a small number of markets is a de facto indicator of weakness. Instead, the vast majority of destinations rely on a small number of markets to generate most of their visitors. The study seems to add credence on a global basis to the work conducted by Chen and his colleagues (Chen and Chen 2012; Jang and Chen 2008; Chen, Jang, and Peng 2011) that the optimal number of markets for most destinations ranges from one to six. This figure may be slightly higher depending on the location of the destination and some other conditions. The study revealed that those destinations that had to rely on a substantially larger number of markets potentially had an unhealthy relationship with source markets, as they lack a clear market position, have seen a dramatic decline in outbound travel from core markets or have undergone a prolonged period of political instability.

The axiom that destinations that rely on a small number of markets are at risk was supported to some extent. But, share figure alone can be a misleading indicator. Instead, the article argued the relationship between arrival and departure share is a much more reliable indicator of health for it shows how important a source market sees a destination. Small island destinations and/or current or former colonies are most at risk of being exposed to a destination-dependent/market-irrelevant situation.

All told, almost half of the world’s destination countries/territories were identified as potentially suffering from one of the two conditions explored in this study. Moreover, the causes seem to be systemic, for seemingly unhealthy relationship with markets were no more or less reliant on tourism as a contributor to their GDP, or price competitiveness, and demonstrated no differences in service infrastructure than healthy places. But as a group, they were identified as being less competitive overall and tended to have smaller tourism sectors, were more isolated, and relied heavily on long-stay pleasure markets.

This article begins the discussion of destinations “at risk” by examining the relationship between source market departures and destination arrivals. Building on both Lorde, Li, and Airey’s (2016) observations about habit persistence and Lee et al.’s (2012) evidence of the enduring impact of distance decay, the study suggests that maintaining a healthy relationship with core source markets is the key to destination robusticity, in the long run. Certainly new markets must be developed, but the overall health of a destination relies on maintaining its core market base. This issue is challenging when half of all destinations studied are unhealthy, with many facing systemic challenges to remain either relevant in their core markets or to rebuild market confidence. Applied theory is considered “in progress” and never completed but “true until shown otherwise” (Cohen 1991; Root 1993); hence, further research using empirical data testing to refine and to increase the confidence level of (Cohen 1991; Root 1993) or even disconfirm the theory. It is suggested to consider bringing in destination competitiveness for future research.

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.

ORCID iD

Bob McKercher

Author Biographies

Bob McKercher received his PhD from the University of Melbourne in Australia, a Masters degree from Carleton University in Ottawa, Canada and his undergraduate degree from York University in Toronto, Canada. He has wide ranging research interests. Prior to entering academia, he worked in a variety of operational and advocacy position in the Canadian tourism industry.

Barry Mak is an Associate Professor of Aviation and Tourism in the School of Hotel and Tourism Management. He commenced his career as supervisor with one of the largest travel agencies in Hong Kong. His research interests range from airline and airport management through culture and tourism and tourism movements.

References

Águas

Costa

Rita

2000. “A Tourist Market Portfolio for Portugal.” International Journal of Contemporary Hospitality Management 12 (7): 394–401.

Águas

Veiga

Reis

2010. “Competitive Destination Analysis in Southern European Countries.” Worldwide Hospitality and Tourism Themes 2 (4): 364–75.

Andonian

Kuwabara

Yamakawa

Ishida

. (2016) The Future of Japan’s Tourism: Path for sustainable growth towards 2020. McKinsey Japan and Travel, Transport and Logistics Practice. https://www.mckinsey.com/~/media/mckinsey/industries/travel%20transport%20and%20logistics/our%20insights/can%20inbound%20tourism%20fuel%20japans%20economic%20growth/the%20future%20of%20japans%20tourism%20full%20report.ashx.

Baker

Page

Meyer

2002. “Modeling Tourism Crime: The 2000 America’s Cup.” Annals of Tourism Research 29 (3): 765–82.

Berno

Douglas

1998. “Tourism in the South Pacific: A Polynesia/Melanesia discussion.” Asia Pacific Journal of Tourism Research 2 (2): 65–73.

Bosworth

Farrell

2011. “Tourism Entrepreneurs in Northumberland.” Annals of Tourism Research 38 (4): 1474–94.

Botti

Goncalves

Ratsimbanierana

2012. “French Destination Efficiency: A Mean-Variance Approach.” Journal of Travel Research 51:115–29.

Çetinsöz

Ege

2013. “Impacts of Perceived Risks on Tourists’ Revisit Intentions.” Anatolia 24 (2): 173–87.

Chaperon

Bramwell

2013. “Dependency and Agency in Peripheral Tourism Development.” Annals of Tourism Research 40:132–54.

10.

Chen

2012. “Identifying Optimal Inbound Market Mixes If the US Tourism Industry.” International Journal of Tourism Sciences 12 (1): 25–46.

11.

Chen

Jang

Peng

2011. “Discovering Optimal Tourist Market Mixes.” Journal of Travel Research 50 (6): 602–14.

12.

Chew

Jahari

2014. “Destination Image as a Mediator between Perceived Risks and Revisit Intention: A Case of Post-disaster Japan.” Tourism Management 40:382–93.

13.

Cohen

1991. Developing Sociological Knowledge: Theory and Method, 2nd ed. Chicago: Nelson-Hall.

14.

Croes

2010. Small Island Tourism Competitiveness: Expanding your destination’s share of paradise. Dick Pope Sr. Institute Publications. Paper 10. https://hospitality.ucf.edu/files/2011/09/Version-2-Small-Island-Tourism-Competitiveness_Jan07_2010.pdf (accessed July 18, 2018).

15.

Croes

Ridderstaat

Rivera

2018. “Asymmetric Business Cycle Effects and Tourism Demand.” Journal of Travel Research 57 (4): 419–36.

16.

Crouch

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

17.

Daye

2010. “Challenges and Prospects of Differentiating Destination Brands: The Case of Dutch Caribbean Islands.” Journal of Travel and Tourism Marketing 27 (1): 1–13.

18.

De Keyser

Vanhove

1994. “The Competitive Situation of Tourism in the Caribbean Area Methodological Approach.” Tourist Review 49 (3): 19–22.

19.

De Vita

. 2014. “The Long-Run Impact of Exchange Rate Regimes on International Tourism Flows.” Tourism Management 45:226–33.

20.

Dupeyras

MacCallum

2013. Indicators for Measuring Competitiveness in Tourism: A Guidance Document, OECD Tourism Papers, 2013/02, OECD Publishing. https://dx-doi-org.web.bisu.edu.cn/10.1787/5k47t9q2t923-en (accessed July 18, 2018).

21.

Dwyer

Edwards

Mistilis

Roman

Scott

2009. “Destination and Enterprise Management for a Tourism Future.” Tourism Management 30 (1): 63–74.

22.

Dwyer

Kim

2003. “Destination Competitiveness: Determinants and Indicators.” Current Issues in Tourism 6 (5): 369–414.

23.

Enright

Newton

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

24.

ETC. 2014. “European Tourism Portfolio Analysis.” European Tourism Commission.

25.

Gomezelj

Mihalic

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

26.

Hendija

1999. “Main Characteristics of the Tourist Flows in Croatia—1999.” Turizam/Tourism 47 (1): 78–79.

27.

Hingten

Kline

Fernandes

Gard McGehee

2015. “Cuba in Transition: Tourism Industry Perceptions of Entrepreneurial Change.” Tourism Management 50:184–93.

28.

Hoti

McAleer

Shareef

2005. “Modelling Country Risk and Uncertainty in Small Island Tourism Economies.” Tourism Economics 11 (2): 159–82.

29.

Hultgren

Coomer

eds. 1989. Alternative Modes of Inquiry in Home Economics Research. Peoria, IL: Glencoe.

30.

ILO. (n.d.). “Executive Summary: Research: Statistical Indicators on Trends in the Demand for Sectoral Competencies in Latin America.” International Labour Organization. http://evc.oitcinterfor.org/pluginfile.php/3004/mod_resource/content/3/trends-tourism.pdf (accessed July 18, 2018).

31.

Ivanovoc

Bogdan

Bareas

2018. “Portfolio Analysis of Foreign Tourist Demand in Croatia.” Ekonomski Vjesnik/Econviews 31:149–62.

32.

Ivars-i-Baidal

Rodrigues-Sanchez

Vera-Robollo

2013. “The Evolution of Mass Tourism Destinations: New Approaches beyond Deterministic Models in Benidorm (Spain).” Tourism Management 24:184–95.

33.

Jalilian

Reyes

2014. “Losing Steam: Crisis Impact at the Macro and Sectoral Levels.” In Surviving the Global Financial Economic Downturn: The Cambodian Experience, edited by Jalilian

Kem

Reyes

Tong

, 40–73. Singapore: Ideas.

34.

Jang

Chen

2008. “Financial Portfolio Approach to Optimal Tourist Market Mixes.” Tourism Management 29:761–70.

35.

King

2017. “Brand Management—Standing Out from the Crowd: A Review and Research Agenda for Hospitality Management.” International Journal of Contemporary Hospitality Management 29 (1): 115–40.

36.

Kozak

Rimmington

1999. “Measuring Tourist Destination Competitiveness: Conceptual Considerations and Empirical Findings.” International Journal of Hospitality Management 18 (3): 273–83.

37.

Lane

1994. “What Is Rural Tourism?” Journal of Sustainable Tourism 2:1–2, 7–21.

38.

Lean

Smyth

2008. “Are Malaysia’s Tourism Markets Converging? Evidence from Univariate and Panel Unit Root Tests with Structural Breaks.” Tourism Economics 14 (1): 97–112.

39.

Lee

Denizci-Guillet

Law

Leung

2012. “Robustness of Distance Decay for International Pleasure Travellers: A Longitudinal Approach.” International Journal of Travel Research 14:409–20.

40.

Loi

2004. “A Portfolio Analysis Model for Tourism: Exemplified by Macao.” In Proceedings of the New Zealand Tourism and Hospitality Research Conference 8-10 December 2004, edited by Smith

Schoot

, 217–29. Wellington, New Zealand: Victoria University of Wellington.

41.

Lorde

Airey

2016. “Modeling Caribbean Tourism Demand: An Augmented Gravity Approach.” Journal of Travel Research 55 (7): 946–56.

42.

Malachovský

Királová

2015. “Invigorating the Destination’s Marketing Strategy? (The Case of Slovakia).” Procedia—Social and Behavioral Sciences 175:393–400.

43.

Mazanec

Wöber

Zins

2010. “Tourism Destination Competitiveness: From Definition to Explanation?” Journal of Travel Research 46:86–95.

44.

McKercher

1995. “The Destination–Market Matrix.” Journal of Tourism and Travel Marketing 4 (2): 23–40.

45.

McKercher

Chan

Lam

2008. “The Impact of Distance on International Tourist Movements.” Journal of Travel Research 47 (2): 208–24.

46.

McKercher

L’Espoir DaCosta

2007. “The Lingering Effect of Colonialism on Tourist Movements.” Tourism Economics 19 (3): 453–74.

47.

Ozcan

Erdogan

2017. “Are Turkey’s Tourism Markets Converging? Evidence from the Two-Step LM and Three-Step RALS-LM Unit Root Tests.” Current Issues in Tourism 20 (4): 425–42.

48.

Papatheodorou

Rosselló

Xiao

2010. “Global Economic Crisis and Tourism: Consequences and Perspectives.” Journal of Travel Research 49 (1) 39–45.

49.

Pechlaner

Smeral

Matzier

2002. “Customer Value Management as a Determinant of the Competitive Position of Tourism Destinations.” Tourism Review 57 (4): 15–22.

50.

Perles-Ribes

Ramón-Rodríguez

Sevilla-Jiménez

2014. “Market Share as a Tourism Destination’s Competitiveness Measure: Sense and Limitations.” Cuadernos de Turismo 34:423–25.

51.

Pike

Mason

2011. “Destination Competitiveness through the Lens of Brand Positioning: The Case of Australia’s Sunshine Coast.” Current Issues in Tourism 14 (2): 169–82.

52.

Podhorodecka

2017. “Tourism Economies and Islands’ Resilience to the Global Financial Crisis.” Island Studies Journal. https://www.islandstudies.ca/sites/default/files/ISJPodhorodeckaTourismCrisis.pdf (accessed July 18, 2018).

53.

Rees

Mann

2004. “Use of the Patient at Risk Scores in the Emergency Department: A Preliminary Study.” Emergency Medical Journal 21 (6): 698–99.

54.

Ritchie

Crouch

2003. The Competitive Destination: A Sustainable Tourism Perspective. Oxon: CABI.

55.

Rodolfo

Domingo

Agner

2010. “Modelling International Demand; Case of the Philippines.” http://nap.psa.gov.ph/ncs/11thNCS/papers/invited%20papers/ips-20/01_Modelling%20International%20Demand_Case%20of%20the%20Philippines.pdf (accessed July 18, 2018).

56.

Root

1993. Philosophy of Social Science: The Methods, Ideals, and Politics of Social Inquiry. Cambridge, MA: Blackwell.

57.

Seaton

1996. “The Competitive Evaluation of Tourism Destination Performance: Scotland and European Tourism 1985-1994,” Report for the Scottish Tourist Board.

58.

Shanaman

2015. “European Tourism: A Comparison of Overseas and European Source Markets.” MSc thesis, Modul University. https://www.modul.ac.at/index.php?eID=dumpFile&t=f&f=5235&token=306ba632cd0955689c39c5e4697f1979c90da42f (accessed July 18, 2018).

59.

Sirse

Mihalic

1999. “Slovenian Tourism and Tourism Policy; A Case Study.” Tourist Review 54 (3): 3447.

60.

Smeral

Witt

2002. “Destination Country Portfolio Analysis: The Evaluation of National Tourism Destination Marketing Programs Revisited.” Journal of Travel Research 40:297–94.

61.

Song

Lin

2010. “Impacts of the Financial and Economic Crisis on Tourism in Asia.” Journal of Travel Research 49 (1) 16–30.

62.

Steffen

2005. “Changing Travel-Related Global Epidemiology of Hepatitis A.” American Journal of Medicine 118 (10): 46–49.

63.

Tang

Tan

2013. “How Stable Is the Tourism-Led Growth Hypothesis in Malaysia? Evidence from Disaggregated Tourism Markets.” Tourism Management 37:52–57.

64.

UNECA (United Nations Economic Commission for Africa). 2013. Sustainable Tourism Master Plan for the Inter-governmental Authority on Development (IGAD) Region. United Nations Economic Commission for Africa. https://www.uneca.org/sites/default/files/PublicationFiles/uneca_stmp2013.pdf (accessed July 18, 2018).

65.

UNWTO (United Nations World Tourism Organization). 2003. Special Report Number 22 Fourth Meeting Tourism Recovery Committee ITB Berlin 2003. Madrid: UNWTO.

66.

Valadkhani

O’Mahony

2018. “Identifying Structural Changes and Regime Switching in Growing and Declining Inbound Tourism Markets in Australia.” Current Issues in Tourism 21 (3): 277–300.

67.

Weaver

2017. “Core–Periphery Relationships and the Sustainability Paradox of Small Island Tourism.” Tourism Recreation Research 42 (1): 11–21.

68.

WEF. 2017. Travel and Tourism Competitiveness Report 2017. Geneva: World Economic Forum. http://www3.weforum.org/docs/WEF_TTCR_2017_web_0401.pdf (accessed July 18, 2018).

69.

Wong

Fong

Law

2016. “A Longitudinal Multilevel Model of Outbound Travel Behavior and the Dual Cycle Model.” Journal of Travel Research 55 (7): 957–70.

70.

Worcestershire

NHS

. (n.d.). Patient at Risk Score (PARS) Clinical Guideline. file:///C:/Users/hmbob/Downloads/PARS%20addition.pdf

71.

Zhang

Botti

Petit

2016. “Destination Performance: Introducing the Utility Function in the Mean-Variance Space.” Tourism Management 52:123–32.