When and Where to Travel? A Longitudinal Multilevel Investigation on Destination Choice and Demand

Abstract

This study aims to reconcile two contrasting research perspectives on tourist decision making. The first perspective contends that tourists are constrained with limited resources and are inhibited to travel, whereas the second perspective contends that tourists search for means to develop a career in traveling and are thereby intrigued to travel to more destinations. This study employs a multilevel design by assessing the relationships of tourist travel frequencies among three outbound markets, namely, in-state, out-of-state domestic, and international markets, through a longitudinal study. By modeling a situational factor—unemployment rate—at the macro level, this study reveals a significant cross-level moderating effect on the relationship between the travel frequencies of domestic and international markets. The findings not only reveal which of these two perspectives is more applicable at a particular situation but also enrich the theories germane to travel career, destination choice, and distance decay.

Keywords

travel behavior travel career destination choice distance decay cannibalism economic situation

Introduction

For years, tourism research has focused on understanding the travel demand and destination choices of tourists. Conceptual and empirical advancements have espoused several lines of theoretical reasoning in answering why tourists prefer and partake certain destinations over others (Karl, Reintinger, and Schmude 2015; Crompton 1992; Sheldon and Mak 1987; Crompton, Botha, and Kim 1999). A dominant view of destination selection rests on the destination choice and decision-making models. The proponents of this view contend that people have an array of needs that can be fulfilled by visiting a destination (Pearce and Caltabiano 1983; Crompton 1979; Prebensen et al. 2013). An implicit premise of this view corroborates the persistency of tourists in fulfilling these needs by developing a travel career (Pearce and Lee 2005; Ryan 1998; Pearce 1988) in which a tourist continuously engages in visiting various destinations (Goossens 2000). An important assumption of this view lies on unlimited resources in which tourists can travel anywhere as often as they wish and as long as they are motivated to do so. Given that tourists opt to develop a travel career and that traveling is a highly engaging and habitual activity, those tourists who have an affinity to travel tend to visit many different places. Therefore, visiting a destination does not lower the propensity of a tourist to visit another destination.

An alternative perspective of travel demand and choice rests on travel resistance and constraints. This view focuses on those factors that inhibit tourists from traveling (Nyaupane and Andereck 2008; Canally and Timothy 2007; Hung and Petrick 2012). Some of these factors pertain to personal resources, such as time, money, and self-efficacy (Lamont, Kennelly, and Wilson 2012). Aside from this view, economic theories consistently acknowledge that humans have scarce resources, thereby forcing them to make tradeoffs with the lowest opportunity cost (Dwyer 2010). That is, scarcity induces humans to make sacrifices and tradeoffs. From this logic, as purported by destination choice theory, tourists are required to make sacrifices before they can travel to specific destinations, such as forgoing their planned visits to other places (Um and Crompton 1990; Crompton 1992; Karl, Reintinger, and Schmude 2015; Prentice 2006). An important assumption of this view is that visiting a specific destination will lower the propensity of a tourist to partake in other destinations, thereby creating a cannibalistic situation in which the gain of a destination is the loss or zero-sum game of another destination.

These two contrasting views present a paradox of destination choice and demand, which creates a void in the literature. Both views have their merits because they are able to explain tourist decisions. Therefore, determining which view is correct is unnecessary considering that both views have been empirically verified in the literature; instead, one must examine which of these views is more applicable in explaining tourist behaviors at a particular time and context. This study aims to investigate the boundary condition of economic situations over time on these two views and determine when destination cannibalization will occur. That is, the foci of this research rests on estimating the moderation of destination choices, whereby in certain periods, the habitual behavior of tourists in visiting a destination is positively associated with their habitual behavior in visiting another destination, while in other periods, the sacrificing behavior of tourists in visiting one destination is negatively associated with their sacrificing behavior in visiting another destination. To address these contradicting views, this study employs a longitudinal, multilevel design by assessing the cross-level moderating effect of economic situations on the relationship between the frequencies of visiting different destinations. This study contributes to the literature by revealing the boundary condition of the economic factor on destination choice and demand. It also seeks to advance the destination cannibalism concept and enriches the theories germane to travel career, destination choice, and distance decay.

Theoretical Background

This study is rooted in multiple research streams pertaining to pleasure travel motivations and behaviors (Goossens 2000; Crompton 1979; Uysal and Hagan 1993), travel career (Pearce and Lee 2005; Pearce 1988), destination choice (Um and Crompton 1990; Karl, Reintinger, and Schmude 2015; Woodside and Lysonski 1989), product cannibalism (Bultez et al. 1989; Kerin, Harvey, and Rothe 1978), and situational (Belk 1975) and travel constraint and negotiation (Lamont, Kennelly, and Wilson 2012; Nyaupane and Andereck 2008; Hung and Petrick 2012). The research framework in Figure 1 suggests that tourist travel behaviors, with respect to travel frequency to a destination (or destination market), is associated with tourist demand in visiting another destination (or destination market). From the research perspectives of travel career (Pearce and Lee 2005) and pleasure travel motivation (Goossens 2000), tourists continue to develop their travel careers by partaking destinations one after another to fulfill their unsatisfied needs. From this theoretical perspective, visiting a destination will improve the chances of a tourist to visit another destination. By contrast, the theoretical underpinnings of travel constraint (Lamont, Kennelly, and Wilson 2012; Nyaupane and Andereck 2008) and destination choice (Um and Crompton 1990) suggest that tourist decisions and actual behaviors are bounded by limited resources as travel inhibitors. The literature on cross-elasticity of demand (Hieser 1955) and cannibalism (Bultez et al. 1989; Harvey and Rothe 1998) also points to a tradeoff between choices in that increased demand of a product would reduce demand on other products. Therefore, tourists are often restricted to visit a destination or may even revoke their travel decisions despite having an array of places in mind in the awareness and evoked sets (Karl, Reintinger, and Schmude 2015; Woodside and Lysonski 1989). In this case, this realm of research alludes to a negative association between visitations of destinations and displacement of one destination from another.

Figure 1.

Hypothesized relationships.

By considering both views, the research framework in Figure 1 proposes a relationship among various destinations (or destination markets). Of more substantive foci, this study estimates how a situational factor may affect such a relationship. Specifically, we focus on the situational factor—unemployment rate—at the macro level that is shared among different actors (i.e., tourists) from the source market. Given that actors’ behaviors are shaped by the socioeconomic system they are embedded within (Scott 2001), we argue that this situational factor does not only directly influence tourist travel behaviors but also serves as a boundary condition that moderates the relationships of travel choice (i.e., specific locations) and demand (i.e., travel frequency) among multiple destinations (or destination markets). In summary, this study examines tourist visitation in three principal outbound markets, namely, in-state domestic, out-of-state domestic,¹ and international markets. These markets have been frequently discussed in the literature and can represent three primary destination sets. This study also employs a longitudinal design to assess the effects of unemployment rate on the relationship among the visitations in these markets.

Destination Choice and Travel Constraint

The destination choice literature has significantly contributed to understanding tourist travel decision making and behaviors. Rooted in the marketing and consumer behavior literature, Crompton and colleague (Crompton 1992; Um and Crompton 1990) propose a three-stage structure of destination choice sets that explain a highly involved destination selection process. The first stage involves the tourist awareness and initial consideration sets, which are often derived based on the previous experiences and passive information of tourists that entail symbolic and social stimuli. The second stage involves the late consideration (evoked) and action sets, which are produced through the substantial reduction of choices from the first stage. At this stage, the decision of a tourist is largely influenced by his or her sociopsychological characteristics, such as personality, motives, values, and attitudes toward destinations. The tourist will also consider situational constraints, which will be described later in detail, during this process. To mitigate the tensions and uncertainties at this stage, the tourist searches for information if the decision is deemed important (high involvement). A final decision is made, and a single destination is selected as the most desirable and feasible place of visit in the last stage of the process (Um and Crompton 1990; Crompton 1992; Woodside and Lysonski 1989; Karl, Reintinger, and Schmude 2015; Prentice 2006).

The seminal work of Plog (2001) on travel personality identifies a continuum of psychographic personality ranging from allocentric (venturer) to psychocentric (dependable). The former archetype delineates adventurer-type tourists who seek new experiences, travel to exotic destinations, and travel frequently, while the latter archetype describes conservative travelers who prefer well-known destinations and travel less frequently. Given that destinations have various ways of attracting different types of tourists, those destinations that possess a specific psychographic position can attract tourists within the corresponding spectrum of the psychographic personality (i.e., allocentric–psychocentric continuum). In other words, destination choice is confined within the psychographic personality traits of destinations that are congruent with the personality of tourists.

Destination decay theory provides an alternative means in understanding destination choice; this theory posits that tourists are exponentially less likely to travel to faraway destinations (Lee et al. 2011; Bao and McKercher 2008; McKercher, Chan, and Lam 2008). However, recent empirical evidence shows that the distance effect must be considered along with tourist motive because of its strong and significant influence for low-novelty seekers (i.e., dependables and near-dependables), but not for high-novelty seekers (i.e., venturers and near-venturers). In other words, venturers willingly visit faraway destinations (e.g., long-haul overseas regions) (Wong and Zhao 2016), whereas dependables prefer to visit destinations in proximity (e.g., short-haul in-state and out-of-state domestic locales). These findings are consistent with the conceptual framework of Yong, Mak, and McKercher (2011) for tourist behavior patterns, which reconciles the contrasting views of Plog (2001), Pearce (1988), and Cohen (1972).

The travel personality framework of Plog (2001) reveals why people refrain from traveling. Although Plog has explained the travel avoidance of people based on the incongruence between the personality of a tourist and that of a destination, recent studies have offered an alternative theory about travel rejection that rests on the domain of constraints. Rooted primarily in leisure constraint and negotiation theory (Crawford, Jackson, and Godbey 1991; Hubbard and Mannell 2001), the travel constraint literature empirically identifies three core factors that limit people from traveling to specific destinations (Nyaupane and Andereck 2008; Pennington-Gray and Kerstetter 2002; Lee, Agarwal, and Kim 2012; Nyaupane, Morais, and Graefe 2004). Intrapersonal constraint refers to personal attributes, such as health, personal ability and interest, and perceived risk, that inhibits people from traveling. Interpersonal constraint refers to attributes, such as the willingness, interest, and ability of families and friends to travel with a tourist. Structural constraint pertains to attributes that are external to the subject, including the cost, time, and distance barriers that are associated with traveling to a specific destination. These attributes also include destination and other situational factors, such as lack of attractions, disasters, weather, and poor economic conditions, that temporarily inhibit tourists from visiting a destination. Given that the travel decisions of tourists are bounded by resource availability (Hong et al. 2005; Hubbard and Mannell 2001) and situational factors, they undergo a negotiation process in which they prioritize their travel plans, concentrate on the most feasible destinations, and give up their other destination choices (Hung and Petrick 2012; White 2008). This logic is in line with the aforementioned destination choice models (Crompton 1992; Woodside and Lysonski 1989; Karl, Reintinger, and Schmude 2015). Therefore, those tourists who have visited a destination are less likely to visit another destination in a specific time frame. Accordingly, the following hypothesis is proposed:

Hypothesis 1a: The visitation (frequency of visit [FOV]) of a destination market is negatively associated with the visitation of other destination markets.

Pleasure Travel Motivations and Career

Contrary to destination constraint theory, travel motivation research centers on those factors that incite the desire of tourists to fulfill their needs (Mansfeld 1992). Aside from applying set theory to explain destination choice (Um and Crompton 1990; Crompton 1992), choice models often suggest that travel motivations have crucial roles in making tourist decisions (Woodside and Lysonski 1989; Karl, Reintinger, and Schmude 2015; Prentice 2006). However, travel motivation literature mostly focuses on articulating the motivational traits in which tourists are driven by two compelling forces, namely, push and pull motives (Baloglu and Uysal 1996; Riley and Van Doren 1992). Push travel motives are intrinsic socio-psychosocial needs, such as novelty, entertainment, arousal, and knowledge, that predispose tourists to pursue a trip. By contrast, pull travel motives are destination-specific attributes that attract tourists to visit a specific place. These motives are extrinsic to tourists and often include several factors, such as the climate, weather, safety, travel cost, distance, accommodations, transportations, leisure activities, events, attractions, dinning, and shopping outlets in a specific destination. Although many tourists tirelessly search for new experiences in a destination, the allocentric–psychocentric paradigm of Plog (2001) suggests that some tourists are inward-looking and embark on travels in which they feel safe and nonadventurous. Similarly, the optimal arousal theory of Iso-Aloha (1982) argues that tourists seek excitement and arousal in a trip yet avoid overexcitement and boredom.

Although these theoretical developments have advanced the knowledge of tourist travel behaviors, Pearce et al. (Pearce 1988; Pearce and Lee 2005) argue that tourists often embark on traveling careers to fulfill their travel needs (see also Filep and Greenacre 2007). In fact, a limitation of destination choice and travel motivation theories rests on the static view toward traveling in the life span of a tourist. However, Pearce et al. show that the travel needs of people change over time based on their lifecycles, thereby viewing traveling as a dynamic evolution of individuals. This contention echoes that of Iso–Aloha, who posits that travel and leisure needs continue to develop throughout the life span of an individual. Pearce et al. (Pearce 1988; Pearce and Lee 2005) further advance travel motivations theory by adopting Maslow’s hierarchy of needs (Maslow 1943) to conjecture a travel-need or career model, which shows that tourists aim to fulfill a constellation of travel needs that mimic human needs (see also Ryan 1998). Therefore, tourists continue to search for avenues and destinations to satisfy their lower-level needs (e.g., safety and physiological needs, such as escaping and seeking excitement and stimulation) before moving to higher-level needs, such as respect, recognition, and achievement (Pearce 1988; Ryan 1998). Given that different destinations provide opportunities to fulfill various needs, tourists continue to travel in order to advance their travel careers and develop a portfolio of travel patterns (Filep and Greenacre 2007; Pearce and Lee 2005). Based on this theoretical underpinning, the travel choice and demand of tourists for a specific destination can lead to additional choices and demands, that is, those tourists who enjoy visiting a specific destination will also enjoy visiting other destinations. This contention is supported by the hedonic tourism motivational model of Goossens (2000), which emphasizes the pivotal role of travel involvement in mediating travel motives and behaviors, that is, the involvement of tourists in pleasure trips is a persistent state of motive that continuously drives people to fulfill their personal needs through traveling. Accordingly, we propose the following hypothesis:

Hypothesis 1b: The visitation (frequency of visit [FOV]) of a destination market is positively associated with the visitation of other destination markets.

Economic Factors and Tourism Outcomes

The tourism economics literature has consistently acknowledged a strong relationship between economy and tourist behaviors in that economic conditions strongly influence tourist demand (Yorucu and Mehmet 2011; Song and Witt 2006) and trip expenditures (Downward and Lumsdon 2003; Wong, Fong, and Law 2016). Income-elastic research emphasizes the influence of financial resource on tourist demand and travel frequency (Davies and Mangan 1992). The general assumption is that people will have a higher propensity to engage in pleasure travel, extend their length of stay in a destination, and have a stronger capability to shoulder trip expenditures as their disposable income increases (Alegre, Mateo, and Pou 2011; Downward and Lumsdon 2003). Wealthy families often have a strong urge to engage in long-haul outbound travels, whereas poor families often struggle to visit other countries (Bao and McKercher 2008; Lim, Min, and McAleer 2008). Those source markets that face intractable financial issues often demonstrate a decline in the number of outbound tourists with curtailed travel expenditures (Eugenio-Martin and Campos-Soria 2014).

Among many economic indicators, unemployment has the most direct influence on the confidence and behaviors of consumers because of its strong correlation with their job prospects, security, and income (Ellonen and Nätti 2015; Mayer 1979). Unlike individual income, unemployment rate has a macro-level influence on the emotions and forward-looking behaviors of people. Based on goal-directed behavior theory, the anticipated emotion of people ultimately influences their actual behaviors (Perugini and Bagozzi 2001). Previous studies show that although employees may be unsatisfied with their organizations, they are unlikely to quit their jobs in cities with high unemployment rates because of job scarcity and the difficulty of finding other available positions (Sun, Aryee, and Law 2007; Trevor 2001).

Following this logic, unemployment has a salient role in directly affecting the travel behaviors of people, particularly their travel frequency. Karl, Reintinger, and Schmude (2015) empirically identifies financial constraints as the most important factors that refrain people from traveling. Although conventional forecasting techniques reveal a significant relationship between economic variables and tourist behaviors (e.g., Fredman 2008; Lim, Min, and McAleer 2008), these forecasts must be cautiously interpreted because inferences must be made only at the macro level instead of at both macro-level economic forces and individual-level behaviors, as critics contend (Wong 2016; Wong, Fong, and Law 2016). This study addresses this methodological limitation via multilevel analysis and proposes the following hypothesis:

Hypothesis 2: The source market economic situation, namely, unemployment rate, has an influence on the outbound travel frequency of individual tourists.

Economic Situations and Cannibalism

Cannibalism is a marketing phenomenon in which the introduction of a new product may lead to the reduced sales of an existing product from the same brand, retailer, or manufacturer (Kerin, Harvey, and Rothe 1978; Copulsky 1976). Cannibalism literature is often rooted on the cross-elasticity of demand theory, which argues that a change in the price demand for a product will influence the change in the price of another product of the same category (Hieser 1955). As the demand of a product increases, the demand of another product decreases, which results in the substitution of products or product cannibalization (Bultez et al. 1989). Although price is among the major factors that lead to cannibalism, other factors, such as product quality and innovativeness, promotion, and distribution, remain important (Kerin, Harvey, and Rothe 1978). Cannibalism may be a strategic decision by which firms acquire “the initiative to supersede one’s own product before a rival entry displaces it” (Conner 1988, 9). Therefore, a firm may intentionally cannibalize an existing product with declining sales by introducing an extension of that product in order to increase its sales (Mazumdar, Sivakumar, and Wilemon 1996).

However, unlike tangible products, the cannibalism of service products rarely brings strategic advantage to firms. If the existing service products are not scarce and are differentiable from others in terms of value, the introduction of new service products will hurt the existing products (Bennett, Seamans, and Zhu 2015). Therefore, similar to products and communication media (Cha 2013), tourism destinations will cannibalize one another in such a way that an increase in the demand of a destination will displace the demand of other destinations. Destination cannibalism may occur in several forms in which the source market travel agencies introduce new outbound markets that may cannibalize the existing markets. For example, the introduction of a new overseas market (e.g., the Caribbean Islands or Fiji) may lead to the substitution of existing out-of-state domestic markets (e.g., Hawaii) and in-state markets (e.g., Long Island).

This logic is consistent with the resource distribution view (Owen and Wildman 1992) in which “one channel’s viewers come at the expense of another” (Cha 2013, 72) because consumers have limited resources (e.g., time and/or money constraints). The resources that are allocated to one channel can reduce the resources that are allocated to another, thereby leading to a zero-sum game and cannibalism. This situation becomes more salient during economic downturns because consumers can face erosion in their resources, especially financial resources, during these periods. In fact, during an economic crisis, consumers often restrain their expenditures (Leong et al. 2008). This situation is particularly acute in long-term-oriented countries because consumers tend to be more forward-looking by saving more and spending less (Powell 2009). Similar findings have been reported in the tourism domain in that a financial crisis often leads to cutbacks in tourism expenditures (Eugenio-Martin and Campos-Soria 2014).

This study contends that economic situations have a dominant effect on the emotions and behaviors of people within a society. For example, organizational studies show that unemployment rate acts as a contextual boundary condition that moderates individual-level relationships (Trevor 2001; Sun, Aryee, and Law 2007). Economic conditions, such as unemployment rate, represents a critical situational factor (Belk 1975). Given its profound effects on consumer behaviors, the destination choice process must consider these effects in which “the linearity of the progression from Stage 1 [i.e., awareness and consideration sets] to Stage 3 [i.e., final travel destination] assumes that situational factors remain reasonably constant throughout the time period of the decision process” (Crompton 1992, 426). Given that the macro environment is dynamic instead of constant, especially in highly volatile economies, the travel decisions of people tend to be contingent upon the ups and downs of economic cycles.

In fact, a stagnated economy will hamper the international tourism industry, leading to rapid destination cannibalism. For example, the phenomenal economic growth of Japan from the 1960s to the 1980s has promoted global tourism in the Japanese outbound market. However, many destinations, such as Hong Kong, Taiwan, Thailand, Hawaii, and Australia, that rely heavily on Japanese tourists have suffered along with the decline of the Japanese economy (McKercher 1995; Department of Business Economic Development & Tourism 2009). With deteriorated economic conditions, Japanese tourists were forced to restrain their travel demands and expenditures, leading to cannibalistic behaviors in that a decision to visit a destination may result in sacrifice of other destinations (Kuo, Liu, and Chen 2014). Accordingly, economic factors, such as unemployment rate, serve as a boundary condition that moderates the relationship of visitations among various destinations. Given the scarce resources, tourists partake of fewer journeys during periods with poor economic conditions such as high unemployment rates. Hence, the following hypothesis is proposed:

Hypothesis 3a: The relationship between FOVs of different destination markets is moderated by the source market economic situation, in that the relationship is negative when the situation is unfavorable (e.g., high unemployment rate).

From a different point of view, favorable economic conditions and outlook help strengthen consumer confidence (Duch and Kellstedt 2011); they also entail positive consumer anticipated emotions, leading to a higher propensity to spend as well as to partake in traveling (Taylor 2016). China provides an excellent example in showcasing this relationship. The burgeoning economy of China has spurred a new frontier in tourism development worldwide (Jin and Wang 2016). The accentuated growth of mainland Chinese travel demand has led to a record high in tourist arrivals and spending in destinations around the globe (Anonymous 2013). With more resources such as disposable income, and relaxation of visa restriction, Chinese tourists are traveling to domestic and overseas destinations as never before in order to gain more travel experience and to build up their travel career, as the literature suggests (Pearce and Caltabiano 1983; Pearce and Lee 2005). This situation reduces destination cannibalism in that traveling to a destination may not diminish a tourist’s propensity to visit another destination. Accordingly, the last hypothesis is proposed:

Hypothesis 3b: The relationship between FOVs of destination markets is moderated by the source market economic situation, in that the relationship is positive when the situation is favorable (e.g., low unemployment rate).

Methods

Sample and Data Collection

This study employed a longitudinal design in which the data were collected from Hong Kong residents who engaged in outbound pleasure travel within the past 12 months, between the years of 2001 and 2010. A simple random sampling method was adopted to ensure the representation and generalizability of the population of interest. A sample frame that included the phone numbers of the residents was obtained. The respondents were interviewed one-to-one over the phone by using a random dialing system. Filter questions were asked to exclude minors, nonresidents, and nonpleasure travelers from the sample.

The data included 12,634 complete responses, which corresponded to an average of 1,263 respondents per time wave. The sample distributions were as follows: 59.3% were males, 24.9% were between the ages of 36 years and 45 years, 20.2% were between the ages of 46 years and 55 years, 18.3% were between the ages of 26 years and 35 years, 29.3% received college education, 71.7% had no college education, 47.4% had a monthly income of up to HKD19,999, 19.7% had a monthly income of between HKD20,000 and HKD29,999, and only 6.2% had a monthly income of HKD70,000 or above.

Measures

This study focuses on the relationships of tourist visitation among three major destination markets, namely, international or overseas, in-state domestic, and out-of-state domestic destinations. These three regions represent three primary destination markets for Hong Kong residents. That is, visitation to any of these markets entails a travel choice (i.e., a destination market) a tourist makes over other choices (i.e., other destination markets). On the other hand, a tourist’s visitation frequency (or frequency of visit [FOV]) to a destination market represents his/her actual travel demand for that market. In this study, the FOV to overseas is a proxy for the international market, FOV to Guangdong is a proxy for the in-state market,² and FOV to China is a proxy for the out-of-state domestic market. To assess tourists’ travel choices and demands, three destinations were presented in the questionnaire with respect to the question “Over the past 12 months, how many overnight pleasure trip(s) lasting at least 1 night have you taken to:” (a) Guangdong province, (b) other parts of mainland China, and (c) outside of China. These choices correspond to the FOVs to international destinations (i.e., FOV_{International}), Guangdong (i.e., FOV_In-State), and other parts of mainland China (i.e., FOV_Out-of-State), respectively. These measures were assessed using a ratio scale. The scale ranges, means, and standard deviations are presented in Table 1. These measures assess destination choice with respect to the travel decisions of tourists to specific markets and travel demand with respect to the travel frequencies to each of these markets. Given that tourist demographic characteristics were often reported to affect tourist travel behaviors (e.g., Pearce and Lee 2005), they were controlled in this study. Specifically, the gender, age, education, and household income of the residents were controlled.

Table 1.

Variable Description and Range.

Variable	Description	Range	Mean (SD)
FOV_{International}	Number of overnight pleasure trips to overseas international destinations	0–20	0.88 (1.25)
FOV_In-State	Number of overnight pleasure trips in Guangdong	0–250	2.12 (7.45)
FOV_Out-of-State	Number of overnight pleasure trips in other parts of mainland China	0–40	0.47 (1.62)
Unemployment rate	Unemployment rate in Hong Kong	3.52%−7.92%	5.46% (1.46%)

Unemployment rate was used as a proxy for economic situational factor. It was assessed according to the data retrieved from the Hong Kong Statistics Bureau (2012). A preliminary analysis indicated that the unemployment rate is highly correlated with other economic indicators in Hong Kong, such as GDP per capita, foreign direct investment, performance of the stock market, consumer price index, and inflation, with r ≥ .86 (p < .001). Inclusion of these factors violates the independence assumption and causes multicollinearity. Hence, only unemployment rate is used in the current study. The unemployment rate demonstrated a fairly volatile distribution between 2001 and 2010. Specifically, the unemployment rate increased from 4.9% in 2000 to 5.1% in 2001 after the collapse of the dot-com bubble. The rate peaked at 7.9% in 2003 because of the SARS epidemic that led to a series of negative economic sentiments and consequences in Hong Kong. The economy of Hong Kong gradually recovered, and the unemployment rate reached a record low of 4.0% and 3.5% in 2007 and 2008, respectively. However, the collapse of Lehman Brothers in late 2008, which triggered the global financial crisis, negatively affected the employment rate in 2009, which improved to 4.3% in 2010.

Data Diagnostics

Prior to data analysis, multicollinearity was assessed via variance inflation vectors (VIFs). All VIFs are below 1.10, which indicates that multicollinearity is not a concern. Common method bias (CMB) was assessed using the single-factor method in which the variables were forced to load onto a single factor using the maximum-likelihood method. χ² ₍₁₄₎ = 491.98, p < 0.001, which suggests that CMB is not a limitation of the study.

Findings

This study used multilevel analysis through hierarchical linear modeling (HLM) because economic situations reside at the macro level, whereas tourist behaviors reside at the micro level. HLM can assess the relationships of the variables at both levels in an omnibus model. The appropriateness of HLM was also diagnosed considering that individual behaviors could vary across time. The variances of the three dependent variables can be accounted for by time (F-values of FOV_{International} = 204.14, p < 0.001; FOV_In-State = 3.57, p < 0.01; and FOV_Out-of-State = 111.57, p < 0.001). Following the recommendation of Raudenbush and Bryk (2002) null models (i.e., models with no predictors but only the dependent variables) were also applied for these three variables, and the results demonstrated a significant relationship in the time variances of these three variables, with χ²₍₉₎ ≥ 32.54, p < 0.001. In other words, a significant portion of the variances in FOV to international, out-of-state domestic, and in-state markets is observed between time. According to intra-class correlations (ICC₍₁₎s), 23%, 1%, and 21% of the variances in FOV_{International}, FOV_In-State, and FOV_Out-of-State, respectively, were observed between time, respectively. The visitation pattern to in-state destinations is relatively stable over time and accounts for only 1% of the variance, whereas the visitation patterns to farther destinations (i.e., out-of-state and international locales) are relatively volatile. These results suggest that farther destinations from the source market will lead to more volatile visitation patterns. The visitation to the domestic markets is relatively more stable than that to the international market. A series of HLM analyses was performed to test the hypotheses and account for volatility over time.

Hypothesis 1 postulates that the tourist FOV to a destination market is related with the FOV to another destination market. Zero-order correlations show that the tourist visitations among international, in-state, and out-of-state markets are positively related (r ≥ 0.06, p < 0.001). We further tested this hypothesis in models 1a, 2a, and 3a. It is important to note that Model 1 tests relationships leading from FOV_In-State and FOV_Out-of-State to FOV_{International}; Model 2 tests relationships leading from FOV_{International} and FOV_Out-of-State to FOV_In-State; and Model 3 examines relationships leading from FOV_{International} and FOV_In-State to FOV_Out-of-State. Results in Table 2 reveal that only FOV_Out-of-State can significantly predict FOV_{International} (b = 0.07, p < 0.001) and FOV_In-State (b = 0.67, p < 0.001). FOV_{International} (b = 0.13, p < 0.001) and FOV_In-State (b = 0.04, p < 0.001) are the predictors of FOV_Out-of-State (see Table 2). These results partially support Hypothesis 1.

Table 2.

Parameter Estimates for Frequency of Visit.

	Model 1a:FOV International	Model 1b:FOV International	Model 2a:FOV In-State	Model 2b:FOVIn-State	Model 3a:FOVOut-of-State	Model 3b:FOV Out-of-State
Control variables
Gender	0.11**	0.12***	−1.23***	−1.22***	−0.04	−0.04
Age	0.00	0.00	0.20**	0.20**	0.07***	0.07***
Education	0.12***	0.12***	−0.36***	−0.36***	−0.03	−0.03
Income	0.14***	0.14***	0.09†	0.09†	0.06***	0.06***
Main effects
FOV_{International}	–	–	0.05	−0.07	0.13***	0.05
FOV_In-State	0.00	0.00	–	–	0.04***	0.04***
FOV_Out-of-State	0.07***	0.03†	0.67***	0.65***	–	–
Cross-level effects
Unemployment rate (UR)		−0.36		−0.29		−0.78†
FOV_{International} × UR		–		−0.14		−0.11**
FOV_In-State × UR		−0.01		–		−0.01
FOV_Out-of-State × UR		−0.04*		−0.02		–
R ²	0.16	0.20	0.40	0.40	0.15	0.17
∆R²		0.04		0.00		0.02

Note: Parameter estimates are unstandardized.

†

p < 0.10, *p < 0.05, **p < 0.01, and ***p < 0.001.

Hypothesis 2 posits a negative cross-level direct relationship between economic situation, namely, unemployment rate (UR), and the FOVs of the three markets. Models 1b, 2b, and 3b show that although a negative relationship is observed, only UR-FOV_Out-of-State relationship is significant (γ = −0.78, p < 0.10), thereby partially supporting Hypothesis 2. Hypothesis 3 proposes the cross-level moderation of unemployment rate on the relationships among the FOV of the three markets. Cross-level interactions of FOV_Out-of-State × UR on FOV_{International} (b = −0.04, p < 0.05) and FOV_{International} × UR on FOV_Out-of-State (b = −0.11, p < 0.01) are observed. However, the moderating effect of UR is not significant for FOV_In-State, thereby partially supporting Hypothesis 3.

To illustrate the interaction effect graphically, we followed Aiken and West’s (1991) simple slope analysis and defined the moderator as high and low UR as well as high and low FOV by referring to one standard deviation from the corresponding means. This procedure yields four outcome values, which are then plotted in Excel for illustration of the interaction effect. Figure 2 shows that the effect of FOV_Out-of-State on FOV_{International} remains positive when the UR is low (i.e., favorable economic situation). However, the relationship becomes negative when the UR is high (i.e., unfavorable economic situation). Figure 3 shows that the FOV_{International}−FOV_Out-of-State relationship remains positive when the UR is low, but becomes negative when the UR is high. These findings demonstrate that UR is a boundary condition whereby those tourists who visit overseas destinations tend to visit domestic destinations more frequently under favorable economic conditions with a low unemployment rate. However, the opposite phenomenon is observed during poor economic conditions with a high unemployment rate whereby those tourists who visit overseas destinations more frequently will visit domestic destinations less frequently and vice versa. This situation can lead to destination cannibalism. However, the in-state market seems fairly robust and stable from the influence of economic conditions and demand from other markets.

Figure 2.

FOV_Out-of-State × Economic Situation interaction on FOV_{International}.

Figure 3.

FOV_{International} × Economic Situation interaction on FOV_{Out-of-State.}

The effects of the demographic variables are also noteworthy. Specifically, females engaged in more overseas travels (b = 0.11, p < 0.01), whereas males engaged in more trips to in-state destinations (b = −01.23, p < 0.001). Age is positively correlated with FOV_In-State (b = 0.20, p < 0.01) and FOV_Out-of-State (b = 0.07, p < 0.001), which indicates that older travelers visit domestic markets more often than younger travelers. With regard to education, those people who have received more education engage in international travel more often (b = 0.12, p < 0.001), while those who have received less education travel within the state more often (b = −0.36, p < 0.001). Higher income levels are attributed to higher levels of travel frequency to all three markets (b ≥ 0.06, p < 0.10). In summary, the proposed models explain 15% to 40% of the variances in the visitation frequency to the three destination markets.

Discussion

The destination choice literature, which is exemplified by Crompton and colleague, acknowledges the process of pleasure travel decision making in which the final decision of a tourist on visiting a specific destination is driven by a cognitive selection process (Um and Crompton 1990; Crompton 1992). However, most studies have assessed destination choices and travel behaviors according to attitudinal and perceptual evaluations instead of actual travel behaviors and demand (e.g., Karl, Reintinger, and Schmude 2015; Hu and Zhang 2014; Moore et al. 2012), thereby leaving several questions unanswered. One of these questions is whether a tourist who visits a destination (or destination market) more often will visit another destination (or destination market) less often or vice versa. This question triggers additional questions, such as will domestic destinations cannibalize international destinations? Can in-state destinations be cannibalized by other domestic and overseas destinations? If so, when will this cannibalization occur? In fact, destination choice studies often assume that the visitation of a destination precludes the visitation of other destinations, which results in destination cannibalism, by which a gain of a destination is a loss of another.

An equally important question on destination choice is when and how situational factors matter in the selection process. Echoing the call from the consumer behavior literature, Um and Crompton (1990) suggest that situational factors, such as “situational constraints should be integrated into decision choice frameworks” (p. 445). Despite numerous attempts to explore the role of situational factors and constraints in addressing this call, the focus of these studies remains limited to individual perceptions. However, the situational constraints, such as societal and economical factors, that go beyond the individual level and are commonly shared as a social context among individuals within a constituency are largely ignored. Therefore, research questions about the role of macro-level situational factors on destination choice remain unanswered.

To address these questions, this study examines the tourist visitation to three principal outbound markets, namely, in-state, out-of-state domestic, and international markets. A longitudinal design is employed to assess the influence of the situational factor—unemployment rate—on the relationship among the visitation frequencies (or demand) to these markets. A multilevel design is used to examine the relationships between the macro-level situational variable and the micro-level individual tourist behaviors. In essence, this study assesses the actual decisions (destination choices) rather than hypothetical ones because of the significant differences between these decisions (Um and Crompton 1990). Therefore, instead of using tourist attitudes or perceptions as proxies for actual choices, this study utilizes the actual behaviors of tourists as evidence for their choices and demands. Based on empirical evidence, several theoretical and managerial implications can be discerned as follows.

Theoretical Implications

From a broad theoretical standpoint, this study rationalizes the proposed relationships and answers the aforementioned questions by synthesizing the extant literature from different streams. On the one hand, destination choice models and travel constraint theory often take the resource view by assuming that the visitation to a destination connotes a sacrifice of visitation to another destination. On the other hand, those theories pertaining to pleasure travel motivation and travel career conjecture a path in which tourists aim to fulfill different levels of travel needs and to accumulate travel experiences continuously. This study predicts how these two views provide a fuller explanation of tourist destination choice and demand. Therefore, the first contribution of this study lies in its ability to reconcile these two divergent views by acknowledging the macro-level situational factor, which serves as a key boundary condition that alters the applicability of these two views in explaining tourist decisions and actual behaviors. In essence, this study opens an avenue to accommodate both theoretical perspectives to yield a better understanding of tourist choice and demand by incorporating socioeconomic contingency factors. Unlike previous research on tourism economics that primarily uses macro-level data to infer aggregated tourist behaviors without accounting for individual differences, this study articulates the effects of both individual- and economic-level factors on actual individual tourist behaviors.

The findings may also help shed light to the current understanding of the destination development cycle as proposed by Plog (2001) and Butler (1980), who contend that the increase in visitation to a destination may be attributed to the increasing propensity of tourists to travel to more places especially during times of economic progression, thereby resulting in the expansion of a global tourism market. In a similar vein, the decrease in the visitation to a destination may be attributed to the propensity of tourists to reduce their desire to travel, especially during times of economic downturn, thereby resulting in destination cannibalism. This phenomenon erodes market share and further suppresses tourist demand. To this end, this study offers an alternative explanation to the travel personality model of Plog (2001), which contends that psychographic personality types ranging from psychocentric (dependable) to allocentric (venturer) are the underlying causes for the increasing and decreasing visitations to tourist destinations. The findings point to the salient role of economic condition on tourist behaviors and destination choices. Specifically, during times of economic progression, more people will exhibit an allocentric tendency by traveling to both in-state and international markets more frequently. In contrast, during times of economic downturn, more people will exhibit a psychocentric tendency by traveling to both markets less frequently. Therefore, this study goes beyond the model of Plog that relies on individual psychographic traits to rationalize the socioeconomic forces that are enacted upon individual travelers. It also extends the literature on cannibalism, specifically advancing the theory of destination cannibalism, to demonstrate when and how tourist travel decisions may lead to cannibalism among destinations.

This study provides two novel perspectives toward travel career theory. First, the theory purports that the travel career of a tourist is affected by his or her travel experience and life stage, that is, the tourist continues to accumulate travel experience and fulfill various needs; in this case, older tourists accumulate more travel experiences (Pearce and Lee 2005; Pearce 1988). This study supports this notion by positing that those tourists who visit a specific destination more often also tend to visit other destinations more frequently. Older tourists also accumulate more travel experiences by visiting domestic destinations more often than their younger counterparts. However, age does not have an apparent effect on the visitation to the international market, which suggests that travel career theory must consider the boundary condition of destination orientation. Aside from this finding, this study posits that the life stage of tourists must also consider other demographic traits, including gender, education, and income, which are significant predictors of tourist travel experience with respect to travel frequency. Specifically, male tourists and those with lower education levels visit in-state destinations more often, while female tourists and those with higher education levels visit international destinations more often. In contrast, income is fairly consistent in explaining tourist visitation to all markets. The empirical evidence shows that female, better-educated, wealthier tourists tend to develop a travel career by focusing on international travel experiences. In contrast, male, less educated, wealthier, and older tourists tend to embark on travel careers by going on domestic trips.

Second, although Pearce and Lee (2005) emphasize the importance of contingency factors in understanding tourist travel career, these factors are seldom investigated in empirical research. This study fills this void by examining the contingency effect of economic situation on tourist travel behaviors. The findings extend travel career theory by revealing the cross-level moderating role of economic condition in that the relationships between out-of-state domestic and international visits are contingent upon the fluctuations in the economy. That is, the travel career of an individual is bounded within socioeconomic forces. In other words, during times of economic progression, a tourist aims to accumulate more experiences to advance his or her travel career. Therefore, he or she engages in both domestic and international journeys. However, during times of economic downturn, tourists make sacrifices to advance their travel careers and are forced to choose between an out-of-state domestic or an international trip. However, the travel career of an individual with respect to in-state travel experience remains fairly robust and stable without being influenced by situational factors because only 1% of the variability of this market is explained over the course of 10 years. These findings demonstrate the importance of considering the destination choices and travel careers of tourists in advancing the tourism literature.

This study also advances the literature by demonstrating that the tourist travel demand to some destinations (e.g., international market) may be volatile and dynamic, that is, such demand can change across time, while the demand to other destinations (e.g., in-state market) remains stable. To this end, this study enriches distance decay theory (McKercher, Chan, and Lam 2008; Lee et al. 2011) from two perspectives, namely, demand volatility and changes over time. This theory posits that the travel demand is exponentially reduced along with the traveled distance. The findings contribute new insights to this theory by illustrating that the travel demand curve is not falling in an exponential distribution, but also fluctuates over time as shown in Figure 4. The volatility of the curve is also dependent on three factors, namely, distance, socioeconomic forces, and life stage, as well as other demographic factors. From a general perspective, the demand volatility of closer destinations is small, while the demand volatility of farther destinations is acute. Travel demand and its volatility are also dependent on the lifecycle and personal traits of an individual, such as age, income, gender, and education, as well as on socioeconomic situational factors, such as unemployment rate and other economic indicators. Figure 4 shows that instead of viewing the distance effect as static, the findings perceive such effect as dynamic, whereby the demand curve fluctuates across various economic cycles. The empirical findings suggest that distance does not serve as a constraint during the up cycles of an economy. When tourists become more affluent as manifested by higher income levels, they become more inclined to travel to international markets that are often far from their homes (see the unstandardized parameter estimates in Table 2). In addition, the interaction of domestic and international markets signifies a potential cannibalization between these two markets, particularly during the down cycles of an economy.

Figure 4.

Extended Model of Distance Decay.

Managerial Implications

The travel destinations around the globe have a high potential to cannibalize one another. The destination choices of tourists, which are often confined within limited resources, are also limited. However, with more destination options, researchers and practitioners often wonder whether cannibalism may occur in the global tourism industry and, if so, which destination (or destination market) and when will this situation occur? This study offers an avenue to answer these questions that are of relevance to tourism practitioners.

The findings show that the in-state market is not cannibalistic to other markets regardless of the economic situation. The tourist visitation to a specific destination is habitual and will not displace other destinations even during an economic crisis. However, two markets, namely, out-of-state domestic and international markets, have a great potential to cannibalize each other. These markets peacefully coexist and complement each other during times of economic progression with low unemployment rates. That is, tourists who visit an out-of-state domestic market are more inclined to visit overseas destinations. However, under poor economic situations with high unemployment rates, these markets enter fierce rivalries and cannibalize each other.

Given the scare resources, such as travel budget, during economic downturns, tourists are forced to reduce their evoked set of destinations in the selection process to a single choice for the entire calendar year. Therefore, traveling to a domestic destination requires a tourist to forgo his or her trip to an overseas destination. By contrast, tourists tend to perceive times of economic progression with better economic outlook and work compensation as positive indicators of resource availability. Therefore, tourists tend to select multiple destinations from their evoked set in a specific calendar year. In fact, the statistical evidence in Table 1 shows that tourists travel outside of their provinces more than once every year, whereas a significant and positive relationship is observed between out-of-state domestic travel and overseas travel. The collated evidence provides travel agencies with the opportunity to capitalize from the economic cycle by promoting tourists with more pleasure travels during periods of positive economic conditions. In contrast, during periods of poor economic conditions, travel agencies can help tourists set their travel priorities and find means to reduce destination cannibalism.

Limitations and Future Research Directions

The findings must be cautiously interpreted because they are limited to a single source market. Although this study utilizes both random sampling with a highly representative sample and a longitudinal design, the generalizability of the results may be limited to similar markets with Chinese-dominant developed economies. In addition, this study assesses the visitations to three groups of outbound destinations instead of the relationships among various individual destinations because of the infinite number of destinations around the globe. Given that exploring infinite pairs of destinations is impossible, this study presents a viable avenue for addressing the networked relationship of tourist visitations among key source markets. This study considers unemployment rate as the only variable in the proposed model that assesses the situational effect and serves as a proxy for economic condition. Although this factor is highly sensible to socioeconomic fluctuations, other possible situational factors, such as changes in technology, global tourism development, disasters, and crises, in the destination markets may have salient effects on tourist behaviors. Therefore, future research must consider these factors.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was partly supported by a research grant from the Hong Kong Polytechnic University.

1.

We use the term “state” throughout this article to denote the division of administrative regions within a country. Although some countries, such as China, are divided by province, we use “state” to refer to the division of administrative regions and provinces for simplicity.

2.

Although Hong Kong is a special administrative region of China and is not considered part of Guangdong from a political standpoint, this region is geographically subsumed within the Guangdong province with many commonalities, such as dialects, lifestyles, and cultural beliefs. In fact, Hong Kong belonged to the Guangdong province before its colonization by the British Empire.

Author Biographies

IpKin Anthony Wong, PhD, is a Professor at theFaculty of International Tourism and Management, City Universityof Macau. His current research interests include tourism and hospitality marketing, consumer behaviors, service quality, event management,and casino marketing, and information system management.

Rob Law, PhD, is a Professor at the School of Hotel and Tourism Management, the Hong Kong Polytechnic University. His research interests are information management and technology applications.

Xinyuan (Roy) Zhao, PhD, is an Associate Professor at Business School, Sun Yat-Sen University (SYSBS). His research focuses on employee and customer behaviors in the tourism and hospitality industries.

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