Abstract
Tourism as an extension of people’s daily life is becoming prevalent in today’s society. However, understanding tourists is still a demanding and changeable task, and the preference structures and decision patterns of different tourists are complex. Research on tourism behavior can help address these issues. In recent decades, research on tourism behavior has attracted considerable attention and has become a cornerstone of tourism market strategy and action. Tourism is not a transient behavior, but is repeated over time and is interrelated with daily life. To understand tourists’ lifestyle and decision-making processes, long-term observations of tourism behavior are needed. A life-oriented approach cannot ignore tourism behavior because it is an important part of life. This chapter analyzes recent research on tourism behavior, summarizes the pertinent concepts, characteristics, determinants, and shortcomings in existing studies on tourism behavior, and suggests directions for future research.
Access provided by CONRICYT-eBooks. Download chapter PDF
Similar content being viewed by others
Keywords
- Tourism behavior
- Spatial and temporal choices
- Social influence
- Integrated behavior model
- Generation theory
- Medical tourism
- Health
- Quality of life
- Qualitative research
8.1 Introduction
Tourism behavior refers to a person’s decisions and actions during the tourism process. It is an activity from which people want to experience pleasure that cannot be satisfactorily experienced in their daily lives. Decisions on tourism behavior usually involve a number of separate but interdependent choices that are made over time and across space, such as destination, travel party, duration, travel route, and activity participation during travel. Some of the choices might result from long-term decisions (e.g., destination, season/duration, and travel party), which are further associated with other life choices, and others might be made during travel (e.g., travel route and shopping). The objects of research are often different types of people who take part in tourism, for example, mass tourists, business travelers, lifestyle travelers, and backpackers. The tourism industry has traditionally treated tourism behavior as consumer behavior.
Consumer behavior research deals not only with individuals, but also with groups or organizations. It is concerned with the decision-making processes consumers use to select, use, and dispose of products/services and the impacts that consumer choices have on consumers themselves and society (Swarbrooke and Horner 2007). Consumer behavior models usually consist of three stages: pre-purchase, consumption, and post-consumption (Engel et al. 1995). Consumer behavior is central to tourism decisions and to academic research. In recent decades, many tourism studies have empirically examined consumer decisions and behaviors from various perspectives (Kozak and Decrop 2009). The topic of consumer behavior in the tourism context is the key to the foundation of all marketing activities that are implemented to establish, advertise, and sell tourism products.
Tourism behavior research is interdisciplinary, often requiring knowledge from fields such as sociology, cultural studies, and psychology. Tourism behavior topics vary according to different perspectives. Research on tourism behavior has mainly focused on tourist-based, market-based, and destination-based aspects. For tourist-based research, the typology of tourists is a popular subject of debate. Swarbrooke and Horner (2007) suggested differences in the consumer behavior of tourists and travelers. Sharply (2014) noted that “tourists” often involved package tourism products and were an integral element of the tourism production process, while “travelers” were often on a limited budget. Cohen (1979) distinguished five types of tourists, i.e., recreational, diversionary, experiential, experimental, and existential tourists. Each acts differently in both tourism behavior and experience. Decrop and Snelders (2005) defined six types of vacations in the decision-making process. Developing typologies of tourists is an important means to explain tourism behavior. Research on tourists’ satisfaction, motivation, and loyalty is also important for the study of tourism consumer behavior (Yoon and Uysal 2005; Del Bosque and San Martín 2008). To understand tourists better, individual influences, including consumers’ demographics, personality traits, lifestyles, emotions, and values are vital concerns for the study of tourism behavior (Frew and Shaw 1999; Alexandra 2013). Market research divides tourists into groups of people who have similar needs, wants, or demands. Market segmentation is aimed at serving the needs of marketers. Johns and Gyimóthy (2002) discussed the factors that influenced tourism behavior based on an “active” and an “inactive” market. Understanding the main characteristics of different groups of tourists would help marketers to predict consumer behavior. Most consumer research in tourism is destination based (Mehmetoglu and Altinay 2006) and has evolved toward an informed empirical and theoretical basis and the widespread adoption of rigorous scientific research methods (Jafari 2005). Chen and Tsai (2007) proposed an integrated model and found that destination image had both direct and indirect effects on behavioral intentions. Questions such as why people choose a certain destination (Um and Crompton 1990; Mazzarol and Soutar 2002), how to get visitors to revisit that place (Jang and Feng 2007), and how to collect information on the destination all require the integrated analysis of tourism behavior (Gursoy and McCleary 2004). However, in the tourism industry, destination, tourists, and market are closely bound together, and any tourism behavior topic should comprehensively consider these three aspects.
8.2 Conceptual Issues
As a complex behavioral phenomenon, tourism behavior has been studied in a variety of disciplines such as geography, psychology, sociology, marketing science, regional and urban planning, transportation, archaeology, cultural anthropology, and agriculture. To draw a general picture about a tourism behavior, a comprehensive classification is helpful. Here, we roughly classify tourism behavior into the following dimensions: (1) information search and use; (2) the social aspect; (3) resources; (4) the spatial aspect; (5) activity participation; and (6) the temporal aspect.
Information search and use: Better scheduling needs reliable and comprehensive information, which includes pre-travel, during-travel, and post-travel information. Pre-travel information is used to decide on a travel, during-travel information is used to modify the planned schedule and support smooth decisions during travel. Information might also be needed after travel (i.e., post-travel information), for example, to evaluate the realized and unrealized activities and to communicate travel experiences to other people (i.e., word-of-mouth information).
Social aspect: The social aspect refers to whether and how tourists decide to travel with other people and/or make use of travel agencies/guides. In the case of traveling with other people, tourists are influenced by coupling constraint, that is, a person has to stay together with another person(s) at a specific place and time. In addition, tourists’ decisions usually involve some group decisions, especially in the case of traveling with other people (e.g., family members, friends, and colleagues). Sometimes, tourists consult travel agencies as well as other experienced persons before making their decisions.
Resources: Time and money (e.g., income) are the main resources for performing travel activities. Because of the availability and scarcity values of these resources, participation in various activities is constrained and, consequently, the resources might affect where to visit (destination choice), how long to stay, with whom to travel, as well as the way in which tourists allocate their available time and money to the various activities during travel.
Spatial aspect and activity participation: Choices of destination, accommodation, travel mode, traveling route, stopping behavior, on-site activity participation (e.g., dining, shopping) are examples here. Performing on-site activities is the main purpose of travel and it is usually influenced by authority constraints such as programs of some on-site events, opening hours of attractions, shops, and stores. To realize and support the abovementioned behavioral dimensions, travel mode choice is indispensable. In this study, travel mode choice behavior is regarded as a part of spatial aspects and activity participation.
Temporal aspect: The temporal aspect refers to when and for how long spatial choices are made. The notion that tourist behavior changes over time is also relevant. This means that the temporal aspect might be correlated with all of the preceding dimensions.
It is expected that decisions related to these behavioral dimensions are interrelated from context to context, requiring the development of integrated frameworks of tourist behavior.
8.3 Information Search and Use
Information may be in a spoken, written, or in pictorial format (Goossens 2000; Van Raaij and Crotts 1994), and may come from personal/impersonal and commercial/noncommercial sources (Fondness and Murray 1997) and/or past experiences. Information needs can be classified into functional, hedonic, innovation, aesthetic, and sign (Vogt and Fesenmaier 1998). Functional needs are defined as motivated efforts that are directed at or contribute to a purpose. Hedonic needs support the view that tourists are pleasure-seekers. Innovation needs are defined by novelty seeking, variety seeking, and creativity. Information related to aesthetic needs is viewed as a stimulus to visual thinking, imagery, and envisioning of a place that is real and obtainable. Finally, the sign need describes the interpersonal, social, symbolic, or more general aspects of information acquisition and knowledge transfer.
People’s primary motives for undertaking search of pre-travel information are to enhance the quality of travel (Goossens 2000) and to reduce the risk of travel decisions (Money and Crotts 2003). Pre-travel information is first used to motivate people to plan a trip. Such information can play the role of either push factors (e.g., feelings of pleasure, excitement, and relaxation) or pull factors (e.g., attractions like sunshine, friendly people, and culture). Pre-travel information is also used to make a detailed decision about primary choices such as destination (Snepenger et al. 1990) and travel party.
During-travel information is used to make on-site decisions such as choices of travel mode, attractions, locations, activities, and lodging (Snepenger et al. 1990) and choices of traveling route to, in, and/or around a destination. Especially nowadays, Internet-based information systems provide increasingly more accessible, reliable, and comprehensive travel information (Rayman-Bacchus and Molina 2001). Tourists can also easily access the necessary travel information via mobile phone during the course of travel, and car navigation systems are used widely, as in Japan, and can provide not only dynamic information for route guidance, but also information about on-site attractions, restaurants, and so on.
Roles of post-travel information have been mainly studied in the fields of tourist satisfaction and service quality. As argued by Westbrook and Oliver (1991), satisfaction is a post-consumption evaluative judgment about both destination performance and tourists’ entire travel process including scheduling behavior. Satisfaction or dissatisfaction about the visited destination(s) is crucial because it may affect expectations for the next visit (Westbrook and Newman 1978; Kozak 2001). Another outcome of the post-evaluation of travel is word-of-mouth information. The influences of word-of-mouth information have been confirmed with respect to various aspects of consumer behavior (Boulding et al. 1993; Zeithaml et al. 1996). Word-of-mouth information usually plays two roles. First, tourists could enjoy post-travel pleasure by showing (talking about) their experiences to other people. Second, such information is also related to tourists’ willingness to recommend the visited destinations to other people (Kozak 2001).
Thus, various studies have analyzed the role of information search and use in travel decisions. However, little has been studied with respect to the influence of travel information on tourists’ scheduling behavior across space and over time.
8.4 Social Aspect
Using data on household decisions on travel involving airlines, collected in 1968, it was shown that husbands play the predominant role in initiating the idea to take a trip, suggesting a destination and selecting an airline; in contrast, the decision on where to go was a mutual decision (Davis 1976). In their vacation-sequence model, Van Raaij and Francken (1984) emphasized the importance of family members’ influence on the decision-making process of tourism service purchases, and incorporated the interaction of household-related variables (e.g., lifestyle, power structure, role, and decision-making style) with individual-related factors. Cosenza and Davis (1981) showed that household members’ involvement appears to vary across stages in the household life cycle. For pre-travel decisions, wives are highly involved in selection of a destination and collection of information (Zalatan 1998). Thornton et al. (1997) found that children influence the behavior of the travel party either through their physical needs or through their ability to negotiate with parents. On the other hand, Moutinho (1987) argued that travel decisions are also affected by the behavior of reference groups. Friends and relatives sometimes provide information to the individual decision-making process (Gitelson and Kerstetter 1994). The coupling constraint (Hagerstrand 1970), in which a person has to be together with other people at a certain place and time, is also related to the social aspect.
Concerning the role of travel agencies in tourists’ decisions, there is no doubt that information from travel agencies has traditionally been one of major sources for the selection of tourism destinations (e.g., Baloglu and Mangaloglu 2001; Gartner and Bachri 1994; Nolan 1976). Nowadays, however, because use of the Internet to search for travel information is becoming increasingly common, many functions of travel information provision by travel agencies are being replaced by online resources (e.g., Buhalis 1998). However, travel agencies can provide not only information, but also advice. Travel agencies can perform better than travel websites in terms of the human touch and personal service (Law et al. 2004).
8.5 Spatial Aspect
Research about destination includes analysis of destination choice behavior, image making, and evaluation of destination (satisfaction, expectation, attitude, and service quality). Distance is one of the strongest influences on scheduling behavior. Distance can be regarded as a proxy variable for time in the scheduling decision. Indeed, it is quite difficult for tourists to measure accurately actual distance. Many studies have reported that cognitive distance, a mental representation of actual distance, is significantly different from actual distance (e.g., Bratfisch 1969; McNamara 1986a; Ankomah et al. 1996). It has also been noted that cognitive or subjective distance is a better indicator than actual distance when representing spatial choice behavior (e.g., Ankomah et al. 1996). Seddighi and Theocharous (2002) argue that spatial choice needs a multistep decision-making process. A tourist is usually first faced with two alternatives when deciding to take a holiday, namely whether to take a domestic or foreign vacation, and then to choose the travel mode after determining a destination. Spatial choices with different scales usually generate differing levels of time pressure on decisions.
Various models have been proposed to represent spatial choice at different spatial scales. At the international, national, or regional level, econometrics, including time series modeling, has been applied to analyze the influential factors of tourism flows (Song and Witt 2000; Gallet and Braun 2001; De Mello et al. 2002). At less aggregate spatial levels, discrete choice models under the principle of random utility maximization have been widely applied using either revealed preference data or stated preference data (e.g., Haider and Ewing 1990; Morley 1994; Crouch and Louviere 2001; Huybers 2003a, b). In line with the research stream of discrete choice models, especially focusing on how to represent observed and unobserved similarities among alternatives in the choice set, Zhang et al. (2008) developed a new choice model by integrating their proposed nested paired combinatorial logit (NPCL) model and relative utility-based model, where the former is used to describe the unobserved similarity (e.g., liking, hobby, and character) and the latter to explain the observed similarities (e.g., overlapped routes, similar attributes of destinations, and spatial closeness). The relative utility choice model argues that tourist behavior is context (or reference)-dependent (Zhang et al. 2004). Focusing on the choice interdependence between travel party and destination, Wu et al. (2009) represented the heterogeneous nested choice structure involved in the choices of these two decision aspects by combining the latent class and nested logit modeling approaches.
Stopping behavior at a particular travel facility prior to the completion of a travel segment also affects scheduling behavior. As argued by Wansink and Van Ittersum (2004), travel itself is motivated or initiated by the traveler’s primary need; in contrast, stopping decisions during travel result from the secondary needs. There are likely two major types of stopping behavior; the first is to acquire information to make the next decisions or confirm the decided schedule, and the second is to meet the needs for gasoline, food, or taking a break. In such cases, tourists have to make trade-offs between the time for stopping and the time eventually allocated to their intended destinations.
In tourism research, few studies have focused on travel mode and route choice, possibly because these are usually determined together with destination. Multidestination choice is another aspect of tourism behavior that has not been well covered to date.
8.6 Temporal Aspect
Zimmermann (1982) argued that there are three temporal dimensions: period effects, life cycle, and cohort effects. Period effects refer to annual changes in tourist arrivals, which are a common concern in all countries. Life cycle relates to variations of individuals’ behaviors due to family structure. Different cohorts might show a variety of behavior patterns. Clearly, all three dimensions closely relate to long-term decisions. Oppermann (1995) concluded that there were comparatively few studies on family life cycle applications in tourism and leisure, and research on generational or cohort differences in tourism patterns was rare; the situation has not changed notably since that time.
Furthermore, we believe that short-term decisions related to time allocation (use) decisions during travel should also be appropriately represented in the literature. However, there is also a dearth of work in this area. The existence of temporal constraints (e.g., available holidays and available time in a day) may force tourists to decide how to make effective use of their available and limited time during travel. The more time tourists spend traveling, the less time they spend at their destination. Even though the importance of time use research in tourism has been recognized since the late 1980s (e.g., Pearce 1988), there are few relevant studies. Fennel (1996) proposed a space-time model that describes tourists’ behavior by dividing a space into core, transition, and periphery, reflecting their perceptions about the space and the pressures caused by social, environmental, and economic (SEE) impacts of the space. However, the way that the space is classified and how perceptions and SEE are defined is arbitrary. Fujiwara and Zhang (2005) applied Becker’s (1965) time allocation theory to represent how a tourist allocates his/her available time to various activities during a one-day car trip within an integrated modeling framework (this is explained below). Linked with resource aspects, Zhang et al. (2009) developed a context-sensitive tourist’s time use and expenditure behavior model by explicitly incorporating the influence of spatial closeness of destination and interdestination similarities, representing three types of interdestination interactions—time-to-time, expenditure-to-expenditure, and time-to-expenditure interactions—as well as the relative importance of destinations in decision making.
Another important decision aspect is timing. Timing decisions include both long-term and short-term aspects. The long-term decision concerns when to go on a trip, (e.g., which season), for what special occasions or events (e.g., wedding anniversary, birthday, or in celebration of finding a new job), or as determined by available vacation period. The short-term decision mainly refers to decisions during travel, such as when to depart from home/the hotel on the day of travel, when to visit a place, and when to go back home/to the hotel. An explicit representation of timing makes it possible to describe a meaningful value of time by focusing on momentary experience. In this context, Zhang et al. (2006) developed a multidimensional timing decision model under the principle of random utility maximization by representing the influence of timing constraints and censored timing. The derived model not only allows for the temporally varying utility of a timing decision, but also incorporates sequential correlation between the neighboring timings. The model can also endogenously specify the sequences of activities/trips as well as heterogeneous preferences about the timing.
With a focus on temporal change of tourist behavior, Jang and Feng (2007) explored the effects of tourists’ novelty seeking and destination satisfaction on revisit intentions measured on short-term, mid-term, and long-term bases. However, no study has examined how to represent such temporal change.
8.7 Integrated Tourism Behavior Models
Tourism behavior involves a decision-making process with various interrelated choices. Early tourist behavior models were built in the 1950s mainly based on the so-called “grand models” of consumer behavior, which were used to explain decisions on tangible or manufactured products instead of services (Sirakaya and Woodside 2005). A tourist’s decision-making process usually follows the following six steps: (1) search for information; (2) evaluation of alternatives; (3) purchase; (4) delivery; (5) consumption; and (6) post-consumption feedback (Engel et al. 1995). Traditional tourist behavior models mainly focused on a certain part of tourist behaviors rather than on a set of tourist behaviors.
Over the last four decades, discrete choice models have proven to be very powerful tools for consumer behavior analyses in various fields including tourism research. The multinomial logit (MNL) model has become the most widely used choice model, probably due to its simple mathematical structure and ease of estimation. Such discrete choice models assume that a decision maker chooses the alternative with the highest utility from the alternatives in the choice set under the principle of random utility maximization. The MNL model has been widely used to represent tourists’ destination choice, travel mode choice, and route choice (Perdue 1986; Schroeder and Louviere 1999; Kemperman et al. 2009). However, because it is assumed that the error terms of the utility function are independently and identically distributed across alternatives, the MNL model is characterized by the independence of irrelevant alternatives (IIA) property, which states that the odds of choosing a particular alternative are independent of the existence and the attributes of any other choice alternative in one’s choice set. To date, various non-IIA discrete choice models have been proposed to overcome the shortcomings of the MNL model. These non-IIA choice models can be classified into three categories (Zhang et al. 2004).
The first group of non-IIA models avoids the IIA property by relaxing the assumption of identically and independently distributed error terms, or allowing for different variances of error terms, or allowing for positive correlations between error terms. For example, Nicolau and Mas (2006) adopted the random coefficient logit model to analyze tourism destination choice. The model is used to deal with the unobserved heterogeneity of tourists, by assuming that the coefficients of the variables vary among tourists. Wu et al. (2013a) used a mixed logit model to represent the influence of social interactions on tourism participation behavior. The model assumed that members in the same social group share a common random parameter, which can create correlation patterns between error terms.
The second group of non-IIA models circumvents the IIA property by extending the utility specification to account explicitly for similarity between choice alternatives. To account for future dependency in tourism destination choice, Wu et al. (2012a) employed the universal logit model, which includes attributes of other alternatives in the utility function of the target alternative. Therefore, such cross effects can allow for correlations between alternatives and can avoid the IIA assumption.
The third group of non-IIA models assumes a hierarchical or sequential decision-making process. The best-known model that can be represented by a hierarchical decision structure is the nested logit (NL) model. In the tourism research field, the NL model has been used to represent sequential choices, such as tourism generation and destination-type choice (Nicolau and Mas 2008), destination and travel companion choice (Wu et al. 2011a), tourism participation, and destination and travel mode choice (Wu et al. 2012b).
In addition to discrete choice, another important aspect of tourist behavior is temporal choice, including tourist’s length of stay and time use decisions on different activities. Most studies focus on the total time that tourists spend during a tour trip. These studies use a survival model to analyze a tourist’s length of stay at a certain destination (Gokovali et al. 2007; Martinez-Garcia and Raya 2008; Thrane 2012).
Models have also been proposed to address some specific issues in tourist behavior. As noted above, the mixed logit model has been applied to capture the unobserved heterogeneity of tourists, by assuming that the parameters of variables vary randomly across individuals (Correia et al. 2007; Grigolon et al. 2014; Nicolau and Mas 2006). Some studies have adopted the latent class model to accommodate tourists’ heterogeneous choice structures (Alegre et al. 2011; Wu et al. 2011a). To account for the mechanism of loss aversion, some models incorporate prospect theory, which argues that individuals’ decisions are more sensitive to losses than to gains. Nicolau (2011, 2012) applied this theory to investigate tourists’ asymmetric reactions to travel cost and its effect on destination choice.
Sirakaya and Woodside (2005) noted that one of the first foundational integrated models of travel decision making is that of Clawson and Knetsch (1966), who proposed an outdoor recreation experience model with a five-phase decision-making process starting with the anticipation phase, followed by travel to actual site, on-site experiences and activities, travel back, and concluding with recollection of experiences. Woodside and MacDonald (1994) introduced the concept of trip frame that describes a set of interdependent travel choices (i.e., destination, route/mode, accommodation, activity performance, and visiting shops), which are made at different points in time.
Dellaert et al. (1998) proposed a conceptual framework to represent and understand multifaceted tourist travel decisions that involve subsequent choices for different facets of a single trip as well as the constraints that may limit the number of feasible travel alternatives. They empirically identified some interdependencies in the following choice process after deciding to travel: (1) pre-travel choices (destination, accommodation, travel party, travel mode, departure time, and duration of travel), and (2) during-travel choices (special attractions to visit, travel route to follow, day-to-day expenditure and rest and food stop locations, and timing). Dellaert et al. argued that to account for the above interdependencies, multidimensional choice models like the NL or probit-type models can be applied. Because these choice models cannot directly incorporate timing decisions, they further suggested applying hazard-based duration models. However, duration models are statistically oriented and cannot properly reflect the behavioral mechanisms in timing decisions.
To elucidate the relationship between traveling to one destination versus to several destinations during a trip, King and Woodside (2001) undertook a qualitative comparative analysis of a travel and tourism purchase–consumption system, which is the sequence of mental and observable steps that a consumer undertakes to buy and use several products, for which some of the products purchased lead to a purchase sequence involving other products. King and Woodside also conceptualized a purchase–consumption framework for leisure travel, which begins with information search and use, followed by three sequential levels: level 1, choices of destination, activity, and attraction; level 2, choices of accommodation and mode/route to destination; and level 3, on-site shopping and dining behavior and choice of mode/route in and around the destination. Post-travel evaluation is also included in the proposed purchase–consumption system. Woodside and Dubelaar (2002) extended the King and Woodside model by defining a tourism consumption system as the set of related travel thoughts, decisions, and behaviors by a discretionary tourist prior to, during, and following a trip (Becken and Gnoth 2004).
Focusing on car tourists’ one-day tours, Fujiwara and Zhang (2005) developed a new scheduling model by combining a destination/route choice model with a nested paired combinatorial logit (NPCL) structure and a time allocation (TA) model. The NPCL model represents choices of destination and route, where the lower level indicates choice of destination and the upper level refers to choice of route. In addition, utility of destination choice is influenced by the time spent at each site. Different route choices result in hourly variant level of service of the road network, which consequently gives rise to varying available time use in the TA model. These are reflected in the NPCL model. Moreover, the TA model endogenously incorporates the influence of hourly variant level of service at the site of interest, which is further affected by the allocated time. Consequently, an iteration estimation procedure is proposed to estimate consistently the parameters in the NPCL and TA models.
In terms of time allocation in tourism activities, some studies have attempted to analyze tourists’ time allocation decisions using a time budget method (Cooper 1981; Fennel 1996), which is a method of measuring the duration and sequence of activities engaged in by an individual during a specific period of time. Activities that tourists participated in were recorded, including starting time and finishing time of each activity, from which tourists’ space-time patterns can be derived. More recently, Wu et al. (2011b) applied a multiple discrete-continuous extreme value (MDCEV) model to analyze tourists’ time-use behavior involving multiple activities. The model is used to represent simultaneously tourists’ decisions on what activities to participate in and how much time to allocate to each activity.
Because tourists face many aspects of choices and have to deal with spatial and temporal constraints, tourist choice behavior is a multidimensional process and its decision-making mechanisms are complex. It is expected that interdependencies exist between different behavior aspects, and some models attempt to represent such interdependency. To represent interrelations between two discrete choices (i.e., destination and travel mode choices), Fukuda and Morichi (2002) developed a framework for modeling recreational travel behavior using a bivariate dichotomous probit model. However, their model can only be used to analyze binary choice behavior. Therefore, some studies used a NL model to incorporate more choice aspects and, at the same time, represent the relationship between them with the help of an inclusive value, which is, in fact, the maximal utility of the alternatives in the choice set of the lower level nest (Wu et al. 2012b). In addition, some models have been developed to represent interdependence between discrete and continuous choice. For example, Alegre et al. (2013) used a Heckman model to analyze households’ tourism expenditure decisions, which was treated as an interrelated two-stage process: first, whether to make a trip and, second, how much to spend on it. Their model is a type of discrete-continuous choice model that uses a binary logit model to represent tourism participation behavior and ordinary least squares (OLS) regression to analyze tourism expenditure. At the same time, the model assumes the error terms in two functions follow a bivariate normal distribution. Wu et al. (2013b) employed a similar discrete-continuous choice model to represent these two choice aspects. In their study, tourism participation choice was analyzed with a Scobit model, which includes a skewness parameter to relax the assumption made in a binary logit model that the sensitivity of individuals to changes in explanatory variables is highest for those who have indifferent preferences over participation and nonparticipation.
8.8 Relationship Between Tourism Behavior and Other Life Choices
Tourism experience not only increases individuals’ satisfaction with the leisure-life domain, but has also been found to influence other life domains. Trenberth et al. (1999) explored the role of tourism experience in the domain of work and showed that tourism could be useful in coping with work-related stress because of its active-challenge and passive-recuperative natures. Strauss-Blasche et al. (2002) conducted a survey of 53 company employees and found that a restful vacation may buffer occupational stress with respect to physical complaints and life satisfaction. Their survey results also suggested that leisure travel moderates stress primarily when stress levels are relatively high. Sirgy et al. (2011) described how specific tourism experiences contribute to positive and negative effects in various life domains, such as social engagement, love, culture, family, and physical well-being, which spill over to overall life satisfaction.
The relationship between tourism and other life domains also results from the fact that different life domains impose mutually exclusive demands on individuals’ limited resources of time. The more time an individual expends on tourism activities, the less time they have for fulfilling their roles in other life domains. Such conflict causes a cross-domain spillover effect, which may have an impact on overall life satisfaction (Rice et al. 1992). Thompson and Bunderson (2001) indicated that conflicts between different life domains increase when individuals fail to allocate the appropriate time to work, family, community, religion, and tourism activities. High job stress, caused by work not being done during the vacation, for example, is associated with poorer well-being (Strauss-Blasche et al. 2002). On the other hand, tourism satisfaction is observed to exert a moderating effect between work/leisure conflict and quality of life (Lin et al. 2013).
8.8.1 Tourism and Generation Theory
Tourism behavior is a part of life choices and it changes over time. To compare the difference through life time, one of the most common and useful way is to classify people by their ages. However, people distinct not only in ages, but by the common events that help shape their lives (Travel Industry Association 2006, p. 8). Thus, tourism researchers try to gain insights through the lens of generation theory instead of simple classification of age. The idea of generation theory was derived from Manheim’s arguments about “the problem of generations”, and was expanded to apply in the social and cultural processes to classify generations for the purpose of sociological study (Pilcher 1994). Generation theory explains that the era in which a person was born affects the development of their view of the world (Codrington 2008). Lehto et al. (2008) stated that the growth of one generation is influenced by historical, political, economic and social events of the time, as well as educational opportunities and lifestyle changes. The most prevalent classification of generation is the division of American society: Silent Generation, Baby Boomers, Generation X, and Generation Y have become fairly well known and well used in recent years to describe groups of people of different ages in academic research. In generation theory, people of the same age are likely to have similar underlying value systems, which are the drivers of behavior and attitudes, and are good predictors of behavior and expectations (Codrington 2008).
In tourism research, generation theory was firstly introduced to identify homogeneous travel patterns and market segmentations, when marketers recognized the need to target different groups of tourists, rather than the whole market (Pennington-Gray et al. 2003). It is expected that better understanding of tourism behaviors of different generations could forecast the changes over time in a more accurate way. The existing studies are mainly focus on two aspects: tourism consumer behavior and tourism attitude/experience during travel. Most studies are about consumer behavior. For example, Furr et al. (2001) used data from 13,000 individuals to analyze generational consumption and behavioral patterns (including information-seeking behavior, purchase behavior and booking travel behavior) of Internet use to compare the differences in different generations and found that Generation X’ers and Baby Boomers groups were more actively online than the senior group. Beldona (2005) distinguished changes in online travel information search behavior and found that although younger people tended to be more eager for new things and quickly adapted to online behavior, elders also adopted new things earlier than traditionally assumed. Pennington-Gray et al. (2003) made an analysis of cohort and examined the changes in preferences for travel over time. Li et al. (2013a) examined the attitudes and behaviors of American international travelers using a generational analysis, and found that characteristics of generation affected travel characteristics in terms of information usage, previous destination experience and future choice. As for studies on tourism attitude/experience based on generation theory, Lehto et al. (2008) analyzed the tourism experiences sought and actual vacation activities of Silent Generation and Baby Boomer generation and found that the differences in cohort-induced lifestyles and values permeated into vacation experience and activity. Compared to the number of research on consumer behavior and generation theory, research on the perception of tourists is limited. However, the understanding of tourism attitude and experience based on generation theory appears to be a more useful basis for addressing different preferences and behaviors of tourists.
Pennington-Gray and Blair (2009) suggested that more theory-based research is needed to document different generations’ travel attitudes and behavior, especially related to the four major generations in American. While we are happy to witness the increase in generational analysis in tourism literature of American tourism market, we also notice that few research in developing countries has considered the influence of generation theory on tourism. Given that different countries experienced different nationally significant events at different times, it seems impossible to develop a single generational theory that applies around the world (Codrington 2011). Codrington (2011) raised up the application of generation theory in Asian regions, unlike American society, generations in different countries have their own classifications for its social development. As more and more international tourists from developing countries have participated in tourism activities, applying the generation theory would gain more insightful understandings of their tourism attitudes and behaviors. On the other hand, existing studies about generation theory and tourism behavior are mainly focus on tourists’ revealed preference, like consuming preference and information searching preference. Few of them have focused on the future choice or the decision-making process of different generations. However, research on tourists’ intention or attitude of different generations may help to predict the future decision on tourism behavior, which could help destination managers gain a lead in the market in terms of strategic planning and marketing.
Above all, the current studies on tourism and generation theory are still not enough to explain the complex process of tourism behavior, more research is needed under the exploration of generation difference among different region, especially in the Eastern hemisphere.
8.8.2 Senior’s Tourism Behavior
Tourists present different tourism attitudes and behaviors in different generation, among which, older generation is one of the main components. With the changing landscape of tourism industry, many academic articles have studied the market of those aged 55 or older, which includes pre-seniors (those 50–64) and seniors (65 and older), to better understand their tourism preferences and behaviors (Shoemaker 2000). The importance of seniors as a market segment in tourism has been recognized for years (Nichols and Snepenger 1988; Lehto et al. 2008). Unlike other generations, seniors have more discretionary time and disposable time, which makes them become main consumers in tourism industry.
Current studies on senior tourism behavior varies from many aspects and different regions. You and O’leary (2000) conducted a cohort analysis of older Japanese travelers and showed that older travelers demonstrated a more active participation pattern than a decade ago. Focusing on tourists in Taiwan, Huang and Tsai (2003) examined the senior travelers in Taiwan through their travel motivations, selection modes and travel satisfactions and adopted Ridit (Relative to an Identified Distributed) analysis of destination selection attributes and factor analysis to perceive the gap between travel agents and senior travelers and predict seniors’ future behavior. Chen and Shoemaker (2014) used generation theory to analyze the psychological characteristics and travel behavior of American senior leisure tourists and confirmed that changes in travel preferences, attitudes, and behaviors among seniors might be minimal, but the difference between two generations, like older senior over 65 and younger seniors under 65, is large because of physiological deterioration, which will make a more specific direction for senior tourism segmentation. Kim et al. (2015) investigated the relationship between seniors’ tourism behavior and their overall quality of life and found that when they were satisfied with their trip experience, their overall quality of life would be improved.
To date, the number of senior tourism research is still increasing. Existing studies have revealed that younger and older seniors may have different attitudes and behaviors. On the other hand, such differences may change over time. It is therefore necessary to use longitudinal study to trace seniors’ changes in their tourism attitudes and behaviors for better understanding the needs of senior travelers and providing better tourism services for them and improving their overall quality of life.
8.8.3 Young People’s Tourism Behavior
According to the WTO, “youth travel includes all independent trips for periods of less than one year by people aged 16–29 which are motivated, in part or in full, by a desire to experience other cultures, build life experience and/or benefit from formal and informal learning opportunities outside one’s usual environment” (Richards 2008).
Poon (1993) described a shift within tourism and tourism behavior, the change being from “old tourism,” the mass tourism of the 1970s and 1980s, to “new tourism”, a flexible, segmented, customized, and diagonally integrated market. Young people are different from older generations because they are more adventurous and seek more autonomy during their travels (Sparks and Pan 2009). Young travelers are not a homogeneous group, their travel style and motivation reflects a changing trend (Cohen 2004). Unlike the older generation, the primary motivation for young travelers is the quest for personal growth instead of leisure, which is achieved through autonomy in decision making, stimulation in daily life, learning through exposure, and detachment and transient yet intense interpersonal relationships (Vogt 1976).
Current research on young people’s tourism behavior is limited. How highly developed Internet and mobile technology has changed young people’s tourism decisions is one of many areas of debate (Xiang and Gretzel 2010; Bizirgianni and Dionysopoulou 2013). Bui et al. (2013) examined how young Asians construct the concept of an “imagined West” from their travel. They analyzed young independent travelers in Asia and found that novelty seeking, unique experience, cultural capital accumulation, and education were goals when traveling in Western countries. Thus, traditional tourism behavior research could not explain some choices or decisions of young people, because of the more complex demands they made of the process. Traditional areas of segmentation studies in tourism behavior (such as demographic, geographical, and socioeconomic segmentation) did not apply to all kinds of young individuals. New factors should have been studied, for example, psychological characteristics. Pizam et al. (2004) investigated 1429 students and found that high risk taking and sensation seeking had significant effects on travel behavior. Lepp and Gibson (2008) also investigated the relationship between sensation seeking and tourism behavior. Ting et al. (2015) explored young Malaysians’ travel lifestyles and outbound tourism intentions based on tourists’ holiday decisions.
With the development of technology and high-level exchanges of social value, young people today have distinct characteristics compared with their elders. Normative theory cannot fully explain their behavior change in tourism, and there remains significant room for the study of young people’s tourism behavior from a variety of perspectives. In an effort to understand young people’s behavior better, some comparisons of different generations have been traced in tourism behavior research. Beldona (2005) distinguished changes in travel information search behavior in the US market and explained strong cohort effects for the period 1995–2000. Chung et al. (2015) utilized generational cohort theory to enhance understanding of tourism motivation and experience of cross-strait (mainland China–Taiwan) tourism and found that generational differences played an important role. Research based on generation theory not only provides a new perspective on young people’s tourism behavior, but also benefits knowledge about the senior tourism market (Chen and Shoemaker 2014).
In sum, the youth tourism market and young people’s tourism behavior still lack relevant research. As the youth segment is an emerging market, scholars should consider the differences between mature tourism markets and youth tourism markets. The differences caused by social development, value, and lifestyle cannot simply be explained by normative criteria. Advanced models and theories should be established for young people’s tourism behavior, which is a significant part of tourism behavior and will have many implications for practical market strategies. On the one hand, youth tourism is a rapidly growing segment of the tourism market. On the other hand, the preferences, personality, attitude, and lifestyle of young people toward tourism are different from traditional mass tourists. This gap between young and older tourists should be investigated through behavior study. Furthermore, because young people are often changeable and demanding, their tourism behavior is difficult to describe with a single choice model or theory. The data on young people’s attitudes, emotions, cognition, and experience in tourism are difficult to collect from normative questionnaires. Thus, research on young people’s behavior calls for advanced models and improved research methods.
8.8.4 Tourism and Health
In a report submitted to the OECD, Lunt et al. (2011) defined medical tourism as “when consumers elect to travel across international borders with the intention of receiving some form of medical treatment.” Medical tourism has become an important part of the tourism industry. Although the treatment in medical tourism may span the full range of medical services, the most common treatments include dental care, cosmetic surgery, elective surgery, and fertility treatment (Lunt et al. 2011). According to the World Travel & Tourism Council (WTTC), in 2011 medical tourism contributed 9 % of global GDP (more than US$6 trillion) and accounted for 255 million jobs.Footnote 1 Even though individuals have travelled abroad for health benefits since ancient times (Lunt et al. 2011), no academic studies of medical tourism are found in the literature before Goodrich and Goodrich (1987). Some research regards medical tourism as a special case of patient mobility (see Glinos et al. 2010).
In an extensive study of various countries, Connell (2006) found that affordability of international flights, foreign currency exchange rates, aging of wealthy postwar baby boomers, diffusion of the Internet, existence of brokers connecting patients to hospital networks, progress of health care systems based on advanced technologies in major countries (India, Thailand, Malaysia, and Singapore) are major factors pushing the growth of medical tourism. Costly medical treatments in developed countries are further identified as an important factor for the growth of medical tourism in developing countries. Focusing on medical tourism in Pattaya, Thailand, Mechinda et al. (2010) revealed that loyalty to medical tourism is influenced by satisfaction, trust, perceived values, and familiarity with, and image of, the destination. According to Moghimehfar and Nasr-Esfahani (2011), factors affecting medical tourism destination choices include pull-and-push factors. Pull factors include tourism attractiveness of destination, advantages of cost, access to destination (distance and cost), while push factors include service quality and lack of advanced medical technologies and services at the residence location. Furthermore, it was noted that religious affinity was the most important factor when Muslim couples with infertility chose the destination for reproductive medical treatments. Hallem and Barth (2011) showed that functional dimensions largely affect value perception of the experience of medical tourism, while Internet usage involves social values, knowledge, and functional values. As a comparative study across countries, Yu and Ko (2012) found that Korean tourists attached the highest importance to medical activities and tourism activities, Japanese tourists mainly emphasized convenience of medical treatment and care services, stay and cost, information, and insurance, while Chinese tourists were most sensitive to stay and cost. In addition, while Chinese tourists showed higher interest in minor surgery, beauty and health care, Japanese tourists were more interested in major surgery and rehabilitation. Connell (2013) reported that culture, availability of health care, and service quality are influential in medical tourism behavior. Focusing on an oriental medicine festival in South Korea, Song et al. (2014) noted that the image of the host city and perceptions of oriental medicine affected visitors’ attitude, which together with subjective norms and positive expectations influenced visitors’ desires, in turn influencing behavioral intention. Pan and Chen (2014) identified eight motivations that tourists from mainland China had for visiting Taiwan for medical tourism: media advertising and marketing by travel agencies, recommendations by friends and relatives, desire of tourists to understand the condition of their own physical body, low quality of medical service at their place of residence, ease of communication based on Mandarin, lifting of the ban to visit Taiwan by the mainland government, shorter flight time by direct flight, and low cost of medical tourism. They further showed that itinerary, quality of accommodation facilities, and transportation arrangements are crucial to the demand for medical tourism. As a special case of medical tourism, dental tourism has increasingly attracted attention. Leggat and Kedjarune (2009) observed that demand for dental tourism was mainly determined by the cost of dental treatment and long waiting times as well as lack of dentists at their place of residence, availability of dental treatment services in other countries, existence of cheaper international flights, and the availability of access to the Internet, while difficulties of post-treatment follow-up and latent costly hospital visits due to retreatment were major barriers to participation in dental tourism.
Influenced by Western lifestyles, spa and wellness tourism targeting body care has shown high growth and diversity (Gustavo 2010). Spas include club spas, cruising spas, medical spas, hot spring spas, and spas at resort hotels. Targeting Portuguese tourists, Gustavo (2010) found that although spa was still a new leisure, among all of the spa-goers, 20 % regularly visited spas, while the main spa users were female clients, aged 30–39 years, highly educated persons, managers and professionals, people without dependents, urban dwellers, and people with an average monthly income of EUR 3000. The most important information source about spa usage comes from family members and friends, followed by Internet usage. Spa usage treats obesity, during which balanced intake of nutrition, physical exercise, use of traditional therapies, and intake of vitamins are usually practiced. On the other hand, the concept of wellness emphasizes not only physical and mental elements but also social elements, where enjoying leisure time (e.g., spending time with family members and friends) plays a central role. Smith and Kelly (2006) noted six dimensions of wellness tourism: medical and beauty, physical, relaxation, hedonic and experiential, existential and psychological, and spiritual and community-oriented dimensions. Each dimension has different requirements for physical space.
According to Lunt et al. (2011), medical tourism is related to the broader notion of health tourism, but is distinguished from health tourism by virtue of the differences in the types of intervention, setting, and inputs. Little is known about health tourism in the literature, with the exception of Runnel and Carrera (2012) who proposed a conceptual framework for health tourism. The framework includes a process that starts with identification of needs (basic health care: vaccinations, prevention, and screening; medical treatment: emergency treatment of illness and injury; health promotion: including cosmetic surgery; and optimal health: wellness and overall health), followed by information search related to needs and treatment options, weighting of various treatment options in order. After the above steps, people specify whether their preferred treatment options (combinations of type and place) belong to medical tourism. If medical tourism is preferred, people may directly contact medical brokers or medical concierges overseas, or medical providers via those medical brokers in order to consult them. If the consultation suggests medical treatment abroad, medical tourism takes place. Furthermore, inbound travel is undertaken if post-treatment care is required. If medical tourism is not preferred, people may consult with local doctors to decide whether to receive medical treatment at their own locality. If this decision cannot be made, people may proceed to the above process, in which case medical tourism is preferred. Regardless whether medical tourism is performed or not, travelers will eventually receive recuperation and follow-up services.
8.9 Tourism and Quality of Life
The contribution of tourism to Quality of Life (QOL) has recently attracted substantial research interest. Current tourism studies have begun to support the view that tourism activities can be a means of pursuing a higher level of QOL. It is argued that tourism plays a triple role in contributing to QOL by providing: (1) physical and mental rest and relaxation; (2) personal development space and the pursuit of personal and social interests; and (3) symbolic consumption to enhance status (Richards 1999).
8.9.1 Direct Effects of Tourism on QOL
Studies generally support a direct enhancement effect of tourism activities on individuals’ QOL, indicating that tourism activities have positive effects on individual satisfaction, psychological well-being and health, and helping individuals to cope with their stress (Strauss-Blasche et al. 2002). Gilbert and Abdullah (2004) examined whether the tourism experience has any impact on the life satisfaction or subjective well-being of those taking vacations. They found that people who went on holiday experienced higher life satisfaction than those who did not, both before and after their trips. Similarly, Boelhouwer and Stoop (1999) found that people who had recently taken a holiday trip scored higher on overall happiness than those who had not. Some studies emphasize the effect of tourism activities on a certain group of individuals. For example, tourism trips have been shown to improve the lives of people with a disability (Card et al. 2006); increase the intellectual functioning of women over 65 (Sands 1981); generate positive attitudes and greater QOL in patients with mental illness (Pols and Kroon 2007); and improve the QOL of seniors (Lee and Tideswell 2005).
While a tourism trip may contribute to tourists’ psychological well-being and QOL, its impact would vary depending on different factors, such as length of stay (Neal et al. 2007) and different phases of the trip (Neal et al. 1999). For example, Neal et al. (2007) examined the moderating effect of length of stay and showed that tourism experience has a positive influence on QOL. Moreover, this positive influence is more evident for tourists with extended stays compared with tourists with shorter stays. Tourism activities potentially contribute to QOL in different stages. It is noteworthy that individuals’ sense of well-being significantly increases before traveling through planning and anticipating the trip. However, the positive post-trip effects on QOL do not last very long, with some studies suggesting that tourism activities may have short-term effects on QOL. Findings indicate that the positive effects of a vacation fade within a short time (Eden 1990; Westman and Eden 1997). Strauss-Blasche et al. (2002) reported that improvement in QOL lasted no longer than five weeks.
Dolnicar et al. (2012) argued that tourism experience is not important to everyone. People assign different levels of importance to each of the life domains that determine their QOL (Scalon 1993). Some people regard family as the most important contributor to QOL, while others view their work as playing a key role. The impact of tourism experience on QOL may depend on different stages in life and other background variables that may influence the degree of importance of travel. When identifying domains that contribute to QOL constructs, Dolnicar et al. (2013) suggest that it is necessary to take into account that the hierarchy of needs varies across and within individuals over time. Models ranking domain importance should weight domain satisfaction by the importance a person attributes to that specific domain. They proposed a dynamic, individual, hierarchical model to demonstrate the role that tourism activities play in people’s lives at any given point in time.
8.9.2 Indirect Effects of Tourism on QOL
Some studies have found that although tourism activity by itself may not be a strong influence on QOL, there might be some intervening variables. Neal et al. (1999) demonstrated that the influence of tourism experience on QOL occurs through the mediation effect of satisfaction with tourism services. They investigated the significance of tourism on leisure life and overall QOL, and showed that satisfaction with tourism services contributed to satisfaction in leisure life, which in turn affected overall QOL. Similarly, Chen et al. (2016) examined the relationships between tourism experiences and life satisfaction through the mediation effect of tourism satisfaction. It was found that individuals who felt relaxed and satisfied with their tourism experience were more likely to be satisfied with their life in general.
In addition to the mediation effect of tourism satisfaction, other intervening variables have also been explored. Sirgy (2010) proposed a conceptual framework that incorporates goal theory to analyze the relationship between tourism and QOL. Sirgy argued that the choice of leisure travel goals (e.g., intrinsic vs. extrinsic, abstract vs. concrete) is important, because those with more attractive and attainable travel goals and those who take actions to implement their goals are more likely to experience higher levels of subjective well-being as a consequence of their leisure travel. Nawijn (2011) found that although people were generally happier when they took a tourism trip, factors such as attitude and holiday stress can influence their levels of happiness, and overall they found no significant improvement in their life satisfaction. Woo et al. (2016) examined the link between tourism motivations and QOL among the elderly, and found that motivations were related to growth needs (knowledge seeking, rest and relaxation, social interaction, and self-fulfillment) rather than basic needs, and that these motivations positively affected their overall life satisfaction.
Although studies have generally supported the view that tourism activity contributes to overall life satisfaction, the complex relationship between tourism and QOL requires further investigation.
8.10 Determinants of Tourism Behavior
Tourism behavior is multi-faceted, including participation in tourism, choices of destination and travel party, choices of travel modes accessing destinations, time use and expenditure at destinations, etc. Generally speaking, even though various factors may affect human choice behaviors, they can be grouped into three categories: factors specific to choice alternatives, factors specific to individual decision-makers, and factors common to all decision-makers (Zhang et al. 2004). Here, determinants of tourist behavior are reviewed following this categorization.
8.10.1 Alternative-Related Determinants
People make a travel for visiting tourism resources (e.g., natural resource, historical and cultural resources, health tourism). A variety of infrastructures are expected to play diverse roles of supporting tourism visits. Accordingly, tourism resources and their supporting infrastructures are two major determinants from the perspective of tourism choice alternatives.
8.10.1.1 Tourism Resources
Natural resources
Natural resources are traditional attractions and important components of the tourism resources and of increasing significance to world tourism industry (Priskin 2001). The term nature-based tourism is generally applied to tourism activities depending on the use of natural resources which remain in a relatively undeveloped state, including scenery, topography, waterways, vegetation and wildlife (Deng et al. 2002). As people become more and more environmentally sensitive, nature-based tourism has received increasing attention from tourists. Millions of people travel to see and experience natural environment. Tourists’ satisfaction with and expectation about natural resources are associated with their decisions on choosing nature-based tourism. The tourism managers often search new natural resources for satisfy tourists’ diverse demand; however, natural resources are limited and cannot meet all the demand of tourists. Such dilemma calls for creating a sustainable form for balancing nature protection and tourist demand. It is also necessary to enhance tourist’ awareness about negative impacts of their visiting behaviors, consequently resulting in their voluntary behavioral changes in consuming natural resources.
Historical and cultural resources
Historical and cultural resources are often linked to cultural heritages, which include both physical assets (e.g., architecture, paintings and sculptures), intangible culture (e.g., folklore), and interpretative arts (e.g., storytelling and drama). These resources can be displayed in museums, heritage sites, exhibitions, and theatres. Cultural resources can be a key sector of some local regions and helps to build unique images. Unlike natural tourism resources, the core of historical and cultural resources needs people’s “feel” rather than “gaze” (Poria et al. 2003). Therefore, the links between cultural resources and tourists are more complex. Destinations with historical and cultural resources need to be better managed in a way of being understood and accepted by tourists. This is because different presentations of the resources may bring different perceptions to people, which may arouse or suppress tourists’ interest in historical and cultural resources.
Health tourism
With the high cost of medical treatment in the original destination and fewer barriers to travel, the idea of availing healthcare in another city or country is gaining greater appeal to many travelers. (Carrera and Bridges 2006). The decisions to engage in health tourism is complex and are driven by patients’ unmet need. Runnels and Carrera (2012) adopted a sequential decision-making process in opting for or against medical care abroad in terms of the required treatments, locations and quality and safety issues attendant to seeking care. Health tourism enhances individual’s wellbeing in mind and body though medical interventions and the combination of daily healthy activities with tourism activities may also arouse people’s intellectual curiosity and desire for new discoveries (Sung et al. 2012). As health tourism is becoming a new trend in tourism market, especially in senior tourism market, the traditional behavior of tourists would be changed. The aging phenomenon in many countries may contribute to the progress of health tourism, as older people have more discretionary time and are more concerned about their health than other generations.
Urban tourism
Cities, as destinations, receive the greatest volume of tourists (Ashworth and Page 2011) and are determinant attractions to some tourists. Urban tourism provides a set of tourist recourses or activities located in towns and cities, to show tourists the history, culture and modernism of the destination. The development of urban tourism can also have influence on tourists’ behavior. For instance, sustainable urban tourism will encourage tourists engage in pro-environmental behaviors (recycling, green transports and green energy), changing people’s behavior in city context (Miller et al. 2015). On the other hand, conflicts between urban residents and tourists should be paid enough attention in the design and management of urban tourism.
Event tourism
Events are animators of destination attractiveness and keys to marketing propositions in promotion of places to attract more tourists. Stimulated by event tourism, the destination provides a substitutable form of demand between residents and visitors. Event tourism expands the tourism potential and capacity beyond traditional leisure-based tourism (Getz and Page 2016). The types of event tourism often contain business (conventions, exhibition and marketplaces); entertainment (concerts, shows and award ceremonies); festival and culture (festivals, religious sites and art exhibitions); sports (professional leagues, participator and annual games). For some tourists the event in the destination is the only reason for travel and some destinations become famous all over the world for holding special events.
8.10.1.2 Supporting Infrastructure
Transportation network
When people decide to travel, the first thing to solve is how to get to the destination. The accessibility of tourism destination would determine whether this travel will happen or not in the first place. The essential role of transportation network in tourism development is recognized by many scholars (Prideaux 2000; Khadaroo and Seetanah 2007). It is influential to both tourism development and tourists’ choices.
A good and attractive transportation system rests to a large extent on quality and availability of transportation infrastructure comprising air services and airports, land transport systems and routes and water transport infrastructures as well (Khadaroo and Seetanah 2008). It has crucial influences on the tourism attractiveness of destinations.
Because of time available to travel, most tourists prefer a destination with good transportation access. For tourists who want to visit multiple destinations during one trip, the transportation network is even crucial for their choices. Improved transport infrastructure and services (e.g., cheaper airlines, high-speed railway, road capacity improvements, reduced fuel consumption, and discounted transit fare) reduce time and cost spending on moving between places, and consequently accelerate the tourism development.
Accommodation
Accommodation is another important issue for tourists, most people prioritize accommodation when planning a trip and spend most of their planning time and effort on selecting the right option (Li et al. 2015). Many studies have been conducted to study the selection criteria that affect consumers’ choice intentions. For example, Lockyer (2005) intensified factors such as location, price, facilities and cleanliness as strong factors on tourists’ hotel selection. Sohrabi et al. (2012) conducted an exploratory study of Tehran hotels and found that hotel comfort factors (hotel services, room comfort, car parking and pleasure, etc.) and hotel compensatory factors (including expenditure, news information, security and protection) were often the determinant factors for accommodation choices. For the consideration of multiple criteria in hotel selection, Li et al. (2013b) introduced a new fuzzy decision support technique based on an aggregation function named the Choquet Integral to discover the preferences among travelers that affect their hotel selection. Hotel selection is a complex process as the wide range of selection criteria. Li et al. (2015) adopted Emerging Pattern Mining concept to discover changes and trends in travelers’ intentions, which help the hotel managers to identify features of interest to specific groups and meet their guests’ expectations.
While nowadays the most common accommodation for travelers is the standardized hotel chains, new types of accommodation have emerged when tourists seek more than just sleeping, for example, the flourish of family inn, which is also called B&B or homestay. The properties of family inn are small and personal in nature and the benefits of such new type of accommodation include quiet, private atmosphere, lower cost and closer interactions between guests and hosts (Nuntsu et al. 2004), which are the main factors affecting people’s choices, especially young travelers who seek for novelty. Unlike other business modes of accommodation, there is also a free hospitality exchange network online called Couchsurfing. Travelers who choose Couchsurfing have totally different tourism behaviors compared to the mass tourists. In the study of Couchsurfing, not only traditional studies of accommodation, like selection criteria and operation mode, should be noted, more ethical and moral aspects should be considered to explain how putting trust in strangers and managing relationships by various social networking mechanisms (Molz 2013).
Restaurants, souvenirs, and other hospitality facilities/services
Eating local foods has been considered a key attraction for tourists. Many destinations attempt to provide tourists with culinary experiences (Cohen and Avieli 2004). In the decision-making process, food is becoming a key element in tourists’ consumer behavior and to increase tourism satisfaction (Tsai and Wang 2016). In addition, delicate artifacts, convenient information centers and other convenient tourism facilities would also give tourists good impression about the destination and affect their choices during travel. Furthermore, with the rapid development of social media, facility managers should pay more attention to the online evaluations for creating more positive images of the destination by electronic word-of-mouth.
8.10.2 Decision Maker Related Determinants
Sociodemographic
Tourists can be categorized into different groups and types based on their sociodemographic characteristics. Segmentation studies are heavily utilized in tourism behavior research. Commonsense segmentation involves division by gender, age, origins, income, etc. (Nichols and Snepenger 1988; Frew and Shaw 1999). While segmentation studies explain some differences in tourism behavior, the criterion for categorization is sometimes viewed as unsophisticated. Expanded variables should be introduced for categorization and more discriminating criteria selected (Frochot and Morrison 2000). Park and Yoon (2009) studied Korean rural tourism empirically, segmenting the tourists by motivation. Prayag et al. (2015a, b) used bagged clustering on the push-and-pull factors of Western Europe to segment potential young Chinese travelers and offered implications for the young Chinese outbound tourism market. Segmentation criteria need to be specified to explain this new tourism market and tourist behavior.
Emotions and affective states
Tourism is mainly about recreation, feeling better both mentally and physically. Some people even treat it as a means of self-analysis. As an expenditure behavior, tourism decisions are highly emotional as well as influenced by other people (Swarbrooke and Horner 2007: 73). However, the importance of emotion seems to be ignored while tourism behavior research focuses on market segmentation and destination competition.
The main realm of emotion in tourism behavior research is satisfaction and loyalty research. The core question concerns the relevant emotional responses during the consumption or experience of different services. Studies cover tourists in general or at a particular destination (Baloglu 2001; Yoon and Uysal 2005). Emotion and destination image are another realm, and the impact of destination image influences decision making (Chi and Qu 2008). The relationship between emotion and tourism experience seems to be a rather new field. Tourists gain emotional experience through tourism activities and this may change their travel decision process and their choice of the next destination or whether to revisit the place (Prayag et al. 2015a, b).
8.10.3 Environmental Determinants
Social interaction
Studies have noted that social interaction has an important influence on behavior (Powell et al. 2005; Moretti 2011). An individual’s behavior is influenced by their reference groups in social interaction, normally for two main reasons: word-of-mouth (WOM) information and social norms. Bansal and Voyer (2000) suggested the important role of WOM information in various types of tourism behavior. WOM information and consumers’ feedback review are closely related to hospitality management and consumer satisfaction (Litvin et al. 2008; Ye et al. 2011). Lam and Hsu (2006) found social norms to be an important factor in influencing tourists’ intentions to visit a certain destination. Lopez-Mosquera and Sanchez (2012) analyzed how normative beliefs determine the visitors’ willingness to purchase. Han (2015) merged value–belief–norm theory with the theory of planned behavior to understand travelers’ proenvironmental intentions in a green lodging context. By collecting information from reference groups, an individual’s behavior can change during the process of travel or visit. Research on tourism behavior should fully consider such social interaction.
Influence of IT and media
When Thomas Cook established the first travel agency in 1845, he could never imagine how information technologies (IT) would dramatically change the tourism sector and the practices of professionals. Today, with IT connecting individuals and cultures, the shape of the tourism industry has changed and impacts the way people access and use travel-related information. Tools such as search engines have become a predominant force that influences travelers’ access to tourism products (Xiang et al. 2008). The adoption of smartphones and their apps provides further sources of information for travelers in making travel decisions (Wang et al. 2016).
With the prevalence of computers and mobile technology, the tremendous growth of social media has changed the dynamics of online communications (Sigala et al. 2012). As a creation of online communication, electronic word of mouth (E-WOM) is directed at consumers through Internet-based technology related to the use or characteristics of particular goods and services, or their sellers (Westbrook 1987). Litvin et al. (2008) investigated the influence of both positive and negative WOM in tourism products and studied the significant role that WOM has traditionally played as an information source in travel and tourism. Yoo and Gretzel (2011) reported some aspects of consumer-generated media and defined them as “a new form of word-of-mouth that serve informational needs by offering non-commercial, detailed, experiential, and up-to-date information with an access beyond the boundaries of one’s immediate social circle.” Social media has significantly impacted tourism system.
Cross-cultural differences
In the last two decades, the tourism and travel industry has experienced an extraordinary increase in international tourism, not only in mature destinations such as Europe and the USA, but rapid growth has also emerged in the Asia-Pacific region, the Middle East, and Africa. When travelers from different backgrounds gather in the same place, it is necessary to appreciate how cultural differences may lead to different tourism behavior and how to translate this understanding into effective communication, thereby leading to better destination management and strategies.
Pizam and Sussmann (1995) and Pizam and Jeong (1996) suggested that nationality influences tourism behavior; that is, different behavioral characteristics are found in different countries. Hudson and Ritchie (2001) investigated different tourist attitudes toward the environment, demonstrating different cross-cultural tourism behaviors. Kozak (2002) found that nationality caused motivational differences in the decision-making process. Lee and Sparks (2007) compared the differences in travel lifestyles of Koreans in Australia and Korea. Hall and Mitchell (2000) pointed to a dearth of research on the cultural differences and similarities of tourists. However, some scholars have criticized the lack of an integrating theory of cross-cultural study in tourism behavior (Clark 1990) and noted that the assessment of national characteristics is often biased by ethnocentrism (Dimanche 1994). Despite these criticisms, the influence of cross-cultural research in tourism behavior is attracting increasing attention. Given rapid globalization, the investigation of cross-cultural determinants in behavioral research is an obvious research innovation.
8.11 Conclusions
Tourism research is an interdisciplinary field. It is often linked with research on social development, personal development, and values, which are further associated with various life choices. In this chapter, we reviewed the many tourist behavior research programs. We now summarize the issues facing integrated tourist behavior models, emphasizing the importance of qualitative research, and discuss how life-oriented tourism research can better inform the design of tourism services and policies.
8.11.1 Issues of Integrated Tourist Behavior Models
Tourist behavior usually involves a complex decision-making process with many dimensions. Interdependencies between behavioral dimensions across space and over time not only lead to competition between destinations, but also require collaboration between destinations, with consideration of tourists’ variety seeking and revisit behavior. Exploring tourist behavior may provide useful insights into both public policy decisions and marketing strategies in the private sector. In this chapter, although it is difficult to say that we have given a complete review of all the major studies of tourist scheduling behavior models, our review has nevertheless revealed a number of important unresolved issues.
Although many studies deal with a single facet of tourism behavior, research that includes multifaceted modeling frameworks is very limited. With a focus on spatial choices, multidestination choice behavior has not been well described from the perspectives of both sequential decisions and interdependencies. Regarding the temporal aspect, we find that the timing decision has been ill specified; further research is required to explore time use and expenditure behavior, while temporal changes of scheduling behavior have been ignored. Travel information is becoming increasingly important in supporting and influencing tourists’ decisions; however, the influence of travel information on scheduling decisions has not been clarified. Furthermore, although a number of descriptive integrated scheduling models have been proposed, the interdependencies involved in travel decisions have been incorporated only to a limited extent. Because tourism policy decisions are required to take into account various aspects of tourists’ behavior at the same time, the integrated models should be further improved to incorporate more behavioral aspects in a systematic way from both long-term and short-term perspectives. Because tourists usually regard satisfaction as a crucial indicator to evaluate their travel, the quality of scheduling decision and behavior should be given appropriate evaluation, which to date is lacking.
8.11.2 Importance of Qualitative Research
The first characteristic of tourism behavior research is that most of it is quantitative and consumer or destination based (Mehmetoglu and Altinay 2006). Reliance on consumer-based and destination-based studies ensures that researchers generate normative knowledge (Hunt 1976) and contribute to practitioners’ knowledge. Large-scale surveys and questionnaires often help to offer suggestions as to how destinations may attract more visitors or improve customer satisfaction. For example, Wu et al. (2012c) collected data from 1253 respondents in Japan to study the choice-making process of Japanese tourists. Phithakkitnukoon et al. (2015) examined the relationship over one year between personal mobility and tourism behavior in Japan by adopting a large-scale (country-level) opportunistic mobile sensing approach, in which mobile phones are used as tracking devices. Although a survey is a practical way of gathering data from a large number of people, it is not very effective at discovering the meanings and motives of different kinds of people.
With these shortcomings in mind, tourism behavior research should shift its focus from how individuals do their travel to how individuals choose their travel. Research should aim at gaining insight into tourists’ line of reasoning that makes them behave in the way they do. With the development of self-concepts and external environment, tourists are looking for optimum decisions instead of standard satisfactions. Thus, a qualitative research method calls for understanding the nature of tourism behavior (Mehmetoglu and Altinay 2006). For example, Uriely et al. (2002) used in-depth interviews to analyze the tourism behavior and experience gained by backpackers, revealing the heterogeneous nature of backpackers and distinguishing characteristics from other typologies. Martin and Woodside (2008) used grounded theory to construct tourism behavior; grounded theory is explicitly emergent and enables useful mapping and description of flows of thoughts and decisions in the research situation.
Although quantitative research methods still dominate behavior research, the need to develop qualitative research methods is becoming recognized. In-depth interviews, long-term observation, and other qualitative research methods are called for to describe the nature and new developing trends in the choice process.
8.11.3 Life-Oriented Tourism Research and Service Design
Research on tourism from the QOL perspective can provide useful insights into tourism service design. Coghlan (2015) conceptually discussed how to apply positive psychology to inform the design of travel experiences for a specific health outcome, namely enhanced participant well-being or mental health. The suggested design is based on the charity challenge model, in which participatory, group travel events are combined with extended physical activity, awareness raising, and fund-raising for charity. By taking part, the participants demonstrate the pathways to well-being, e.g., being active, doing something meaningful, giving, and connecting with others. Voigt and Laing (2010) showed that the concept of life cycle stages associated with reproduction can be used to develop and market new tourism products and experiences, and argued that parents-to-be and new parents form a new tourism niche market. As for tourism marketing and policy, segmentation is essential. In this regard, the concept of stage-of-life cohorts has been widely adopted (Pennington-Gray et al. 2003). Related to this segmentation associated with QOL, Dolnicar et al. (2013) showed that not everybody enjoys tourism and argued that effective tourism marketing should be based on better segmentation using QOL. They measured the QOL based on eight life domains—family, job, other persons, leisure, money, health, vacation, and spiritual life—and found that vacation was not valued at all by ~30 % of respondents. As for lifestyle, promoting cultural tourism is especially relevant because the consequences of cultural tourism are the improvement of lifestyle, values, family relationships, attitudes, customs, traditions, behavioral patterns, and many other economic and social components (Alinejad and Razaghi 2012). Alinejad and Razaghi further argued that cultural tourism is also the most appropriate way to recognize the cultural interdependence of nations, and tourism’s human-oriented nature has made the remarkable role of human beings very noticeable in its development. Lee (2015) showed that the public and private sectors in Gangwon, South Korea should consider health-concerned lifestyles in promoting green health tourism.
The tourism industry faces a number of uncertainties, one of which comes from seasonal variation in tourist arrivals; mobile workers play an important role in accommodating these uncertainties. Tuulentie and Heimtun (2014) analyzed the characteristics of these workers’ mobility, their relationships to seasonal workplaces, and their potential to become permanent residents in sparsely populated Arctic tourism destinations such as Finnish Lapland and Nordkapp (North Cape) in Norway. They found that mobility varies from lifestyle mobility to more economic and necessity-based mobility; furthermore, they noted that without year-round jobs, it was difficult to persuade these mobile workers to reside permanently in the region.
Tourism can also contribute to resolving social exclusion issues by removing not only physical barriers, but also internal, cultural, and social barriers that hinder persons with disabilities from participating in tourism (Kastenholz et al. 2015). Engaging in tourism activities can contribute to slowing the progress of dementia in elderly people (Page et al. 2015).
Notes
- 1.
http://travel.cnn.com/cheapest-facelifts-world-786941/ (accessed March 9, 2016).
References
Alegre J, Mateo S, Pou L (2011) A latent class approach to tourists’ length of stay. Tour Manag 32:555–563
Alegre J, Mateo S, Pou L (2013) Tourism participation and expenditure by Spanish households: the effects of the economic crisis and unemployment. Tour Manag 39:37–49
Alexandra V (2013) Consumer behavior in tourism and the influencing factors of the decision making process. Revista Economica 65(2):186–198
Alinejad ME, Razaghi Z (2012) Culture and its role in tourism development. Life Sci J (ACTA Zhengzhou University, Overseas Edition) 9(3):1593–1597
Ankomah PK, Crompton JL, Baker D (1996) Influence of cognitive distance in vocation choice. Ann Tourism Res 23(1):138–150
Ashworth G, Page SJ (2011) Urban tourism research: recent progress and current paradoxes. Tour Manag 32(1):1–15
Baloglu S (2001) An investigation of a loyalty typology and the multidestination loyalty of international travelers. Tourism Anal 6(1):41–52
Baloglu S, Mangaloglu M (2001) Tourism destination images of Turkey Egypt Greece and Italy as perceived by US-based tour operators and travel agents. Tour Manag 22:1–9
Bansal HS, Voyer PA (2000) Word-of-mouth processes within a services purchase decision context. J Serv Res 3(2):166–177
Becken S, Gnoth J (2004) Tourist consumption systems among overseas visitors: reporting on American German and Australian visitors to New Zealand. Tour Manag 25:375–385
Becker GS (1965) A theory of the allocation of time. Econ J 75:493–517
Beldona S (2005) Cohort analysis of online travel information search behavior: 1995–2000. J Travel Res 44(2):135–142
Bizirgianni I, Dionysopoulou P (2013) The influence of tourist trends of youth tourism through social media (SM) and information and communication technologies (ICTs). Procedia-Social Behav Sci 73:652–660
Boelhouwer J, Stoop I (1999) Measuring well-being in the Netherlands. Soc Indic Res 48:51–75
Boulding W, Kalra A, Staelin R, Zeithaml VA (1993) A dynamic process model of service quality: from expectations to behavioral intentions. J Mark Res 30:7–27
Bratfisch O (1969) A further study of the relation between subjective distance and emotional involvement. Acta Psychol 29:244–255
Buhalis D (1998) Strategic use of information technologies in the tourism industry. Tour Manag 19(5):409–421
Bui HT, Wilkins HC, Lee YS (2013) The ‘imagined West’ of young independent travelers from Asia. Ann Leisure Res 16(2):130–148
Card J, Cole S, Humphrey A (2006) A comparison of the accessibility and attitudinal barriers model: travel providers and travelers with physical disabilities. Asia Pac J Tourism Res 11:161–175
Carrera PM, Bridges JFP (2006) Globalization and healthcare: understanding health and medical tourism. Expert Rev Pharmacoeconomics Outcomes Res 6(4):447–454
Chen SC, Shoemaker S (2014) Age and cohort effects: the American senior tourism market. Ann Tourism Res 48:58–75
Chen CF, Tsai DC (2007) How destination image and evaluative factors affect behavioral intentions? Tour Manag 28(4):1115–1122
Chen C, Huang W, Petrick J (2016) Holiday recovery experiences tourism satisfaction and life satisfaction—is there a relationship? Tour Manag 53:140–147
Chi CGQ, Qu H (2008) Examining the structural relationships of destination image tourist satisfaction and destination loyalty: an integrated approach. Tour Manag 29(4):624–636
Chung JY, Chen CC, Lin YH (2015) Cross-strait tourism and generational cohorts. J Travel Res
Clark T (1990) International marketing and national character: a review and proposal for an integrative theory. J Mark 54(4):66–79
Clawson M, Knetsch JL (1966) Economics of outdoor recreation. The John Hopkins Press, Baltimore
Codrington G (2008) Detailed introduction to generational theory. Available at: www.tomorrowtodayuk.com. Retrieved 10 May 2016
Codrington G (2011) Detailed introduction to generational theory in Asia. Tomorrow Today 1–22
Coghlan A (2015) Tourism and health: using positive psychology principles to maximise participants’ wellbeing outcomes—a design concept for charity challenge tourism. J Sustain Tourism 23(3):382–400
Cohen E (1979) A phenomenology of tourist experiences. Sociology 13:179–201
Cohen E (2004) Backpacking: diversity and change. In: Richards G, Wilson J (eds) The global nomad: backpacker travel in theory and practice. Channel View Publications, Clevedon, pp 43–59
Cohen E, Avieli N (2004) Food in tourism: attraction and impediment. Ann Tourism Res 31(4):755–778
Connell J (2006) Medical tourism: sea sun sand and surgery. Tour Manag 27:1093–1100
Connell J (2013) Contemporary medical tourism: Conceptualisation culture and commodification. Tour Manag 34:1–13
Cooper C (1981) Spatial and temporal patterns of tourist behavior. Reg Stud 15:359–371
Correia A, Santos C, Barros C (2007) Tourism in Latin America: a choice analysis. Ann Tourism Res 34:610–629
Cosenza RM, Davis DL (1981) Family vacation decision making over the family life cycle: a decision and influence structure analysis. J Travel Res 17:17–23
Crouch GI, Louviere JJ (2001) A review of choice modeling research in tourism hospitality and leisure. In: Mazanec JA, Crouch GI, Brent Ritchie JR, Woodside AG (eds) Consumer psychology of tourism hospitality and leisure, vol 2. CABI Publishing, Wallingford, pp 67–86
Davis HL (1976) Decision making within the household. J Consum Res 2:241–260
De Mello M, Pack A, Sinclair T (2002) A system of equations model of UK tourism demand in neighbouring countries. Appl Econ 34(4):509–521
Decrop A, Snelders D (2005) A grounded typology of vacation decision-making. Tour Manag 26(2):121–132
Del Bosque IR, San Martín H (2008) Tourist satisfaction: a cognitive-affective model. Ann Tourism Res 35(2):551–573
Dellaert BGC, Ettema DF, Lindh C (1998) Multi-faceted tourist travel decisions: a constraint-based conceptual framework to describe tourists’ sequential choices of travel components. Tour Manag 19(4):313–320
Deng J, King B, Bauer T (2002) Evaluating natural attractions for tourism. Ann Tourism Res 29(2):422–438
Dimanche F (1994) Cross-cultural tourism marketing research: an assessment and recommendations for future studies. In: Uysal M (ed) Global tourist behavior. The Haworth Press, Binghamton, pp 123–134
Dolnicar S, Yanamandram V, Cliff K (2012) The contribution of vacations to quality of life. Ann Tourism Res 39:59–83
Dolnicar S, Lazarevski K, Yanamandram V (2013) Quality of life and tourism: a conceptual framework and novel segmentation base. J Bus Res 66:724–729
Eden D (1990) Acute and chronic job stress strain and vacation relief. Organ Behav Hum Decis Process 45:175–193
Engel JF, Blackwell RD, Miniard PW (1995) Cross-cultural tourism marketing research: an assessment and recommendations for future studies. Consumer behavior, 8th edn. The Dryden Press, Florida
Fennel DA (1996) A tourist space-time budget in the Shetland Islands. Ann Tourism Res 23(4):811–829
Fondness D, Murray B (1997) Tourist information search. Ann Tourism Res 24(3):503–523
Frew EA, Shaw RN (1999) The relationship between personality gender and tourism behavior. Tour Manag 20(2):193–202
Frochot I, Morrison AM (2000) Benefit segmentation: a review of its applications to travel and tourism research. J Travel Tourism Mark 9(4):21–45
Fujiwara A, Zhang J (2005) Development of car tourists’ scheduling model for 1-day tour. Transp Res Rec 1921:100–111
Fukuda D, Morichi S (2002) Recreation demand model as a mixture of interrelated travel behaviors. J Eastern Asia Soc Transp Stud 4:47–62
Furr HL, Bonn MA, Hausman A (2001) A generational and geographical analysis of Internet travel-service usage. Tourism Anal 6(2):139–147
Gallet CA, Braun BM (2001) Gradual switching regression estimates of tourism demand. Ann Tourism Res 28(2):503–507
Gartner WC, Bachri T (1994) Tour operators’ role in the tourism distribution system: an Indonesian case study. J Int Consum Mark 6(3/4):161–179
Getz D, Page SJ (2016) Progress and prospects for event tourism research. Tour Manag 52:593–631
Gilbert D, Abdullah J (2004) Holidaytaking and the sense of well-being. Ann Tourism Res 31:103–121
Gitelson R, Kerstetter D (1994) The influence of friends and relatives in travel decision-making. J Travel Tourism Mark 3(3):59–68
Glinos IA, Baeten R, Helble M, Maarse H (2010) A typology of cross-border patient mobility. Health Place 16:1145–1155
Gokovali U, Bahar O, Kozak M (2007) Determinants of length of stay: a practical use of survival analysis. Tour Manag 28:736–746
Goodrich G, Goodrich J (1987) Health care tourism—an exploratory study. Tour Manag 8(3):217–222
Goossens C (2000) Tourism information and pleasure motivation. Ann Tourism Res 27(2):301–321
Grigolon A, Borgers A, Kemperman A, Timmermans HJP (2014) Vacation length choice: a dynamic mixed multinomial logit model. Tour Manag 41:158–167
Gursoy D, McCleary KW (2004) An integrative model of tourists’ information search behavior. Ann Tourism Res 31(2):353–373
Gustavo NS (2010) A 21st century approach to health tourism spas: the case of Portugal. J Hospitality Tourism Manag 17:127–135
Hagerstrand T (1970) What about people in regional science? Paper Reg Sci Assoc 23:7–21
Haider W, Ewing G (1990) A model of tourist choices of hypothetical Caribbean destinations. Leisure Sci 12(1):33–47
Hall CM, Mitchell R (2000) Wine tourism in the Mediterranean: a tool for restructuring and development. Thunderbird Int Bus Rev 42(4):445–465
Hallem Y, Barth I (2011) Customer-perceived value of medical tourism: an exploratory study—the case of cosmetic surgery in Tunisia. J Hospitality Tourism Manag 18:121–129
Han H (2015) Travelers’ pro-environmental behavior in a green lodging context: converging value-belief-norm theory and the theory of planned behavior. Tour Manag 47:164–177
Huang L, Tsai HT (2003) The study of senior traveler behavior in Taiwan. Tour Manag 24(5):561–574
Hudson S, Ritchie JRB (2001) Cross-cultural tourist behavior: an analysis of tourist attitudes towards the environment. J Travel Tourism Mark 10(2–3):1–22
Hunt SD (1976) The nature and scope of marketing. J Mark 40(3):17–28
Huybers T (2003a) Modelling short-break holiday destination choices. Tourism Econ 9(4):389–405
Huybers T (2003b) Domestic tourism destination choices—a choice modelling analysis. Int J Tourism Res 5:445–459
Jafari J (2005) Bridging out nesting afield: powering a new platform. J Tourism Stud 16(2):1–5
Jang SCS, Feng R (2007) Temporal destination revisit intention: the effects of novelty seeking and satisfaction. Tour Manag 28(2):580–590
Johns N, Gyimóthy S (2002) Market segmentation and the prediction of tourist behavior: the case of Bornholm Denmark. J Travel Res 40(3):316–327
Kastenholz E, Eusebio C, Figueiredo E (2015) Contributions of tourism to social inclusion of persons with disability. Disabil Soc 30(8):1259–1281
Kemperman A, Borgers A, Timmermans HJP (2009) Tourist shopping behavior in a historic downtown area. Tour Manag 30:208–218
Khadaroo J, Seetanah B (2007) Transport infrastructure and tourism development. Ann Tourism Res 34(4):1021–1032
Khadaroo J, Seetanah B (2008) The role of transport infrastructure in international tourism development: a gravity model approach. Tour Manag 29(5):831–840
Kim H, Woo E, Uysal M (2015) Tourism experience and quality of life among elderly tourists. Tour Manag 46:465–476
King RL, Woodside AG (2001) Qualitative comparative analysis of travel and tourism purchase-consumption systems. In: Mazanec JA, Crouch GI, Brent Ritchie JR, Woodside AG (eds) Consumer psychology of tourism hospitality and leisure, vol 2, CABI Publishing, Wallingford, pp 87–105
Kozak M (2001) A critical review of approaches to measure satisfaction with tourist destinations. In: Mazanec JA, Crouch GI, Ritchie JRB, Woodside AG (eds) Consumer psychology of tourism hospitality and leisure, Volume 2, CABI Publishing, Wallingford, pp 303–320
Kozak M (2002) Comparative analysis of tourist motivations by nationality and destinations. Tour Manag 23(3):221–232
Kozak M, Decrop A (2009) Handbook of tourist behavior, theory and practice. Routledge, London
Lam T, Hsu CHC (2006) Predicting behavior intention of choosing a travel destination. Tour Manag 27:589–599
Law R, Leung K, Wong J (2004) The impact of the internet on travel agencies. Int J Contemp Hospitality Manag 16(2):100–107
Lee JH (2015) The influence of well-being lifestyle on green health tourism motivation and visit attitude in Gangwon area. J Hotel Resort 14(2):279–297
Lee SH, Sparks B (2007) Cultural influences on travel lifestyle: a comparison of Korean Australians and Koreans in Korea. Tour Manag 28(2):505–518
Lee S, Tideswell C (2005) Understanding attitudes towards leisure travel and the constraints faced by senior Koreans. J Vacation Mark 11:249–263
Leggat P, Kedjarune U (2009) Dental health ‘dental tourism’ and travelers. Travel Med Infect Dis 7:123–124
Lehto XY, Jang SC, Achana FT, O’Leary JT (2008) Exploring tourism experience sought: a cohort comparison of baby boomers and the silent generation. J Vacation Mark 14(3):237–252
Lepp A, Gibson H (2008) Sensation seeking and tourism: tourist role perception of risk and destination choice. Tour Manag 29(4):740–750
Li X, Li XR, Hudson S (2013a) The application of generational theory to tourism consumer behavior: an American perspective. Tour Manag 37:147–164
Li G, Law R, Vu HQ, Rong J (2013b) Discovering the hotel selection preferences of Hong Kong inbound travelers using the Choquet Integral. Tour Manag 36:321–330
Li G, Law R, Vu HQ, Rong J, Zhao XR (2015) Identifying emerging hotel preferences using emerging pattern mining technique. Tour Manag 46:311–321
Lin J, Wong J, Ho C (2013) Promoting frontline employees’ quality of life: leisure benefit systems and work-to-leisure conflict. Tour Manag 36:178–187
Litvin SW, Goldsmith RE, Pan B (2008) Electronic word-of-mouth in hospitality and tourism management. Tour Manag 29(3):458–468
Lockyer T (2005) Understanding the dynamics of the hotel accommodation purchase decision. Int J Contemp Hospitality Manag 17(6):481–492
López-Mosquera N, Sánchez M (2012) Theory of planned behavior and the value-belief-norm theory explaining willingness to pay for a suburban park. J Environ Manage 113:251–262
Lunt N, Smith R, Exworthy M, Green ST, Horsfall D, Mannion R (2011) Medical tourism: treatments markets and health system implications: a scoping review. Organization for Economic Co-operation and Development, Paris
Martin D, Woodside AG (2008) Grounded theory of international tourism behavior. J Travel Tourism Mark 24(4):245–258
Martinez-Garcia E, Raya J (2008) Length of stay for low-cost tourism. Tour Manag 29:1064–1075
Mazzarol T, Soutar GN (2002) “Push-pull” factors influencing international student destination choice. Int J Educ Manag 16(2):82–90
McNamara TP (1986) Mental representations in spatial relations. Cogn Psychol 18:87–121
Mechinda P, Serirat S, Anuwichanont J, Gulid N (2010) An examination of tourists’ loyalty towards medical tourism in Pattaya Thailand. Int Bus Econ Res J 9(1):55–70
Mehmetoglu M, Altinay L (2006) Examination of grounded theory analysis with an application to hospitality research. Int J Hospitality Manag 25:12–33
Miller D, Merrilees B, Coghlan A (2015) Sustainable urban tourism: understanding and developing visitor pro-environmental behaviours. J Sustain Tourism 23(1):26–46
Moghimehfar F, Nasr-Esfahani MH (2011) Decisive factors in medical tourism destination choice: a case study of Isfahan Iran and fertility treatments. Tour Manag 32:1431–1434
Molz JG (2013) Social networking technologies and the moral economy of alternative tourism: the case of couchsurfing org. Ann Tourism Res 43:210–230
Money RB, Crotts JC (2003) The effect of uncertainty avoidance on information search planning and purchases of international travel vacations. Tour Manag 24:191–202
Moretti E (2011) Social learning and peer effects in consumption: evidence from movie sales. Rev Econ Stud 78:356–393
Morley C (1994) Experimental destination choice analysis. Ann Tourism Res 21(4):780–791
Moutinho L (1987) Consumer behavior in tourism European. J Mark 21(10):3–44
Nawijn J (2011) Determinants of daily happiness on vacation. J Travel Res 50:559–566
Neal J, Sirgy M, Uysal M (1999) The role of satisfaction with leisure travel/tourism services and experiences in satisfaction with leisure life and overall life. J Bus Res 44:153–164
Neal J, Uysal M, Sirgy M (2007) The effect of tourism services on travelers’ quality of life. J Travel Res 46:154–163
Nichols CM, Snepenger DJ (1988) Family decision making and tourism behavior and attitudes. J Travel Res 26(4):2–6
Nicolau J (2011) Differentiated price loss aversion in destination choice: the effect of tourists’ cultural interest. Tour Manag 32:1186–1195
Nicolau J (2012) Asymmetric tourist response to price: loss aversion segmentation. J Travel Res 51:568–576
Nicolau J, Mas F (2006) The influence of distance and prices on the choice of tourist destinations: the moderating role of motivations. Tour Manag 27:982–996
Nicolau J, Mas F (2008) Sequential choice behavior: going on vacation and type of destination. Tour Manag 29:1023–1034
Nolan D (1976) Tourist’s use and evaluation of travel information. J Travel Res 14(2):6–8
Nuntsu N, Tassiopoulos D, Haydam N (2004) The bed and breakfast market of Buffalo City (BC) South Africa: present status constraints and success factors. Tour Manag 25(4):515–522
Oppermann M (1995) Travel life cycle. Ann Tourism Res 22(3):535552
Page SJ, Innes A, Cutler C (2015) Developing dementia-friendly tourism destinations: an exploratory analysis. J Travel Res 54(4):467–481
Pan TJ, Cheng WC (2014) Chinese medical tourists—their perceptions of Taiwan. Tour Manag 44:108–112
Park DB, Yoon YS (2009) Segmentation by motivation in rural tourism: a Korean case study. Tour Manag 30(1):99–108
Pearce DG (1988) Tourist time-budget. Ann Tourism Res 15(1):106–121
Pennington-Gray L, Blair S (2009) Nature-based tourism in North America: is generation Y the major cause of increased participation. In: Benckendorff P, Moscardo G, Pendergast D (eds) Tourism and generation Y. CABI Publishing, Wallingford, pp 73–84
Pennington-Gray L, Fridgen JD, Stynes D (2003) Cohort segmentation: an application to tourism. Leisure Sci 25(4):341–361
Perdue R (1986) Traders and nontraders in recreational destination choice. J Leisure Res 18(1):12–25
Phithakkitnukoon S, Horanont T, Witayangkurn A, Siri R, Sekimoto Y, Shibasaki R (2015) Understanding tourist behavior using large-scale mobile sensing approach: a case study of mobile phone users in Japan. Pervasive Mobile Comput 18:18–39
Pilcher J (1994) Mannheim’s sociology of generations: an undervalued legacy. Br J Sociol 45(3):481–495
Pizam A, Jeong GH (1996) Cross-cultural tourist behavior: perceptions of Korean tour-guides. Tour Manag 17(4):277–286
Pizam A, Sussmann S (1995) Does nationality affect tourist behavior? Ann Tourism Res 22(4):901–917
Pizam A, Jeong GH, Reichel A, Van Boemmel H, Lusson JM, Steynberg L, State-Costache O, Volo S, Kroesbacher C, Kucerova J, Montmany N (2004) The relationship between risk-taking sensation-seeking and the tourist behavior of young adults: a cross-cultural study. J Travel Res 42(3):251–260
Pols J, Kroon H (2007) The importance of holiday trips for people with chronic mental health problems. Psychiatr Serv 58:262–265
Poon A (1993) Tourism, technology and competitive strategies. CABI Publishing, Oxon
Poria Y, Butler R, Airey D (2003) The core of heritage tourism. Ann Tourism Res 30(1):238–254
Powell LM, Tauras JA, Ross H (2005) The importance of peer effects cigarette prices and tobacco control policies for youth smoking behavior. J Health Econ 24:950–968
Prayag G, Disegna M, Cohen SA, Yan H (2015a) Segmenting markets by bagged clustering young Chinese travelers to Western Europe. J Travel Res 54(2):234–250
Prayag G, Hosany S, Muskat B, Chiappa GD (2015b) Understanding the relationships between tourists’ emotional experiences perceived overall image satisfaction and intention to recommend. J Travel Res (in press)
Prideaux B (2000) The role of the transport system in destination development. Tour Manag 21(1):53–63
Priskin J (2001) Assessment of natural resources for nature-based tourism: the case of the Central Coast Region of Western Australia. Tour Manag 22(6):637–648
Rayman-Bacchus L, Molina A (2001) Internet-based tourism services: business issues and trends. Futures 33:589–605
Rice R, Frone M, McFarlin D (1992) Work-nonwork conflict and the perceived quality of life. J Organ Behav 13:155–168
Richards G (1999) Vacations and the quality of life: patterns and structures. J Bus Res 44:189–198
Richards G (2008) Youth travel matters: understanding the global phenomenon of youth travel. World Tourism Organization (WTO)
Runnels V, Carrera PM (2012) Why do patients engage in medical tourism? Maturitas 73:300–304
Sands J (1981) The relationship of stressful life events to intellectual functioning in women over 65. Int J Aging Hum Dev 74:11–22
Scalon T (1993) Value desire and quality of life (Chap. 15). In: Nussbaum M, Sen A (eds) The quality of life. Oxford University Press, New York
Schroeder H, Louviere J (1999) Stated choice models for predicting the impact of user fees at public recreation sites. J Leisure Res 31(3):300–324
Seddighi HR, Theocharous AL (2002) A model of tourism destination choice: a theoretical and empirical analysis. Tour Manag 23:475–487
Sharpley R (2014) The consumption of tourism. In: Sharpley R, Telfer DJ (eds) Tourism and development: concepts and issues (2nd edn), Channel View Publications, Clevedon, pp 358–366
Shoemaker S (2000) Segmenting the market: 10 years later. J Travel Res 39(1):11–26
Sigala M, Christou E, Gretzel U (2012) Social media in travel tourism and hospitality: theory practice and cases surrey. Ashgate Publishing, UK
Sirakaya E, Woodside AG (2005) Building and testing theories of decision making by travelers. Tour Manag 26(6):815–832
Sirgy M (2010) Toward a quality-of-life theory of leisure travel satisfaction. J Travel Res 49:246–260
Sirgy M, Joseph P, Kruger S, Lee D, Yu G (2011) How does a travel trip affect tourists’ life satisfaction? J Travel Res 50:261–275
Smith M, Kelly C (2006) Wellness tourism. Tourism Recreation Res 31(1):1–4
Snepenger DJ, Meged K, Snelling M, Worrall K (1990) Information search strategies by destination-naive tourists. J Travel Res 29(2):13–16
Sohrabi B, Vanani IR, Tahmasebipur K, Fazli S (2012) An exploratory analysis of hotel selection factors: a comprehensive survey of Tehran hotels. Int J Hospitality Manag 31(1):96–106
Song HY, Witt SF (2000) Tourism demand modeling and forecasting: modern econometric approaches. Pergamon, Amsterdam
Song HJ, You GJ, Reisinger Y, Lee CK, Lee SK (2014) Behavioral intention of visitors to an oriental medicine festival: an extended model of goal directed behavior. Tour Manag 42:101–113
Sparks B, Pan GW (2009) Chinese outbound tourists: understanding their attitudes, constraints and use of information sources. Tour Manag 30(4):483–494
Strauss-Blasche G, Ekmekcioglu C, Marktl W (2002) Moderating effects of vacation on reactions to work and domestic stress. Leisure Sci 24:237–249
Sung J, Woo JM, Kim W, Lim SK, Chung EJ (2012) The effect of cognitive behavior therapy-based “forest therapy” program on blood pressure salivary cortisol level and quality of life in elderly hypertensive patients. Clin Exp Hypertens 34(1):1–7
Swarbrooke J, Horner S (2007) Consumer behaviour in tourism. Routledge, London
Thompson J, Bunderson J (2001) Work-nonwork conflict and the phenomenology of time: beyond the balance metaphor. Work Occupations 28:17–39
Thornton PR, Shaw G, Williams AM (1997) Tourist group holiday decision-making and behaviour: the influence of children. Tour Manag 18(5):287–297
Thrane C (2012) Analyzing tourists’ length of stay at destinations with survival models: a constructive critique based on a case study. Tour Manag 33:126–132
Ting CS, Chiu LK, Kayat K (2015) Travel lifestyles and outbound tourism intentions of young Malaysians. Am J Tourism Manag 4(2):40–42
Travel Industry Association (2006) Travel across the generations. Travel Industry Association, Washington DC
Trenberth L, Dewe P, Walkey F (1999) Leisure and its role as a strategy for coping with work stress. Int J Stress Manag 6:89–103
Tsai CTS, Wang YC (2016) Experiential value in branding food tourism. J Destination Market Manag (in press)
Tuulentie S, Heimtun B (2014) New rural residents or working tourists? Place attachment of mobile tourism workers in finnish lapland and Northern Norway Scandinavian. J Hospitality Tourism 14(4):367–384
Um S, Crompton JL (1990) Attitude determinants in tourism destination choice. Ann Tourism Res 17(3):432–448
Uriely N, Yonay Y, Simchai D (2002) Backpacking experiences: a type and form analysis. Ann Tourism Res 29(2):520–538
Van Raaij WF, Crotts JC (1994) Introduction: the economic psychology of travel and tourism. J Travel Tourism Mark 3(3):1–19
Van Raaij WF, Francken DA (1984) Vocation destinations activities and satisfactions. Ann Tourism Res 11(1):101–112
Vogt JW (1976) Wandering: youth and travel behavior. Ann Tourism Res 4(1):25–41
Vogt CA, Fesenmaier DR (1998) Expanding the functional information search model. Ann Tourism Res 25(3):551–578
Voigt C, Laing JH (2010) Journey into parenthood: commodification of reproduction as a new tourism niche market. J Travel Tourism Mark 27(3):252–268
Wang D, Xiang Z, Fesenmaier DR (2016) Smartphone use in everyday life and travel. J Travel Res 55(1):52–63
Wansink B, Van Ittersum K (2004) Stopping decisions of travelers. Tour Manag 25:319–330
Westbrook RA (1987) Product/consumption-based affective responses and post purchase processes. J Mark Res 24(3):258–270
Westbrook RA, Newman JW (1978) An analysis of shopper dissatisfaction for major household appliances. J Mark Res 15:456–466
Westbrook RA, Oliver RL (1991) The dimensionality of consumption emotion patterns and consumer satisfaction. J Consum Res 18:84–91
Westman M, Eden D (1997) Effects of a respite from work on burnout: vacation relief and fade out. J Appl Psychol 82:516–527
Woo E, Kim H, Uysal M (2016) A measure of quality of life in elderly tourists. Appl Res Quality Life 11:65–82
Woodside A, Dubelaar C (2002) A general theory of tourism consumption systems: a conceptual framework and an empirical exploration. J Travel Res 41(2):120–132
Woodside AG, MacDonald R (1994) General system framework of customer choice processes of tourism services. In: Gasser R, Weiermair K (eds) Spoilt for choice, pp 30–59
Wu L, Zhang J, Fujiwara A (2009) A model of heterogeneously nested structure of tourists’ destination and travel party choices. In: Proceedings of the 15th APTA (Asia Pacific Tourism Association) annual conference, Incheon, 9–12 July (CD-ROM)
Wu L, Zhang J, Fujiwara A (2011a) Representing tourists’ heterogeneous choices of destination and travel party with an integrated latent class and nested logit model. Tour Manag 32(6):1407–1413
Wu L, Zhang J, Fujiwara A (2011b) Representing tourist’s time use behavior based on a multiple discrete-continuous extreme value model. J Eastern Asia Soc Transp Stud 9:796–809
Wu L, Zhang J, Fujiwara A (2012a) A tourist’s multi-destination choice model with future dependency. Asia Pacific J Tourism Res 17(2):121–132
Wu L, Zhang J, Fujiwara A (2012b) Dynamic analysis of Japanese tourists’ three stage choices: tourism participation destination choice and travel mode choice. Transp Res Rec 2322:91–101
Wu L, Zhang J, Fujiwara A, Chikaraishi M (2012c) Analysis of tourism generation incorporating the influence of constraints based on a scobit model. Asian Transp Stud 2:19–33
Wu L, Zhang J, Chikaraishi M (2013a) Representing the influence of multiple social interactions on monthly tourism participation behavior. Tour Manag 36:480–489
Wu L, Zhang J, Fujiwara A (2013b) Tourism participation and expenditure behaviour: analysis using a scobit based discrete–continuous choice model. Ann Tourism Res 40:1–17
Xiang Z, Gretzel U (2010) Role of social media in online travel information search. Tour Manag 31(2):179–188
Xiang Z, Woeber K, Fesenmaier DR (2008) Representation of the online tourism domain in search engines. J Travel Res 47(2):137–150
Ye Q, Law R, Gu B, Chen W (2011) The influence of user-generated content on traveler behavior: an empirical investigation on the effects of e-word-of-mouth to hotel online bookings. Comput Hum Behav 27(2):634–639
Yoo KH, Gretzel U (2011) Influence of personality on travel-related consumer-generated media creation. Comput Hum Behav 27(2):609–621
Yoon Y, Uysal M (2005) An examination of the effects of motivation and satisfaction on destination loyalty: a structural model. Tour Manag 26(1):45–56
You X, O’leary JT (2000) Age and cohort effects: an examination of older Japanese travelers. J Travel Tourism Mark 9(1–2):21–42
Yu JY, Ko TG (2012) A cross-cultural study of perceptions of medical tourism among Chinese Japanese and Korean tourists in Korea. Tour Manag 33(1):80–88
Zalatan A (1998) Wives’ involvement in tourism decision processes. Ann Tourism Res 25(4):890–903
Zeithaml VA, Berry LL, Parasuraman A (1996) The behavioral consequences of service quality. J Mark 60:31–46
Zhang J, Timmermans HJP, Borgers A, Wang D (2004) Modeling traveler choice behavior using the concepts of relative utility and relative interest. Transp Res Part B 38:215–234
Zhang J, Fujiwara A, Sawara J (2006) Multi-dimensional timing decisions: a case study in tourism behavior analysis. Tourism Anal 11:319–330
Zhang J, Hakoda Y, Fujiwara A (2008) Modeling of touring behavior along the Asian highway incorporating inter-alternative similarities: a comparative analysis between Japanese and Korean tourists. Infrastruct Planning Rev 25:783–792 (in Japanese)
Zhang J, Zhang H, Wu L, Fujiwara A (2009) Representing tourists’ context-sensitive time use and expenditure behavior. In: Proceedings of the 15th APTA (Asia Pacific Tourism Association) annual conference, Incheon, 9–12 July (CD-ROM)
Zimmermann CA (1982) The life cycle concept as a tool for travel research. Transportation 11:51–69
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Japan KK
About this chapter
Cite this chapter
Zhang, L., Wu, L., Zhang, J. (2017). Life-Oriented Tourism Behavior Research. In: Zhang, J. (eds) Life-Oriented Behavioral Research for Urban Policy. Springer, Tokyo. https://doi.org/10.1007/978-4-431-56472-0_8
Download citation
DOI: https://doi.org/10.1007/978-4-431-56472-0_8
Published:
Publisher Name: Springer, Tokyo
Print ISBN: 978-4-431-56470-6
Online ISBN: 978-4-431-56472-0
eBook Packages: EnergyEnergy (R0)