Tourist motivation: an integral approach to destination choices

Chang-Keun Yoo (Department of Airlines and Tourism, Uiduk University, Gyeongju, Republic of Korea)
Donghwan Yoon (Department of Hotel and Tourism, The University of Suwon, Hwaseong, Republic of Korea)
Eerang Park (School of Management, Victoria University of Wellington, Wellington, New Zealand)

Tourism Review

ISSN: 1660-5373

Publication date: 14 May 2018



The purpose of this study is to discuss prevalent socio-psychological models which examine how tourists’ needs and motivations affect their destination choices by collectively considering Plog’s (1974) psychographic profiles, Cohen’s (1979) tourist typology and Peace’s (1988) travel career ladder. The current study argues that no single model can adequately explain tourists’ destination selection process as well as travel behaviors and introduces a new integrated perspective of existing psychological models.


Examining responses from 202 Hong Kong residents who have travel experience, this study divides the respondents’ psychographics into three types.


Using multinomial logit model (MNL) analysis, the study finds that tourists’ travel motivations and destination settings can be varied by their psychographics types. The findings also reveal that tourist’s psychographic types can be varied by demographics, travel type, frequencies, duration, purpose and destination setting.


The study provides implications for tourism marketers as well as the tourism literature by suggesting an integrative approach for a better understanding of tourist motivations.



Yoo, C., Yoon, D. and Park, E. (2018), "Tourist motivation: an integral approach to destination choices", Tourism Review, Vol. 73 No. 2, pp. 169-185.

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Emerald Publishing Limited

Copyright © 2018, Emerald Publishing Limited

1. Introduction

The choice of destination for tourists has been regarded as a decisive factor influencing the competitiveness and life cycle of the destinations themselves (Plog, 1974). Tourists decide to which destinations they will travel based on their judgments regarding interactions of the internal and external influences. In other words, tourists’ decision-making process in terms of destination is influenced by their own internal characteristics (e.g. motivation, personality and attitude) and the external environment (e.g. travel distance, expense, accessibility and destination authenticity) (Belk, 1975).

A fundamental background regarding factors influencing travel behaviors as well as destination selections has long been discussed in association with motives that are generated by tourist needs. Within the disciplines of psychology and sociology, tourism as a social phenomenon prioritizes an understanding of an individual tourist’s mind set and behavior; therefore, an explanation of the tourists’ internal and external factors that lead them to engage in a mode of travel has been developed in theoretical and conceptual models (Woodside and Lysonski, 1989).

For instance, Goeldner and Ritchie (2006) examine the relationship of an individual’s propensity to travel and the resistance of the link between origin and destination areas, while also considering external (e.g. exotic place, strangeness and authenticity) and internal (e.g. novelty and relaxation) factors in determining demands.

On the other hand, Dann (1981) notes the difference between socio-psychological motives (or push factors) and the attributes of a destination (or pull factors) in shaping choices. More specifically, understanding the travel patterns and individual choices of tourists involves three major theoretical concepts linked to motivation: (a) the psychographic personality, evolved from Plog’s (1974) psychographic profile model; (b) modes of travel experiences as a type of social behavior, established by Cohen’s (1979) tourist typology; and (c) socio-psychological desires based on past experiences, as discussed in Pearce’s (1988) travel career ladder. These three distinct conceptual frameworks explaining the relationship between tourists and destinations have received the most attention from tourism researchers. Although the three models are all tourist-specific concepts and commonly rooted in changes in tourists’ psychology in regard to travel over time, these three models have only been tested independently. Furthermore, given the complex nature of the modern tourism environment, an investigation solely focused on the psychological factors in tourists’ decision-making process cannot precisely examine how tourists choose a certain type of destination.

Indeed, today’s tourists are faced with more sophisticated and complicated factors affecting their involvement in travel and tourism. It has long been known among academics that since 1990s, modern tourists have engaged in a variety of behaviors or tourist roles, needs, wants and expectations; those vary considerably. Therefore, tourism has become a highly specialized commodity and more sophisticated. Accordingly, the tourism industry should respond to diversifying product and services for tourists (Cohen, 1972; Gibson and Yiannakis, 2002).

In this vein, previous studies have suggested numerous motives for the destination choices of tourists. For example, Crompton (1979) empirically examines tourists’ motives that can impact their destination choice and finds that these destination choices consist of seven socio-psychological motives (i.e. escape from a mundane environment, evaluation of self, relaxation, prestige, regression, enhancement of relationships and facilitation of social interaction) and two cultural motives (i.e. education and novelty).

Furthermore, Pearce (1988) suggests a travel career ladder (TCL) based on the needs hierarchy theory of motivation developed by Maslow (1970). The TCL suggests that tourist motivation is formed by a ladder, one that starts from the relaxation needs, followed by safety needs, relationship needs, self-esteem needs and fulfilment needs. Pearce (1988) also contends that motivation changes and patterns of travel can be changed by previous tourism experience.

As such, given that tourists have complicated decision-making processes, prior researchers have suggested different viewpoints of models to explain tourist motivation in destination choices. Despite these varying explanations, the prior literature has been limited in synthesizing holistic understandings with dynamic variables; this should be taken into account when examining tourists’ choices of certain destinations.

To fill this void, the current study examines an integrated perspective of internal and external factors in conjunction with the multidimensional socio-psychology models (e.g. push-pull model) to address tourists’ psychology-related concepts. Specifically, the study’s objectives are:

  • to understand theoretical, conceptual frameworks of tourists’ travel patterns and destination choices by discussing two major streams (i.e. psychological and sociological streams) in the social science disciplines;

  • to examine, compare and classify types of tourists by assessing an integrated relationship of major theoretical concepts – psychographics, motivation and travel experiences; and

  • to conduct an empirical test of a new model, which is derived from integrated perspectives of sociology and psychology, along with an additional factor from the geographical perspective and socio-demographics, to better understand tourists’ destination choices.

2. Literature review

2.1 Conceptual frameworks of tourists’ destination choices

2.1.1 Psychological approach.

Tourists’ choices of destinations and activities are determined by particular purposes. A key factor is tourists’ desires, which primarily propel their perceived necessity of travel (Hall and Weiler, 1992). According to Plog’s (2001) psychographic continuum, which ranges from allocentric to psychocentric, a destination has a life cycle determined by tourist demand; the life cycle corresponds to tourists’ psychographic scale and the direction of market evolution. Specifically, Plog (2001) defines types of destinations and asserts that a destination is discovered by allocentric travelers who seek adventure and the experiences of new cultural and environmental challenges.

As the number of near-allocentric travelers increases, the destination develops and equips itself with tourist establishments. A market technically changes until the last phase of the scale, when it attracts psychocentric tourists through reputation and popularity. At the end of the cycle, the destination loses its viability when it becomes destroyed by tourism growth. In this vein, Holden (2005) supports Plog’s model regarding the inferred meaning of why tourists share motivation and attitudes. The model not only identifies the travel type and pattern but also distinguishes the choice of destination.

Meanwhile, various leisure studies argue that Plog’s scale is conceptualized only in a broader perspective of the locus of control (Lehto, O’Leary, and Morrison, 2002; Madrigal, 1995). In other words, Plog (1974) has defined the market profiles to describe destination types and positions from the destination’s perspective, but in reality, this model seems more valid when discussed from the tourist’s perspective. In addition, this model does not explain the phenomenon of extrinsic background influencing a shift in intrinsic motivation.

McKercher (2005) also argues that no definite life cycle can be asserted in regard to destinations, because they are in multiple stages simultaneously. This is because a destination for travelers coming from closer distances and similar cultural backgrounds would be a psychocentric destination, but the same location would be considered an allocentric destination to people from far away and culturally different places.

On the other hand, Pearce (1988) developed the TCL model to understand tourists’ desires to travel. The conceptual model demonstrates how tourists can change their motivations over time – from the lowest need of relaxation to the highest level of fulfillment; motivation levels are influenced by past experiences. This model has strengths in terms of considering dynamic factors influenced by experiences obtained from career history. Thus, the argument that motivation influences travel behavior as well as the overall concept is intuitively sound.

However, Ryan (1998) argues for a contradictory concept of the TCL. That is, the experiences do not necessarily change the motives of travelers, but rather are seen as better meeting their desires. In terms of the psychological point of view, particular motivations conducive to the increasing necessity of travel are significant criteria; however, as discussed in the empirical contradictions, the complex nature of travel may not be compatible with a concrete measurement method for determining motives. Hence, classifying tourists by motivation and psychographic distinctions is still viewed as a challenge.

Unlike previous destination choice models, which have mainly focused on unidimensional aspects of destination choices, including the push-pull model (Dann, 1981), the allocentric–psychocentric theory (Plog, 1974), the escape-seeking theory (Ross and Iso-Aloha, 1991) and the TCL (Pearce, 1988), the current study aims to apply a different spectrum of destination choices of tourists by integrating the choice models.

2.1.2 Sociological approach.

Prior research has viewed tourism as a social phenomenon, and researchers’ attempts at developing a theoretical understanding originated from the point of view of critiquing modern mass tourism as a cultural phenomenon (Cohen, 1972, 1979). Moreover, they have concerned themselves with tourists’ motivation; specifically, the novelty and strangeness of experiences and travel outside of the daily environment are regarded as motivations.

However, tourists also tend to feel secure in a boundary in which familiar settings provide a level of protection from strangeness. The defined travel behaviors are comparable to Boorstin’s (1964) critique of mass tourists who seek and enjoy pseudo-events (e.g. trivial and superficial experiences) as opposed to experiencing reality (e.g. authentic experiences) (Cohen, 1988).

In this connection, Cohen (1972) examines the relationship between tourists and a destination’s touristic settings, ultimately finding that a standardized and advanced setting in a destination becomes a stronger barrier because the genuine traits of the destination disappear. Cohen (1972) further substantially discusses a concrete typology. More specifically, in the five modes of tourist experiences developed by Cohen (1979) – recreational (i.e. entertainment), diversionary (i.e. a mere escape from routine), experiential (i.e. experiencing another authenticity), experimental (i.e. comparing different options to meet one’s desire) and existential (e.g. committed to an elective spiritual center such as traveler’s native society and culture) modes – he asserts that pleasure travel consists of new experiences provided “out there.” The degree of disequilibrium in the social environment and alienation from the center creates motives to travel, and the experiences of the five types of tourists are differentiated by their needs and desires.

Therefore, the necessity for quality and meaning in tourist experiences increases in the social phenomena of tourism. As such, the experiences of tourists are in contrast to Boorstin’s (1964) view of the superficial nature of tourists’ experiences.

In terms of the significance of experiences in social behavioral travel, Hall and Weiler (1992) emphasize the motivations associated with travel choices that are fed by the desire to pursue novelty, uniqueness and authenticity as part of the travel experience. Those desires are understood in accordance with changing social lifestyles as well as consumption patterns, which naturally cause a variety of tourist types, needs and travel propensities.

2.1.3 The integrated relationship of tourists’ personality, motivation and travel experiences.

The three major theoretical concepts of travel – personality, motivation and behavioral choices and meaning – have strengths and drawbacks that have been recognized before. On the other hand, based on the literature review, it is recognized that these three models commonly address psychological maturity steps, and the advancement is embodied in destination choice behavior. In addition, motivation, often regarded as a predictor of behavioral patterns, is positioned in the middle of the decision-making process, and researchers studying the three conceptual models all deal with motivation because of its role as a key factor in directing travel and tourism.

However, researchers in different disciplines use varying factors from their own academic and methodological perspectives when examining travel motivations, decisions and behaviors. Psychologists emphasize personal dissatisfaction as creating motivation; thus, travel is examined as a reflection of personality. Meanwhile, sociologists stress the disequilibrium in a social setting, so that travel is viewed as a behavioral attempt to offer a change from an unsatisfying environment.

Given that the shared logic of the three models is that the elevation of motivation levels is deeply related to the destination choice and can define the type of tourists as well as their behavior in order of maturity, these three conceptual models cannot independently represent tourists’ psychology and the social behavior of travel. Therefore, research adopting a single model cannot accurately make conclusions about tourists’ decision-making processes or about the influence of the destination choice. That is, tourists hold innate psychographic attributes, but their needs are created by personal dissatisfaction or social disequilibrium. Thus, a destination choice driven by a level of desire to leave actually has a multitude of meanings behind it, and the types of experiences available in a certain destination are distinguished.

Based on each group’s behavioral and psychological features iterated by Plog (1974), Cohen (1979), Pearce (1988) and other researchers, this study explicitly suggests an integrated relationship between the psychographic personality, tourist typology and the travel career ladder. In contrast to the previous research, this integrated model will enrich the understanding of tourists’ intrinsic motivational factors, including dynamic variables from multidimensional viewpoints.

The importance of a new, integrated perspective of the existing psychological models is sought to rise above the limitations of the existing models. More specifically, neither Plog’s (1974) psychographic model nor Pearce’s (1988) is empirically proven to be admitted as a theory, but the logic intuitively makes sense; hence, the significance of both these models has been sustained. Moreover, the skewness of the proportion of the market categorized by the five tourist typologies (i.e. recreational, diversionary, experiential, experimental and existential) is approximately estimated in the reflection of the level of motivation and the distinctions of travel experiences.

Moreover, Plog’s (1974) normal curve of the psychographic distribution of tourists indicates that the five types of tourist travel patterns and preferences may not be a precise shape, especially when taking into account the wide range of motivational and psychological characteristics. Finally, psychographic profiles, the primary backbone of the overall conceptual framework, were established based on research conducted on travelers residing in regions of the USA (Litvin, 2006). However, individuals’ mindsets, behavioral patterns and norms are affected and formed by the social, cultural, political and religious settings of each country.

Likewise, the concepts and models of tourists’ destination choices suggested by Plog (1974), Cohen (1979) and Pearce(1988) still not only include only tourists’ inner-directed changes, but also focus on tourists within a certain geographic level. However, more complicated modern tourism mechanisms include subsequent intervening factors (e.g. motivations, individual characteristics, distance and travel experience), and these factors are deeply related with psychographic profiles and motivation.

2.1.4 Intervening factors influencing tourists’ choice of destination.

Tourists’ choices of destinations are determined not only by their inner-directed parameters but also by the destination itself. In the complex movements of tourism systems, distance and socio-demographic interfering factors are more obvious at the macro level of international tourism (McKercher and Lew, 2004). Distance decay theory, which asserts that spatial interaction decreases as distance increases, can be discussed in conjunction with tourists’ socio-psychology and the motivation-driven destination choice. The degree of time, money and effort all increase in longer-distance travels, and allocentric travelers are more likely to travel longer journeys in an active, adventurous travel manner (Lew and McKercher, 2006).

From a psychographic personality point of view, the intervening factors are more diverse, resulting in the fact that the cultural-distance concept is well explained in conjunction with psychographic profiles. Additionally, tourists display the differences in the extent of their knowledge about destinations, as well as in their motivations to explore new destinations (Lew and McKercher, 2006; McKercher and Wong, 2004). A higher level of seeking new experiences in culturally different environments is more likely to be sought from tourists who fall under the matured level in the psychographic scale. Tourists positioned in this level are willing to engage in longer-distance travel and first-time visiting, and openness owed to their motivation to experience genuine local culture.

More interestingly, the psychographic scale and the level of motivation to travel can clearly interpret the second peak at the tail of the latest distance decay curve (McKercher and Lew, 2003). Specifically, the distance decay curve indicates that the number of tourists is proportionally decreased according to distance, and the second peak implies that when destinations have strong pull factors, this peak can be generated even over a long distance. For instance, McKercher and Lew (2003) examine the decaying effect according to air trip distance, and the results indicate that the first peak exists between 1,000 and 1,500 miles, and 70 per cent of all trips were taken in fewer than 2,000 miles. However, travel demand also increased, even over long distances, indicating the second peak occurring between 4,501 and 5,000 miles and the third and stronger peak located between 6,001 and 7,000 miles.

On the other hand, socio-demographic variables are taken into account as an intervening factor (Bao and McKercher, 2008; Crouch, 1994), regardless of tourists’ attitudes or willingness to take longer journeys to engage in cultural experiences with which they have not previously been acquainted. This finding also contradicts the result of Lehto et al.’s (2002) research, given that demographic characteristics (e.g. gender and age) are not effective predictors of destination choices, but that psychographic variables accurately distinguish the market of tourists.

The conceptual models discussed earlier are linked together and deeply related to each other, but they cannot be fully applicable to tourism studies without the consideration of physical and cultural distances, as well as the concomitant circumstances of individuals. Regarding this issue, Table I summarizes tourists’ psychology, motivations, destination choices, physical and cultural distance decay, travel experiences and socio-demographic attributes. Accordingly, this research study conducts the empirical test of tourists’ destination choices in consideration of the integral variables summarized in Table I.

3. Methodology

3.1 Survey instrument

The questionnaire is composed of five sections, asking about the individual’s travel experiences in terms of the number of international trips made and destinations visited in a certain period of time, travel motivation, psychographic personality, destination settings of choice and socio-demographic factors of the respondent.

3.2 Measures

The travel motivation section contains 25 statements, which were adopted from Kozak (2002) and Lehto et al. (2002), and the psychographic personality part includes 14 variables, which were adapted from Brayley (1990) and Plog (1974, 2001). Finally, the seven variables of destination settings of choice were adopted from Cohen (1972). Answers to the respondents’ level of agreement or disagreement with the statements were measured using a five-point Likert scale, ranging from 1 representing “strongly disagree” to 5 representing “strongly agree.”

3.3 Samples, data collection and analysis

The target population of this study consists of international tourists visiting Hong Kong, which means that it includes various international tourists came from other countries as well as mainland China. Indeed, Hong Kong as one of the major tourism destinations in the world has an advantage in having a good spectrum for various visitors coming from many regions of the world. Sampling was framed to include overnight, leisure tourists who were traveling to selected tourist attractions. Convenience sampling as one of the non-probability sampling methods was used to get the samples (i.e. international, overnight and leisure tourists in Hong Kong).

The survey was conducted at major tourist attractions in Hong Kong. Because Hong Kong has two geographically distinct regions in the Kowloon Peninsula and Hong Kong Island, tourists in a major attraction on each side – that is, the Avenue of Stars in Kowloon and the Peak in Hong Kong Island – were approached. To ensure that the respondents were Hong Kong residents who had traveled to any foreign countries in the previous three years, a screening question was carried out before the questionnaires were distributed.

On-site interviews were conducted using self-administered questionnaires, and all answered questionnaires were collected at the same location. From a total of 220 surveyed, 18 incomplete responses were excluded from the data analysis, and 202 were found as valid responses. Consequently, the valid responses were used for a series of quantitative analyses.

The collected data were analyzed by conducting descriptive analysis to see the overall profile of the samples and identify the distribution of psychographic types in comparison to Plog’s (2001) and other existing models. Cross-tabulation analysis was used to investigate the relationship between the psychographic types of tourists and their socio-demographic factors; factor analysis was used to identify travel motivation. Multinomial logit (MNL) analysis was applied to classify international tourists in Hong Kong into the groups of five psychographic types (Brayley, 1990; Plog, 2001) in association with other variables – that is, motivation, distance, destination settings and socio-demographic factors.

4. Results

4.1 Profiles of respondents

Participating international tourists in this research were characterized as young, well-educated professionals, mostly from mainland China (47.5 per cent), Europe, Africa and the Middle East (19.8 per cent). Of the 202 respondents, the number of female travelers (51 per cent) was slightly higher than that of males (49 per cent); 48 per cent were in their 20s, 29.2 per cent were in their 30s and 11.4 per cent were in their 40s. Approximately half of respondents were from mainland China (47.5 per cent), while 19.8 per cent were from Europe, Africa and the Middle East, followed by America (9.9 per cent) and South/Southeast Asia (8.4 per cent).

According to occupation, professionals and sub-professionals showed 36.6 and 10.9 per cent, respectively. The respondents were single (51.5 per cent) and married (44.6 per cent). The overall annual household income was fairly low, with 9.9 per cent earning under US$25,000 and 12.4 per cent earning between US$25,000 and US$49,999 for the international tourists, excluding the mainland Chinese respondents. A low-income level was seen in about 13 and 12 per cent of tourists from mainland China in the category of CNY50,000 to CNY74,999 and CNY100,000 to CNY199,999, respectively; those amounts of CNY represent US$30,000 or below. Around 78 per cent of respondents were educated at the college or postgraduate level or more. Table II shows the demographic profiles in details.

4.2 The psychographic types

As previously discussed, this study adopted 14 items of the psychographic personality attributes developed and used by Brayley (1990) and Plog(1974, 2001). The items were rated on a five-point Likert scale. Examples of the scale items are “I prefer a package travel which includes pre-arranged accommodations, itineraries, and transportations (M = 3.20, SD = 1.14)”, “I purchase local artisanal crafts over souvenirs (M = 3.49, SD = 0.93)”, “I would like to travel to well-known, familiar destinations (M = 3.44, SD = 0.99)” and “Accommodations with insufficient or a lack of convenient facilities are not important to me while traveling. (M = 2.88, SD = 1.06)”. Accordingly, the accumulated points for each respondent were ranged from 32 to 57. Then, we categorized the respondents as five groups according to each point: psychocentric (32 to 36), near-psychocentric (37 to 41), midcentric (42 to 46), near-allocentric (47 to 51) and allocentric (52 to 57).

The empirical test result of psychographic types is compared to the results of Plog’s (1974) and other existing models. As shown in Table III, the distribution of the tourists’ psychographic types in this study was similar to Plog’s model: psychocentric (4.5 per cent), near-psychocentric (30.7 per cent), mid-centric (47.0 per cent), near-allocentric (13.4 per cent) and allocentric (4.5 per cent). It also shows the differences among three empirical tests to the samples of a particular nationality in different geographical regions – that is, Plog’s (1974) test to US travelers, Litvin’s (2006) research on Singaporean travelers, and this study to the international travelers.

As Figure 1 demonstrates, the differences between the curves are distinct. The ideal destination curve analyzed by Litvin and based on Plog’s model is not supported by the other two empirical tests. This finding highlights the criticism of Plog’s model by showing that classifying tourists by their psychographic distinctions may not reflect the diverse considerations of travel-directing factors and that different people in various geographic areas are likely to have distinct leisure behaviors.

Accordingly, it can be argued that the tourists’ psychographics may evolve over time and that a single criterion of the psychographic personality scale cannot generalize the types of tourists to fully understand their destination choices.

4.3 Cross-tabulation analysis between psychographic types and socio-demographic factors

A cross-tabulation analysis was conducted to determine whether demographic variables are significantly related to the psychographic classification. As a result, travel modes were discovered as being significantly related to the psychographic personality (χ2 = 13.306, p < 0.012). In addition, the significant differences of the column proportions were confirmed by adjusted residual (Z-score) values (ranged from 2.1 to 2.6 > 1.96) (Table IV).

Specifically, the percentages of the general independent travel (50.7 per cent) was associated with a higher percentage in the psychocentric type compared with the full-package tour (19.7 per cent). Meanwhile, the general independent travel (71.6 per cent) showed the highest percentage in the mid-centric type than the other three travel modes. The backpack travel (27.8 per cent) indicated the highest percentage in the allocentric type than the other three travel modes.

4.4 Determinant factors classifying psychographic types

To analyze tourist motivation in relation to the psychographic types, a factor analysis was conducted. Table V shows the results of exploratory factor analyses using the varimax rotation for tourist motivations. The principal components method was used to extract underlying factors. Results of the factor analysis using 25 motivation items produced six underlying domains, in which all items’ eigenvalues were greater than 1.0 on the screen-plot, except three items that were eventually excluded from the model. The factors were labeled “scenery and exotic experience”, “culture”, “relaxation”, “self-actualization”, “physical refreshment” and “pleasure”. The factor model explained 60.52 per cent of the variance. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy was 0.80, indicating that it validates extracting the factor structure. Factor loadings were greater than 0.41 on 22 items. The reliability alphas within the six domains ranged from 0.62 to 0.74, so the internal consistency within the extracted factors was guaranteed.

4.5 Multinomial logit analysis

Tourists were originally categorized into five types (i.e. psychocentric, near-psychocentric, midcentric, near-allocentric and allocentric) in Plog’s (1974, 2001) psychographic personality study. However, the number of samples for psychocentric and allocentric types in this study was small. In other words, each sample size of psychocentric (N = 9) and allocentric (N = 9) was less than 30, which does not meet the sample size (N = 30) for the assumption of normality (Maas and Hox, 2005). Thus, the psychocentric and near-psychocentric groups were combined as ‘psychocentric’ (N = 71), while allocentric and near-allocentric were combined as ‘allocentric’ (N = 36) (Table II).

As a result, the tourist types were reclassified into three types (psychocentric, midcentric and allocentric), and the major influencing factors of the three psychographic types were reviewed. To examine such effects, the multinomial logit (MNL) model was applied by including demographic characteristics, travel modes, motivations of selecting a destination and destination attributes, representing the destination settings as independent variables against the psychographic types of dependent variables. The definitions of variables and descriptive statistics are presented in Table VI.

Table VII highlights the coefficient values and significances of the MNL model. The result shows that white collar (occupation), scenery/exotic experience (Motivation 1), cultural experience (Motivation 2), pleasure seeking/fantasy (Motivation 6) and superb scenery (Destination 2) factors significantly influenced the psychocentric group. Further, income, independent travel (travel mode) and cultural experience (Motivation 2) factors significantly impacted the mid-centric type.

As shown in Table VIII, the marginal effect shows different effects by each of the three categories classified by the tourists’ psychographic types.

First, with regard to demographic characteristics, significant negative effects on income were shown in the mid-centric type (p < 0.1) and significant positive effects appeared in the allocentric type (p < 0.05). It can be interpreted that those with high income levels were mainly shown to fit into the allocentric type, and the mid-centric type was associated with respondents with a relatively low-income level. Specifically, high-income travelers increase the probability of being in the allocentric type by 1.0 per cent, while low-income travelers decrease the probability of being in the mid-centric type by 1.8 per cent. Regarding the occupation groups, only the psychocentric type showed negative effects (p < 0.1). Tourists who were not white collar tended to be from the psychocentric type. Specifically, other occupations rather than white collar have a 17.0 per cent higher probability to belong to the psychocentric type than the other types.

Second, the analysis on the marginal effect of the travel mode shows that the group most likely to choose independent travel was the mid-centric type (p < 0.05). Independent travel increases the probability of being in the mid-centric type by 19.1 per cent.

The frequency of travels and length of travel periods display significant effects on the allocentric type at 5 and 10 per cent significance levels, respectively. More travel experiences and longer travel periods have 1.3 and 0.1 per cent of higher probabilities of being in the allocentric type than the other types, respectively. This finding corresponds to the attempt to gain an integral understanding of tourist behavior addressed in Table I; the allocentric type has more travel experiences and tends to travel for longer periods.

Third, among the motivation factors for destination choices, cultural experience (p < 0.05), physical refreshment (p < 0.1), pleasure seeking/fantasy (p < 0.1) and touristic establishment (p < 0.05) significantly influence the psychocentric type; within these results, cultural experience and pleasure seeking/fantasy decrease the probability of being in the psychocentric type by 19.1 and 12.0 per cent, whereas physical refreshment increases the probability as much as 14.7 per cent.

Although the mid-centric type does not show any significant effects, the allocentric type shows a significant positive relationship with the cultural experience (p < 0.01) and pleasure seeking/fantasy (p < 0.05) factors. Cultural experience and pleasure seeking/fantasy have 16.6 and 8.7 per cent higher probabilities to belong to the allocentric type.

Finally, only well-developed touristic establishments of destination-setting variables showed a statistically significant effect on the psychocentric type, which had a positive effect (p < 0.05), and the allocentric type, which had a negative effect (p < 0.01). Touristic establishments increase (decrease) the probability of being in the psychocentric (allocentric) type by 15.9 per cent (10.4 per cent). That is, tourists in the psychocentric type tend to be influenced by well-developed travel infrastructure in a destination, whereas the allocentric type of tourist is less affected by infrastructure.

5. Discussions and conclusions

Prevalent socio-psychological models examining tourist needs and motivations affecting destination choices have been discussed by utilizing Plog’s (1974) psychographic profiles, Cohen’s (1979) tourist typology to reflect on modes of travel experiences, and Pearce’s (1988) travel career ladder, mainly to depict the process of motivation maturity.

Considering the various models and different perceptions used to examine human psychology and behavior, this study argues that no single model can adequately explain tourists’ destination-selection process or their travel behaviors. Because the insufficiency of the single-dimensional application to modern tourism behavior research has been admitted, this study not only integrates common elements of these models to offset the drawbacks of each, but also links additional factors such as distance decay and socio-demographic elements to the tourists’ destination decision-making process.

Although the findings do not fully support the proposed model of this study, the statistically significant findings partially support the relationship between motivation and dichotomized tourists’ psychographic types. Therefore, the proposed model provides the potential to integrate variables while also understanding tourists’ destination choices and their behavior more effectively.

The results of this empirical study indicate that the backbone of Plog’s (1974, 2001) dichotomy of tourists’ psychographic personality does not completely apply to international tourists; this study attempts to overcome the limitations of previous studies that have tested tourists from particular nations. Meanwhile, the integrated model – which contains various internal and external influences for tourists’ destination choices – was partially supported by statistically significant test results. This finding can suggest that the shared logic in understanding tourists’ destination choices should be discussed through an interdisciplinary approach rather than defined within discrete models.

According to the test results, tourists’ motivation to travel and their choices of destination, psychological characteristics, intervening factors such as travel distance and demographic factors can all be integrated to achieve a fuller understanding of their travel behaviors. In contrast to the conventional view of distance decay in association with the tourists’ destination choices being grounded in psychological characteristics, this research proves that there is no statistically significant relationship between travel distance and tourists’ psychographic characteristics. This finding supports the argument of McKercher (2005), in that travel distance is subjective to the origin market and that different markets show unique relationships with the destination.

The present study offers both theoretical and practical contributions regarding the impact of both sociodemographic and travel-related psychographic variables on destination choices. From an academic perspective, this research contributes to the body of literature in relation to tourists’ destination choices by not only examining the variables of the three major constructs – personality, motivation and travel behavior – but also by integrating the intervening factors of travel distance and socio-demographic characteristics. The results of the study provide a more comprehensive and holistic understanding of the relationship between tourists and destinations and serve as an indicator for the focus of any further research into the relationship of other influential factors that might better explain tourists’ destination choices.

From the practical perspective, the study provides information for destination marketers how tourist psychographics (i.e. psychocentric, mid-centric and allocentric) can be differentiated by demographic characteristics (i.e. age, incomes, occupations, motivations and destination characteristic).

Multifactor-led integral concepts are more likely to provide opportunities for destinations and to identify the direction of their growth. The development and the rise and fall of the popularity of destinations are undoubtedly initiated by tourist-specific aspects; at a certain phase, though, the decisive factors shift to destination-specific aspects. As Figure 1 shows, the ideal positioning of destinations is necessary to to maintain a volume share of the market, and growth is associated with the rate and number of visitors, as well as with the use of infrastructure. A beneficial strategy for managing tourism growth would be to avoid decreasing allocentric tourism interests by providing an adequate level of genuine experience opportunities and gradually involving travelers other than those allocentric tourists.

However, empirical test results indicate that the proportion of psychological profiles cannot accurately suggest the direction a tourism destination should take in managing its growth. In particular, if a destination’s market structure follows a similar pattern to this study’s sample profiles, then the popularity of the destination may rely on well-developed tourism establishments, as the dominant market for the peak of popularity will be the near-psychocentrics. That is, destinations should pay attention to managing their resources as well as to their possible impact on the local community, as the pychocentrics defined in this research are less likely to value cultural experiences or adventure. Additionally, the test results for travel distance and destination choice in this study are not significantly related. Therefore, a destination may have the opportunity to succeed in the market regardless of any distance barriers.

This study does have limitations that need to be discussed. First, the conceptual framework does not consider external factors such as natural disasters or adverse political situations. These factors undoubtedly have an effect on tourism and destination choices.

Second, the relatively smaller sample size in each type of psychographic characteristic could not successfully group tourists into the five categories of Plog’s (1974, 2001) model. Nevertheless, the study itself can be meaningful in the tourism literature, and it is recommended that future studies can test our model with different or more samples to improve the stability of the model. In other words, the current study can be considered as exploratory and shows an integrative approach regarding tourists’ destination choices. It is recommended that researchers can further investigate this model with different settings and larger samples.

Third, the weighted sample size in a particular group can cause a lack of generalizability. The current research was conducted in Hong Kong, where the tourism market composition has been rapidly changing since the transfer of sovereignty over Hong Kong from the UK to China, encompassing more than 50 per cent of mainland Chinese. Around 47 per cent of the respondents from mainland China in this study could influence the test results, because they still tend to belong to the lower-income level and, in consideration of language barrier, prefer packaged travel. In turn, although this study used a wider range of international tourists than the previous research, mainland Chinese tourists’ specific travel patterns cannot be overlooked. Thus, future studies should target other tourist markets so that the research can address a broader spectrum of tourist characteristics using the integrated model in the study.

Fourth, the current study followed Brayley’s (1990) perspective which categorizes tourist groups according to tourists’ psychographic preferences, while Plog’s (1974, 2001) model categorized the groups into five types based on destinations that tourists visited. In this connection, future studies can improve the accuracy of the psychographic types by combining the two different perspectives. As a result, tourist motivations can more precisely be measured.

Fifth, the study was limited to individuals’ types on destination choice, in turn, could not consider influences of family and travel party on motivations. Future researchers can include them in the motivational factors, and it can provide more in-depth information about relationships between the factors and the psychographic types.

Finally, one of the major goals of this study – to examine the relationship between travel distance and tourist behavior – was not statistically proven. Therefore, a more refined methodology will enhance test results in future research to actually come to a conclusion about the interplay between these two factors.


Comparison between previous research models and the study result

Figure 1

Comparison between previous research models and the study result

Intrinsic and extrinsic factors in relation to tourists’ psychology and destination choices

Motivation Destination setting Distance relationship Socio-demographics/travel experiences
Psychographic characteristics of allocentrics
Host-site involvement
Cultural experience
Nature-based experience
Exotic place
Long distance
Culturally distinct
Longer stay
First-time tourist
More experienced
Psychographic characteristics of psychocentrics
Touristic establishment
Short distance
Culturally close
Shorter stay
Repeat tourist
Less experienced

Demographic profiles (N = 202)

Variable Category (%) Variable Category (%)
Gender Male
Marriage Single
Age 20s
60s and older
Residential place Europe
North America
South America
Northeast Asia
Asia (Others)
Mainland China
Education Less than high school
High school graduate
Some college
College graduate
Postgraduate study or more
for international tourists
Under USD 25,000
USD 25,000–49,999
USD 50,000–74,999
USD 75,000–99,999
USD 100,000–199,999
USD 200,000 and higher
Occupation Professional
Blue collar
Service industry
Government employee
for mainland Chinese tourists
Under CNY 169,999
CNY 170,000–339,999
CNY 340,000–509,999
CNY 510,000–679,999
CNY 680,000–135,999
CNY 136,000 and higher

Tests of psychographic types of tourists in comparison with previous research

Psychographic type Plog’s model (%) Test results
Ideal destination (%) Litvin (2006) (%) Result of this study (%)
Psychocentric 4.0 3.0 57.0 4.5
Near-psychocentric 13.0 10.0 17.0 30.7
Midcentric 68.0 74.0 23.0 47.0
Near-allocentric 12.0 11.0 3.0 13.4
Allocentric 2.5 3.0 4.5

Relationship between psychographic types and travel modes

Travel mode
Psychographic type General independent travel Backpack travel Full-package tour Air-hotel package or semi-package tourTotal
Psychocentric 36 (50.7%)a
13 (18.3%)
14 (19.7%)
8 (11.3%)
71 (100%)
Mid-centric 68 (71.6%)
8 (8.4%)
11 (11.6%)
8 (8.4%)
95 (100%)
Allocentric 23 (63.9%)
10 (27.8%)
1 (2.8%)
2 (5.6%)
36 (100%)
Total 127 (100%) 31 (100%) 26 (100%) 18 (100%)

Notes: Chi-square = 16.306 (p < 0.012); a% within psychographic type; bAdjusted residual

Factor analysis with varimax rotation for travel motivation

Motivation items Factor loadings Eigen value Variance explained Reliability alpha
Factor 1: Scenery and exotic experience
Natural aesthetics 0.732 5.343 24.28 0.736
Unique environmental feature 0.711
Experiences of natural habitat (birds/animal/wildlife) 0.591
Novelty 0.475
Factor 2: Culture
Local people 0.803 2.412 10.96 0.728
Friendship/social interaction 0.715
Knowledge of new places 0.666
Historical/cultural sites 0.626
Educational value 0.409
Factor 3: Relaxation
Emotionally/physically refreshed 0.823 1.890 8.59 0.732
Relaxation 0.818
Good weather 0.535
Fun 0.533
Factor 4: Self-actualization
Self-actualization 0.725 1.351 6.13 0.625
Self-fulfillment 0.719
Factor 5: Physical refreshment
Engaging in sports 0.747 1.263 5.73 0.658
Mingling with fellow tourists 0.620
Being active 0.597
Health and Fitness 0.461
Factor 6: Pleasure seeking/fantasy
Getting away from home 0.787 1.057 4.08 0.619
Escaping from everyday life 0.733
Seeking adventure 0.604

Notes: KMO (Kaiser-Meyer-Olkin) = 0.793, Total variance 60.52 %; N = 202

Definition of variables and descriptive statistics

Summary of variables Name of variables Definitions of variables Mean Standard deviation
Dependent variable Type Psychocentric = 1
Midcentric = 2
Allocentric = 0
1.23 0.76
Demographic variables Gender Male = 1
Female = 0
0.489 0.50
Marital status Married = 1
Not married = 0
0.449 0.49
Income USD 87,995 55,915
Occupation White collar = 1
Other = 0
0.637 0.48
Travel mode Mode Independent travel = 1
Other = 0
0.604 0.49
Frequency Number of travel (times) 4.97 6.50
Period Travel period (days) 12.0 31.58
Distance Within the continent = 1
Outside the continent = 0
0.369 0.48
Motivation of selecting tourism destinations Motivation 1 Scenery and exotic experience 3.63 0.68
Motivation 2 Cultural experience 3.64 0.58
Motivation 3 Relaxation 3.97 0.66
Motivation 4 Self-actualization 3.54 0.78
Motivation 5 Physical refreshment 3.21 0.64
Motivation 6 Pleasure seeking/fantasy 3.36 0.85
Destination setting Destination 1 Exotic place 3.94 0.76
Destination 2 Superb scenery 4.08 0.72
Destination 3 Familiarity 2.85 0.90
Destination 4 Touristic establishment 3.61 0.89

Coefficient values of the multinominal logit model  (N = 202)

Variable Tourist psychographic group
β (t-value)
β (t-value)
Gender −0.691  (1.11) 0.381 (0.69)
Marital status −0.670 (1.04) 0.629 (1.11)
Income 0.055 (1.33) −0.030 (2.01)**
Occupation −0.239 (3.10)*** 0.171 (0.31)
Mode −0.274 (0.43) 1.005 (1.78)*
Frequency −0.253 (2.41) 0.111 (1.25)
Period −0.002 (0.21) 0.008 (0.70)
Distance −0.001 (1.20) 0.004 (0.03)
Motivation 1 −1.283 (1.97)** 0.040 (1.37)
Motivation 2 −2.113 (3.38)*** 0.060 (1.91)*
Motivation 3 0.129 (0.23) −0.196 (0.41)
Motivation 4 −0.396 (0.76) 0.245 (0.54)
Motivation 5 0.607 (1.09) −0.505 (−1.06)
Motivation 6 −1.369 (3.37)*** 0.415 (1.16)
Destination 1 0.149 (0.94) −0.179 (0.37)
Destination 2 1.01 (2.18)** 0.063 (1.22)
Destination 3 0.520 (1.35) 0.620 (1.57)
Destination 4 0.518 (1.20) −0.520 (1.38)
Constant 14.18 (3.85) 7.806 (2.38)

Notes: Log-L = −109.54; Rest. Log-L = −156.81; Chi-squared = 94.53  (p < 0.000);

*p < 0.1;

**p < 0.05;

***p < 0.01

Marginal effects of the multinominal logit model  (N = 202)

Variable Tourist psychographic group
β  (t-value)
β  (t-value)
β  (t-value)
Gender −0.079  (−0.84) 0.124  (1.28) −0.044  (2.00)
Marital status −0.091  (−0.91) 0.097  (0.91) −0.005  (0.61)
Income 0.007  (0.83) −0.018  (−1.82)* 0.010  (1.95)**
Occupation −0.170  (−1.72)* 0.145  (1.37) 0.029  (0.49)
Mode −0.120  (−1.29) 0.191  (1.96)** −0.071  (−1.24)
Frequency −0.022  (−1.61) 0.009  (0.70) 0.013  (2.13)**
Period −0.003  (−1.03) 0.001  (0.61) 0.001  (1.75)*
Distance −0.109  (−1.02) 0.025  (0.22) 0.084  (1.22)
Motivation 1 −0.125  (−1.33) 0.080  (0.83) 0.044  (0.76)
Motivation 2 −0.191  (−2.12)** 0.025  (0.26) 0.166  (2.67)***
Motivation 3 0.012  (0.13) −0.008  (−0.08) −0.003  (−0.07)
Motivation 4 −0.007  (−0.10) 0.055  (0.70) −0.047  (−1.12)
Motivation 5 0.147  (1.69)* −0.069  (−0.78) −0.077  (−1.64)
Motivation 6 −0.120  (−1.86)* 0.033  (0.50) 0.087  (2.09)**
Destination 1 0.046  (0.52) −0.050  (−0.56) 0.004  (0.07)
Destination 2 0.035  (0.32) 0.001  (0.01) −0.035  (−0.67)
Destination 3 0.018  (0.32) 0.012  (0.21) −0.031  (−0.89)
Destination 4 0.159  (2.52)** −0.055  (−0.82) −0.104  (−2.66)***
Constant 0.565  (1.07) −0.127  (−0.237) −0.438  (−1.432)

Notes: Log-L = −109.54; Rest. Log-L = −156.81; Chi-squared = 94.53  (p < 0.000);

*p < 0.1;

**p < 0.05; and

***p < 0.01


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Corresponding author

Donghwan Yoon can be contacted at:

About the authors

Chang-Keun Yoo is Assistant Professor at the Department of Airlines and Tourism, Uiduk University, Gyeongju, Republic of Korea.

Donghwan Yoon is Visiting Professor at the Department of Hotel and Tourism, The University of Suwon, Hwaseong, Republic of Korea.

Eerang Park is Lecturer in Tourism Management at School of Management, Victoria University of Wellington, Wellington, New Zealand.