• tourism motivation;
  • grid-group analysis;
  • Chinese


  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References

Marketing theorists have widely proposed that culture is one of the underlying determinants of consumer behavior and that increasing globalization is creating a multicultural marketplace. Empirical inquiries in the field of tourism remain scarce, particularly regarding the understanding of behavioral influences. This study aims to fill this gap by investigating the influence of sub-cultures on tourism motivation. Drawing on grid-group cultural theory, an analysis of survey data from 727 Chinese tourists reveals that respondents classified as different cultural types had different travel motivations. Contributions and limitations of the study are discussed, and future research directions are suggested. Copyright © 2013 John Wiley & Sons, Ltd.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References

Tourism motivation has long been conceptualized in the literature and is central to understanding tourist behavior. The study of motivation has received a fair amount of attention since the 1960s. Many scholars have acknowledged the difficulty and importance of understanding tourism motivation, and substantial progress has been made. Several theories and models have been developed to guide the empirical study of tourism motivation, such as the push-pull model (Dann, 1981), allocentric-psychocentric theory (Plog, 1974), escape-seeking theory (Ross and Iso-Aloha, 1991) and the travel career ladder (Pearce, 1988). Among other disciplines, sociology and anthropology have opened up opportunities to study tourism motivation by looking at the social context and structure within which that motivation is interpreted. In particular, Cohen (1972, 1979) argued that tourist motivation is determined first and foremost by the relationship between tourists and tourism businesses and the destination. By classifying tourists into five typologies within the social structure, Cohen concluded that mass tourists, who depend largely on tourism institutions and business arrangements, act quite distinctly in comparison to individual tourists. In this regard, tourists’ individual heterogeneities in terms of personality, attitude, satisfaction and social-demographics matter less to tourist behavior than the social relationships that tourists are bundled with. This probably relates to the role that culture plays in determining human behavior.

Culture – defined as a set of beliefs or standards shared by a group of people (Goodenough, 1971) – is widely accepted by marketing theorists as one of the underlying determinants of consumer behavior, including that of tourists. Culture in its various manifestations has a significant influence on tourist behavior. Given that cultural elements are subtly inculcated into individuals from an early age (Pizam et al., 1997) and are resistant to change (Hofstede, 1997), the incorporation of culture into the study of tourism motivation is crucial. However, culture is too broad to be theoretically justified if it is treated as a factor in explaining human behavior. Research has thus addressed the cultural characteristics of tourist behavior rather than culture as a determinant of human behavior (Cohen, 1972, 1979; MacCannell, 1976). Moreover, the operationalization of culture as a determinant in explaining tourist motivation remains unexplored.

This study was designed to address this gap by employing grid-group cultural theory to operationalize culture and explain tourists’ motivation in relation to cultural types. The grid-group theory asserts that there are four types of culture based on two fundamental dimensions of sociality: the group and the grid. The four major social types are labeled as individualist, fatalist, hierarchist and egalitarian. Individuals with different lifestyles have distinctive personal identities and behavior. More specifically, the study aimed to achieve two research objectives: to delineate the primary tourism motivations of individuals of each social type and to identify the differences and similarities in tourism motivation among the four social types. A quantitative approach was adopted using data collected from a questionnaire survey. The study results are expected to provide a culturally based understanding of tourism motivation.

Literature Review

  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References

Tourism motivation

Motivation is considered as the cause of human behavior (Mook, 1996). It is a state of need or a condition that drives an individual toward certain types of action that could satisfy needs. Given the importance of travel motivation in determining behavior, studies of travel motivation are abundant, and various conceptualizations of travel motivation have been developed. However, many of these empirical studies were based on social psychological theories, which have been blamed for their failure to explain why individuals would choose to satisfy their needs through traveling rather than by other means such as family or religion, or why they choose one destination over another (Ryan and Glendon, 1998; Jamal and Lee, 2003). To address this, many sociologists have included tourism motivation in their studies (Rojek, 1995; Wang, 2000).

In contrast to psychological perspectives, which suggest that human beings are born with basic innate needs and that travel provides alternatives to satisfy these needs when they experience disequilibrium in their need systems, sociological approaches emphasize the influence of the structure of society on an individual's travel behavior (Jamal and Lee, 2003). It is argued that tourism is not something given. It is rather socially and culturally produced, constructed and generated (Wang, 2000).

The first sociological account of tourism appeared in Germany in 1930 (Cohen, 1984). Since then, several distinctive approaches or perspectives emerged (Wang, 2000), such as the Weberian (tourism as meaningful action and motivation, Dann, 1977, 1981), the Durkheimian (tourism as ritual and myth, Graburn, 1989; MacCannell, 1976), the Marxian (tourism as false consciousness and ideology, Thurot and Thurot, 1983), the structural-functional (tourism as social therapy, Krippendorf, 1987), the structural-conflictual (tourism as the conflict of interests between the core and periphery, Turner and Ash, 1975), the symbolic interactionist (tourism as communication of identity and as symbolic display of status, Brown, 1992), the phenomenological (tourism as experiences, Cohen, 1979), the feminist (tourism as gender inequality Kinnaird and Hall, 1994) and the post-structuralist (tourism as sign, discourse, and representation, Dann, 1996).

One fundamental approach that sociologists apply to tourism is the contextualism of modernity, which frames motivation in a broad context of global structure and social changes. According to this approach, the formation of tourism is not merely an issue of bio- or psychogenesis at the level of the individual but rather a matter of sociogenesis at the levels of society and culture (Wang, 2000). Any changes in the global environment may influence the needs and desires of individuals and their subsequent motivations (Burns and Holden, 1995; Wang, 2000). The influence of the global structure on motivation is affected more directly by the home environment of the individual. The modernization of society greatly changes people's lifestyle in that they tend to experience more fragmentation in their daily life. Interpersonal relationships also become more fragmented and less authentic (MacCannell, 1976). These changes result in anomie in the life of individuals, which forces them to escape from their home environment and seek authenticity and self-enhancement at destination, through the experience of the products, services and facilities provided there (Dann, 1981).

One of the elements of the home environment – culture – has yet been fully explored in the study of tourism motivation, despite the fact that the importance of incorporating cultural elements has been widely recognized in the study of consumer behavior (Douglas, 1997; Luna and Gupta, 2001). Due to the increasingly diversified and sophisticated behavior of tourists, the incorporation of cultural elements in the study of tourism motivation is critically needed. An extensive review of the literature on culture and tourism motivation revealed that most of the studies were conducted in the context of cross-cultural comparison. With few exceptions, previous studies have largely employed nationality as the surrogate for culture. Contributing enormously to the understanding of travel motivation, the use of a collective cultural proxy as a discriminating variable actually assumes cultural homogeneity within a national or ethnic boundary, whereas the layers of culture have been ignored. Li and Cai (2012) addressed this by exploring the effect of values on travel motivation and behavioral intention. Personal values, as one of the four manifests of culture, were found to exert a significantly positive effect on travel motivation. However, the study did not explain how individuals with distinct cultural values are urged by different travel motivations.

Cultural theory and grid-group analysis

This study draws on grid-group cultural theory, also known as grid-group analysis, cultural theory or the theory of socio-cultural viability (Figure 1). The theory has been developed over the past 40 years through the work of the British anthropologists Mary Douglas and Michael Thompson, the American political scientist Aaron Wildavsky, and many others (Mamadouth, 1999). The fundamental idea of grid-group cultural theory is that what people do or want is culturally biased. Grid-group theorists claim that culture can be classified across two dimensions of sociality: through individuation in the group dimension and through social incorporation in the grid dimension (Douglas, 1982). The group dimension covers incorporation into a bounded group, which is strong when the individual is a member of one corporate group and weak when an individual does not belong to such a group. The grid dimension is ‘the cross-hatch of rules to which individuals are subject in the course of their interaction’ (Douglas, 1982: 192). Personal identity is determined by individuals’ relationships to groups, and personal behavior is shaped by social prescription.

These dimensions address two fundamental questions about human existence: who am I, and how should I behave? (Schwarz and Thompson, 1990). This claim is based on the assumption that people derive a great many of their preferences, perceptions, opinions, values and norms from their adherence to a certain way of organizing social relations, which is revealed by their preference for the two basic dimensions of social life: group (incorporation or boundedness) and grid (regulation or prescription).

The two dimensions form four major social types with corresponding ideologies (Caulkins, 1999): individualists, fatalists, hierarchists and egalitarians. Individualists are characterized by weak group incorporation and weak regulation or role prescriptions. They are relatively free from external constraints and their ability to control others is a measure of their position in the network. Individualists pursue personal rewards in competitive environments.

Fatalists are characterized by weak group incorporation and binding prescription. They are strictly constrained by external factors and have little influence on the way in which they live. Hierarchists are characterized by strong group boundaries and binding prescriptions. They have highly differentiated roles and maintain hierarchical social relations. Egalitarians have strong group boundaries and few regulations. They share an opposition to the outside world and are thus closely bound.


Figure 1. Grid-group theory (based on Douglas, 1978).

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Grid-group cultural theory has been widely applied by an interdisciplinary variety of scholars includes interpretation of environmentalism (Douglas and Wildavsky, 1982; Grendstad and Selle, 1997), perception of risk (Dake, 1991), a critique of rational choice theory (Douglas and Ney, 1998), technology policy (Schwarz and Thompson, 1990), public administration (Hood, 1996) and religious communities (Atkins, 1991). However, its application in the hospitality and tourism literature has been very limited. Houghton (1994) explained the organizational diversity of the hospitality industry by referring to the cultural attributes of the markets to which it caters. Duval (2006) applied grid-group cultural theory to explore the relationship between migration and tourism. Fisher (2009) used the theory to illustrate how a given individual can exhibit different behavioral patterns in a variety of situations within the context of tipping. However, as these studies were descriptive, they were only able to portray the characteristics of tourists in each dimension and failed to validate the descriptions with empirical data.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References

The data used in this study were collected through a self-administrated survey of Chinese package-group tourists visiting Taiwan. Tour guides distributed the questionnaires to the tourists and collected them during the return flights between January and July 2010. Each household was allocated one survey. A total of 727 completed surveys were collected for use in the study.

The survey design was based on the literature review. The variables used for the study included tourism motivation, cultural traits and other variables pertaining to tourists’ profiles and behavior. The operationalization of motivation drew upon on the individuals’ psychological attributes. Thirty-seven items were selected from the existing tourism motivation scales (Crompton, 1979; Dann, 1981; Fodness, 1994; Hsu et al., 2011) and rephrased in statements that were consistent with the context of the present study. The statements were presented to the respondents, who were asked to evaluate them on a Likert scale ranging from 1 to 7. Cultural traits were measured using the British edition of Dake's Cultural Biases Questionnaire (Dake, 1992). The respondents were asked to evaluate each of the 28 statements on a Likert scale ranging from 1 ‘strongly disagree’ to 7 ‘strongly agree’.

The instrument was first developed in English and then translated into Chinese using a combination of parallel blind translation and modified direct translation, as described by Guthery and Lowe (1992). The questions were first translated by two bilingual English/Chinese speakers simultaneously, and the two target versions were compared before a consensus was reached. The translated questionnaire was then reviewed by an expert panel, and revisions were made accordingly.

A progressive series of statistical analyses were carried out. First, a frequency analysis was conducted to examine the profile of the respondents. A confirmatory factor analysis (CFA) was then conducted to verify the measurement scale of cultural bias in the Chinese context. The CFA was followed by an exploratory factor analysis (EFA) to identify the underlying motivational dimensions. Cronbach's alpha test was employed to verify the reliability of the variables generated by the EFA. To investigate the effect of cultural bias on various dimensions of tourist motivation, a series of multivariate analysis of variance (MANOVA) procedures was conducted to test for any significant differences in the underlying dimensions of motivation with different cultural types. In the MANOVA procedure, motivational factors emerging from the factor analysis were the dependent variables, and the cultural types were the independent variables. Four multivariate test statistics for MANOVA were conducted. They are Pillai's trace, Wilks’ lambda, Hotelling's trace and Roy's largest root; 0.001 was used as the cut-off line for significance. Two sets of ANOVAs were further performed as post hoc analysis to examine, which dimensions of tourist motivation were exactly affected by cultural types and which cultural types affected those tourist motivation dimensions. Prior to performing ANOVA, the underlying assumptions for ANOVA were examined beforehand, including the normality of the data, homogeneity of variance and the independence of the observations.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References

Profiles of the respondents

The respondents’ profiles are presented in Table 1. There were slightly fewer males (42.4%) than females (47.7%). With respect to age, the 35–44 age group had the highest proportion of respondents (19%), followed by 25–34 (14.4%), 55–64 (12.8%) and 45–49 (11.3%). Regarding education, 24.5% of the respondents had an Associate degree, 22.3% had a high school degree, 18% had a Bachelor's degree and over 4% had a Master degree or above. In terms of employment, the largest proportion of respondents (almost 20%) classified themselves as retired, followed by manager/executive (13.6%), owner/self-employed (11.7%) and worker (10.3%). In terms of monthly income, approximately 20% of the respondents indicated that they earned from RMB 3000 to RMB 4999, 15.7% earned between RMB 2000 to RMB 2999, 11.8% earned between RMB 5000 to RMB 7999, 10.6% earned between RMB 1000 and RMB 1999, and those who earned less than RMB 1,000 and above RMB 10,000 accounted for around 3% and 5%, respectively.

Table 1. Social demographics of the respondents *
Gender  Occupation  
  1. Notes: * Cases with missing values are excluded.

Female34747.7Government officials273.7
Age  Military/police50.7
> 65547.4   
Education  Income  
Doctoral degree40.6< 1,000223.0
Master's degree263.61,000–1,9997710.6
Bachelor's degree13118.02,000–2,99911415.7
Associate's degree17824.53,000–4,99914920.5
High school16222.35,000–7,9998611.8
Junior high school10814.98,000–9,999243.3
Primary school and below253.5> 10,000385.2

A cultural typology of tourists

Confirmatory factor analysis of cultural bias

As Dake's (1991) cultural bias scale is a well-developed instrument that has been applied in other contexts (Marris et al., 1998), a confirmatory factor analysis of cultural bias was deemed appropriate to examine whether it was valid in the Chinese context. The results indicated poor goodness-of-fit between the original measurement model and the data (Table 2). Examination of the modification indices suggested that four items used to measure cultural bias in the Western context may not apply to the Chinese context. Three of these items loaded on egalitarianism and were used to measure food-related cultural bias, and the other loaded on individualism and was used to measure social security related cultural bias. After a respecification of Dake's (1992) cultural bias measurement model, we retained 24 items that loaded on the four types of cultural bias: hierarchy, individualism, egalitarianism and fatalism.

Table 2. Confirmatory factor analysis of cultural bias
  • Notes: SMC refers to as the squared multiple correlation for a measurement variable.

  • ***

    p < 0.001.

  • a

    Original model consists of 28 items, which are precisely drawn from Dake's (1991) cultural bias scale with minor modifications.

  • b

    Specification 1 consists of 25 items with three items measuring food-related cultural bias deleted.

  • c

    Specification 2 consists of 24 items as the final cultural bias, with the item measuring social security deleted.

People should be rewarded according to their position0.713***1.000 0.508
We have gone too far in pushing equal rights0.406***0.6230.0630.165
It is important to carry on family traditions0.568***0.7330.0540.323
I am more strict than most people about what is right and wrong0.621***0.8570.0580.386
Support the introduction of compulsory National Service0.680***0.9650.0610.463
I think there should be more discipline in the youth of today0.592***0.8620.0620.351
In a fair system people with more ability should earn more0.595***1.000 0.354
A person who has the get up-and go to acquire wealth should have the right to enjoy it0.698***1.1370.0840.487
It is just as well that life tends to sort out those who try harder from those who don't0.691***1.1060.0820.478
In this country, the brightest should make it to the top0.555***0.9460.0820.308
What this country needs is a ‘fairness revolution’ to make the distribution of goods more equal0.822***1.000 0.675
The world could be a more peaceful place if its wealth were divided more equally among nations0.788***0.9990.0440.621
The difference between rich and poor nations isn't right0.541***0.6350.0440.293
Those who get ahead should be taxed more0.651***0.6930.0390.424
The government should make sure everyone has a good standard of living0.748***0.8620.0410.559
If people in this country were treated more equally we would have fewer problems0.645***0.6710.0380.416
There is no use in doing things for people0.765***1.000 0.586
Cooperation with others rarely works0.810***1.0590.0300.656
The future is too uncertain to make plans0.806***1.0870.0470.650
I have often been treated unfairly0.811***1.0690.0460.658
A person is better off if s/he doesn't trust anyone0.879***1.1870.0460.772
Making friends only because it's useful0.802***1.0920.0480.643
Life is like a lottery0.852***1.1680.0480.725
I don't worry about politics because I can't influence things very much0.714***1.0300.0520.510
Model specificationχ2χ2/dfGFICFITLIRMSEA
Original a1479.4354.403 (336)0.8640.8940.8800.068
Specification 1b1091.2254.165 (262)0.8840.9160.9040.066
Specification 2 (Final)c868.3473.603 (241)0.9040.9340.9240.060

The results show that the measurement model achieved a modest fit through two steps of the respecification (χ2 (241) = 868.347, p < 0.001; GFI = 0.904, CFI = 0.934, TLI = 0.924 and RMSEA = 0.060). To check the reliability and validity of this measurement, Cronbach's α and the average variance extracted (AVE) were calculated (Table 3). Cronbach's α was above the cutoff of 0.70 for all four factors, indicating a high reliability of the scale. The AVE for the hierarchy (F1), individualism (F2) and egalitarianism (F3) factors was below the cutoff of 0.50, indicating unsatisfactory convergent validity of the measurement model in the Chinese context. The AVE for these three factors was, however, greater than the squared correlation coefficients for the corresponding inter-constructs, indicating modest discriminant validity.

Table 3. Reliability and validity of the measurement of cultural bias
F1: Hierarchy1.000   
F2: Individualism0.831 (0.691)1.000  
F3: Egalitarianism0.528 (0.279)0.511 (0.261)1.000 
F4: Fatalism0.485 (0.235)0.428 (0.183)0.382 (0.146)1.000
Cronbach's α0.7730.7270.8580.932
Categorizing cultural bias of the respondents

As the 24-item cultural bias scale was shown to be valid, we proceeded to calculate the value, or factor score, for each of the four types of cultural bias. The factor-based scores approach was adopted for computing factor scores as it is a straightforward and reliable method of estimation (Alwin, 1973; Marris et al., 1998); it estimates the value of a latent variable as the equally weighted sum of all of its items (Alwin, 1973). Following this approach, the grand mean for each cultural type was calculated in two phases. First, for each individual respondent, the mean of each cultural type was calculated by summing the scores for the relevant items for this cultural type divided by the number of items. Second, the grand mean of each cultural type was calculated by aggregating the means for the individual respondents for each cultural type divided by the sample size.

Table 4 shows the grand mean and standard deviation for each cultural type. Egalitarianism had the largest mean of 5.096 with a modest standard deviation of 0.042, indicating that the respondents were more culturally biased toward this cultural type. In contrast, fatalism had the smallest mean of 3.019 and the largest, yet still acceptable, standard deviation of 0.059, indicating that it was not a dominant cultural value among the respondents. Modest means and standard deviations were found for hierarchy (M = 4.753, SD = 0.036) and individualism (M = 4.748, SD = 0.042), suggesting that the respondents had a modest attitude toward both hierarchy and individualism.

Table 4. Categories of cultural bias

The 727 respondents were then classified into the four cultural types using the Marris et al. (1998) approach. To categorize a respondent as having a particular type of cultural bias, the respondent must score above the mean for that cultural type and below the means for the other three culture types (Marris et al., 1998). Following this classification approach, 59 respondents were categorized as fatalist, representing the largest proportion (8.1%), 57 were categorized as individualist (7.8%), 50 were categorized as egalitarian (6.9%) and 36 were categorized as hierarchical (5.0%). The results also show that a substantial number of respondents, 72.2%, could not be categorized as any of the four cultural types (Table 4). In other words, only about 28% of the respondents demonstrated clearly classifiable cultural values. This result may not be surprising as a large number of respondents in the Marris et al. (1998) study were either unclassified or of mixed cultural bias.

Tourist motivation profiles

Eleven rounds of EFA were conducted on tourist motivation to refine the measurement model. The criteria for the refining process were whether items had communality below the cutoff value of 0.50, had a cross-loading above 0.50 on at least two items and had a factor loading below the cutoff value of 0.50. According to the three cutoff values, 11 items were deleted from the measurement of tourist motivation and 26 items were retained in the final EFA. Regarding the factor structure of tourist motivation, the Kaiser-Meyer-Olkin measure of sampling adequacy was 0.925, and Bartlett's test of sphericity was χ2 (325) = 7691.598, p < 0.001, indicating that EFA was appropriate. According to Kaiser's (1974) criterion, factors with an eigenvalue greater than 1 were extracted, which resulted in a five-factor structure (Table 5). The five-factor structure was satisfactory as all communalities were above 0.50.

Table 5. Result of the exploratory factor analysis of tourist motivation
FactorFactor loadingEigen-valueCommunalityVariance explainedCronbach's αMSD
  1. Notes: Kaiser-Meyer-Olkin measure of sampling adequacy is of 0.925, Bartlett's test of sphericity is of 7691.598, df = 325, p < 0.001.

  2. Extraction method: principal component analysis.

  3. Rotation method: Varimax with Kaiser normalization.

F1: Self-development and Escape 10.065 38.7100.9453.6361.588
Gaining a sense of accomplishment0.841 0.765    
Visit the featured places0.827 0.737    
Being away from daily routine0.818 0.766    
Having unprecedented experience0.792 0.693    
Feeling excitement0.775 0.674    
Having romantic relationships0.768 0.715    
Being free to act the way I feel0.761 0.705    
See the actor/actress in movies or dramas0.728 0.658    
Understanding more about myself0.722 0.633    
Getting away from my family0.687 0.700    
Having others know that I have been there0.684 0.614    
Feeling inner harmony/peace0.650 0.627    
F2: Novelty 3.426 13.1780.8215.6361.024
Feeling the special atmosphere of the vacation destination0.861 0.778    
Experiencing something different0.820 0.731    
Experience the local conditions and social customs of Taiwan0.731 0.681    
Enjoy the natural scenery of TW0.653 0.612    
Visiting places related to my personal interests0.513 0.515    
F3: Knowledge 1.732 6.6620.7595.3041.152
Visit the cultural and heritage sites0.733 0.638    
Experience the traditional Chinese culture and conventions that are well kept0.700 0.562    
Visit the places I have read from books0.694 0.584    
Know more about the political, social, and cultural environment0.602 0.550    
F4: Entertainment 1.241 4.7710.7004.2671.565
Shopping0.732 0.637    
Going places friends have not been0.616 0.680    
Having fun or being entertained0.506 0.696    
F5: Relaxation 1.153 4.4330.8314.8841.704
Resting and relaxing0.866 0.848    
Release my work pressure0.818 0.817    

As the factor structure matched well with Pearce's (1988, 1993) motivation typologies, the first factor was termed self-development and escape, comprising 12 items with factor loadings ranging from 0.650 to 0.841 and explaining 38.710% of the variance. The second factor was novelty, comprising five items with factor loadings ranging from 0.513 to 0.861 and explaining 13.178% of the variance. The third factor was knowledge, comprising four items with factor loadings ranging from 0.602 to 0.733 and accounting for 6.662% of the variance. The fourth factor was entertainment, comprising three items with factor loadings ranging from 0.506 to 0.732 and explaining 4.771% of the variance. The fifth and final factor was relaxation, comprising two items with factor loadings between 0.818 and 0.866 and explaining 4.433% of the variance.

Tourist motivation in the grid-group matrix: a MANOVA analysis

The effect of culture on tourist motivation

Table 6 shows the results of four multivariate test statistics for MANOVA: Pillai's trace, Wilks’ lambda, Hotelling's trace and Roy's largest root. All of these statistics were statistically significant at a 0.001 level, indicating that culture had a significant effect on overall motivation. In other words, tourists that were classified into the four cultural typologies had significant differences in their motivation. To further examine which dimensions of tourist motivation were affected by culture, a post hoc analysis with univariate ANOVAs was performed. The results show that culture had statistically significant effects on four dimensions of tourist motivation: self-development and escape (F (3, 127) = 2.838, p < 0.05), novelty (F (3, 127) = 10.059, p < 0.001), knowledge (F (3, 127) = 5.454, p < 0.01) and entertainment (F (3, 127) = 3.255, p < 0.05) (Table 7). Thus, although there was no evidence that culture affected the relaxation dimension, it did explain tourist motivation to a large extent, as it significantly affecting four of the factor's other dimensions.

Table 6. Multivariate test statistics for the MANOVA
Test statisticsValueF-ratioP
Pillai's trace0.4864.837 (15, 375)0.000
Wilks' lambda (Λ)0.5665.192 (15, 340)0.000
Hotelling's trace (T)0.6765.486 (15, 365)0.000
Roy's largest root (Θ)0.50412.589 (5, 125)0.000
Table 7. Differences in the dimensions of tourist motivation (ANOVA)
MotivationM   F-ratiop
 Hierarchy (22)Individualism (36)Egalitarianism (27)Fatalism (46)  
  1. Notes: Uncategorized was not calculated for the mean values in this table; therefore, the means are different from the previous EFA table.

  2. For the size of each group, cases with missing values are not included.

F1: Self-development3.02273.20603.23463.82792.838 (3, 127)0.041
F2: Novelty5.75455.86675.71114.813010.059 (3, 127)0.000
F3: Knowledge4.98865.26395.11114.40225.454 (3, 127)0.001
F4: Entertainment4.15154.66673.56794.21743.255 (3, 127)0.024
F5: Relaxation4.59095.13895.00004.36962.337 (3, 127)0.077
Effect of different cultural types on tourist motivation

As tourists were grouped according to cultural typologies, we examined which typologies accounted for the differences in tourist motivation identified above. We compared the differences between each of the cultural typologies on each dimension. Thus, comparisons were made between six pairs for each of the five dimensions of tourist motivation (Table 8). As Field (2009) suggested, three post hoc tests – Bonferroni, Gabriel and Games-Howell – were employed to determine the significance of the differences between each pair. There were no significant differences between the six comparisons for either self-development or escape and relaxation (Table 8). This is consistent with the previously mentioned result, suggesting that culture did not have a statistically significant effect on relaxation. Importantly, there was a significant difference between tourists classified under the typology of fatalism and those classified under the other three typologies in terms of novelty, as all three post hoc tests were statistically significant for the four pairs. Those in the fatalist group also differed from those in individualist and egalitarian groups in terms of knowledge, as the Bonferroni and Gabriel tests were statistically significant. A difference between the individualist and egalitarian groups was also found for entertainment, as all three post hoc tests were statistically significant.

Table 8. Differences of tourist motivation across four cultural biases (ANOVA)
Motivation Group pairs   
H × IH × EH × FI × EI × FE × F
  1. Notes: H, I, E and F denote hierarchy, individualism, egalitarianism and fatalism respectively.

  2. a, b and c denote the significance for Bonferroni, Gabriel and Games-Howell tests, respectively.

F1: Self-development–0.1833–0.2118–0.8052–0.0285–0.6219–0.5933
F2: Novelty–0.11210.04340.9415abc0.1556abc1.0536abc0.8981abc
F3: Knowledge–0.2753–0.12250.58650.15280.8617abc0.7089ab
F4: Entertainment–0.51520.5836–0.06591.0988abc0.4493–0.6495
F5: Relaxation–0.5480–0.40910.22130.13890.76930.6304


  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References

Informed by grid-group cultural theory, this study investigated the influence of culture on tourism motivation through a questionnaire survey of 727 tourists visiting Taiwan. The four-dimensional typology of culture was confirmed in a Chinese context through confirmatory factor analysis with some slight adjustments. Four of the 28 statements were eliminated as they failed to explain respondents’ cultural preferences/biases in the study setting. Although the respondents showed a bias toward egalitarianism, as indicated by the largest mean value for that typology, less than 30% of respondents demonstrated a clear preference for any of the four types of cultural values. Over 70% of the respondents were either unclassified or of mixed cultural bias. This finding is consistent with that of Marris et al. (1998).

Travel motivation was found to comprise five underlying factors. In descending order of importance, these were novelty, knowledge, relaxation, entertainment, and self-development and escape. All but one of these dimensions had a mean value of above 4, indicating that they are critical in motivating Chinese tourists to take pleasure trips. Although the self-development and escape dimension explained the most variance, it had a mean value of only 3.636, indicating that the Chinese tourists did not consider it to be an important motivation. The motivational factors were in the same order of importance for each of the four cultural types, indicating that national culture plays a critical role in tourist behavior.

The results of the MANOVA revealed that tourists associated with the different cultural typologies traveled for different reasons. Cultural type had statistically significant effects on four out of the five dimensions of motivation: self-development and escape, novelty, knowledge, and entertainment. Post hoc analyses showed that respondents classified as different cultural types were relatively consistent on the motivational dimensions of self-development and escape and relaxation.

Further investigations into each cultural type revealed that individuals classified as hierarchical and egalitarian had more similarities in terms of travel motivation than those classified as fatalist or individualist. This finding implies that in the grid-group dichotomy of cultural types, the dimension of group exerts greater influences on an individual's tourism motivation than the dimension of grid, as both hierarchism and egalitarianism are in the high-group continuum.

Those classified as fatalist were statistically different from the other three typologies on the motivational dimension of novelty. Although they still considered novelty to be the most prominent motivation, fatalists placed significantly less importance on this motivation than did respondents in the other three typologies. In addition, fatalists scored higher than the other three types on the dimension of self-development and escape, indicating that though still not important, this dimension was more likely to be the reason for those classified as fatalists to take pleasure trips.

Fatalists are characterized by binding prescriptions in combination with weak group incorporation. This type of person is strictly controlled by external rules and regulations and, therefore, has little to say about how they live their life. Fatalists feel they have little individual autonomy and are hardly able to gain it from their own efforts. Fatalists’ passive nature could thus explain their relatively low rating on all five dimensions of motivation. As the respondents in this cultural type feel that they have little control over their lives, they are more likely to regard travel as a means to escape from their daily routines and be free to act the way that they feel. Fatalists are also averse to risks, and are less likely than other cultural types to travel to experience novelty because they prefer to avoid possible risks to their personal safety.

Individualists, characterized by weak group incorporation and weak regulation, are more autonomous and feel less restricted by other people and by rules. They have strong self-confidence and a perception of self-control and are thus more active in making travel decisions, as indicated by the high ranking on four of the five motivational dimensions. This type of person considers tourism more as an opportunity to seek novel experiences, knowledge, relaxation and entertainment than a means by which to escape.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References

This study compared tourism motivation among different cultural groups using the grid-group cultural theory. The differences/similarities of tourism motivations were identified across cultural groups through a questionnaire survey of 727 Chinese tourists. This study extends the body of knowledge on cross-cultural tourism motivation. Although research on generic marketing and consumer behavior has shifted significantly over the past decade toward global or international topics, cross-cultural consumer research in hospitality and tourism has been largely neglected in scientific journals, even though travel and tourism are international phenomena. This is especially true for the study of tourism motivation (Li, 2012). The current study thus contributes to the literature by examining tourism motivation in a cross-cultural context. Moreover, it goes beyond the influence of national culture to investigate behavioral differences among subcultures.

The cultural theory of Douglas and her colleagues has been applied to a wide spectrum of cross-cultural topics (Dake, 1991), including risk perception (Marris et al., 1998), political science (McLeod, 1982), and environmental issues (Lima and Castro, 2005). The findings from this study demonstrate that cultural theory can also be assessed at the individual level of analysis. This not only adds a powerful set of tools to the study of how individual, social structural and cultural biases influence one another in the context of tourism but also expands the conceptual agenda of tourism motivation research.

The study's third contribution lies in the cultural assessment variables used. Despite their contribution to the understanding of tourism behavior in cross-cultural contexts, previous studies examining cross-cultural travel motivation have used nationality as the sole proxy for cultural affiliation. However, the use of national culture as a discriminating variable to explain differences in tourism motivation assumes that cultural homogeneity exists within national boundaries, neglecting the layers of culture and existence of sub-cultures. Any differences observed from such studies may thus be due to many effects other than culture, leading to ambiguous or even erroneous conclusions (Nakata and Pokay, 2004). In this sense, the current study contributes to the literature by employing cultural bias as the discriminating variable to explain differences in tourism motivation.

In addition, the study addresses the data equivalence issues that have been largely ignored in cross-cultural tourism research (Li, 2012) by considering a context with shared beliefs, values, traditions and language at a macro level. The language, measurement, construct and scalar equivalences are ensured, and the differences identified can thus be attributed to the different values associated with each subculture.

The findings from the study also have some implications for the industry. Practically, demographic and geographic information has been the most widely adopted variables to identify the market because they are easily measured and classified. However, demographic and geographic segmentation is often criticized of being somewhat outdated and blurred (Shoemaker and Shaw, 2008). To serve a diverse market profitably, the destination managers must have complete and accurate information about the individuals who make up each segment, and the addition of information about cultural values will greatly enhance the effectiveness of any effort, from destination planning to marketing. The findings from this study could be translated into marketing programs. For example, destination marketers could emphasize the possibility of achieving values through travel to a destination or even use particular value items in their advertisement campaigns to certain evoke travel motivation.

Notwithstanding these significant findings, this study does have some limitations. Dake's Cultural Biases Questionnaire was used to gain an understanding of cross-cultural differences in tourism motivation. Due to coding issues, however, over 70% of the respondents were either unclassified or of mixed cultural bias. The relationship between cultural predisposition and tourism motivation thus remains unexplored for this group of respondents, and future research efforts are warranted to investigate into this. This limitation also resulted in the unequal sample sizes of different cultural types, and the future study should collect equal number of responses to obtain robust results of ANOVA. In addition, the survey was exploratory and conducted with respondents from one region in China, which limits the generalizability of the findings. Future studies should replicate this study using different populations in other places. In addition, although the typology of cultural bias is supposedly context free, the study results reveal that the four types of cultural bias have some common features in terms of tourism motivation. Therefore, future studies should investigate whether this typology can go beyond national culture or whether it is contextually bound within a country.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References

The work described in this article was fully supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. PolyU 5467/10H).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Literature Review
  5. Methodology
  6. Results
  7. Discussion
  8. Conclusion
  9. Acknowledgements
  10. References
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