Predicting Leisure Satisfaction in Mainland China,Taiwan,and Brazil: An Individual Participant Data Meta-Analysis

Abstract

Numerous studies show that individuals’ satisfaction with their leisure relates to other aspects of their lives such as perceived quality of life, life satisfaction, subjective wellbeing, and self-rated health. However, the factors that lead to differences in leisure satisfaction are less well known, especially those involving culture. The purpose of this study is to determine which, among several variables common to samples from locations in Mainland China, Taiwan, and Brazil, best predict leisure satisfaction. An individual participant data meta-analysis indicates that age, education, income, and marital status are significant predictors. Frequency of participation in active leisure pursuits and the perceived importance of interpersonal leisure constraints significantly predict leisure satisfaction, as well. Finally, as our primary interest is in the predictive value of culture-related variables, we examine cultural competence, knowledge of the degree to which individuals know the cultural model of the importance of various leisure activities to a good lifestyle, and cultural consonance, the frequency in which individuals claim to participate in those activities, as predictors. Results indicate that cultural consonance with respect to activities agreed upon as high in importance to a good lifestyle is a robust predictor of leisure satisfaction but that cultural competence is not.

Keywords

leisure satisfaction cultural consonance China Taiwan Brazil multilevel modeling dominance analysis

In his 1881 introduction to anthropology, E. B. Tylor divided human culture into the “Arts of Life” (chapters eight through eleven) and the “Arts of Pleasure” (chapter twelve). In the former he included the development of tools, the food quest, housing, clothing, transportation, food preparation, technology, and commerce. In the latter, he addressed poetry, music, dance, drama, art, play, and games. The Arts of Life described how humans make a living while the Arts of Pleasure addressed how we spend free time and give meaning to life (Tylor, 1881). Hence, interest in leisure dates to the very beginnings of anthropology as a discipline and there is a rich tradition in anthropological research devoted to particular types of leisure, such as games (e.g., Roberts et al., 1959), sport (e.g., Besnier et al., 2018), festivals (e.g., Addo, 2009), music (e.g., Nettl, 2005), dance (e.g., Royce, 1977) and art (e.g., Layton, 1991). Anthropological research on leisure as a more general category of human activity is less common (Chick, 1998, 2020), but recent studies do exist.

Rubin et al. (1986), for example, showed that four groups of Amazonian natives adjusted their levels of active and passive leisure, particularly among children and adolescents, to regulate energy use in relatively abundant versus relatively depleted environments. They thereby managed their balance of active and passive leisure to adapt to their environments. Among the Tsimané of the Bolivian Amazon, Reyes-García et al. (2009) found that “social, not solitary, leisure has a positive and statistically significant association with subjective well-being” (p. 432). Godoy et al. (2009) observed that “sharing leisure time with kin and friends” was an important contributor to happiness among the Tsimane’ (p. 564). In introducing the concept of “cultural consonance,” Dressler (1996) examined the concept of lifestyle in a Brazilian community by asking a sample of individuals to rate the importance of 39 items, 30 of which were material possessions, such as a color television or automobile, and 9 leisure activities, such as attending the cinema or watching television, in having “successful lifestyles” (Dressler, 1996, p. 6). Using cultural consensus analysis (Romney et al., 1986), Dressler found that individuals exhibited consensus in rating the items. He then calculated the percentage of those items owned or participated in by the informants that received a consensus rating of 2 or higher on a 1-3 scale and. He termed this a measure of the informants’ “cultural consonance” (Dressler, 1996, p. 7). Dressler found that cultural consonance was a stronger predictor of informants’ systolic blood pressure than several sociodemographic measures such as age, sex, occupation, or education. Later research by Dressler and colleagues shows that cultural consonance in four domains, lifestyle, family life, social support, and national identity, is positively associated with physical and mental health in individuals (reviewed in Dressler, 2018).

To have positive effects such as those indicated above, however, leisure should be enjoyable and satisfying (Pressman et al., 2009; Shin & You, 2013; Weziak-Bialowolska et al., 2023). Leisure satisfaction reflects the degree to which individuals have leisure experiences that fulfill needs or desires for expression, rest and relaxation, entertainment, and other personal interests. Leisure satisfaction is therefore not directly observable, such as activities themselves or time spent in leisure, but a subjective outcome of participation. As such, leisure satisfaction relates positively to numerous other aspects of life such as life satisfaction (e.g., Brown & Frankel, 1993; Chick et al., 2023; Walker & Ito, 2017), perceived quality of life (e.g., Tercan Kass et al., 2023; Vong, 2005; Zhou et al., 2021), physical and mental health (e.g., Ateca-Amestoy et al., 2008; Chick et al., 2014; Chick et al., 2016; Fancourt et al., 2021), and wellbeing (e.g., Chen et al., 2022; Kuykendall et al., 2015; Newman et al., 2014). Satisfaction is most often explained via psychologically oriented perspectives such as Expectation-Confirmation Theory which holds that satisfaction results from the comparison of pre-activity expectations and whether those expectations were fulfilled in post-activity perceptions (Yuan & Marzuki, 2024) or motivation-based perspectives based on interactions between past experiences, behavioral intentions, and anticipated outcomes (Li et al., 2023). The influence of culture-related variables on leisure satisfaction is largely unexplored, however.

To address this gap, we examine variables common to data gathered in two studies in Mainland China, two in Taiwan, and one in Brazil as possible predictors of leisure satisfaction. These include age, gender, education, marital status, income, leisure constraints, and self-reported levels of participation in active and in passive leisure pursuits. We also include cultural competence and cultural consonance as culture-related variables. In the present study, cultural competence refers to the degree to which individuals know or agree with a model of leisure activities organized in terms of their importance to a good lifestyle. Cultural consonance denotes the extent to which individuals behave in accordance with that model (Dressler, 1996, 2018).

We examine possible predictors of leisure satisfaction in a mini meta-analysis (Goh et al., 2016) of the included studies. In each, sample members shared an agreed upon cultural model of leisure activities in terms of their importance for experiencing a good lifestyle. This model presumably provided initial conditions and satisfaction expectations for leisure activities that individuals could compare with their own experiences. We anticipated that the frequency of participation in leisure activities agreed upon as important to a good lifestyle would predict leisure satisfaction better than the other variables included in this study. Further, we anticipated that greater satisfaction would result from more frequent participation in leisure activities that are culturally defined as high in importance for a good lifestyle than in leisure activities culturally defined either as low or medium in importance. This study contributes to understanding of the predictors of leisure satisfaction, to cultural consonance theory, and to the influence of culture on leisure satisfaction, more generally.

Literature Review

In the review below, we first address our dependent variable, leisure satisfaction. Next, we examine the predictor variables common to the five data sets, how they have been related to leisure satisfaction in past research, and include hypotheses to be tested where appropriate. We then cite a definition of culture that permits it to be operationalized and measured in terms of cultural models that provide guidelines for individuals’ behavior and their ability to comprehend and predict the behavior of others. Finally, based on a cultural model of leisure activity importance, we present our primary hypothesis linking cultural consonance with respect to that model to leisure satisfaction.

Leisure Satisfaction

Individuals commonly experience satisfaction when their needs or desires are fulfilled. Campbell (1961), for example, defined satisfaction as “a comparison of what people have to what they think they deserve, expect, or may reasonably aspire to” (p. 22). Satisfaction is therefore a consequence of motivation: individuals are motivated to be something, to do something, to feel something, or to experience something, and the degree to which they manage to accomplish these ends results in their level of satisfaction. Leisure satisfaction involves the positive feelings or perceptions experienced during leisure (Beard & Ragheb, 1980) and is an important subdomain of life satisfaction, itself a component of subjective wellbeing (SWB) (Newman et al., 2014). Kuykendall et al. (2015) examined the influence of leisure engagement on subjective well-being (SWB) in a meta-analysis of 37 studies finding a modest relationship (r = .26) mediated by leisure satisfaction. Newman et al. (2014) concluded that “… Leisure activities and recreation satisfaction have been shown to be even greater predictors of life satisfaction and quality of life than sex, education, religiosity, marital status, age, health, employment status, and income” (pp. 572-573).

As leisure satisfaction connects leisure participation to other life domains, the determination of the variables that most strongly influence leisure satisfaction is an important research goal. While each of the variables described below may influence leisure satisfaction, our focus is on the degree to which the behavior of individuals matches a model of leisure provided by their cultures. Our theoretical perspective is based on research and theorizing by Dressler and colleagues indicating that individuals’ inability or failure to match shared cultural models in their own behavior is stressful (see Dressler, 2018, for a review). Moreover, we propose that even if individuals’ overall behavior accords with an agreed upon model of leisure activity importance, the level of stress they experience is influenced by whether the activities in which they engage most frequently are agreed upon as low, medium, or high in importance to a good lifestyle. We theorize that more frequent participation in activities agreed upon as low in importance to a good lifestyle is more stressful, or less stress-reducing, than participation in those agreed upon as medium in importance, and, in turn, those agreed upon as high in importance.

Demographics and Leisure Satisfaction

Among members of a sample from Finland, Haavio-Mannila (1971) found the highest correlation between leisure satisfaction and life satisfaction for unmarried employed men (r = .53) followed by married employed women (r = .29), married employed men (r = .28) and, finally, unmarried employed women (r = .19). In a Dutch sample, Francken and van Raaij (1981) determined that higher leisure satisfaction was associated with older individuals who held a more optimistic outlook with respect to future leisure while the converse was true for individuals with lower leisure satisfaction. Moreover, individuals from higher social classes reported the greatest leisure satisfaction while those from middle social classes perceived more external barriers to leisure and reported lower leisure satisfaction. Brown and Frankel (1993) established that the correlation between leisure satisfaction and physical activity remained relatively constant between the ages of 18 and 70+ for members of a Canadian sample but was low (r = .11). Leisure satisfaction also had low positive correlations with both age (r = .14) and gender (r = .09) while those between education and income and leisure satisfaction approached zero (r = .03 for both). Ateca-Amestoy et al. (2008) found no relationship between household income and leisure satisfaction but suggested that relative income, in terms of social class and expenditure capacity, may impact leisure satisfaction.

Gender was unrelated to leisure satisfaction among overseas Chinese in Australia (Tsai & Coleman, 1999) and Canada (Spiers & Walker, 2008). Vong (2005), however, found that men in Macao had higher leisure satisfaction than women but that age, marital status, education, and income were unrelated to it. Huang and Li (2019) found that married seniors in five cities in China had higher levels of leisure satisfaction with those 75 and older more satisfied than younger individuals while gender and educational attainment had no effect. Given the inconsistency of these findings as well as our focus on the predictive efficacy of culture-related variables, we have no predictions regarding the possible influences of demographics on leisure satisfaction in the present study.

Leisure Constraints and Leisure Satisfaction

Jackson (2000) defined leisure constraints as “factors that are assumed by researchers and/or perceived or experienced by individuals to limit the formation of leisure preferences and/or to inhibit or prohibit participation and enjoyment in leisure” (p. 62). Common leisure constraints include lack of time and financial resources as well as aspects of gender, race, class, ethnicity, and age, access to leisure sites and resources, partners, health, and cultural norms (Crawford & Godbey, 1987; Godbey et al., 2010). In 1987, Crawford and Godbey introduced an influential classification of leisure constraints as (1) intrapersonal, or psychological characteristics of individuals that affect the development of leisure preferences and behaviors, (2) interpersonal, or social factors that influence leisure preferences and participation, and (3) structural, or factors that intervene between leisure preferences and participation. Intrapersonal constraints include items such as lack of interest and lack of skill. Examples of interpersonal constraints include the lack of companions and disapproval by others. Finally, structural constraints include things like lack of time and lack of money.

Leisure constraints typically relate negatively to quality-of-life issues such as health, well-being, and leisure satisfaction (Chick et al., 2014, 2016; Spiers & Walker, 2008). Tian et al. (2023), for example, found that middle aged to older individuals who felt lower levels of leisure constraints experienced greater wellbeing after controlling for age, sex, health, and income. Kono and Ito (2023) found that leisure constraints are also associated negatively with enjoyment experienced in leisure. We anticipate that the degree of importance of each type of leisure constraints (i.e., intrapersonal, interpersonal, and structural) will relate negatively to individuals’ leisure satisfaction.

Active Leisure, Passive Leisure, and Leisure Satisfaction

Brown and Frankel (1993) found a modest correlation between participation in physically active recreation and leisure satisfaction. In a study of adolescents in South Korea, Shin and You (2013) found that active, passive, and social leisure impacted males and females differently. For males, active leisure, such as sport participation, affected leisure satisfaction positively while, for females, active leisure also influenced leisure satisfaction positively but passive leisure and social leisure had negative effects. Wei et al. (2015) determined that passive leisure activities contributed to happiness in China but active pursuits did not while Joudrey and Wallace (2009) found that participation in active, but not passive, leisure was important in reducing depression among a sample of Canadian attorneys. While the relationships between active leisure, passive leisure, and other variables appear to be somewhat inconsistent, we predict that more frequent participation in active leisure pursuits will have a positive relationship with leisure satisfaction while more frequent participation in passive leisure will have either a negative or no relationship.

Culture and Leisure Satisfaction

Culture is frequently invoked as a plausible explanation for the diverse beliefs, values, and behaviors found across human societies. When leisure researchers have employed culture as an explanatory construct it has most often been via a comparison of aspects of leisure between two or, sometimes, three societies with presumably distinct cultures (e.g., Ito et al., 2017; Ito et al., 2020). In such studies culture is not actually measured but is assumed to differ in some relevant manner between or among the groups examined. There are at least three problems with this approach. First, Campbell (1961) claimed that differences in samples taken from two ostensibly distinct groups cannot be definitively attributed to culture but, instead, could be influenced by numerous other variables. Munroe and Munroe (1991) advised that at least four groups are necessary for observed differences to be attributed to culture. Second, the aspects of culture that allegedly lead to differences in leisure-related variables are rarely articulated. Finally, even if relevant cultural traits can be articulated, their measurement has long been problematic because of the lack of a consensus definition of culture that permits its operationalization as an explanatory construct.

Cultural Models

In 1957, Goodenough characterized a society’s culture as “whatever it is one has to know or believe in order to operate in a manner acceptable to its members.” He then indicated that culture “is not a material phenomenon; it does not consist of things, people, behavior, or emotions. It is rather an organization of those things. It is the form of things that people have in mind, their models for perceiving, relating and otherwise interpreting them” (p. 167). Goodenough’s (1957) definition portrays culture as information shared by individuals who claim a communal identity and are recognized by others as having that identity. It provided an early definition of cultural models as well as the foundation for cultural model theory. Following Goodenough (1957), Dressler (2018) characterized cultural models as the schematic or modular knowledge contained in cultural domains, such as “kinds of fish,” “kinship terms,” or “leisure activities.” Cultural models consist of the elements in the domains, the relationships among those elements, and information about how domains relate to each other. Cultural models are therefore mental phenomena that are shared by groups and help group members think about things, behave appropriately, and interpret the behavior of others (D’Andrade, 1995; Dressler, 2018; Dressler et al., 2017). In this view, as Dressler et al. (2005) put it, “Culture is not regarded as an integrated whole but as a set of cultural models for various cultural domains” (p. 334). Evidence suggests that culture, when so considered, affects how leisure is experienced, understood, and described (Chick et al., 2014, 2016; Dressler et al., 1996; Sidik & Chick, 2022).

While common in anthropology and other social sciences studies utilizing cultural models are rare in leisure research. Examples include Paris (2012) who found that flashpackers, a subgroup of relatively affluent individuals characterized by their involvement in backpacking by choice rather than necessity and their use of mobile communication devices, shared a cultural model of backpacking with the larger community of backpackers. Paris et al. (2015) later determined that Asian and Australasian backpackers share some, but not all, aspects of backpacker culture. Ribeiro (2011) examined cultural models of beliefs about spring break and spring break behavior in Panama City, Florida, USA. He found that men and women did not share the same model and that women were more knowledgeable of their model of spring break behavior than men were of theirs. Other studies show that more frequent participation in leisure activities that are part of an agreed-upon cultural model of their importance to a good lifestyle are positive and robust predictors of self-rated health (Chick et al., 2014, 2016) and leisure satisfaction in mainland China and in Taiwan (Chick et al., 2021; Sidik & Chick, 2022). Each of these studies utilized cultural consensus analysis to demonstrate the existence of a shared cultural model of the selected cultural domain.

Culture as Consensus

While being shared is a defining quality of culture, the degree of sharing of information about specific cultural domains, such as “perceptions of diseases” (Romney et al., 1986), or “types of leisure activities” (Chick et al., 2014), is invariably uneven. Physicians and recreation programmers typically have more within-group shared information about diseases and recreational activities, respectively, than do individuals in other occupations. Romney et al. (1986) developed cultural consensus theory and cultural consensus analysis (CCA) in response to the problem of within-culture variability in knowledge and belief. CCA provides a method for determining whether individuals agree on the content of cultural domains to the degree that their knowledge can, in fact, be regarded as cultural rather than idiosyncratic. Second, CCA provides a measure of individual knowledge of a cultural domain, and, finally, CCA provides “culturally correct” answers to questions about the domain under study (Romney et al., 1986).

CCA has been used extensively in anthropology, linguistics, and other social sciences including several leisure and tourism related studies. Parr and Lashua (2004) determined via CCA that leisure service practitioners and non-leisure service practitioners largely agreed on the meanings of leisure (i.e., as free time, activity type, and involving state of mind). Chick and colleagues (Chick et al., 2014, 2016, 2020; Sidik & Chick, 2022) used consensus analysis to determine that informants from communities in mainland China and in Taiwan shared a cultural model of the importance of sets of leisure activities to a good lifestyle while Paris (2012) and Paris et al. (2015) used CCA in the comparative studies of cultural models shared by backpackers. Ribeiro (2011) employed CCA in his analysis of college students’ spring break behavior in Florida. In these studies, cultural consensus theory and CCA provided researchers the tools to measure the content of relevant cultural models that presumably influence the behavior of individuals.

Cultural Competence

Consensus analysis is based on the premise that the level of agreement between any two individuals is a function of the degree to which each has knowledge of an objective set of facts or reality. In consensus analysis, knowledge of these facts by individuals is referred to as their “cultural competence.” Cultural competence can be estimated for individuals as the correlation between that their responses and the aggregate of the other members of the group (Borgatti & Halgin, 2011). In a study of religious festival sponsorships in a Mexican village, for example, Chick (1981) found that correlations between knowledge of the cultural model of the order in which villagers should sponsor particular festivals for each of 31 informants and the mean of the other 30 ranged between .39 and .88. Copeland (2018) examined knowledge of the self-management of HIV/AIDS among Kenyan women who tested positive but were not receiving medical treatment. She found that such knowledge—their cultural competence as measured using cultural consensus analysis—was a significant predictor of better overall health after controlling for demographic variables as well as how long they had known they were HIV-positive.

However, in an examination of the association of cultural competence and psychological distress, Dressler et al. (2018) found that cultural competence was not strongly associated with psychological distress and varied with the cultural domain wherein competence was measured. Marginally significant associations were found only in the domains of family life and occupational and educational aspirations but not in lifestyle, social support, or national identity. Nonetheless, we anticipate that individuals who know more about the importance of various leisure activities to a good lifestyle, that is, are more culturally competent, will experience greater leisure satisfaction.

Cultural Consonance

Dressler (2018) defined cultural consonance as “the degree to which individuals, in their own beliefs and/or behaviors, approximate the prototypes for belief and behavior encoded in cultural models” (p. 2). Therefore, cultural consonance connects culture, a collective entity, to the individual (Dressler, 2018) and permits examination of how the degree to which individuals engage in behaviors consistent with salient cultural models affects other aspects of their lives. In a series of studies, Dressler and colleagues found cultural consonance in lifestyle, operationalized as possessing desirable items, such as a car, a television, or a refrigerator, and participating in favored leisure activities, to be a strong and consistent predictor of measures of physical and mental health, including blood pressure, stress, and depression, while controlling for variables such as age, gender, education, occupational status, and income (reviewed in Dressler, 2018).

Chick et al. (2014, 2016) found that cultural consonance with respect to leisure activities judged to be high in importance to a good lifestyle significantly predicted informants’ self-rated health. Chick et al. (2021) determined that cultural consonance in leisure activities more strongly predicted leisure satisfaction than demographics, leisure constraints, perceived stress, or self-rated health among members of a sample from Taiwan. Finally, Sidik and Chick (2022) found cultural consonance in leisure activities agreed upon as high in importance to a good lifestyle to be strongly associated with leisure satisfaction among members of a sample in Xinjiang Province, China.

In other leisure-related research, Reyes-Garcia et al. (2010) showed that low cultural consonance in the culturally valued lifestyle, including participation in certain leisure-time activities, among the Tsimane’, a forager-farmer group in the Bolivian Amazon, predicted higher psychological distress. Ribeiro (2011) found high cultural consonance for both self-reported and objectively measured behaviors among spring breakers in Florida. Bae and Han (2020) determined that a shared cultural model of the trustworthiness of online hotel reviews predicted behavior in using those reviews in travel planning among South Korean tourists. Snodgrass et al. (2021) used cultural consonance theory and analysis to illustrate how individuals’ relative consistency with respect to their cultural values regarding video game play interacted with general cultural norms in both stimulating and ameliorating psychological distress. Chick et al. (2014) determined that cultural consonance with respect to leisure activities agreed upon as important to a good lifestyle had a significant direct effect on leisure satisfaction in a sample from Mainland China while Chick et al. (2018) showed that cultural consonance mediated the effects of leisure constraints on leisure satisfaction in Taiwan. Chick et al. (2020) found that cultural consonance with respect to leisure activities agreed upon as high in importance to a good lifestyle was the best predictor of leisure satisfaction among several variables, including demographics, residence location (urban, suburban, or rural), community satisfaction, leisure constraints, perceived stress, self-rated health, and cultural consonance with respect to leisure activities agreed upon as low or medium in importance. Based on these studies, we anticipate that cultural consonance with respect to leisure activities agreed upon as high in importance to a good lifestyle will be the strongest predictor of leisure satisfaction among the included independent variables.

Research Objective and Hypotheses

As indicated above, our overall goal in this study is to determine which of the variables described above best predicts leisure satisfaction in five studies involving informants from 24 locations in mainland China, Taiwan, and Brazil. To that end, we offer the following hypotheses.

(1) Intrapersonal, interpersonal, and structural leisure constraints will each negatively predict leisure satisfaction (H1a, H1b, H1c).

(2) More frequent participation in active leisure will positively predict leisure satisfaction while more frequent participation in passive leisure will have either a negative or no relationship with leisure satisfaction (H2_a, H2_b).

(3) Cultural competence with respect to models of the importance of leisure activities to a good lifestyle will positively predict leisure satisfaction (H3).

(4) More frequent participation in leisure activities agreed upon as low, medium, or high in importance to a good lifestyle will positively predict leisure satisfaction (H4_a, H4_b, H4_c).

(5) More frequent participation in leisure activities agreed upon as high in importance to a good lifestyle will be the strongest predictor of leisure satisfaction among the included variables (H5).

Methods

Meta-Analysis

Meta-analyses synthesize the results of multiple studies that address the same, or very similar, problems or questions to estimate overall population effect sizes. Meta-analyses involve studies that are empirical rather than theoretical, quantitative rather than qualitative, address the same variables, and provide results that can be organized in comparable statistical form (Wilson, 2010). Meta-analyses of leisure-related phenomena are rare but, in addition to those cited above by Kuykendall et al. (2015) and Newman et al. (2014), Tercan Kaas and Tarcan İçigen (2022) obtained an average correlation of .46 between leisure satisfaction and life satisfaction in a meta-analysis of 21 studies published between 1999 and 2019. Chick et al. (2023) found an average correlation of .57 between the same two variables in a meta-analysis of 4 earlier studies.

Each of these investigations involved aggregated data (AD) analyses, the use of a weighted average of effect sizes across multiple studies. Common effect size indicators “include the standardized mean difference, the correlation coefficient, the odds-ratio, and the risk ratio” (Wilson, 2010, p. 184). The five original research projects included in the present study have 14 variables in common, utilized essentially identical data collection methods, and addressed similar research questions, making them effectively replications. Therefore, we chose to employ an individual participant data (IPD) meta-analysis based on the original data rather than summary indicators of effect size. Because they employ original raw data rather than summary statistics, IPD meta-analyses are generally preferred over AD meta-analyses (Cooper & Patall, 2009).

Additionally, the present study is an example of what Goh et al. (2016) termed a “mini meta-analysis,” that is, a meta-analysis of a small number of studies, often conducted by the same researchers, that pursue identical or, at least, very similar research questions and are either replications or near replications. Because they are replications or near replications, researchers can utilize the original data from each study rather than summary statistics. Published reports based on the first 4 studies include Chick et al. (2014), Chick et al. (2015), Chick et al. (2020), and Sidik and Chick (2022). No reports based on the data collected in Brazil have been published but variable operationalizations were identical to those in the previous studies. As the data involved individuals nested within respondent residence locations nested within studies, we employed multilevel mixed-effects linear regression as implemented in Stata 17 in our analyses.

Mixed-Effects Models

A problem with applying linear regression to nested models is that the cases within groups are typically more similar than between groups. For example, if math achievement at several elementary schools is compared, students at the same school will likely be more similar among themselves than with students at other schools. Mixed-effects models (also known as multilevel models or hierarchical models) permit analysis of data having a hierarchical or nested structure by partitioning total variance at each level of the hierarchy. In the case of this two-level example, variance would be calculated at the within-school level and the between-school level. Mixed models may have more than two levels and, as in standard multiple regression, the significance of individual predictors can be calculated while controlling for other predictors.

Mixed models contain both random and fixed effects. Fixed effects involve the same independent variables found in standard linear regression and are typically measured at the level of individuals. Random effects are due to the grouping or clustering variables. In the present study, random effects could occur at the level of the studies and informant residences while fixed effects at the level of individual responses. Examination of a null model including only the random effects, the study and resident location sites in the present project, permits determination of whether both, only one, or neither of the levels should be included in the final model.

Determining Predictor Importance: Dominance Analysis

Dominance analysis (DA) is a statistical technique that rank orders independent variables in a regression model in terms of their relative contribution to predicting a dependent variable by evaluating their unique and shared contributions to the overall model fit (Budescu, 1993). DA involves running multiple regression analyses using all possible subsets of predictors and then comparing average predictor power across all the regressions. A dominance statistic is then calculated for each of the predictor variables that sum to the total R² for a multiple regression model. When standardized, general dominance statistics indicate the percentage of the total variance explained by each variable. This permits the ranking of predictors in terms of their influence on the outcome variable. DA was originally designed for use with population level data. DA modules are now available for two-level mixed-effects multilinear regression models (Luo & Azen, 2013). We employed a user-supplied command for Stata 17, mixdom, that extends DA to two-level mixed linear regression models (Luchman, 2021).

Data Sources

The first of the included five data sets was collected in Beijing, Hangzhou, Shanghai, Shenzhen, Qingdao, and Chengdu, China, in 2006 and the second in Taipei, Taichung, Kaohsiung, Hualien, Hsinchu, and Taitung, Taiwan, in 2008. Data for the third study were collected in 2011 and 2012 via intercept sampling at the Sun Moon Lake National Scenic Area, a well-known tourist destination in Taiwan. Respondents provided their residence and were grouped into ten locations that included six cities and four suburban and small-town locations. The fourth data set was collected in the Xinjiang Uyghur Autonomous Region in western China in 2013, primarily in the city of Ürümqi. The fifth data set was collected in Ponta Grossa, Brazil, in 2020. We obtained these data sets and permission to use them from the original researchers.

In each of the studies, prior to the primary data collection via face-to-face or online surveys, samples of individuals free listed leisure constraints and leisure activities they regarded as important in the local context. Results were then used in constructing surveys, one for each of the studies. In the survey phase of the studies, the original researchers collected demographic data, including age, gender, marital status, education, and income, as well as informant reports on the degree to which the listed leisure constraints affected them, their evaluation of the importance of listed leisure activities to a good lifestyle, and their estimate of the frequency in which they participated in those activities.

Variable Operationalization and Measurement

Demographics

In each of the studies, gender and marital status were measured as two valued (i.e., female, male; not married, married) variables. In the studies conducted in Taiwan as well as the Brazil study, informants were asked to indicate their age in years while in the Mainland China projects they were requested to locate their age within one of five categories (i.e., 18–30, 31–49, 41–50, 51–60, 61 and older). Therefore, we recoded the data collected in Taiwan and Brazil into the five categories used in the other studies. Similarly, in the studies conducted in Mainland China, informants indicated their level of education in terms of four categories (“less than high school,” “high school/vocational school graduate,” “college or university graduate,” “graduate school degree”). The other studies included up to 6 levels of education, including elementary school or less and middle school categories, as well as master’s degree and doctoral degree categories. We collapsed these into the “less than high school” and “graduate school degree” categories respectively, providing a 4-level scale for education. Finally, in each of the studies survey respondents were asked to report their income on either an 8- or a 9-level scale in their national currency. Rather than attempt to recode income into US dollars, for example, we ordered informants in terms of whether they were in the lowest 20%, 2^nd 20%, 3^rd 20%, 4^th 20%, or highest 20% of earners in each of the 5 studies. We feel that this provides a more comparable arrangement of informant incomes than conversion to a single currency.

Leisure Satisfaction

In the initial mainland China study, respondents responded to a single question, “How satisfied are you with your current leisure lifestyle?” on a 7-point Likert-type scale. In each of the remaining four studies, leisure satisfaction was measured with an 8-item unidimensional leisure satisfaction scale with items measured on 7-point Likert-type scales developed by Chick and Yeh (2008). This scale has demonstrated excellent reliability in previous research and results relate strongly to other constructs, including life satisfaction and self-reported health (Chick et al., 2021).

Cultural Competence and Cultural Consonance

The initial step in measuring cultural competence and cultural consonance involved determining whether cultural consensus was present in the domain of perceived importance of listed leisure activities for a good leisure lifestyle as indicated by informants. Each of the studies utilized lists of both leisure constraints and leisure activities developed from free listing. Using CCA, the original researchers determined that consensus existed for the importance of the listed leisure activities to a good lifestyle in each of the five data sets. The leisure activities included in each of the instantiations of the cultural model were then ordered from least to most important and divided into three groups: (1) low importance, (2) medium importance, and (3) high importance. Respondents’ self-reported levels of participation in the activities in each of the three importance-level groups were then averaged. This resulted in three new variables for individual informants that indicated their level of self-reported participation in leisure activities agreed upon as (1) low in importance, (2) medium in importance, and (3) high in importance to a good lifestyle. CCA also provided cultural competence estimates for informants, as noted above.

Leisure Constraints

As indicated above, leisure constraints were initially free listed by samples of informants in the five studies and subsequently rated by members of separate samples in terms of their importance to them. They were then divided into the intrapersonal, interpersonal, and structural categories proposed by Crawford and Godbey (1987) in each of the five data sets. Only those constraints that unambiguously fit into only one of the categories were retained. Individuals responded to constraints in terms of their importance to them using 1–5 Likert-type scales.

Active and Passive Leisure

Dardis et al. (1994) described active leisure as activities requiring physical effort, such as jogging, biking, or basketball, and passive leisure as activities that required no physical effort on part of the participants. We did not include their third category of leisure, social entertainment, as it can be either active, as in dancing, or passive, as in chatting with friends. Additionally, we applied the physical effort criterion more strictly than Dardis et al. (1994) as they had included fishing and photography as active leisure. Such activities may require significant physical effort in some instances but virtually none in others. Hence, our active leisure category included activities such as walking, playing badminton, and hiking, where physical effort is always present, while we classified activities, such relaxing, going to the theatre, and reading, where physical exertion is very rarely or never present, as passive. We eliminated ambiguous activities from consideration.

Because frequency of participation in active leisure, passive leisure, and low, medium, and high importance activities was measured on 3-point scales in the initial Mainland China study but on 5-point scales in each of the others, we standardized those variables in each of the data sets before conducting analyses for the present study. We did the same when categorizing leisure activities into active and passive but used raw scores for importance of leisure constraints ratings as these were measured on the same scale across the studies.

Results

Characteristics of the survey respondents are indicated in Table 1 below.

Table 1.

Sociodemographic Characteristics of the Survey Respondents (N = 3,714)^a

Variables	Possible value	China	Taiwan1	Taiwan2	Xinjiang	Brazil
Variables	Possible value	(N = 772) N (%)	(N = 1,766) N (%)	(N = 882) N (%)	(N = 187) N (%)	(N = 107) N (%)
Age	18-30	405 (52)	512 (29)	270 (31)	103 (55)	68 (64)
	31-40	238 (31)	349 (20)	257 (29)	16 (9)	19 (18)
	41-50	94 (12)	326 (19)	194 (22)	41 (22)	16 (15)
	51-60	28 (4)	319 (18)	126 (14)	25 (13)	3 (3)
	60& older	4 (1)	256 (15)	35 (4)	2 (1)	1 (1)
Gender	Female	371 (50)	923 (52)	538 (61)	113 (60)	76 (71)
Gender	Male	369 (50)	843 (48)	342 (39)	74 (40)	31 (29)
Education	< High School	55 (7)	285 (16)	33 (4)	5 (3)	10 (9)
	High School	121 (16)	503 (29)	156 (18)	15 (8)	37 (35)
	College Graduate	487 (64)	818 (47)	544 (62)	143 (76)	29 (27)
	Graduate Degree	100 (13)	148 (8)	149 (17)	24 (13)	31 (29)
Marital Status	Unmarried	351 (46)	669 (41)	342 (40)	91 (49)	87 (81)
Marital Status	Married	417 (54)	959 (59)	513 (60)	96 (51)	20 (19)
Income	Lowest 20%	311 (41)	204 (12)	253 (29)	40 (21)	38 (36)
	2nd 20%	304 (40)	315 (18)	200 (23)	21 (11)	37 (35)
	3rd 20%	70 (9)	392 (23)	170 (20)	47 (25)	11 (10)
	4th 20%	37 (5)	316 (18)	111 (13)	45 (24)	9 (8)
	Highest 20%	40 (5)	495 (29)	134 (15)	34 (18)	12 (11)

^aThe 2nd through 6th columns indicate frequencies per demographic category with percentages in parentheses. Column totals may vary due to missing cases and percentages may not sum to 100 due to rounding error.

Means, standard deviations, and ranges for the dependent variable, leisure satisfaction, and the leisure-related independent variables are provided in Table 2 below. Possible values for leisure satisfaction range from 1–7. Cultural competence ranges between −1.00 and 1.00. For the first Mainland China study data, possible values for the frequency of participation in active and passive leisure range from 1 (“never”), through 2 (“occasionally”), to 3 (“regularly”). The same scale applies to frequency of participation in activities agreed upon as low, medium, and high importance to a good lifestyle. For the other data sets, these variables are scaled 1–5 (“never,” “seldom,” “occasionally,” “often,” and “very frequently”).

Table 2.

Means and Standard Deviations for Leisure-related Variables in 5 Studies (N = 3,802)^a

Variables	China	Taiwan1	Taiwan2	Xinjiang	Brazil
Leisure satisfaction	4.35 (1.56)	4.82 (1.06)	5.36 (.93)	4.67 (1.06)	5.28 (1.09)
Cultural competence	.53 (.15)	.50 (.29)	.58 (.16)	.58 (.24)	.66 (.18)
Active leisure	1.38 (.27)	1.76 (.41)	1.97 (.50)	2.51 (.72)	4.02 (.44)
Passive leisure	1.59 (.26)	2.73 (.49)	1.81 (.42)	3.05 (.05)	4.01 (.42)
Low importance activities	1.19 (.22)	1.55 (.42)	.92 (.55)	2.13 (.72)	2.31 (.66)
Medium importance activities	1.45 (.29)	2.13 (.44)	1.54 (.50)	3.22 (.51)	3.40 (.53)
High importance activities	1.89 (.34)	2.95 (.49)	2.38 (.41)	3.43 (.52)	4.21 (.43)
Intrapersonal constraints	2.49 (.92)	2.31 (.76)	2.04 (.86)	3.14 (.74)	3.36 (.73)
Interpersonal constraints	2.38 (.96)	2.36 (.85)	2.12 (.81)	3.29 (.78)	3.44 (.93)
Structural constraints	2.99 (.91)	2.73 (.77)	2.22 (.79)	3.40 (.76)	3.79 (.70)

^aValues for participation in active leisure, passive leisure, and participation in activities agreed upon as low, medium, and high in importance to a good lifestyle were measured on 1-3 scales for the China data but on 1-5 scales in the other four studies.

Examination of the null model, which included both the study and informant residence sites, indicated that both the study level (coefficient = .13, SE = .09, 95% CI = .04, .51) and site within study level (coefficient = .02, SE = .01, 95% CI = .01, .05) were significant. However, the intraclass correlation coefficients (ICCs), which show the degree of similarity between individuals in the same groups (Holodinsky et al., 2020), were .09 at the study level but only .11 at the level of the sites within the studies. Because including the within-site level explained only 2% of the model variance, we chose to employ a two-rather than 3-level mixed effects multilinear regression for our analysis.

Table 3 below presents the results of the mixed-effects multilinear regression analysis.

Table 3.

Results of Mixed Multilinear Regression Analysis of Leisure Satisfaction

Predictor^a	Coefficient	Std. Error	z	p	95% confidence interval (lower bound)	95% confidence interval (upper bound)
Age	.13	.02	6.08	.00	.09	.17
Gender	−.02	.04	−.41	.68	−.09	.06
Education	.06	.03	2.23	.03	.01	.11
Marital status	−.14	.05	−2.64	.01	−.24	−.04
Income	.04	.02	2.59	.01	.01	.07
Competence	−.11	.09	−1.26	.21	−.28	.06
Active leisure	.11	.04	3.13	.00	.04	.18
Passive leisure	−.06	.05	−1.11	.27	−.17	.05
Low imp	−.13	.03	−4.22	.00	−.19	−.07
Medium imp	.09	.03	2.91	.00	.03	.16
High imp	.30	.04	8.66	.00	.23	.37
Intrapersonal	−.22	.03	−6.32	.00	−.28	−.15
Interpersonal	−.03	.03	−.87	.39	−.09	.04
Structural	−.01	.04	−.37	.71	−.08	.06
Constant	5.17	.22	23.27	.00	4.73	5.60

^aThe predictors Low imp, Medium imp, and High imp refer to frequency of participation in activities consensually rated as low, medium, and high in importance to a good lifestyle while Intrapersonal, Interpersonal, and Structural refer to rated importance of leisure constraint types.

The random-effects coefficient for the study level was significant (coefficient = .15, SE = .10, 95% CI = .04, .54). The ICC (.12, 95% CI = .03, .33) indicates that 12% of the residual variation in leisure satisfaction is due to the study in which informants participated. While this value falls within its confidence interval, it also indicates that the within-study similarity of leisure satisfaction responses among individuals is modest.

Among the leisure constraint types, contrary to our expectation, only intrapersonal leisure constraints predicted leisure satisfaction negatively, as hypothesized, while interpersonal and structural constraints were not significant. H1_a is therefore supported but H1_b and H1_c are not.

As we anticipated, frequency of participation in active leisure pursuits is a positive predictor of leisure satisfaction, supporting H2_a. Frequency of participation in passive pursuits, however, is not significant so H2_b can be accepted in the sense of being unrelated, although not negatively related, to leisure satisfaction. Cultural competence with respect to the importance of listed leisure activities to a good lifestyle was not a significant predictor of leisure satisfaction. H3 is therefore rejected.

Cultural consonance with respect to frequency of participation in leisure activities agreed upon as low, medium, and high in importance to a good lifestyle are each significant. However, while the coefficients for medium and high importance to a good lifestyle are both positive, the coefficient for low importance activities is negative. Therefore, H4b and H4c are supported while H4a is not.

Results of the dominance analysis while accounting for clustering at the study level are presented in Table 4 below. As we had no predictions involving the demographic variables, we included them (age, gender, education, marital status, and income) as covariates in all combinations of models.

Table 4.

Rank Orders of Importance of Leisure Satisfaction Predictors Based on Dominance Analyses of 5 Data Sets

Variable	Dominance Statistic	Standardized Dominance Statistic	Rank
High importance leisure activities	.034	.172	1
Intrapersonal leisure constraints	.017	.083	2
Passive leisure activities	.011	.058	3
Medium importance leisure activities	.009	.047	4
Active leisure activities	.007	.037	5
Interpersonal leisure constraints	.004	.022	6
Low importance leisure activities	.004	.021	7
Structural leisure constraints	.004	.020	8
Cultural competence	.001	.003	9

The dominance analysis indicates that cultural consonance with respect to leisure activities agreed upon as high in importance to a good lifestyle is the strongest predictor of leisure satisfaction in these samples with the importance of intrapersonal leisure constraints second and participation in passive leisure activities third. Notably, the standardized dominance statistics indicate that participation frequency in activities agreed upon as high in importance for a good lifestyle accounts for slightly more than double that accounted for by the importance of intrapersonal leisure constraints (17.2% vs. 8.3%). H5 is therefore supported.

Discussion

The goal of this study was to determine the relative efficacy of several variables in predicting leisure satisfaction. We were particularly interested in the contributions of cultural competence and cultural consonance with respect individuals’ self-reported frequency of participation in leisure activities agreed upon as high in importance to a good lifestyle in predicting leisure satisfaction.

Cultural competence fails to predict leisure satisfaction. Apparently knowing about culturally salient activities is not enough (Dressler, 2020). One must participate or, at least, claim to participate in activities consensually judged to be medium or high in importance for leisure satisfaction to be positively influenced. This is the opposite of Copeland’s (2018) finding that cultural competence regarding the self-management of HIV/AIDS among Kenyan women who tested positive but were not under treatment predicted their health status. It may be that either cultural context or the cultural domain under study influences the results.

While informants’ rating of the importance of intrapersonal leisure constraints is a strong, although inverse, predictor of leisure satisfaction, the importance of interpersonal and structural constraints is not. Given the claim that continued leisure participation depends on individuals successfully negotiating intrapersonal, interpersonal, and structural leisure constraints sequentially (Godbey et al., 2010) our results suggest, at minimum, that intrapersonal constraints are the most important to negotiate in achieving leisure satisfaction.

Numerous studies have linked participation in both active and passive leisure to positive mental health, physical health, and wellbeing (e.g., Bian & Xiang, 2023; Mock & Smale, 2023; Pressman et al., 2009). Some research, however, indicates that certain forms of passive leisure, such as social media use and TV watching, often related negatively to health and wellbeing (e.g., Kukendall et al., 2015; Kuper et al., 2022; Newman et al., 2014) or that health and wellbeing effects of both active and passive leisure are mediated by factors such as specific activity types, such as TV watching versus reading, and individual characteristics including personality traits, age, gender, income, and education level (Bian & Xiang, 2023; Fancourt et al., 2021; Park, 2023; Roy & Orazem, 2021). It is likely, therefore, that grouping all sedentary leisure pursuits, such as TV watching, reading, listening to music, and playing board games, allows the positive effects of some to be cancelled by the negative effects of others.

Our primary hypothesis (H5) was that more frequent participation in leisure activities agreed upon as high in importance to a good lifestyle would be the best predictor of leisure satisfaction among the variables included in the study. Our results support that hypothesis but also indicate that frequent participation in activities agreed upon as low in importance to a good lifestyle has a negative relationship to leisure satisfaction while participating more frequently in activities agreed upon as medium in importance is a moderately strong predictor of leisure satisfaction. These results therefore support grouping the content of cultural models, when organized in terms of a ranked feature such as importance, into low, medium, and high. Dressler (1996) utilized a similar strategy in his initial formulation of cultural consonance by analyzing only lifestyle items rated at least 2 (“somewhat important”) on a 3-point scale while disregarding those rated as 1 (“not at all important”) (p. 6). We feel, however, that including low ranked items in analyses adds both nuance and information to research involving items scaled in terms of their importance. This is especially true in the present study. While all activities in the cultural model of their importance to a good lifestyle exhibit cultural consensus in each of the five included studies, some are agreed upon as more important than others. And participation in those judged to be relatively low in importance has a negative relationship with leisure satisfaction.

As indicated earlier, our theoretical perspective derives from Dressler and colleagues’ research on cultural consonance and stress (Dressler, 2018; Dressler et al., 2017, 2018). Other studies have shown that stress correlates negatively with more leisure participation (Iwasaki et al., 2005; Liu et al., 2021) and increased leisure satisfaction (Kim & Brown, 2018; Mausbach et al., 2012). We speculate that participation in leisure activities culturally agreed upon as low in importance to a good lifestyle either engenders more stress or ameliorates it less than participation in activities agreed upon as medium or high in importance to a good lifestyle. Future research should address the relationships among leisure participation, leisure satisfaction, cultural consonance, and stress.

Finally, our results suggest that it is important for leisure providers know the local culture, including what kinds of leisure pursuits are agreed upon as important to leisure satisfaction and a preferred leisure lifestyle. This reflects the concept of market segmentation (Burns et al., 2011; Ferguson et al., 2018) and enhances opportunities to provide culturally salient leisure opportunities to customers.

Limitations

None of the samples used in this study were random and nearly all survey respondents were residents of urban areas. They also tended to be younger and were generally well educated with most being college graduates. Additionally, the societies included in this study are generally characterized as collectivistic rather than individualistic (Triandis, 1993). Adeclas et al. (2024), for example, found that different leisure motivations led to leisure satisfaction among French and South Korean informants, the former representing a presumably individualistic society while the latter is collectivistic. Individually oriented activities may lead to greater satisfaction in individualistic societies while group-oriented activities may be more satisfying in collectivistic societies. Whether variation in this and possibly other societal characteristics, such as cultural complexity (Chick, 1997) or cultural tightness and looseness (Pelto, 1968), influence the constructs examined in this study suggest the need for similar investigations to be conducted in locations that exhibit contrasting cultural syndromes.

Our most concerning issue, however, is informant accuracy. That is, can informants accurately recall and reliably report their own frequency of participation in various leisure activities? Research on informant accuracy, especially the ability of individuals to recall their own past behavior, led Bernard et al. (1984) to conclude “The results of all of these studies leads [sic] to one overwhelming conclusion: on average, about half of what informants report is probably incorrect in some way” (p. 503). Unfortunately, studies wherein informant recall and external reports, rather than self-reports, of their behavior are compared are very rare (but see, e.g., Chase & Godbey, 1983; Chick, 1981; Ribeiro, 2011). Despite these concerns, we feel that our findings are likely to generalize beyond the samples included in this meta-analysis.

Conclusions

Our results indicate that the ability and the choice to engage in leisure pursuits that are agreed upon as important to a good lifestyle is the best predictor of leisure satisfaction among the leisure-related variables examined in this study. On the other hand, participation in activities agreed upon as low in importance to a good lifestyle is significantly, but negatively, related to leisure satisfaction when the other variables are held constant. Hence, we feel that this study contributes to greater understanding of the variables that influence leisure satisfaction but also to cultural consonance theory when the variable in question, such as the frequency of participating in culturally agreed upon activities that are important to a good lifestyle, can be ranked from low to high rather than treated as a single value.

Footnotes

ORCID iDs

Garry Chick

Erwei Dong

Robert Burns

Jasmine Cardozo Moreira

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data generated and analyzed in the current study are available from the first author upon request.

Author Biographies

Garry Chick has a Ph.D. in anthropology from the University of Pittsburgh and is a professor emeritus in the Department of Recreation, Park and Tourism Management at The Pennsylvania State University. His primary research interests include the influence of culture on behavior, expressive culture, and adult play and games. He is a past president of The Association for the Study of Play and the Society for Cross-Cultural Research.

Erwei Dong received his Ph.D. in Recreation, Park, and Tourism Management (Leisure Studies) from Pennsylvania State University in 2006. He has been affiliated with several universities, including Shanghai University of Sport (China), Shenyang University of Sport (China), Shandong University (China), Huanggang Normal University (China), Xi'an Eurasia University (China), University of Ryukyus (Japan), Kyung Hee University (Korea), University of KwaZulu-Natal (South Africa), New York University, and Arizona State University where he has held positions including distinguished professor, research professor, honorary professor, guest professor, visiting professor, and Clinical Professor. He is currently a professor at the City University of Macau.

Robert C. Burns is a professor of Human Dimensions of Natural Resources in the West Virginia University School of Natural Resources and the Environment. He received a Ph.D. in Leisure Studies from Penn State University (2000), M.S. in Public Administration, and B.A. in Criminology. Burns is a 2022 Fulbright Scholar; a Fellow in The Academy of Leisure Sciences; and he was awarded the Society of American Foresters Award in Forest Science in 2019. Burns is a 2020 graduate of the LEAD 21 leadership academy, and he serves nationally as Executive Board member of the National Association of University Forest Resource Programs (NAUFRP), and US non-governmental representative to the International Union of Forest Research Organizations (IUFRO). He is co-editor of the 2016 book entitled Outdoor Recreation Planning and past Chief Editor of the Journal of Park and Recreation Administration. He has authored/co-authored over 100 peer-reviewed publications on the human dimensions of land and water resources.

Chih-Kuei Yeh has Ph.D. from the Department of Recreation, Sport, and Tourism at the University of Illinois at Urbana-Champaign. He is a former professor in Department of Tourism, Leisure, and Recreation Studies at National Dong Hwa University in Hualien, Taiwan, with research interests are philosophy of leisure, sociology of leisure, and leisure and culture.

Jasmine Cardozo Moreira is Professor at Ponta Grossa State University, in Brazil. She teaches in the Tourism curriculum at the undergraduate level and the Land Management / Geography program at the graduate level. She is head of LABTAN, a university laboratory that focuses on tourism, outdoor recreation and public use in protected areas. Her expertise is on human dimensions of tourism planning in Brazil National Parks and Geopark projects. She is also a member of the International Academy for the Development of Tourism Research in Brazil, TAPAS Group (The Tourism and Protected Areas Specialist Group), and the Geoheritage group from the International Union for Conservation of Nature.

Kunduz Sidik has a Ph.D. in Land Resources Management from the China University of Geosciences, Wuhan, China, and is a founder and director of the Yultuz & Kunduz Education Charity Fund. Her research interests are leisure and culture.

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