How Motivations, Constraints, and Demographic Factors Predict Seniors Overseas Travel Propensity

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1 Asia Pacific Management Review 14(3) (2009) How Motivations, Constraints, and Demographic Factors Predict Seniors Overseas Travel Propensity Ching-Fu Chen a,*, Chine-Chiu Wu b a Department of Transportation and Communication Management Science, National Cheng Kung University, Taiwan b Department of Tourism Department, Nan Hua University, Taiwan Accepted 30 August 2008 Abstract This study investigates the influences of travel motivations, travel constraints, and demographic variables on seniors overseas travel intentions. A survey of Taiwanese seniors was conducted for the purpose of identifying the significant predicators of overseas travel. Using factor analyses, four motivations (relaxation, novelty, escape, and socialization) and three travel constraints (perceived risks, time commitments, and personal reasons) are delineated. The results from the logistic regression analysis suggest that the main factors predicting seniors overseas travel propensity include age, income source, employment status, relaxation motive, novelty motive, socialization motive, and personal reasons constraint. Keywords: Seniors, travel motivation, constraint, travel propensity, logistic regression 1. Introduction 1 As the trend in aging societies is growing all over the world, the travel and tourism market for senior citizens has attracted much attention recently from both practitioners and academics. The market shows great potential, not only because of its considerable population proportion, but also because of senior citizens great contribution to a tourism economy. It is generally accepted that seniors in developed countries on average possess a relatively large share of discretionary income and time and are still in good health. These characteristics enable them to travel more, and many seniors have the time to travel and are willing to spend a significant amount of their savings on travel (Fleischer and Pizam, 2002). In addition, the travel behaviour of seniors is characterized as traveling more frequently, going longer distances, staying away longer, spending more money, and relying more on travel agents (Rosenfeld, 1986). Hence, senior travellers are important to the tourism industry and will grow in importance as their segment grows in size and wealth (Reece, 2004). Understanding seniors travel decision behaviour is a crucial issue to travel marketers who compete for this important market. The proportion of Taiwan s population age over 50 accounted for 23.09% by the year 2004, which is evidence that Taiwan s population is stepping into the era of an aging society like other developed countries (Snyder, 2001). Given its potential contribution to the travel and tourism market, this senior segment should gain more attention in terms of market research. Unfortunately, to date only a few studies can be found that explore Taiwanese seniors travel behaviors, especially travel motivations (Huang and Tsai, 2003; Jang and Wu, * Corresponding author. cfchen99@mail.ncku.edu.tw 301

2 2006). However, the issue on how the affecting factors such as travel motivations and travel constraints influence senior traveler decision behaviors requires more research efforts. This study attempts to provide insights into this special market segment on this issue in Taiwan. Many facets of the senior market have been studied, including travel motivations (Anderson and Langmeyer, 1982; Blazey, 1987; Chun, 1989; Huang and Tsai, 2003; Iso- Ahola and Crowley, 1991; Guinn, 1980; Romsa and Blenman, 1989; Shoemaker, 1989; 2000), travel constraints (Fleischer and Pizam, 2002; Hong et al., 1999; Huang and Tsai, 2003; Romsa and Blenman, 1989), market segmentation (Horneman et al., 2002; Hsu and Lee, 2002; Mathur et al., 1998; Shoemaker, 1989; 2000), the effects of various senior citizens destination choices (Zimmer et al., 1995), travel modes (Baloglu and Shoemaker, 2001), and vacation behaviors (Romsa and Blenman, 1989). The theory of tourism demand states that it is influenced by three major determinants: economic factors, socio-psychological factors, and exogenous factors (Uysal, 1998). The economic factors include per capita income, cost of living, exchange rate, tourism prices, and transportation costs, among others. The social-psychological factors are associated with the decision-making of travelers such as demographic factors, travel motivations, constraints, images of destinations, and travel preferences, among others. The exogenous factors are associated with the business environment. This study explores the travel motivations and constraints of Taiwanese senior travelers and, furthermore, investigates the effects of the main decision-making factors on seniors travel propensity - e.g. demographic factors, travel motivations, and travel constraints. To the best of our knowledge, this study is the first attempt to explore the issue in the Taiwanese senior travel context. The results of this study can provide more understandings of the factors associated with senior travel decision behaviors, and suggestions for travel marketers to compete for the senior travel market in Taiwan. 2. Literature review 2.1 Travel motivation Ryan (1991) emphasizes the significance of the psychological determinants of demand in explaining some reasons why tourists travel and select particular destinations. Motivation is defined as the driving force behind all behavior (Fodness, 1994). From the perspective of the traveler s decision-making process, travel motivations are seen as the energizers of demand that promote an individual to decide on a holiday (Page and Hall, 2003). Travel behavior can be predicted by underlying motivations (Pearce and Caltabiano, 1983). Hence, those who are highly motivated might be those who are most likely to overcome constraints and participate in more leisure activities (Fredman and Heberlein, 2005). The concept of travel motivations is based on the existence of push and pull factors. This has been extensively discussed and is a generally accepted concept (Crompton, 1979; Dann, 1981; Jang and Cai, 2002; Pearce and Caltabiano, 1983). Push motivation refers to an individual s internal energy and an increase in the desire for people to travel, while pull motivation refers to a force external to an individual that influences where people travel, given the initial desire to travel (Dann, 1981). Ryan (1991) finds that tourist travel motivations could be identified as wish fulfillment, shopping, escaping from a mundane environment, rest and relaxation, an opportunity for play, strengthening family bonds, prestige, social interaction, and educational opportunities. The most common motivations identified by related research regarding seniors travel motivation are rest and relaxation, escaping from a mundane environment, social interaction, physical exercise, learning, nostalgia, visiting friends and relatives, and excitement (Fleischer and Pizam, 2002; Horneman et al., 2002; Shoemaker, 1989; 2000). Shoemaker (1989) notes that 302

3 among fourteen motivations, rest and relaxation as well as escaping from a mundane environment are the most salient two motivations, while knowing friends of a different gender and playing golf are the least important two motivations for senior travelers in Pennsylvania. Interestingly, after ten years, Shoemaker (2000) finds that the main motivations of senior travelers shifted to visiting new places and experiencing new things. Regarding seniors travel motivation, some main classifications can be identified in the literature such as rest/relaxation, social interaction, health, learning, seeking, escaping, attracting, cost, nostalgia and visiting historical sites (Huang and Tsai, 2003). Huang and Tsai (2003) reported that Get rest and relaxation (35.6%) and Meet people and socialization (20.1%) are found as the main travel motivation of Taiwanese seniors. In addition, considering both push and pull dimensions of Taiwanese senior travel motivations, Jang and Wu (2006) found the push motivations include ego-enhancement, self-esteem, knowledge seeking, relaxation, and socialization while the pull motivations encompass cleanliness and safety, facility, event and cost, and natural and historical sites. 2.2 Travel constraints Unlike motivations that serve as energizers, constraints towards traveling function as filters for tourism demand, preventing the decision makers from engaging in travel even though the motivation may exist (Page and Hall, 2003). Leisure constraints can provide a conceptual framework that may help understand why individuals do not participate in specific tourism activities. The hierarchical model proposed by Crawford and Godbey (1987) and Crawford et al. (1991), is the most accepted theoretical framework of leisure constraints (Nyaupane et al., 2004). Their studies categorize leisure constraints into three hierarchically organized levels: intrapersonal, interpersonal, and structural constraints. Furthermore, participation in leisure activities is seen as a process of overcoming the various constraints. The intrapersonal constraints are defined as individual psychological states and attributes, such as stress, anxiety, attitudes, and perceived self skill, that might inhibit one from participating in leisure activities (Fredman and Heberlein, 2005). They exist when people fail to develop leisure preferences due to problems or misconceptions associated with personality needs, prior socialization, personal ability, and perceptions of reference group attitudes. The interpersonal barriers result from social interactions with friends, family, and others. The structural constraints include factors that block people s intentions from taking actions such as economic resources, availability of time, and accessibility (Fredman and Heberlein, 2005; Jackson and Scott, 1999). The most often cited constraints to travel in related research are a lack of time, financial considerations, physical and emotional costs, health status (objective and self-reported), perceived disability, age, security concerns, lack of information, family approval, and family responsibilities (Blazey, 1987; Fleischer and Pizam, 2002; Mcguire, 1984; Mcguire et al., 1986; Romsa and Blenman, 1989 ). Regarding the dimensionality of leisure constraint measurement, Jackson (1993) identifies six dimensions of leisure constraints from eight studies: social isolation, accessibility, personal reasons, costs, time commitments, and facilities. The social isolation dimension can be deemed as an interpersonal dimension that is based on characteristics involving interaction between/among people. The accessibility dimension includes factors such as a lack of or limited access to transportation or getting there. The personal reasons dimension is an intrapersonal dimension that includes items pertaining directly to an individual s abilities or motivations. The cost dimension covers items related to the outlay of money. The time dimension represents a collection of items referred to as reasons that affect levels and intensity of participation among adults. Finally, the facility dimension relates specifically to leisure settings and individuals perceptions (Hultsman, 1995). 303

4 3. Method 3.1 Sample design and data collection To collect the data, this study employs a non-probability sampling method called convenience sampling. It is frequently used in research when probability sampling is not a feasible option. Although various definitions of a senior tourist are found in past studies, in order to cover the growing trend of pre-retirement (Tsai and Huang, 2003), we operationalize the senior tourist in this study as being 50 years old and above. Due to the cost and time constraints, a self-administered questionnaire was distributed to individuals at five senior community centres and senior societies in Kaohsiung city in southern Taiwan who are over 50 years old. The purpose of the survey was explained to the participants and agreement to participate in the survey was obtained before the survey was given. The interviewers only provided assistance to respondents with difficulty in reading by filling in the questionnaire based on their opinions. The data collection was conducted from November to December in Three hundred surveys were distributed during the data collection period, and 224 (i.e. 74.7%) usable samples were obtained after eliminating incomplete questionnaires. 3.2 Survey instrument The survey instrument is a four part questionnaire. The questions in the questionnaire are based on a review of the related literature. The questionnaire was pre-tested by 30 respondents to uncover any potential problems. Some revisions were made based on the pre-test result and respondents comments. Hence, the content validity of the questionnaire was deemed adequate. Section 1 of the questionnaire contains two items pertinent to overseas travel experience in the past three years and the propensity to travel overseas in the following year. A binary categorical scale is employed to measure both questions. Section 2 deals with the measurement of travel motivations with 23 items. Section 3 deals with the measurement of the constraint to travel with 15 items. Respondents are asked to indicate their agreement level of each item in Sections 2 and 3 on a five-point Likert scale anchored by strongly disagree (= 1) to strongly agree (= 5). The final section presents a respondent s demographic information with six items, such as gender, age, marital status, and education level, by means of a categorical scale. 3.3 Data analysis The data analysis was carried out in three main stages. First, descriptive statistics analyzed the profile of respondents. Second, a principal factor analysis with a varimax rotation method is conducted on travel motivations and travel constraints for data reduction purposes. Third, a binary logistic regression is employed to investigate the predict utility of travel motivations, travel constraints, and demographic variables on the intention to travel for seniors. This study specifies future travel propensity (0 = no, 1 = yes) as the dependent variable for the logistic regression, while travel motivations, travel constraints, and demographic variables are the independent variables. 4. Results 4.1 Respondents profile Table 1 shows the demographic profile of respondents. Among the 224 usable questionnaires, females make up the great majority of respondents (57.6%), a slight majority of them are ages (39.3%); and 22.3%, 20.1%, and 18.3% respectively represent 304

5 respondents of ages 55-59, 60-64, and 65 or older. In all, 41.9% hold a college or university degree and higher, while 41% have achieved the education level of high school (junior or senior). The dominant majority are married (91.5%), slightly over half of the respondents are retired, while 34.3% are full-time workers and 15.2% are part-time workers. Regarding income source, 48.7% state they have their own salary, while 23.7% of respondents indicate their source is from their savings, followed by pensions (14.3%) and donations from their children (14.3%). Table 1. Respondents profiles (N = 224). Demographic variables Frequency Percentage (%) Gender Male Female Age and older Education Illiteracy Primary Junior high Senior high College University and higher Martial status Single Couple Widow Divorced Employment Work full-time Work part-time Unemployed or retired Income source Pension Own savings Children s donation Own salary 4.2 Factor analysis of travel motivations The principal component factor analysis with a varimax rotation was used to generate the factors underlying the 23 travel motivation items. Those factors with an eigenvalue greater than 1 were chosen. The eigenvalues suggest that a five-factor solution explains 58.9% of the total variance. The Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy is 0.88, and the value of Bartlett s test of sphericity is (d.f. = 253, p = 0.00), which suggests that the factor analysis is appropriate and the variables show good predictive power for the dimensions (Hair, Anderson, Tatham and Black, 1995). Five items with a factor loading value of less than 0.5 (the cutoff value) were removed. Cronbach's α was calculated to test the reliability of each factor. Based upon the minimum value of 0.5 as an indication of reliability (Nunnally, 1978), one two-item factor with α less 305

6 than 0.5 was removed for further analysis. In the end, the α values for all remaining factors range from 0.52 to Table 2 summarizes the factor analysis results of travel motivations. According to the characteristics of the items under each factor, the four motivation factors are named as: relaxation, novelty, escape, and socialization. The factor scores of the four motivation factors are calculated and used in the logistic regression analysis. Motivation factor/ item Table 2. Factor analysis of travel motivation. Factor loading Eigenvalue Variance explained (%) Cumulative variance explained (%) Cronbach s α Relaxation (3.80) * Having opportunities.827 for doing sports Resting and relaxing.783 Spending some time.698 with family Visiting local.648 museums and historical sights Being close to nature.571 Wanting to have a sense of discovery.551 Novelty (3.66) Increasing knowledge.799 of new things Exploring new places.758 Experiencing different.584 ways of life Experiencing different cultures.573 Being curious and trying new things.572 Escape (2.66) Getting away from work.867 Escaping from daily routine.768 Socialization (2.70) Visiting relatives and friends.833 Meeting new people.634 Traveling while health is good.523 Note: * The value in the parenthesis is the factor mean. 4.3 Factor analysis of travel constraints Applying the same method and criteria in the motivation factor analysis, three constraint 306

7 factors were extracted from 15 constraint items. The eigenvalues suggest that the three factors explain 65.3% of the total variance. The Kaiser-Meyer-Olkin overall measure of sampling adequacy is 0.85 and the value of Bartlett s test of sphericity is (d.f. = 66, p = 0.00), indicating that the factor analysis is appropriate and the variables have good predictive power for the dimensions. Three items were removed due to each factor loading value being less than 0.5. Cronbach s α value of the three factors range from 0.72 to 0.86, well above the minimum acceptable value of 0.5. Table 3 summarizes the factor analysis results of the travel constraints. According to the characteristics of items under each factor, the four motivation factors are labeled as: perceived risks, time commitments, and personal reasons. The factor scores of the three constraint factors are used in the logistic regression analysis. Constraint Factor/ Item Table 3. Factor analysis of constraints to travel. Factor loading Eigen -value Variance explained (%) Cumulative variance explained (%) Perceived Risks (3.24) * Perceived safety.845 about destination Worry about.844 healthcare resource at destination Perceived risk about.721 destination Foreign language skill.695 in communication Product failing.611 Time Commitments (2.96) Lack of time.775 Lack of family.734 support Family commitment.666 Financial.514 consideration Personal Reasons (3.06) Fear of feeling unease.829 away from home Physical ability.821 Age problem.580 Note: * The value in the parenthesis is the factor mean. Cronbach s α 4.4 Logistic regression analysis A logistic regression is a suitable technique to predict the likelihood of an event to occur and uses a dichotomous dependent variable. Likewise, it can accommodate independent variables that are measured on either a continuous or categorical scale. In this study future 307

8 travel intention is expressed as a discrete variable Y with a value 1 if the respondent is likely to travel in the next year, and a 0 if not - that is: * Yi = α ' Z i + β i, * where Y i is an unobservable variable reflecting the likelihood of travel participation. The * observed counterpart of Y i is Yi with an observed value. * Y = 0 if Y i 0, * Y = 1 if Y i 0. Among the 224 usable questionnaires, 77 respondents report that they intend to travel in the future (i.e. Y = 1), while 147 respondents indicate no future travel intention. Four factors of travel motivations, three factors of travel constraints, and selected demographic variables (age, education level, martial status, income source, and employment) are specified as the dependent variables. The factors of motivations and constraints are continuous measures while the demographic variables are discrete measures. For simplicity purposes, the demographics were converted to dichotomous measures (as shown in Table 4) before the logistic regression analysis. Table 4. Definitions of dichotomous demographic variables. Variables Dichotomous value Gender female = 1 male = 0 Age over 60 = 1 other = 0 Education college or over = 1 other = 0 Marital status couple = 1 other = 0 Income source own salary = 1 other = 0 Employment retired = 1 other = 0 Table 5 reports the results of the logistic regression. To assess the model s goodness of fit, the chi-square value and the Hosmer and Lemeshow goodness-of-fit index are computed. As shown in Table 4, the model chi-square is significant (i.e. χ2 = , d.f.= 12, p = 0.000), indicating that the classification of seniors into intent to travel overseas and not to travel overseas could be predicted from the study s independent variables. In addition, the Hosmer and Lemeshow goodness-of-fit index is (d.f. = 8, p = 0.055), meaning that the model fits quite well. The second column of Table 5 shows the logit estimates of the parameter β for all independent variables. The fifth column shows the significant p-value for these sample statistics. Three out of four motivation factors - namely, relaxation, novelty, and socialization - are significant at p <.01. Here, the βs of relaxation (0.850) and novelty (0.561) are positive, while the βof socialization (-0.520) is negative. This implies that the more travel motivations of relaxation and novelty that respondents have, the more likely they are to travel. However, the more travel motivations of external interaction/socialization the respondents have, the less likely they are to travel. 308

9 Although the theoretical hypothesis assumes a positive impact of travel motivation on travel propensity, it is acceptable to see a negative impact of socialization motivation on travel intention in this study. The decision of whether to travel overseas or to travel domestically involves different decision-making considerations. The push power of travel motivation, such as socialization in general, can motivate seniors to travel in the domestic context due to familiar cultural and/or social customs, languages, etc. However, traveling overseas might be a barrier for seniors due to an unfamiliar or foreign environment. This is indirectly supported by about 70% of retirees choosing group package tours as their overseas travel mode in Taiwan (Wang, 2006). The odds ratios of both relaxation and novelty are greater than 1, revealing that they predict which type of respondent is more likely to travel. On the other hand, the odd ratio of socialization is less than 1, indicating that it predicts the type of respondents who are less likely to travel. Regarding travel constraints, only the personal reasons constraint is significant at p <. 05. In addition, the β of personal reasons (-0.468) is negative, and its odds ratio is less than 1, showing that the greater the intrapersonal constraint is that a respondent perceives, the less likely the respondent will travel. Regarding the demographic variables, only age, income, and employment are significant at p <.01. As a reminder, this study assigns a value of 1 to those seniors over 60 years of age, whose income comes from their own salary, and those who are retired. The βs of these variables are positive and their odds ratios are greater than 1. The odds of propensity to travel increase if the respondent is older (over 60), supports him/herself financially (own salary), and has more time (retired). Table 5. Results of binary logistic regression. (Dependent variable: travel intention, n = 224). Variables (1) β(2) SE (3) Z (4) Sig.(5) Exp(β) (6) Relaxation 0.850** Novelty 0.561** Escape Socialization ** Perceived risks Time commitments Personal * reasons Age 1.475** Education Marital status Income source 3.142** Employment 1.731** Constant Log-Likelihood = Goodness-of-fit Chi-Square : (DF = 12, p-value = 0.000) Hosmer-Lemeshow: (d.f.= 8, p = 0.055) Notes: (a). Age (> 60) = 1, education (college or higher) = 1, marital status (couple) = 1, income source (own money) = 1, employment (retired) = 1. (b). **: p < 0.01, *: p <

10 5. Conclusions This study has empirically investigated the influences of travel motivations, leisure constraints, and socio-demographic variables on seniors overseas travel propensity. A survey of Taiwanese seniors was conducted for the purpose of identifying the significant predicators. Four motivations are delineated - relaxation, novelty, escape, and socialization - that are also frequently cited in previous studies (Fleischer and Pizam, 2002; Horneman et al., 2002, Shoemaker, 1989; 2000). The results of factor analysis can provide clearer dimensions of Taiwanese senior travel motivation by considering multiple motivation items. Furthermore, it helps overcome the reliability problem of motivation measurement scales (Jang and Wu, 2006). In addition, three constraints to travel are identified - perceived risks, time commitments, and personal reasons constraints - that are in generally consistent with those major dimensions in past studies as reported by Crawford and Godbey (1987) and Jackson (1991). By applying a binary logistic regression analysis, this study contributes to seniors travel choice model by incorporating travel motivations, leisure constraints, and socio-demographic variables. The results from the binary logistic regression analysis reveal that age, income source, employment status, relaxation motive, novelty motive, socialization motive, and the personal reasons constraint are the main factors affecting seniors overseas travel propensity. This shows that in order to understand seniors overseas travel propensity, one should take into account not only socio-demographic, but also psychological factors such as motivations and perceived constraints. This study also concludes that seniors who are older (over 60), retired and financially self supporting, are more likely to travel. Both relaxation and novelty motivations have significantly positive effects on travel likelihood, while external interaction and socialization motivation as well as intrapersonal constraint have negative influences. It is worthwhile noting that the negative influence of external interaction and socialization motivation might result from the nature of overseas travel in this study. To travel overseas for seniors means to go to a relatively or completely foreign environment and be away from one s own daily environment. Due to language barriers and different customs and culture, the intuitive push factors of external interaction and socialization do not facilitate the propensity to travel overseas, specifically for Taiwanese seniors. We believe that this motivation has a positive influence on seniors propensity to travel domestically. Hence, a comparative study on the effects of the determinants on travel propensity in the context of domestic or overseas destinations will attract further research attention. The findings herein are also limited to the location scope in this study. The sample of this study is only obtained from urban seniors. However, whether or not there is a significant difference in travel motivations and constraints between urban seniors and rural seniors, and in turn to what extent such a difference would affect their travel propensity, is an interesting issue for further investigation. Last but not least, empirical data by employing a probability sampling method rather than convenience sampling method can improve the representativeness of research objects. References Anderson, B.B., Langmeyer, L. (1982) The under-50 and over-50 travelers: A profile of similarities and differences. Journal of Travel Research, 20(4), Baloglu, S., Shoemaker, S. (2001) Predication of senior travelers motorcoach use from demographic, psychological, and psychographic characteristics. Journal of Travel Research, 40(1),

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