Abstract
Nontravel behavior has been studied in some depth, with the intent generally to find ways to motivate the segment to travel. This research examined an aspect of nontravel behavior previously unexplored: their “at-home” behavior. The results are very informative and reflect a highly sedentary lifestyle. The nontravelerers were found to be far less active in their daily lives, both recreationally and culturally, than were those who traveled. This sedentary lifestyle, not before identified, is an important characteristic adding to the difficulty of motivating the nontravel segment to travel.
Introduction: Why Nontravelers Do Not Travel
The nontravel segment has attracted a good deal of recent interest in the tourism literature, generally geared toward understanding the reasons behind the segment’s lack of travel in order to motivate these potential consumers to do so. The current study adds to our understanding of the segment.
Perhaps the initial nontravel research can be attributed to a qualitative study by Haukeland (1990). The key findings presented in Haukeland’s Scandinavian-based research were that nontravelers were not a homogeneous group, and that nontraveler segments had distinctly different motives for not having traveled. Haukeland (1990) crafted a 2 × 2 model that segmented nontravelers based on several sociodemographic and situational variables. These included age, income, personal health, and familial situation. His model incorporated a Y axis anchored by the variables “restrained” and “unrestrained” social factors, and an X axis that divided nontravelers based on “restrained” versus “unrestrained” economic factors. Placement of nontravelers within one of the four quadrants provided a framework for future study, allowing researchers to consider why members of each segment opted not to travel. Haukeland’s (1990) model was successfully replicated using Canadian-based quantitative data by Smith et al. (2009). Haukeland’s (1990) model, however, useful as it was, did not provide much depth of understanding as to the motivations for nontravel. Smith and Carmichael (2005) sought to close this lacuna. Based on analysis of a large nationally representative (U.S.) data set, these authors developed a nontravel index intended to aid understanding of the causes for nontravel. The Smith and Carmichael (2005) index was modified and adopted by Statistics Canada and incorporated in their 2006 Travel Activities and Motivations Survey. (TAMS, a significant periodic survey research initiative of the Canadian government focused on the U.S. outbound market, is discussed further in the research methods section that follows.) This allowed for specific measures of nontravel not previously available to researchers at a nationally representative level. Utilizing these data in conjunction with a wide range of demographic and geographic variables extracted from the 2006 TAMS, research by Smith, Fralinger, and Litvin (2011) used cluster analysis to better understand nontravel motives. These authors determined that U.S. nontravelers segmented into a range of diverse homogeneous groups, each with distinctive demographic characteristics and nontravel motivations. Based on the identified clusters, the authors suggested marketing strategies that would allow marketers to better encourage these consumers to make vacation travel a part of their lives.
Why the continued interest in nontravelers? Simply, the number of nontravelers is surprisingly large. Smith and Carmichael (2005) reported that one in three Canadians had not taken an overnight trip (at least 80 km) during the years 1999-2000. McKercher (2009), having defined a nontraveler as one who had not taken a pleasure trip over the past 12 months nor intended to do so in the following 12 months, noted annual Hong Kong nontravel rates (2000-2004) that fluctuated between 27 and 34 percent. Europeans are typically regarded as heavy travelers. Yet a now fairly dated study (Commission of European Communities 1986) found that 44 percent of the EU population had not taken a three-nights or longer holiday away from their usual place of residence during the previous year. Jackson, Schmier, and Nicole’s (1997) Australian study yielded similar results, with 38 percent of Australians having failed to have taken a vacation trip during the five-year period examined. As a final example, the aforementioned TAMS 2006 study reported that 21 percent of adult Americans (U.S.) had not taken an overnight trip during the years 2004 and 2005. Given a current U.S. population estimate of 311 million (U.S. Census Bureau 2011) and assuming a consistent rate of nontravel in the half decade since, American nontravelers thus represent a potential market of more than 65 million people. The consensus of the nontravel literature—if we could better understand nontravel motivations and could find means to convert a share of these nonusers to travel consumers—their travel dollars would add significantly to the financial success of the industry. Thus, we continue to strive to learn more about the nontravel segment.
The presumption of much of the relevant tourism literature is the belief that nontravelers want to travel, and given a viable option will choose to do so, while others argue that many nontravelers fail to do so because they do not have “the travel bug.” Mansfield (1992) and McKercher (2009) have both explored these premises, finding that many situational factors can influence the nontravel decision. The current study adds another layer to the discussion, suggesting that one’s “at home” behavior may be the best predictor of nontravel, and the largest obstacle in changing nontravel behavior. The research findings discussed below inform that nontravelers do not simply not travel but rather, as the title of this note suggests, tend to be generally more sedentary people. While by definition travel is not a part of their lives, neither are a wide range of activities, both recreational and cultural, that more active people more actively pursue—a parsimonious and valuable, if somewhat discouraging, revelation that renders as ineffective many of the recommendations prior authors have provided for getting the nontraveler to travel. The discussion that follows explains. But first, a discussion of the research method used.
Research Method
TAMS 2006 data were made available to one of the authors by the Ontario Ministry of Tourism. TAMS 2006, the most recent periodic TAMS exercise, “examined the recreational activities and travel habits of Americans looking at their travel behavior” for the years 2004-2005 (Ontario Ministry of Tourism 2007, p. 8). TAMS 2006 employed a panel design with a mail-back survey, garnered a response rate of 71 percent, and yielded a sample of more than 60,000 respondents. Its random sampling and significant scale provided a sample that was a good overall reflection of the general U.S. population.
Two key dimensions were extracted for study. The first allowed division of the sample into traveler versus nontraveler segments. Of the 60,000+ respondents, 21 percent (12,282 respondents) were classified as nontravelers, that is, those who had not vacationed during the previous two years. The second reflected the respondents’ participation in a wide range of at home recreational, cultural, and entertainment activities (TAMS question: “In a typical year, how often do you participate in each of the following activities when not traveling, that is, while you are not traveling out-of-town?”). These included active recreational (golfing, camping, jogging, fishing, swimming, etc.), passive recreational (park-outings, gardening, attending sporting events, etc.), cultural (attending an opera, visiting museums, etc.), and entertainment activities (dining at restaurants, visiting casinos, and going to jazz clubs). The response options provided for each activity were “frequently,” “occasionally,” “rarely,” and “never.” (A fifth option, “not available where I live,” was treated as a missing response.) For analysis sake, these responses were recoded into a dichotomous variable. Respondents who indicated “frequently” and “occasionally” were classified as “participants.” Those who indicated “rarely” and “never” were classified as “nonparticipants.” Analysis using the statistical package PASW (version 18.0) utilized the Cochran–Mantel–Haenszel’s chi-square test designed for two nominal variables independent in each stratum. In addition, phi, employed as a confirmatory measure, was computed and reported.
Findings
When comparing the participation levels of travelers versus nontravelers, it was noted that travelers were notably, often dramatically, more recreationally and culturally active than were the sample’s nontraveler segment. Neither activity-cost nor physical intensity helped to explain the dichotomy, evidenced by the lower participation rate of nontravelers for such diverse activities as golf, downhill skiing, and attending the opera, as well as jogging, park-outings and going dancing. In fact, without exception, all 44 at-home activities included in the TAMS 2006 study reflected a statistically higher participation level by travelers than nontravelers. Table 1 reflects these results, with statistically significant chi-square results and Phi test scores found for all 44 activities. (Given the size of the data set, finding statistically significant differences was not surprising. However a review of the results in Table 1 point to the obvious “real” differences between the two segments. Percentage differences of double digits were common, and those activities with smaller differences tended to be niche activities where the gap represented a large percentage differential across the activity segment. The statistical results, given that all 44 variables reflected the same dichotomy, have clear “face validity.”)
“At-Home” Activities of Travelers versus Nontravelers
Note: Activities for which less than 5 percent of the sample have participated (cross country skiing, skateboarding, snowboarding, and snowmobiling) have been excluded from the table.
The percentage of participation reflects those respondents who indicated they participated in the activity “frequently” and “occasionally.” It excluded those who responded “rarely” and “not at all.”
Responses from those who either failed to answer the question or indicated the activity was not available where they lived were excluded. The resulting number of respondents ranged from a low of n = 47,478 for downhill skiing to a high of n = 58,338 for the activity “visiting festivals or fairs.”
Cochran–Mantel–Haenszel’s chi-square test for two dichotomous variables.
Difference between groups, for each activity, is statistically significant at p <0.01.
A further question that required address was the potential impact of moderating variables. For example, it was conceivable that age was determinant as to whether a person was both someone who traveled and was an active person when at home. Perhaps, it could be presumed, younger people were in a better position to be both active and to travel, while the elderly were more likely to both not travel and to be more sedentary. Similarly, other variables, to include marital status, income, and gender could logically have been important moderators. To test this, the sample was segmented demographically based on these four characteristics, with activity participation rates again calculated. All 44 at-home activities were tested (for brevity’s sake, Table 2 reflects a half dozen of the tested variables). None of the 44 was found to have demographics as a moderating variable. For example, while older folks were considerably more likely to visit historic/heritage sites than were their younger counterparts, within both age groups it was the traveler who was the more likely site visitor. Similarly, while a virtually identical rate of males and females dined in restaurants, a significantly higher percentage of male travelers than male nontravelers did so—as did a significantly higher percentage of female travelers versus female nontravelers. Young or old, male or female, rich or poor, married or single—regardless of demographics tested, those who travel tend to lead more active recreational and leisure lives while those who do not travel tend to be more sedentary.
Comparison of Travel versus Nontraveler Participation in Selected Activities, Segmented by Demographic Variables
The percentage of participation reflects those respondents who indicated they participated in the activity “frequently” and “occasionally.” It excluded those who responded “rarely” and “not at all.”
Responses from those who either failed to answer the question or indicated the activity was not available where they lived were excluded.
Cochran–Mantel–Haenszel’s chi-square test for two dichotomous variables.
US$75k was the income split prescribed within the TAMS 2006 data set.
Difference between groups, for each activity, is statistically significant at p <0.01.
Discussion
Much of the previous nontravel research has worked on the assumption that certain segments of nontravelers could be converted to travelers if the right products and offerings were made available to the consumer (Commission of the European Communities 1986; Jackson, Schmier, and Nicole 1997; Smith and Carmichael 2005; Smith, Fralinger, and Litvin 2011). Both Mansfield (1992) and McKercher (2009) however questioned this assumption, indicating that no degree of product manipulation would be effective when it is either a consumer’s choice or structural constraints that keep him or her from traveling. The results of this study indicate that the Mansfield (1992) and McKercher (2009) interpretation is likely correct; with the more likely of the two barriers (choice or constraints) being traveler’s choice—specifically isolated in this study as the nontraveler’s innate general lack of desire to do things, to include travel.
But why do nontravelers say they do not travel? When nontravelers were asked in the TAMS 2006 study to indicate their reasons for not having done so, a lack of interest in travel was cited by only 8 percent of the subjects. Instead, the plurality pointed to financial constraint as their primary barrier (43 percent ticked the response “a lack of money to do so”). Perhaps such an answer was motivated by a social desirability bias, for the family income breakdown reflected in Table 2 does not suggest financial restraint to be the case. Furthermore, looking at the at-home activity participation rates reflected in Table 1, it is noted that less than 50 percent of nontravelers went on park-outings, and only about half as many nontravelers versus travelers jogged, visited a botanic garden, or attended amateur sporting events—all free activities.
While the root cause for the link between a more sedentary lifestyle, as measured by one’s proclivity for engagement in recreational and cultural activities, and travel cannot be explained from these data, the relationship is apparent. Such a linkage has not been identified in prior nontravel research. Given the findings herein, this seems a significant omission in our literature and points to the importance of the current study.
Conclusion
The above results and discussion suggest it is not a lack of money, lack of time, or family status, etc. that keeps many nontravelers from doing so. It is also likely not a lack of an attractive travel product. Instead, the nontraveler simply seems to lack the drive to do things, is stereotypically more sedentary with fewer recreational or cultural interests, and unfortunately for the hospitality and tourism industry, this lack of drive extends to travel. Even those with limited financial resources, limited discretionary time, or even reduced mobility can enjoy many of the activities included in the TAMS study, such as walking, gardening, going to museums, even dining out at inexpensive restaurants, but the nontraveler participates significantly less frequently in these and all other tested activities than does their neighbor who travels. The key management implication: efforts invested attempting to induce the nontraveler to travel will likely fail to pay dividends. Their generally more sedentary nature simply makes them a poor segment to pursue.
There are some potential limitations of this work. First, the TAMS 2006 data used is somewhat dated. However, they do benefit from having been collected during a time of relative economic stability, thus eliminating the macroeconomic impacts of the recessionary period that followed as an intervening variable that could have affected the research. A further limitation is the solely U.S. sample. While the results reported were statistically strong, replication in other locations would be useful for sake of validation and generalization. Finally, a psychologically based study that provides hints that could help us to better understand the underpinnings of the relationship between sedentary behavior and nontravel could provide useful insight. With this further knowledge, perhaps we could unhinge these two behavioral characteristics, providing tourism marketers the opportunity to motivate nontravelers to “get off the couch” and make travel an enriching part of their lives.
Footnotes
Declaration of Conflicting Interests
The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.