Sedentary Behavior of the Nontravel Segment

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.

Full text

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Following is the final submission of the paper.
Please see JTR for published paper and cite as:
Litvin, S. W., Smith, W. W., & Pitts, R. E. (2013). Sedentary behavior of the nontravel segment: A
research note. Journal of Travel Research, 52(1), 131-136.
SEDENTARY BEHAVIOR OF THE NON-TRAVEL
SEGMENT: A RESEARCH NOTE
INTRODUCTION – WHY NON-TRAVELERS DO NOT TRAVEL
The non-travel 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 non-travel research can be attributed to a qualitative study by
Haukeland (1990). The key findings presented in Haukeland’s Scandinavian-based
research were that non-travelers were not a homogeneous group, and that non-
traveler segments had distinctly different motives for not having traveled.
Haukeland (1990) crafted a 2x2 model that segmented non-travelers based upon
several socio-demographic 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 non-travelers based upon ‘restrained’ versus ‘unrestrained’ economic
factors. Placement of non-travelers 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 non-travel. Smith and Carmichael (2005) sought to close this
lacuna. Based upon analysis of a large nationally representative [USA] dataset,

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these authors developed a non-travel index intended to aid understanding of the
causes for non-travel. 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 upon the USA outbound market, is discussed further
in the research methods section that follows.] This allowed for specific measures of
non-travel 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) employed cluster analysis to better understand non-travel
motives. These authors determined that USA non-travelers segmented into a range
of diverse homogeneous groups, each with distinctive demographic characteristics
and non-travel motivations. Based upon 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 non-travelers? Simply, the number of non-travelers 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 non-traveler as one who had not taken a pleasure trip over the
past twelve months nor intended to do so in the following twelve months, noted annual
Hong Kong non-travel rates (2000-2004) that fluctuated between 27% and 34%.
Europeans are typically regarded as heavy travelers. Yet, a now fairly dated study
(Commission of European Communities 1986) found that 44% 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% 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 found that 21% of adult Americans [USA] had not taken an overnight trip during
the years 2004 and 2005. Given a current USA population estimate of 311 million (U.S.
Census Bureau 2011), and assuming a consistent rate of non-travel in the half decade

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since, American non-travelers thus represent a potential market of over 65 million people.
The consensus of the non-travel literature…if we could better understand non-travel
motivations, and could find means to convert a share of these category non-users 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 non-travel segment.
The presumption of much of the relevant tourism literature is the belief that non-travelers
want to travel, and given a viable option will choose to do so; while others argue that
many non-travelers 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 non-travel decision. The current study adds another
layer to the discussion, suggesting that one’s ‘at-home’ behavior may be the best
predictor of non-travel, and the largest obstacle in changing non-travel behavior. The
research findings discussed below inform that non-travelers 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 non-traveler to travel. The
discussion that follows explains. But first, a discussion of the research method employed.
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 2007:8). TAMS
2006 employed a panel design with a mail-back survey, garnered a response rate of
71%, and yielded a sample of over 60,000 respondents. Its random sampling and
significant scale provided a sample that was a good overall reflection of the general
USA population.

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Two key dimensions were extracted for study. The first allowed division of the
sample into traveler versus non-traveler segments. Of the 60,000+ respondents,
21% (12,282 respondents) were classified as non-travelers, i.e. 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 ‘non-participants’. Analysis using the statistical package
PASW (version 18.0) utilized the Cochran-Mantel-Haenszel’s chi-squared 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 non-travelers, it was
noted that travelers were notably, often dramatically, more recreationally and
culturally active than were the sample’s non-traveler segment. Neither activity-cost
nor physical intensity helped to explain the dichotomy, evidenced by the lower
participation rate of non-travelers 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

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study reflected a statistically higher participation level by travelers than non-
travelers. 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 dataset,
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.’]
\\Please insert Table 1 around here//
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 upon these four characteristics, with activity
participation rates again calculated. All 44 at-home activities were tested (for
brevity 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 non-travelers did so; as did a significantly higher percentage of female
travelers versus female non-travelers. Young or old, male or female, rich or poor,
married or single – regardless of demographic tested, those who travel tend to lead

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more active recreational and leisure lives while those who do not travel tend to be
more sedentary.
\\Please insert Table 2 around here//
DISCUSSION
Much of the previous non-travel research has worked on the assumption that certain
segments of non-travelers could be converted to travelers if the right products and
offerings were made available to the consumer (Commission of European Communities,
1986; Jackson, Schmier & Nicole, 1997; Smith & Carmichael, 2005; Smith, Fralinger &
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/her from traveling.
The results of this study indicate 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 non-
traveler’s innate general lack of desire to do things…to include travel.
But why do non-travelers say they do not travel? When non-travelers 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% of the subjects. Instead, the plurality pointed to financial
constraint as their primary barrier (43% 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.
Further, looking at the at-home activity participation rates reflected in Table 1, it is noted
that less than 50% of non-travelers went on park-outings, and only about half as many
non-travelers versus travelers jogged, visited a botanic garden, or attended amateur
sporting events…all free activities.

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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 non-travel 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 non-travelers from doing so. It is also likely not
a lack of an attractive travel product. Instead, the non-traveler 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 non-traveler 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
non-traveler 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
employed is somewhat dated. However, they do benefit from having been collected
during a time of relative economic stability, thus eliminating the macro-economic
impacts of the recessionary period that followed as an intervening variable that
could have affected the research. A further limitation is the solely USA 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 non-travel could

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provide useful insight. With this further knowledge, perhaps we could unhinge
these two behavioral characteristics, providing tourism marketers the opportunity to
motivate non-travelers to ‘get off the couch’ and make travel an enriching part of
their lives.

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REFERENCES
Commission of the European Communities. (1986). Europeans and their Holidays.
Brussels: Commission of the European Community.
Haukeland, V.J. (1990). “Non-Travelers: The Flip Side of Motivation.” Annals of
Tourism Research, 17: 172-184.
Jackson, M.S., C.L. Schmier, and M. Nicole (1997). “Influences on Tourist Decision-
Making Tourism Research: Building a Better Industry. In Proceedings Australian
Tourism and Hospitality Research Conference, Sydney, 6-9 July 1997.
Mansfield, Y. (1992). “From Motivation to Actual Travel.” Annals of Tourism Research,
19: 399-419.
McKercher, B. (2009). “Non-Travel by Hong Kong Residents.” International Journal of
Tourism Research, 11(6): 507-519.
Ontario Ministry of Tourism (2007). Travel Activities and Motivations of U.S. Residents:
An Overview. Toronto: Ontario Ministry of Tourism.
Smith, W.W. and B.A. Carmichael (2005). “A Geographical Analysis of Non-Travel
Across the Regions of Canada.” Tourism Geographies, 7(3): 257-271.
Smith, W.W., S.W. Litvin, S. Nadav, and B. Carmichael (2009). “Non-Travelers: The
Flip Side to Motivation – Revisited.” Tourism Recreation Research, 34(1): 91-93.
Smith, W.W., E. Fralinger, E. and S.W. Litvin (2011). “Segmenting the USA Non-
Travel Market.” Enlightening Tourism – A Pathmarking Journal, 1(2): 137-151.
U.S. Census Bureau. (2011). Population Clock. http://www.census.gov/.

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Table 1
‘At-Home’ Activities of Travelers versus Non-Travelers
Percent of
travelers
participating
in the activity
while at home a,b
Percent of
Non-travelers
participating
in the activity
while at home a,b
X2 c
Phi
Participative Activities
Camping
29%
16%
695.1 *
0.134*
Canoeing or kayaking
9
3
441.3 *
0.153*
Fishing
33
27
114.9 *
0.083*
Hunting
12
9
50.8 *
0.038*
Cycling
25
14
594.4 *
0.161*
Hiking
35
16
1492.9 *
0.231*
Exercise at home or club
58
42
861.3 *
0.158*
Jogging
19
11
409.2 *
0.122*
Golfing
19
7
972.1 *
0.231*
Racquet sports
13
7
328.3 *
0.122*
Playing team sports
16
9
300.8 *
0.122*
Horseback riding
9
5
146.3 *
0.127*
Downhill skiing
8
2
473.9 *
0.185*
Ice Skating
7
3
210.6 *
0.131*
Rollerblading
8
5
126.5 *
0.094*
Riding ATV
12
8
104.9 *
0.081*
Sailing or boating
21
9
845.2 *
0.182*
Swimming
57
35
1785.5 *
0.130*
Passive Activities
Park outings
66%
49%
1068.1 *
0.185*
Picnicking
50
38
515.0 *
0.168*
Visiting farmers markets
34
29
84.2 *
0.105*
Gardening
55
48
187.3 *
0.099*
Visiting botanic gardens
21
12
496.1 *
0.182*
Attending amateur sports
40
23
1135.8 *
0.196*
Attending pro sports
37
18
1338.4 *
0.212*

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Table 1 continued
Cultural Activities
Visiting art galleries
29%
14%
1090.3 *
0.229*
Attending ballets
8
3
236.4 *
0.138*
Attending opera
6
3
155.9 *
0.105*
Classical music events
16
9
337.3 *
0.139*
Historic/heritage sites
41
22
1255.2 *
0.228*
Attend live theatre
31
14
1162.5 *
0.216*
Visit museums
39
20
1279.4 *
0.236*
Entertainment Activities
Visit bars with rock bands
25%
15%
459.3 *
0.182*
Visit jazz clubs
9
5
122.4 *
0.112*
Go dancing
21
14
245.5 *
0.135*
Eating in restaurants
94
78
2650.8 *
0.161*
Visiting day spa
11
4
437.2 *
0.154*
Visit festivals or fairs
68
49
1317.6 *
0.191*
Visit casinos
30
18
544.0 *
0.140*
Attend rock concerts
22
13
472.1 *
0.152*
Attend a rodeo
9
6
52.7 *
0.076*
Theme/amusement parks
39
26
585.4 *
0.160*
Zoos or aquariums
48
32
908.5 *
0.200*
B&B in hometown
10
5
331.6 *
0.115*
NOTES:
a. 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’.
b. 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.’
c. Statistical test employed: Statistical test employed: Cochran-Mantel-Haenszel’s chi-
squared test for two dichotomous variables.
* Difference between groups is statistically significant based upon at p < 0.01.
Activities for which less than 5% of the sample have participated (cross country skiing,
skateboarding, snowboarding, and snowmobiling) have been excluded from the table.

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Table 2
Comparison of Travel versus Non-Traveler Participation in Selected Activities,
Segmented by Demographic Variables
Percent of
travelers
participating
in the activity
while at home a,b
Percent of
Non-travelers
participating
in the activity
while at home a,b
X2 c
Jogging
Males
21
12
214.2 *
Females
18
10
193.1 *
Family income < US$75k d
22
16
32.3 *
Family income ≥ US$75k d
22
15
39.4 *
Married
19
11
191.0 *
Not-married
20
11
217.7 *
Age < 40 years
26
17
151.0 *
Age ≥ 40 years
14
08
178.7 *
Pro sports events
Males
41
23
535.1 *
Females
33
14
810.0 *
Family income < US$75k
46
30
108.1 *
Family income ≥ US$75k
45
30
121.1 *
Married
38
19
670.5 *
Not-married
35
18
585.5 *
Age < 40 years
39
24
300.4 *
Age ≥ 40 years
35
16
100.5 *

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Table 2 continued
Historic/Heritage Sites
Males
40
22
546.9 *
Females
41
23
709.9 *
Family income < US$75k
45
26
141.9 *
Family income ≥ US$75k
44
26
166.7 *
Married
41
22
649.7 *
Not-married
40
23
567.7 *
Age < 40 years
29
17
217.8 *
Age ≥ 40 years
49
25
1331.2 *
Festivals and Fairs
Males
63
45
527.9 *
Females
72
53
834.1 *
Family income < US$75k
70
53
133.5 *
Family income ≥ US$75k
70
53
166.3 *
Married
68
52
542.7 *
Not-married
66
47
673.9 *
Age < 40 years
65
53
172.8 *
Age ≥ 40 years
70
47
1308.9 *
Visiting Casinos
Males
30
18
289.4 *
Females
30
19
257.8 *
Family income < US$75k
30
18
289.4 *
Family income ≥ US$75k
30
19
257.8 *
Married
31
21
44.4 *
Not-married
31
21
62.4 *
Age < 40 years
28
18
202.1 *
Age ≥ 40 years
33
19
411.9 *

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Table 2 continued
Dining in restaurants
Males
93
76
129.4 *
Females
94
79
1369.2 *
Family income < US$75k
96
84
341.1 *
Family income ≥ US$75k
96
85
366.7 *
Married
95
80
1244.6 *
Not-married
92
75
1097.9 *
Age < 40 years
94
80
756.1 *
Age ≥ 40 years
94
77
1842.7 *
a. 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’.
b. Responses from those who either failed to answer the question or indicated the activity
was not available where they lived were excluded.
c. Statistical test employed: Cochran-Mantel-Haenszel’s chi-squared test for two
dichotomous variables.
d. US$75k was the income split prescribed within the TAMS 2006 dataset.
* Difference between groups is statistically significant based upon at p < 0.01.