Tourism Management 32 (2011) 406e414
Contents lists available at ScienceDirect
Tourism Management
journal homepage: www.elsevier.com/locate/tourman
The impact of age and family life experiences on Mexican visitor
shopping expenditures
David C. Bojanic*
Department of Marketing, College of Business, University of Texas at San Antonio, San Antonio, TX 78249, USA
a r t i c l e i n f o
a b s t r a c t
Article history:
Received 26 March 2009
Accepted 22 March 2010
Mexican Nationals frequently visit border towns and other cities in the United States that are in close
proximity to their areas of residence for the main purpose of shopping at popular malls and outlet
centers. However, it is somewhat difficult to gather the necessary information in order to profile the
visitors and develop marketing strategies for targeting the appropriate market segments. The purpose of
this paper is to identify the key target markets for U.S. shopping malls based on the age and family life
experiences (i.e., marriage and having children) of the Mexican visitors. First, a three-factor ANOVA
analysis is used to examine the impact of these characteristics on shopping expenditures, including the
interaction effects. Then, a cluster analysis is performed in order to segment the market using age and
the family life experience variables. Finally, recommendations are provided based on the expenditures
and trip behavior by family life cycle stage.
Ó 2010 Elsevier Ltd. All rights reserved.
Keywords:
Shopping expenditures
Mexican visitors
Family life experiences
Family life cycle
1. Introduction
Recently, the world faced a global recession sparked by the
financial crises in the United States that started in 2007 and had
effects that lasted through at least 2010. The recession impacted
most of the industrialized countries and tourism was one of the
main sectors of the economy that experienced a major downturn.
When the local economy is weak, it is important for destination
marketing organizations to find areas where there is still good
potential for generating tourism revenue. For example, it was
estimated that the economic impact of Mexican visitors was
approximately $3 billion in the South Texas Rio Grande Valley in
2004 (Canas, Coronado, & Phillips, 2006). Similarly, the economic
impact was estimated at $4.5 billion in California and $1.6 billion in
Arizona around the same time period. Texas has been fortunate in
that there is a segment of the population in nearby Mexico that has
a fairly large amount of disposable income, and the members of this
segment were not as seriously affected by the current economic
crisis. According to the CIA World Factbook, the richest 10% of
Mexicans accounted for 37.9% of the nation’s income in 2006.
However, the development of marketing strategies for targeting
this segment of the Mexican market is contingent upon having
reliable information on the profile of the visitors and their spending
patterns.
* Tel.: þ1 210 458 5373.
E-mail address: david.bojanic@utsa.edu
0261-5177/$ e see front matter Ó 2010 Elsevier Ltd. All rights reserved.
doi:10.1016/j.tourman.2010.03.012
One of the main benefactors of the Mexican tourists is the
shopping industry. In fact, shopping expenditures are often at the
top of the list of overall tourism expenditures based on visitor
studies (Cai, Lehto, & O’Leary, 2001). Also, shopping has been found
to be a destination driver for some countries (Moscardo, 2004;
Yeung, Wong, & Ko, 2004). In Texas, the net exported retail sales
to Mexican visitors for the four major border cities (El Paso, Laredo,
McAllen, and Brownsville e see Fig. 1) was estimated to be $2.3
billion per year from the late 1970s to 2001. This represented 26.4%
of the total retail sales in those four cities (combined), and 2% of the
overall retail sales in Texas (Canas et al., 2006). Pavlakovich-Kochi
and Charney (2008) collected survey data from Mexican visitors
at six Arizona border ports of entry and in two airports (Tucson and
Phoenix) from July 2007 through June 2008. The average party
consisted of 1.8 people, 84% of which were day trippers. Approximately 57.5% cited shopping as their main reason for visiting, and
three Arizona shopping malls were identified as the most popular
shopping destinations. The results were segmented according to
various demographic and geographic variables, but no attempt was
made to form meaningful market segments based on two or more
variables that could be used to tailor marketing programs to
specific segments.
The purpose of this research project was to conduct an intercept
study of the Mexican Nationals who traveled to south central Texas
to shop during the 2008 holiday season in an effort to establish an
accurate profile of the market. In particular, the main objective was
to examine the effects of age, marriage, and having children on the
level of shopping expenditures, and then evaluate the ability to
D.C. Bojanic / Tourism Management 32 (2011) 406e414
407
Fig. 1. TexaseMexico Border. Source: Texas Comptroller of Public Accounts.
segment the market for Mexican shoppers using the three variables. In addition, the general travel behavior of the Mexican
National market (to south central Texas) was investigated in order
to estimate the overall scope of retail spending by Mexican visitors
to the region by FLC stage. Finally, recommendations are provided
regarding the most attractive market segments for targeting by the
destination marketing organization (DMO) in order to take
advantage of opportunities for sustainable growth.
2. Literature review
There are three streams of literature related to this study. The
first stream concerns the use the family life cycle in segmenting
markets for products and services. The second stream discusses the
life course approach, which represents an extension to the family
life cycle approach by combining demographics with life experiences in order to create cohorts, or market segments. The third
stream focuses on how shopping attracts visitors to tourist destinations, and how the market can be segmented.
2.1. The family life cycle concept
The family life cycle (FLC) concept is used to create cohorts
based on age, marital status, and the presence of children, and then
attempt to explain the consumption patterns of individuals as they
progress from early adulthood as a bachelor, to a newly married
couple, to a couple with young children (full nest), to a couple with
older children (empty nest), to a solitary survivor after the death of
his/her spouse (Wells & Gubar, 1966). Basically, the discretionary
income of the various FLC segments changes throughout the life
cycle as people age, get married, have children, and lose spouses.
The results of these early studies suggest that bachelors, newly
married, empty nest, and solitary survivor are the FLC stages with
the highest amounts of discretionary income. This is particularly
important to organizations that market leisure travel. Hisrich and
Peters (1974) looked at the effect of age, social class, and family
life cycle on the purchase of leisure services, including tourism. It
turned out that the FLC was more highly correlated with leisure
activities than age or social class. There are other studies that
include some of the same variables as the FLC in combination with
other demographic and socioeconomic variables (Crask, 1981;
Schul & Crompton, 1983; Taylor, 1986), sometimes including the
FLC as well (Meiden, 1984). Other researchers looked at the effect of
gender on travel decisions over the life cycle and found that travel
peaks for women earlier than men (Collins & Tisdell, 2002). Most
recently, Peterson (2007) focused primarily on age and income and
determined that seniors below the age of 75 demonstrated
different vacation behaviors than seniors age 75 and over.
Lawson (1991) applied the Wells and Gubar (1966) version of
the family life cycle in a tourism context with a survey of international visitors to New Zealand. The main focus of the study was
to identify the expenditure patterns of the people in the various
stages of the FLC. Young singles and empty nesters spent the most
on accommodations, and young singles, young couples (newly
married), and solitary survivors spent the most on shopping. Fish
and Waggle (1996) reviewed the impact that life cycle income had
on the number of vacations taken by families and their trip
expenditures. Interestingly, the percentage spent on trips
increased as the level of total household expenditures increased,
and total household expenditure was the best predictor of the
number of trips and vacation expenditures. Finally, Hong, Fan,
Palmer, and Bhargava (2005) used a consumer expenditures
survey to determine if people in different stages of the FLC
exhibited different spending patterns on leisure travel, and they
found there was a significant relationship between FLC and travel
expenditures.
408
D.C. Bojanic / Tourism Management 32 (2011) 406e414
Another area of research focused on the relationship between the
FLC and the travel decision-making process (Cosenza & Davis, 1981),
including travel expenditures. Fodness (1992) examined the relationship between the FLC and the leisure travel decision-making
process (primarily the information search and final decision stages).
One of the findings of the study was that roles related to family
decision-making change over time. Fodness and Murray (1999) also
examined the correlates of tourist information search in a subsequent study, including individual tourist characteristics (i.e., life
cycle variables). Pennington-Gray and Kerstetter (2002) examined
the effect of FLC stage and age on travelers’ perceived constraints to
nature-based tourism. Nicolau and Más (2005) examined two stages
of the travel process, the decision to take a trip and the level of
tourism expenditures, and concluded that there are real differences
between the various demographic and geographic variables used in
the study, especially the distance traveled.
The legitimacy of the traditional FLC concept came under some
scrutiny because of changing social trends such as divorce, more
women in the workplace, and more couples choosing not to have
children. In response, a ‘modernized’ family life cycle was developed (Derrick & Lehfeld, 1980; Murphy & Staples, 1979). Initially,
these studies made minor modifications to include divorced
persons and childless couples throughout various stages of the FLC.
For example, Bojanic (1992) included single parents and middleaged couples without children and found that both groups take
more overseas trips on average than other FLC segments, and
middle-aged couples without children place more importance on
local customs than all of the other segments. Fig. 2 is one possible
diagram of a modernized family life cycle that depicts the various
alternatives for individuals as they age.
2.2. Cohorts and the life course approach
A similar approach to FLC segmentation is the use of “cohorts” to
group individuals with similar values or lifestyles resulting from
shared life experiences during a certain timeframe (i.e., generation). Pennington-Gray et al. (2003) placed individuals into age
cohorts based on generation with the belief that their behaviors
would be influenced by “epochal” events that transpired during
certain periods in their lives. There were some significant findings
in regard to travel preferences that would suggest that cohort
analysis is useful in the tourism industry because different cohorts
are attracted to different tourist activities at different times.
Upchurch, Rompf, and Severt (2006) utilized a commercial
instrument for cohort segmentation (IXIÔ Cohorts e www.cohorts.
com) that groups consumers into four major categories (married
couples, single females, single males, and “households that defy
classification”) with a total of 31 subcategories or segments. The
purpose of the study was to determine if lifestyle differences
existed among randomly selected vacation club owners, and if this
led to differences in satisfaction levels among owners. The results
indicated that there was an overall sense of general satisfaction
among all cohorts, but only 12 out of the 31 segments were represented in this study. The results support a differentiation
(“niche”) strategy for targeting timeshare customers rather than
a mass marketing approach, and suggest that the lifestyle cohort
scheme used in this study is useful in this regard.
Noble and Schewe (2003) examined the validity of using values
to group consumers into cohorts and trying to determine if individuals in the same cohort are influenced similarly by external life
events. The researchers found that the sample could be placed into
cohorts with some reliability, but the respondents in the same
cohort didn’t necessarily demonstrate the same level of influence
from external life events, thereby raising some doubt about the
validity of cohort segmentation for use in consumer marketing.
Reisenwitz and Iyer (2007) also evaluated the viability of cohort
segmentation by using chronological age to further segment the
baby boomers into younger and older baby boomers, and then
determine if there were differences between the cohorts in regard
to cognitive age and other behavioral variables (e.g., Internet usage
and personal lifestyle characteristics). Once again, cohort
segmentation was proven to have limited applicability in predicting
differences among groups of individuals in this specific context.
The life course approach was developed as an extension of
cohort segmentation and the FLC concept to include social and
cultural norms, and role transitions (i.e., life experiences) that
influence an individual’s behavior at various stages throughout his/
her life (Elder, Johnson, & Crosnoe, 2003; Moschis, 2007). The
advantage over the FLC was the mere inclusion of life experiences
to define market segments, and it extended cohort analysis by
focusing on differences in life experiences as the primary means of
segmentation, rather than assuming members of the same age
cohort share all of the same life experiences in the same manner.
Mathur, Lee, and Moschis (2006) used cluster analysis to group
respondents based on demographics (age, sex, income, living
status, employment status, and education), health status (#medical
Fig. 2. Modernized family life cycle.
D.C. Bojanic / Tourism Management 32 (2011) 406e414
problems and #prescription drugs), and life events (#experienced
and #expected). The respondents ended up in one of four clusters
(unruffled, free birds, chronic strugglers, and full nesters) with the
average age for each cluster ranging from 42 to 63, with the full
nesters the youngest and the free birds the oldest. The life experience numbers were highest for the “chronic strugglers” and the
number of health problems was highest for the “free birds.” There
was no significant difference for international travel (first time or
first time in a long time), but the “chronic strugglers” bought more
gifts than usual and spent more on clothes than usual, compared to
the other three groups.
Other studies have used the life course concept to study
compulsive consumption. Rindfleisch, Burroughs, and Denton
(1997) examined the effect of early life experiences on patterns of
consumer behavior in the United States. Similarly, BenmoyalBouzaglo and Moschis (2009) examined the effects of family
disruption events experienced during adolescence, the perceived
stress of those events, peer communication environments, and
levels and types of family support on compulsive shopping
behavior tendencies. Both studies concluded that stressful family
disruption events experienced during adolescence are associated
with compulsive shopping behaviors in early adulthood. In addition, Benmoyal-Bouzaglo and Moschis (2009) found a relationship
between exposure to peer communication about consumption and
compulsive consumption in early adulthood.
2.3. Shopping as a tourist activity
Shopping has been demonstrated to be a popular tourist activity
and it is often one of the main destination drivers. Shopping was
found to be the most important motivator for tourists’ traveling to
Hong Kong, given that they spent approximately 50% of their
money for the trip on shopping (Heung & Cheng, 2000). Similarly,
Cai et al. (2001) found that shopping expenditures accounted for
the largest percentage of total expenditures in their survey of
outbound Chinese travelers, and shopping was determined to be
the third most important motivational factor for visiting Turkey
(Tosun, Temizkan, Timothy, & Fyall, 2007). However, in contrast,
shopping was only the 12th most important motivator (out of 21)
for tourists traveling to Thailand, and the factor containing the
shopping attribute only explained 9.57 percent of the total variance
for the sample regarding travel motivation (Rittichainuwat, Qu, &
Mongkhonvanit, 2008).
Various segmentation schemes have been used in the tourism
literature to study shopping as a tourist activity. As with most
industries, demographic variables are frequently used in the
process of segmenting markets, and they are often combined with
other types of segmentation variables. For example, Oh, Cheng,
Lehto, and O’Leary (2004) looked at socio-demographic characteristics and trip typologies to determine the predictors of shopping
behavior. Shopping behaviors were examined by the tourists’ level
of involvement in five categories of shop or browse activities. Age,
gender, and trip typology were all found to be significant predictors
of those activities. In another study, Lehto, Cai, O’Leary, and Huan
(2004) used data from a survey of outbound Taiwanese tourists
to examine shopping preferences and behaviors. The basic findings
were that travel purpose, travel mode, age and gender were
significant predictors for the amount of money spent on shopping
and the types of items that were purchased.
Yu and Littrell (2005) discovered that demographics had
a significant influence on shoppers’ behaviors (i.e., they are older,
empty nesters who are well-educated and have more discretionary
income) in their examination of product-oriented versus processoriented shopping. Josiam, Kinley, and Kim (2005) used cluster
analysis to identify tourist-shoppers in an attempt to explain why
409
people living in metropolitan areas travel to other destination to
shop, thereby classifying themselves as tourists. The more highly
involved shoppers turned out to be females and their main motivation for the trip was shopping, but there was not a significant
relationship between level of involvement and the time or money
spent on shopping. Fairhurst, Costello, and Holmes (2007)
segmented tourists into five dimensions (city, historical, active,
alone, and tour groups) using a factor analysis based on tourist
styles. Next, the authors examined the shopping behaviors and
demographics across the dimensions to determine if there were
any significant differences. The results indicated that shopping
behaviors (including information search) vary more for tourists
than for the local shopper, and must be considered when targeting
specific tourist styles.
Finally, some researchers incorporated the use of geographic
origin in their segmentation studies. LeHew and Wesley (2007)
examined the level of satisfaction among area residents and tourists for two shopping malls in the Midwest and two heritage
destination centers on the West Coast (San Francisco and Las
Vegas). The researchers concluded that the area residents demonstrated higher levels of satisfaction with the shopping venues. Lee,
Chang, Hou, and Lin (2008) examined the relationship between
experience and image for foreigners in Taiwan, including foreign
residents (mainly from Southeast Asia) and foreign visitors (mainly
from Japan, Hong Kong and Macao, and the United States) for
shopping (eating) at the night markets. Significant differences were
found by age, gender and type of foreigner for ‘think’ experiences,
by age and country of origin for ‘act’ and ‘related’ experiences, and
only by country of origin for ‘feel’ experiences. In addition, there
were differences for image across categories for all of the sociodemographic variables except gender.
Once again, the purpose of this study is to extend the current
research in the area of shopping as a tourist activity by examining
the effects of marriage and/or having children on tourists within
different age cohorts. This life course approach is different in that it
looks at marriage and having children as two major life experiences
that can impact a tourist’s shopping behavior, including any interaction effect between the two family life experiences and age. This
is coupled with a segmentation analysis using age, marriage, and
having children as the variables for assigning tourists to family life
cycle stages, and then identifying the best target markets for
shopping destinations based on visitor expenditures. The following
research questions are addressed:
1. Do age, marriage, and having children affect the amount of
money spent by Mexican visitors on shopping?
2. Can Mexican visitors be grouped into family life cycle stages,
and are the stages representative of the traditional family life
cycle or the modernized family life cycle?
3. Are there some family life cycle segments that are more
attractive to destinations based on trip expenditures, including
shopping?
3. Methodology
The sample for this study was obtained by intercepting Mexican
tourists at three shopping malls in south central Texas. The Shops at
La Cantera and the North Star Mall are located in San Antonio, and
the San Marcos Outlets are located about 30e40 min northeast of
San Antonio. These three shopping malls were chosen because they
are three of the largest destination malls for tourists in the region
(e.g., The Shops at La Cantera and the San Marcos Outlets are
considered “lifestyle centers”), they represent three distinct shopping districts, and they are all affiliated with the San Antonio
Convention and Visitors Bureau. Interviewers consisted of students
410
D.C. Bojanic / Tourism Management 32 (2011) 406e414
in a tourism destination marketing class at the University of Texas
at San Antonio and employees (staff) at the San Antonio Convention
and Visitors Bureau who were fluent in Spanish.
The process was to walk around the shopping venues and listen
for people speaking Spanish. Then, the adult shoppers (18 years or
older) who were speaking Spanish were approached and asked if
they were visiting from Mexico. If so, they were invited to participate in the study in return for a $5.00 gift card to Starbucks, which
had a store at all three shopping venues. One adult representative
from each family unit was permitted to participate in the survey. In
other words, adult brothers, sisters and friends were treated as
separate units, and husbands, wives, and adult children living at
home were treated as the same family unit.
The intercepts were conducted between the middle of
November, 2008 and early January, 2009, resulting in a total sample
of 328 respondents. It was estimated by the interviewers that 1 in
every 3 or 4 people approached agreed to participate in the study,
resulting in a response rate of 25e30%. This time period was chosen
because there is a high volume of Mexican visitors due to the
holidays and the fact that it is a popular shopping season. Also,
surveys were conducted mainly on weekend (FridayeSunday)
afternoons when the frequency of Mexican visitors is the greatest.
This represented a judgment sample based on the expert knowledge of the San Antonio Convention and Visitors Bureau and the
shopping mall staff who are responsible for the Mexican market
segment. While there is a potential for bias due to temporal effects
and the lack of random sampling, it was felt that the benefits of
obtaining a larger sample of the actual target population in a more
efficient manner outweighed the costs.
The first level of analysis involved an examination of the effects
of age, marriage, and having children on the levels of shopping
expenditures. This required the use of the univariate GLM procedure (a three-factor ANOVA model) in SPSS that measured the main
effects for the three variables, as well as the interaction effects for
all of the possible combinations of the variables. The second level of
the analysis involved segmenting the Mexican visitors into family
life cycle stages using the three variables in a two-step cluster
analysis procedure in SPSS. The two-step cluster technique was
used because the number of clusters was not known (or assumed)
a priori and categorical variables were involved. The two-step
clustering algorithm is based on a distance measure that gives the
best results if mixed-type variables (categorical and continuous)
and/or large datasets are used. The process begins with the
formation of pre-clusters that are then used instead of the raw data
in the standard hierarchical clustering algorithm. The algorithm is
thought to behave reasonably well even when the assumptions (i.e.,
all variables are independent, continuous variables have a normal
distribution, and categorical variables have a multinomial distribution)are not met.
The resulting clusters were examined based on the input variables to determine if they were consistent with distinct stages of
the ‘modernized’ family life cycle. A discriminant analysis using the
cluster membership as the dependent variable and the same three
independent variables as in the cluster analysis (age, marital status,
presence of children) was performed to validate the clusters. Then,
after the clusters were validated and labeled, the FLC segments
were compared to determine if there were any significant differences between the groups for income, trip expenditures, and trip
frequency. All of the data for expenditures and income were
reported in U.S. dollars. For example, the respondents were asked
“how much do you plan on spending on each of the following travel
components on this trip in U.S. dollars,” followed by a list of
components (i.e., lodging, restaurants, shopping, and transportation). These analyses were performed using the chi-square
statistic with crosstabulations for categorical variables and ANOVA
for the metric variables. The results of all of these analyses appear
in the next section, along with an interpretation of the findings.
4. Results
The profile of the overall sample was more males (59.5%), with
household incomes less than $200,000 (74.7%), aged 26e55
(70.3%), married (69.8%), and traveling with children (56.7%). The
average respondent had 3.90 adults and 1.38 children in his party,
and 87.8% had been to San Antonio at least once before. Most of the
respondents lived in either Monterrey and the border towns
(36.0%) or the Mexico City area (30.8%). As for the visitation, the
majority of the sample visited San Antonio less 2e3 times per year
(59.7%), with the most popular seasons being winter (60.1%) and
summer (48.5%). This is expected given that the winter season is
the most popular for shopping, and the summer season is when
families take the most vacation time. The vast majority of the
respondents listed ‘friends/relatives’ as the way they first became
interested in San Antonio and most of them made their own travel
arrangements (69.8%) rather than use a travel agent or rely on
a friend or relative. Finally, the preferred mode of travel was by
private vehicle (82.6%) and the respondents normally chose to stay
in hotels (73.2%), and the main purpose given by the respondents
for traveling to San Antonio was to shop (66.5%).
4.1. Impact of age and life experiences on shopping expenditures
The first step in the analysis is to determine the effects of age and
life experiences on visitors’ shopping expenditures. In other words,
do the expenditures vary between visitors from different age
cohorts, or by the categories for life experiences (i.e., marriage and
having children)? Also, is there any interaction effect between the
three variables on the amount of money spent on shopping? Table 1
contains the means for shopping expenditures per person per day
by all of the possible combinations of age, marriage, and having
children. A cursory examination shows that shopping expenditures
decrease with age, they are higher for people who don’t have
children, and they are virtually the same based on marital status.
A general linear model (GLM) was constructed to analyze the
statistical significance of these relationships using a three-factor
ANOVA design (see Table 2). The only significant relationship was
between having children and the level of shopping expenditures
(F ¼ 6.208, p ¼ .013). The visitors without children spent an average
of $168.65 per person per day on shopping, while those with
children only spent an average of $95.68. The main effects for age
and marital status were not significant in regard to shopping
expenditures. Also, there were no significant interaction effects
between the three variables and shopping expenditures. Therefore,
the answer to the first research question is that having children
does affect shopping expenditures, but age and marriage do not,
and there are no significant interaction effects between age,
marriage, and having children.
4.2. Family life cycle segmentation
For the next stage in the analysis, a two-step cluster algorithm
was used to classify the subjects based on age, marital status, and
the presence of children. These are the three typical variables used
in the family life cycle (FLC) segmentation scheme. The analysis
produced eight segments with distinct differences on the three
variables. Table 3 contains the results of the analysis, along with the
labels arrived at by the researcher based on the results. The eight
categories are: Bachelor I, Bachelor II, Full Nest I, Full Nest II, Full
Nest III, Empty Nest I, Empty Nest II, and Single Parent. The main
deviations from the traditional family life cycle were the addition of
D.C. Bojanic / Tourism Management 32 (2011) 406e414
411
Table 1
Shopping expenditures by age and family life experiences.
Characteristics
N
Total expenditures
Shopping expenditures
Mean
Std. dev.
Mean
Std. dev.
Mean
Std. Dev.
16
31
47
51
25
76
67
56
123
289.18
194.70
226.87
251.15
95.23
199.86
260.23
150.29
210.18
253.52
299.59
285.56
385.35
91.76
327.30
356.89
234.78
311.09
165.48
150.76
155.77
164.67
58.83
129.85
164.86
109.72
139.76
183.73
301.96
265.56
315.19
65.65
264.79
287.98
231.82
264.30
56.39
58.06
57.48
55.69
57.02
56.14
55.86
57.59
56.65
23.39
27.72
26.04
24.84
19.54
23.08
24.32
24.14
24.15
No
Yes
Total
No
Yes
Total
No
Yes
Total
12
66
78
7
3
10
19
69
88
252.79
163.18
176.97
397.50
77.59
301.53
306.10
159.46
191.12
199.60
141.02
153.42
186.99
64.66
219.36
202.84
139.43
165.50
166.50
113.47
121.63
269.05
39.86
200.29
204.28
110.27
130.57
176.79
122.14
132.02
163.42
37.69
174.29
174.89
120.54
138.56
59.42
63.24
62.64
66.78
51.32
62.14
62.13
62.71
62.59
25.83
17.69
19.03
18.46
14.67
18.19
23.12
17.65
18.83
No
Yes
Total
No
Yes
Total
No
Yes
Total
48
54
102
5
4
9
53
58
111
247.27
130.51
185.46
79.58
111.34
93.70
231.45
129.19
178.02
212.00
121.75
179.23
35.37
111.93
74.86
207.77
120.27
174.74
172.71
68.06
117.31
45.00
35.14
40.07
160.66
65.27
110.41
174.05
67.78
138.79
37.08
51.39
42.57
170.02
66.83
134.77
64.53
51.30
57.44
57.10
31.57
45.75
63.79
49.89
56.45
19.37
26.39
24.20
33.10
22.91
30.43
20.77
26.47
24.84
No
Yes
Total
No
Yes
Total
No
Yes
Total
76
151
227
63
32
95
139
183
322
256.97
157.97
191.11
253.79
95.59
200.50
255.53
147.06
193.88
217.13
179.91
198.30
357.77
89.80
304.53
288.33
169.16
234.22
170.21
104.89
126.76
166.77
53.51
127.84
168.65
95.68
127.08
174.16
165.16
170.66
291.84
61.18
244.48
233.99
152.99
195.13
61.91
57.96
59.27
57.06
53.31
55.78
59.68
57.13
58.22
21.38
23.69
22.98
24.76
20.80
23.45
23.04
23.21
23.14
Age
Marital status
Kids
18e35
Married
No
Yes
Total
No
Yes
Total
No
Yes
Total
Single
Total
36e45
Married
Single
Total
46þ
Married
Single
Total
Total
Married
Single
Total
the Single Parent and the existence of two Bachelor categories and
two Empty Nest categories. Also, the Newly Married and Solitary
Survivor categories were eliminated because there were a very
small percentage of respondents who fit the profiles for these two
categories. Upon further investigation, it was clear that the Empty
Nest I segment represented a combination of Newly Married
couples (aged 18e35) and Middle-Aged couples who never had
children (36e45). Similarly, the Solitary Survivors were absorbed
into the Bachelor II segment.
Most of the categories are self-explanatory based on the
percentages of respondents in each of the categories for the classification variables. However, it is worthy to note that the respondents
in the Bachelor II category were older than the Bachelor I
Table 2
Impact of age and family life experiences on shopping expenditures.
Source
Type III sum of
squares
df
Mean square
F
Sig.
Model
Age
Kids
Marital
Age kids
Age marital
Kids marital
Age kids marital
Error
Total
6,004,423.671
96,971.211
229,012.042
43,120.941
38,739.708
35,941.401
25,464.190
84,034.386
11,472,221.677
17,476,645.348
12
2
1
1
2
2
1
2
311
323
500,368.639
48,485.606
229,012.042
43,120.941
19,369.854
17,970.701
25,464.190
42,017.193
36,888.173
13.564
1.314
6.208
1.169
.525
.487
.690
1.139
.000
.270
.013
.280
.592
.615
.407
.321
Note: shopping expenditures are expressed in U.S. dollars per person per day.
Shopping % of total
respondents, and some of the Bachelor II respondents were traveling
with children. They could have had their own children, but not be
married, or they could have been traveling with friends or relatives
who had children. The main distinction between the Empty Nest I
and Empty Nest II categories was that age of the Empty Nest I
members was younger. The three Full Nest categories were all
married and had children, but each FLC segment fell completely in
one of the three age categories (progressing from the youngest to the
oldest), and all of the Single Parents were under the age of 36.
The next step involved running a discriminant analysis in order
to validate the results of the cluster analysis. The same variables
(age, marital status, and presence of children) used to classify
respondents in the cluster analysis were used as independent
variables in a discriminant analysis, and the cluster membership
was used as the grouping variable. This resulted in 84.7% of the
cases being correctly classified, which provides strong evidence for
the validity of the clusters. This was also supported by the face
validity of the results in accordance with the FLC theoretical
foundations. Therefore, the answer to the second research question
is that the Mexican visitors can be grouped into family life cycle
stages, and the results of the cluster analysis (and discriminant
analysis) support a modernized version.
4.3. Shopping expenditures by family life cycle stage
Once the FLC clusters were established, an analysis was performed to see if there were any significant differences in the
amount of trip expenditures overall, as well as the amount spent on
412
D.C. Bojanic / Tourism Management 32 (2011) 406e414
Table 3
Crosstabulations for demographics by family life cycle stage.
Bach I
Full I
Full II
Full III
0.00%
50.00%
50.00%
100.00%
0.00%
0.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
0.00%
100.00%
0.00%
100.00%
40.00%
60.00%
100.00%
0.00%
Age
18e35
36e45
46þ
100.00%
0.00%
0.00%
Marital status
Single
Married
Traveling w/kids
Yes
No
Bach II
Empty I
Empty II
Single parents
0.00%
0.00%
100.00%
57.10%
42.90%
0.00%
0.00%
0.00%
100.00%
100.00%
0.00%
0.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
100.00%
100.00%
0.00%
100.00%
0.00%
100.00%
0.00%
0.00%
100.00%
0.00%
100.00%
100.00%
0.00%
Note: all relationships are significant at the .001 level.
Table 4
Trip expenditures by family life cycle stage.
Bach I
Bach II
Full I
Full II
Full III
Empty I
Empty II
Single parents
Total expenses*
Lodging
Restaurants*
Shopping*
Transportation**
251.15b,c
32.41
23.49a,b
164.67b
30.58a,b
203.08a,b,c
18.46
28.83b,c
120.18a,b
29.59a,b
194.71a,b,c
20.73
14.97a
150.77a,b
8.24a
163.18a,b,c
20.19
18.25a,b
113.47a,b
11.27a
130.51a,b
21.36
17.52a,b
68.07a,b
23.58a,b
273.59c
33.01
36.87c
165.92b
37.79b
247.27b,c
28.02
29.34b,c
172.71b
17.21a,b
95.23a
15.11
13.98a
58.83a
7.32a
Shopping/total*
55.69a,b
54.38a,b
58.06a,b
63.24a,b
51.31a
57.69a,b
64.53b
57.02a,b
Note: all expenditures are expressed as per person per day and length of trip is in days.
*Significant at the .05 level.
**Significant at the .10 level.
Note: figures with same letter are not significantly different.
the various components of tourism spending: accommodations,
restaurants, shopping, and transportation. The expenditures per
person per day were used to eliminate any potential bias as a result
of the length of trip or the number of people in the party. Table 4
contains the results of the comparison by FLC cluster for the
various expenditure categories using a series of ANOVAs. There
were no significant differences for the amounts spent on lodging
(F ¼ 1.639, p ¼ .124) and transportation (F ¼ 1.834, p ¼ .080), but all
of the other expenditure categories, including total expenditures,
had differences that were significant at the .05 level.
Single Parents spent the least overall ($95.23), with the Empty
Nest I ($273.59), Empty Nest II ($247.27), and Bachelor I ($251.15)
respondents spending significantly more. The other FLC segments
overlapped and were not significantly different, except for Full Nest
III ($130.51) and Empty Nest I. Empty Nest I demonstrated the
highest level of expenditures in every other category except for
shopping, where the Empty Nest II category spent the most per
person per day. Conversely, Single Parents were the lowest in
average expenditures per person per day for every category.
Therefore, the answer to the third research questions is that some
FLC segments are more attractive than others given that there was
a significant difference in expenditures for all categories except
lodging.
5. Conclusions
The main purpose of the study was to determine the effects of
age and family life experiences on shopping expenditures for
Mexican tourists visiting south central Texas, including the ability
to segment the market into family life cycle stages. Based on the
respondents obtained through the mall intercepts, it appears that
age and marriage are not significant factors in regard to the level of
shopping expenditures. However, having children does affect the
amount of money spent on shopping per person per day for the
Mexican visitors. In other words, couples and single parents with
children spent significantly less on shopping than their counterparts without children, especially those in the younger age cohorts.
This would suggest that the family life experience “getting married”
does not necessarily change a person’s spending habits on discretionary items, but once couples have children, they do tend to
approach the spending decision in a different manner.
A cluster analysis was performed using the three variables that
produce the family life cycle stages (age, marital status, and presence of children) without any a priori classification of the respondents. The analysis resulted in eight stages that clearly delineated
the main stages of the traditional FLC plus an additional Bachelor
stage, an additional Empty Nest stage (containing Newly Married as
well), and a Single Parent stage consisting mainly of young Mexicans (age 18e35). The results were also validated using a discriminant analysis to see if the same classification variables could be
used to correctly classify the respondents into the previously
assigned clusters. Approximately 85% of the sample was correctly
classified, thereby lending credibility to the original groupings. This
was an important finding because many studies merely pre-assign
the respondents to FLC groups without using an unbiased statistical
procedure, leaving an assumption that the sample would have been
grouped into a similar number of stages.
However, before the destination marketing organization (DMO)
chooses its target segments, it is necessary to examine the trip
expenditures. Overall, the Empty Nesters and the Bachelors spent
the most per person per day on travel-related items (e.g., lodging,
restaurants, and transportation). These would be good target
markets for the destination as a whole because of the revenue they
generate for the local economy. Another area of analysis needs to be
performed by each of the components of the tourism product. First,
the lodging industry would still want to focus on the Empty Nest
stages, but only the Bachelor I stage based on the results of this
study. The respondents in the Bachelor II stage did not spend nearly
as much per person per day on lodging as the Bachelor I respondents. Second, the most valuable target markets for the restaurants
D.C. Bojanic / Tourism Management 32 (2011) 406e414
would be the Empty Nesters, especially the Empty Nest I segment,
and the Bachelor II segment. Finally, the transportation sector (i.e.,
airlines) should focus its efforts more on Empty Nest I and the
Bachelor stages. Apparently, these segments tended to be more
likely to fly and possibly rent cars rather than bring large parties in
SUVs and vans.
This leads to the main expenditure area of the study e shopping.
The segments that spent the most per person per day on shopping
were the two Empty Nest stages and the Bachelor 1 stage, with the
Full Nest I stage slightly behind. The Bachelor segment should be
targeted by stores like American Eagle, Hollister, Banana Republic, J.
Crew, Polo, and Abercrombie and Fitch. As for the Empty Nesters,
they are older and more likely to shop at high-end stores like
Nordstroms, Neiman-Marcus, Coach, and Tiffany’s. Special attention should be given to the Mexican holidays and stores should
have Spanish-speaking personnel. Also, items should be stocked
that sell well to Mexican Nationals based on historical data and
employee observations. One other figure that is interesting is the
amount spent on shopping as a percentage of the total trip
expenditure on travel-related products and shopping goods (see
Table 4). All of the segments spent over 50% of their budgets on
shopping, which is consistent with the findings by Heung and
Cheng (2000) in Hong Kong.
5.1. Limitations
There were some important findings in this study, but there
were some limitations as well. First, the sample was only taken
during the holiday season so there is a potential for some level of
temporal bias. The travel and spending patterns for the respondents in this sample might not be representative of the overall
population of Mexican Nationals who visit south central Texas.
Although, approximately 49% of this sample indicated that they did
travel to San Antonio during the summer, and 20e30% mentioned
the fall and spring as well. Second, the sample is relatively small
(n ¼ 328), and that could also affect the degree to which the sample
is representative of the population being studied. Another artifact
of the small sample is that the number of respondents in each of the
FLC stages was relatively small, thereby decreasing the power of the
statistical tests. Finally, only two types of life experiences were
included in the study, so it does not provide insight into the overall
impact of life experiences on the Mexican visitors’ travel and
shopping behaviors.
5.2. Future research
This study provided a first look at the Mexican tourist market
for San Antonio and south central Texas. It is evident from the
results that the Mexican Nationals provide good potential for
tourism to the region, and they spend as much, or more than the
typical visitor (most of whom are from the state of Texas) on travel
and shopping. The next step is to get a better understanding of the
actual size of the market so that the economic impact of this target
market can be accurately measured. This would involve working
with the U.S. Customs Department and obtaining figures from the
border towns (e.g., Loredo) where most of them enter the state. In
addition, these figures could be supplemented with data from
deplanement statistics, which wouldn’t be difficult because of the
small number of flights between San Antonio and Mexico City or
Monterrey.
At the same time, the DMO could continue to intercept Mexican
Nationals at the three shopping locations throughout the entire
year in order to increase the sample size and remove the temporal
bias. This will result in more accurate percentages for the people in
each of the FLC stages and their expenditures that can be used once
413
there is an estimate for the total number of visitors per year. Also,
the life course approach could be used instead of the FLC approach
by adding more questions concerning the respondent’s life experiences (e.g., health issues, moving, financial issues, retirement, and
other job-related issues) in addition to marriage and having children. This will result in a more detailed analysis of the decisionmaking process, and it could be coupled with more investigation
into the motivations of the visitors and their perceptions of the
destination in terms of image. Finally, it would be interesting to see
if the life course stages and shopping behaviors are consistent
across cultures by conducting similar research in other countries.
References
Benmoyal-Bouzaglo, S., & Moschis, G. (2009). The effects of family structure and
socialization influences on compulsive consumption: a life course study in
France. International Journal of Consumer Studies, 33, 49e57.
Bojanic, D. (1992). A look at a modernized family life cycle and overseas travel.
Journal of Travel & Tourism Marketing, 1(1), 61e77.
Cai, L., Lehto, X., & O’Leary, J. (2001). Profiling the US-bound Chinese travelers by
purpose of trip. Journal of Hospitality and Leisure Marketing, 7(4), 3e17.
Canas, J., Coronado, R., & Phillips, K. (2006). Border benefits from Mexican shoppers.
Southwest Economy (Federal Reserve Bank of Dallas), 3(May/June), 11e13.
CIA World Factbook. https://www.cia.gov/library/publications/the-world-factbook/
geos/mx.html.
Collins, D., & Tisdell, C. (2002). Gender and differences in travel life cycles. Journal of
Travel Research, 41(2), 133e143.
Cosenza, R., & Davis, D. (1981). Family decision making over the family life cycle:
a decision and influence structure analysis. Journal of Travel Research, 20(Fall),
17e23.
Crask, M. (1981). Segmenting the vacationer market. Identifying the vacation
preferences, demographics, and magazine readership of each group. Journal of
Travel Research, 20(Fall), 29e34.
Derrick, F., & Lehfeld, A. (1980). The family life cycle: an alternative approach.
Journal of Consumer Research (September), 214e217.
Elder, G., Johnson, M., & Crosnoe, R. (2003). The emergence and development of
life course. In J. Mortimer, & M. Shanahan (Eds.), Handbook of the life course
(pp. 3e19). New York: Plenum.
Fairhurst, A., Costello, C., & Holmes, A. (2007). An examination of shopping behavior
of visitors to Tennessee according to tourist typologies. Journal of Vacation
Marketing, 13(4), 311e320.
Fish, M., & Waggle, D. (1996). Current income versus total expenditure measures in
regression models of vacation and pleasure travel. Journal of Travel Research, 35
(2), 70e74.
Fodness, D. (1992). The impact of family life cycle on the vacation decision-making
process. Journal of Travel Research, 31(2), 8e13.
Fodness, D., & Murray, B. (1999). A model of tourist information search behavior.
Journal of Travel Research, 37(3), 220e230.
Heung, V., & Cheng, E. (2000). Assessing tourists’ satisfaction with shopping in the
Hong Kong special administrative region of China. Journal of Travel Research, 38
(May), 396e404.
Hisrich, R., & Peters, N. (1974). Selecting the superior segmentation correlate.
Journal of Marketing, 38, 60e63.
Hong, G., Fan, J., Palmer, L., & Bhargava, V. (2005). Leisure travel expenditure
patterns by family life cycle stages. Journal of Travel & Tourism Marketing, 18(2),
15e30.
Josiam, B., Kinley, T., & Kim, Y. (2005). Involvement and the tourist shopper: using
the involvement construct to segment the American tourist shopper at the mall.
Journal of Vacation Marketing, 11(2), 135e154.
Lawson, R. (1991). Patterns of tourist expenditure and types of vacation across the
family life cycle. Journal of Travel Research, 29(4), 12e18.
Lee, S., Chang, S., Hou, J., & Lin, C. (2008). Night market experience and image of
temporary residents and foreign visitors. International Journal of Culture,
Tourism and Hospitality Research, 2(3), 217e233.
LeHew, M., & Wesley, S. (2007). Tourist shoppers’ satisfaction with regional shopping mall experiences. International Journal of Culture, Tourism and Hospitality
Research, 1(1), 82e96.
Lehto, X., Cai, L., O’Leary, J., & Huan, T. (2004). Tourist shopping preferences and
expenditure behaviours: the case of the Taiwanese outbound market. Journal of
Vacation Marketing, 10(4), 320e332.
Mathur, A., Lee, E., & Moschis, G. (2006). Life-changing events and marketing
opportunities. Journal of Targeting, Measurement and Analysis for Marketing, 14
(2), 115e128.
Meiden, A. (1984). The marketing of tourism. The Service Industries Journal, 4(3),
166e186.
Moscardo, G. (2004). Shopping as a destination attraction: an empirical examination of the role of shopping in tourists’ destination choice and experience.
Journal of Vacation Marketing, 10(4), 294e307.
Moschis, G. (2007). Life course perspectives on consumer behavior. Journal of the
Academy of Marketing Science, 35, 295e307.
414
D.C. Bojanic / Tourism Management 32 (2011) 406e414
Murphy, P., & Staples, W. (1979). A modernized family life cycle. Journal of Consumer
Research (June), 12e22.
Nicolau, J., & Más, F. (2005). Heckit modelling of tourist expenditure: evidence from
Spain. International Journal of Service Industry Management, 16(3/4), 271e293.
Noble, S., & Schewe, C. (2003). Cohort segmentation: an exploration of its validity.
Journal of Business Research, 56, 979e987.
Oh, J., Cheng, C., Lehto, X., & O’Leary, J. (2004). Predictors of tourists’ shopping
behaviour: examination of socio-demographic characteristics and trip typologies. Journal of Vacation Marketing, 10(4), 308e319.
Pavlakovich-Kochi, V., & Charney, A. (2008). Mexican visitors to Arizona: visitor
characteristics and economic impacts, 2007e08. Research report prepared for
the Arizona Office of Tourism.
Pennington-Gray, L., Fridgen, J., & Stynes, D. (2003). Cohort segmentation: an
application to tourism. Leisure Sciences, 25, 341e361.
Pennington-Gray, L., & Kerstetter, D. (2002). Testing a constraints model within the
context of nature-based tourism. Journal of Travel Research, 40(4), 416e423.
Peterson, M. (2007). Effects of income, assets and age on the vacationing behavior
of US consumers. Journal of Vacation Marketing, 13(1), 29e43.
Reisenwitz, T., & Iyer, R. (2007). A comparison of younger and older baby boomers:
investigating the viability of cohort segmentation. Journal of Consumer
Marketing, 24(4), 202e213.
Rindfleisch, A., Burroughs, J., & Denton, F. (1997). Family structure, materialism and
compulsive consumption. Journal of Consumer Research, 23, 312e325.
Rittichainuwat, B., Qu, H., & Mongkhonvanit, C. (2008). Understanding the motivation of travelers on repeat visits to Thailand. Journal of Vacation Marketing, 14
(1), 5e21.
Schul, P., & Crompton, J. (1983). Search behavior of international vacationers: travelspecific lifestyle and sociodemographic variables. Journal of Travel Research, 22
(2), 25e30.
Taylor, G. (1986). Multi-dimensional segmentation of the Canadian pleasure travel
market. Tourism Management, 7(3), 146e153.
Tosun, C., Temizkan, S., Timothy, D., & Fyall, A. (2007). Tourist shopping experiences
and satisfaction. International Journal of Tourism Research, 9, 87e102.
Upchurch, R., Rompf, P., & Severt, D. (2006). Segmentation and satisfaction preferences of specific looking glass cohorts profiles: a case study of the timeshare
industry. Journal of Retail & Leisure Property, 5(3), 173e184.
Wells, W., & Gubar, G. (1966). Life cycle concept in marketing research. Journal of
Marketing Research, 3(November), 355e363.
Yeung, S., Wong, J., & Ko, E. (2004). Preferred shopping destination: Hong Kong versus
Singapore. International Journal of Tourism Research, 6(March/April), 85e96.
Yu, H., & Littrell, M. (2005). Tourists’ shopping orientations for handcrafts: what are
key influences? Journal of Travel & Tourism Marketing, 18(4), 1e19.