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.