Fashion and Shopping Orientation of Rural vs. Urban UW-Stout Students. Danielle S. Harnett

1 Fashion and Shopping Orientation of Rural vs. Urban UW-Stout Students by Danielle S. Harnett A Research Paper Submitted in Partial Fulfillment ofthe Requirements for the Master of Science Degree III Applied Psychology A ~ved: 6 Semester qre~\ \ VU'0vC>L-~ Committee Members: It-C) ~/(;~~. -- Dr. Kristina Gorbatenko-Roth ~5 =~ " Dr. Kim Hahn The Graduate School University of Wisconsin-Stout May, 2011

The Graduate School University of Wisconsin-Stout Menomonie, WI Author: Harnett, Danielle S. Title: Fashion and Shopping Orientation of Rural vs. Urban UW-Stout Students Graduate Degree/Major: MS Applied Psychology Research Advisor: Sarah Wood, Ph.D. Month/Year: May, 2011 Number of Pages: 46 Style Manual Used: American Psychological Association, 6 th edition Abstract Undergraduate students (N = 161) from the University of Wisconsin-Stout were surveyed to investigate whether differences exist in the fashion and shopping orientation of students from rural vs. urban backgrounds. The study also examined how self-monitoring affected the relationship between participants rural or urban background and their fashion orientation. The effect that participants rural or urban background had on fashion innovativeness was also examined. A survey measured the population size of where UW-Stout students grew up, selfmonitoring, fashion and shopping orientation, and fashion innovativeness of UW-Stout students. Results indicated that one s rural or urban background was not a predictor of fashion orientation. In addition, self-monitoring predicted fashion orientation, but did not moderate the relationship between participants rural or urban background and their fashion orientation. Results also revealed that rural or urban background was not a predictor of fashion innovativeness. Findings are discussed and implications for marketers and retailers are considered. 2

The Graduate School University of Wisconsin-Stout Menomonie, WI Acknowledgements I would like to thank my advisor, Dr. Sarah Wood, for all of her assistance throughout this project. Her dedication, time commitment, and knowledge greatly contributed to the success of this paper. I would also like to thank my committee members, Dr. Kristina Gorbatenko-Roth, and Dr. Kim Hahn, for their valuable input and advice. Finally, I would like to thank my parents for being so supportive of this thesis and of my education. 3

Table of Contents Abstract.. 2 Chapter I: Introduction.. 7 Chapter II: Literature Review: Fashion and Shopping Orientation 8 Rural vs. Urban Background..10 Self-monitoring.. 13 Fashion Innovativeness.. 16 Summary 17 Research Questions 18 Hypotheses 1a and 1b 19 Hypothesis 2.. 19 Hypothesis 3 20 Chapter III: Methodology.. 20 Participants. 20 Materials 21 Procedure 22 Chapter IV: Results 23 Data Preparation.23 Analysis. 27 Chapter V: Discussion.. 32 References 37 Appendix A: Demographics.. 41 4

Appendix B: Fashion Innovativeness Test 43 Appendix C: Additional Items... 45 5

List of Tables Table 1: What Participants Value when Shopping for Clothing...27 Table 2: Means, Standard Deviations and Skew of Main Variables. 29 Table 3: Fashion Orientation and Self-Monitoring Regression... 30 List of Figures Figure 1: A Model of Path Analysis.. 20 Figure 2: Population Range for Participants ages 9-13...... 26 Figure 3: Current Population Range for Participants ages 18-23... 26 6

Introduction Fashion affects how we view ourselves and how others view us in various ways; such as through first impressions, perceptions of our personality and attractiveness, and through decisions in the professional world. Fashion goods or clothing has been referred to as our second skin and affects how other people see us and react to us as well as how we feel about ourselves (Behling, 1999, p.55). Research has shown that within the first five seconds of meeting someone, they will make up their mind about you (Hampson, 2001). In those first moments, someone will decide whether they feel similar to you and whether they want to forge a connection with you. Within this time, people will also interpret and process the visual clues that are presented. Hair, makeup, and clothes are instantly processed and analyzed. Hampson (2001) believes that in the workplace, people should view their clothing as their packaging. An individual s clothing tells a story about their character, their purpose, and gives clues about their confidence (Hampson, 2001). When a person appears uncomfortable in their clothing, this instantly sends out a message about them. The purpose of this study was to determine whether differences exist among the fashion and shopping orientations of people from rural vs. urban backgrounds. The study was also intended to discover whether self-monitoring moderates the relationship between rural vs. urban background and fashion orientation, as well as investigate whether one s background influences their fashion innovativeness. There is a lack of current research on these topics, and no studies have been done to date that have incorporated all of these variables. Furthermore, the implications of this research may benefit retailers and marketers. By gaining an understanding of the fashion and shopping orientations of rural and urban UW-Stout undergraduate students, retailers will be able to take the first step in gathering useful information to target specific market 7

segments, leading to more effective retail fashion marketing. Additionally, college-age consumers were ideal participants in the current study because young singles are the most active fashion influentials (Gordon et al.,1986, p. 4). Young women are almost always the featured models in fashion magazines, fashion shows, catalogs, and other forms of advertising, thus illustrating western society s beauty ideals and targeted audience. Therefore, using a sample of college students provided an ideal segment for studying fashion and shopping orientations. Fashion and Shopping Orientation Fashion orientation was defined in the current study as perceptions of fashion and apparel shopping. The specific fashion orientation factors studied were fashion leadership, fashion interest, importance of being well-dressed and anti-fashion attitude (Gutman & Mills, 1982). Fashion leadership refers to the importance of being seen as a fashion leader, being aware of fashion trends, confidence in the ability to recognize fashion trends, and viewing clothing as an important way of expressing individuality. Fashion leaders are important to marketers and retailers because they not only purchase products, but are influential because others look to them for fashion ideas and advice. Fashion interest refers to needing a wide variety of clothes, paying attention to fashion magazines, and spending a lot of time and effort on clothing and fashion. People that are interested in fashion enjoy spending time on it and staying current with the newest trends. Importance of being well-dressed includes dressing the part to get ahead, wearing clothes to show success in life, and dressing as a reflection of self-image (Summers, Belleau & Wozniak (1992). An example of the importance of being well-dressed is dressing appropriately for a job interview. Anti-fashion attitude means buying clothing without caring about what is fashionable, viewing fashion as just a way to get more money from the consumer, and resentment towards being told what to wear by fashion experts. People with anti-fashion attitudes 8

do not follow the latest trends, or feel the need to wear name-brand clothing or dress like other people to fit in. They are often apathetic and are not interested in fashion or the shopping process. According to Gehrt & Shim (1998), the premise of shopping orientations is that consumers with differing orientations have different consumer characteristics and market behaviors. These include differences in store preferences and attributes, clothing needs, and preferences for information sources. For example, consumers may shop at certain stores because they like the atmosphere or know the store personnel. The shopping orientation factors that were examined for the present study were; shopping enjoyment, cost consciousness, traditionalism, practicality, planning, and following (Gutman & Mills, 1982). Shopping enjoyment includes attitudes about shopping, frequency of shopping, and enjoying seeing what is new in stores. People shop for a variety of reasons, among them are enjoyment, socializing and self-indulgence. Cost consciousness is buying less clothing because of rising prices, buying clothing only out of necessity, and thinking that spending a lot of money on clothing is frivolous. Cost conscious people are more likely to seek out sales and discounts, and will likely avoid high-fashion clothing because of the price. Cost conscious shoppers may favor shopping at discount or secondhand stores. Traditionalism refers to the preference of wearing and buying traditional clothing that does not stand out from anyone else. Practicality is the preference for wearing sensible clothing and avoiding high-fashion clothing because it goes out of style very quickly. Planning refers to planning shopping trips and one s wardrobe very carefully, as well as shopping for coordinated outfits. Following is the preference for only purchasing and wearing clothing that is well-accepted or worn by admired people. Fashion and shopping followers most likely seek ideas and advice from fashion leaders and innovators. 9

Not a lot of research has been conducted on these specific fashion and shopping orientation factors because they were developed by Gutman & Mills (1982) in order to better understand consumers fashion life style. Gutman and Mills (1982) defined fashion life style as the attitudes, interests, opinions, and behaviors of consumers as they related to fashion and shopping. Summers, Belleau & Wozniak s (1992) study used the items developed by Gutman & Mills and reduced them to five fashion and shopping factors using factor analysis. Other studies have developed their own fashion and shopping orientation measures. For example, Hong & Littrell (2005) developed a specific shopping orientation measure regarding tourists intentions to buy souvenirs. Lumpkin, Hawes, & Darden (1986) studied rural consumers by developing a taxonomy of shopping orientation groups. Gehrt & Shim s (1998) research segmented French shoppers based on their catalog shopping orientations. The studies just described developed different ways to measure fashion and shopping orientations. The consensus of these studies is that marketers and retailers should develop strategies based on the fashion and shopping orientations of consumers (Gehrt & Shim, 1998). The study described in this paper used the items created by Gutman & Mills to gain a greater understanding of college consumers fashion and shopping orientations. Rural vs. Urban Background The present study categorized urban and rural backgrounds using the definitions established by the U.S. Census Bureau. According to the U.S. Census Bureau (1990), an urbanized area (UA) is a continuously developed area with a population of 50,000 or more. Outside of UA s, an urban place is any incorporated place or census designated place with at least 2,500 residents. A rural place has fewer than 2,500 inhabitants. For the purposes of the current study, only UA s were considered urban backgrounds, and all areas with populations 10

below 50,000 were considered rural backgrounds. This was decided because people from areas with populations between 2,500 and 50,000 may not be from incorporated or densely populated areas, thus classifying them as not urban. Lumpkin, Hawes, & Darden s (1986) research categorized populations of 30,000 to 50,000 as being medium-sized communities which have regional shopping malls, but do not have extensive retail options. Lumpkin et al. categorized metropolitan urban areas as having populations of over 50,000, and offering extensive retailing facilities. Furthermore, participants in Summers, Belleau & Wozniak s (1992) study were selected from either rural locations that had populations under 2,500, or urban locations that had populations over 50,000. Differences between rural and urban individuals has not been frequently studied in the context of clothing preferences and personality. While demographic characteristics have been shown to have an influence on fashion attitudes and store patronage, the influence of an increasingly important demographic characteristic- consumers location of residence- has not been fully explored (Summers, Belleau & Wozniak, 1992, p. 84). Understanding the differences between rural and urban consumers is becoming increasingly important to retailers as nationwide demographics continue to rapidly change. According to Summers et al. (1992), population projections have shown that the fastest growing areas in the future will be rural counties and their surrounding areas. Lumpkin et al. (1986) also emphasized the importance of studying rural consumers because research has shown that they represent a significant force in some retail markets, but have been ignored by researchers in the past. Thus, understanding changing consumer demographics will be even more important for rural retailers in geographical areas experiencing population growth and needing new marketing strategies. Hennon and Brubaker (1988) conducted research on rural families and emphasized the 11

need for further research on rural individuals, particularly comparative studies. Hennon and Brubaker (1988) stressed that assumptions should not be made about people from rural backgrounds without conducting a corresponding investigation of people from urban backgrounds, and vice versa. The present study accomplished this objective by comparatively studying rural and urban undergraduate students. Gordon, Infante, and Braun (1986) considered rural vs. urban background in their study, hypothesizing that fashion innovators would differ from non-innovators by having more dramatic communicator styles, being lower in conformity, and being from urban as compared to rural areas. The results confirmed that consumers who scored high in fashion innovativeness were more likely to be from urban rather than rural areas (Gordon et al. 1986). Similarly, Summers et al. (1992) explored whether rural and urban consumers differed on their perceptions of fashion and shopping. However, contrary to expectations no differences were found between rural and urban consumers in their perceptions of fashion and apparel shopping in their study. The results from these two studies seemed to contradict one another suggesting the need for further research. The need for research on whether rural and urban consumers differ on their fashion and shopping orientations is twofold. Prior results have been conflicting, and previous research is outdated. Gordon et al. (1986) found that fashion innovators were more likely to be from urban areas, while Summers et al. (1992) found that urban and rural consumers did not differ on their fashion and shopping orientations. Additionally, these two studies are 19-25 years old and changes may have occurred since then that might impact the fashion and shopping orientations of people in urban and rural areas. For example, there have been significant technological changes (e.g., internet and internet shopping), as well as environmental concerns and historic and societal 12

events. In addition, the current economic recession, inflation, and rising price of gas may greatly affect consumers fashion and shopping behavior. The effect that the recession has had on retail stores has been well-documented. In 2009, it was reported that because major retail chains were closing down stores, vacancies at the 1,500 shopping malls nationwide were at their highest point in almost a decade (Times Topics, 2009). This led to the opening of more downscale chains and temporary stores where the higher-end stores used to be (Times Topics, 2009). Furthermore, a rural individual may shop less often than an urban individual because gas prices are so high, and they live further away from a shopping mall. Currently, analysts are troubled that the recent increase in gas prices is not leaving shoppers with enough money left over to spend on other goods and services (Associated Press, 2011). These changes that have occurred in the last couple decades, directly affect consumers buying behavior and might influence the results of this current study. A potential advantage of the present study is that the sample differs from Summers et al. Specifically, the Summers et al. (1992) sample was comprised of mostly older, married, and employed individuals with families. The sample in the present study was made up of mainly young, unemployed, and unmarried students. Because these samples were so different, it was expected that the current study would find differences in fashion and shopping orientations as a function of consumers rural or urban background. A 2004 study found that college students in the U.S. spend over $122 billion annually on consumer products (Manese-Lee, 2007). Therefore, college students, especially those from urban areas will likely spend more money on clothing, therefore illustrating the effect that background has on fashion and shopping orientation. Self-monitoring According to Browne and Kaldenberg (1997), among the various personality traits that 13

are often associated with consumer behavior, self-monitoring has attracted considerable attention from market researchers (DeBono, 2006; Snyder & Gangestad, 1986). Snyder (1987) indicated that self-monitoring affects consumer behavior because it is associated with portraying an image of the self to other people. Self-monitoring is the extent to which individuals regulate selfpresentation for the sake of desired public appearances (Zhang, Yu & Bi, 2010). It is the tendency to notice cues for socially appropriate behavior and modify one s own behavior to fit the situation accordingly. In other words, it is the amount to which a person desires and is able to stay in tune with other people. For example, people often use self-monitoring to regulate their behavior so they will be perceived favorably by others. Individuals greatly vary on the extent to which they can and are able to monitor their behavior. High self-monitors are very concerned with how they are perceived, and are able to monitor and alter their behavior to fit differing situations. Low self-monitors are less concerned with how they are perceived and behave more consistently across situations. High self-monitors are very concerned with their public self and role-playing in situations, while low self-monitors focus on their personal value systems, and want to act like themselves (Browne & Kaldenberg, 1997). Research has shown that high self-monitors appear to be more concerned with physical appearance and body image than low self-monitors (Sullivan & Harnish, 1990). These characteristics might be important in relation to fashion and shopping because high self-monitors might wear fashionable clothing to promote their image, while low self-monitors may ignore current trends and wear clothes they like, regardless of current fashion. Browne and Kaldenberg s (1997) research confirmed that high self-monitors were significantly more interested in fashion than low self-monitors, and were more likely to believe that it was important that others liked the brands they purchased. Results also showed that high self- 14

monitors, particularly women, were more involved with clothing than low self-monitors. Previous research has been done on product and brand choice and how self-monitoring affects individuals responses to advertising. For example, because high and low self-monitors differ on their concerns for prestige and appearance, a study showed that when high selfmonitors were asked to judge the quality of a sporty Pontiac Fiero vs. a functional Volkswagen Rabbit, they chose the Fiero, while low self-monitors chose the functional Volkswagen (Browne & Kaldenberg, 1997). Exploratory research by Becherer and Richard (1978), examined whether personality was a more meaningful predictor of brand choice when self-monitoring was used as a moderating variable. Findings showed that the low self-monitors personality traits in particular predicted brand choice better than the high self-monitors traits did (Becherer & Richard, 1978). These results showed that low self-monitors desired to stay true to their personalities and dispositions by being brand loyal, while situational factors were more likely to affect high selfmonitors brand choice. Auty & Elliot (1998) used jeans as a vehicle for studying how selfmonitoring impacts brand meanings. They hypothesized that because high-self monitors are more concerned with their image, high self-monitors would have a more positive association towards branded jeans than low self-monitors would. Results found that high self-monitors held more negative attitudes towards unbranded jeans than low self-monitors, however there were no differences found on attitudes toward branded jeans (Auty & Elliot, 1998). In other words, both groups held positive attitudes towards branded jeans, however high self-monitors viewed unbranded jeans negatively. The implications of these findings relate to the present study by suggesting that high self-monitors may be more fashion oriented and possibly wear and buy different brands because they desire to fit in and dress like others, while low-self-monitors will care less about fashion and dressing like others to fit in, and will therefore be more likely wear 15

the same brands they are comfortable with. Although some research has been done on self-monitoring and how it affects brand choice and advertising, more studies are necessary to investigate its impact on consumers fashion and shopping behavior. Becherer and Richard (1978) emphasized the importance of using self-monitoring as a moderating variable, because it may provide important insight on whether consumers behavior is more likely to be self-motivated, or motivated by others. The present study expected self-monitoring to relate to fashion and shopping orientation because high self-monitors are very concerned with their image and being accepted and liked by others, therefore clothing will likely be more important to them. It was expected that high self-monitors would place greater emphasis on their clothing and image because they are concerned with regulating and modifying their behavior to fit differing situations and to appear favorably to others. On the other hand, low self-monitors will care more about wearing clothing that they personally like, regardless of whether it is fashionable or socially accepted. Fashion innovativeness Fashion innovativeness is the willingness to adopt recent fashion trends, and the tendency to buy new products more often and more quickly than other people (Gordon, Infante, & Braun, 1986; Roehrich, 2004). Innovativeness is often measured by the time a product is bought from its introduction to adoption (Goldsmith & Hofacker, 1991). In other words, the quicker a consumer buys a new product, the more innovative they are. Diffusion of innovation is a process by which innovations are accepted and spread throughout a market (Jordaan & Simpson, 2006). Diffusion research, specifically fashion diffusion research, has been of particular interest to fashion marketers, clothing theorists, consumer psychologists, and social science researchers because fashion innovators possess certain characteristics (Goldsmith, Moore, & Beaudoin, 1999). 16

Research has shown that highly innovative people tend to be more risk-taking, influential, and more knowledgeable and involved in new products (Jordaan & Simpson, 2006). Fashion innovators are particularly important to companies because they generate revenue when they buy new fashions, and they play an essential role in spreading information about the product. Furthermore, because fashion innovators are among the first to buy new fashions, their reactions to the product could eventually make or break the success or failure of the style (Goldsmith et al. 1999). The current study expected that fashion innovativeness and urban vs. rural background would relate to one another as they did in Gordon, Infante, and Braun s (1986) research, in which fashion innovators were more likely to be from urban areas. Summary The primary focus of this study is the impact of urban vs. rural background on fashion and shopping orientation. People from urban backgrounds may be more fashion and shopping orientated than people from rural backgrounds. It is likely that individuals from urban backgrounds generally have grown up in closer physical proximity to shopping malls, mass retailing stores, and high-fashion stores than most individuals from rural backgrounds. This means that urban people have greater access to shopping malls, which leads to increased ability to shop, and perhaps frequency. Another factor is that people from urban backgrounds are targeted with more clothing related advertising from retailers than rural individuals. This is thought to be the case because nearby retailers will focus their attention and advertising on residents, and will not spend advertising dollars on individuals that live outside the area who are less likely to shop there. Also, with more exposure to advertising, individuals will probably shop more and be more knowledgeable about fashion. Furthermore, individuals who come from urban backgrounds will have grown up in an environment that is more exposed to fashion trends due to 17

larger and more diverse populations. This leads to increased awareness and exposure to the latest fashion trends and styles that are available, while people from rural backgrounds will be less likely to have this exposure. The closer proximity to stores, increased marketing efforts, and awareness and exposure to new fashion trends also explains why students from urban backgrounds will more likely be fashion innovators. Hypotheses Research question 1: Does fashion and shopping orientation differ among students from rural vs. urban backgrounds? Because participants from urban backgrounds are likely to have more convenient access to malls and retail stores, and are the recipients of more frequent advertising from these stores, it is expected that they will have higher fashion orientation scores than participants from rural backgrounds. Because participants from rural backgrounds likely live further from shopping malls and retail stores, they will have less exposure to advertising, and will not be as knowledgeable or involved in the latest fashions. To define the difference between rural and urban participants, participants were asked to provide the name of the city or town where they grew up in during the age ranges of 9-13, 13-18, and 18-23. They were asked for a population estimate for each age range, and also asked the distance from a major shopping area in miles. The major shopping area was operationalized in the current study as a shopping mall that includes clothing stores and mid-level department stores such as Boston Store, JCPenney s and Macy s. A shopping mall with discount retailers such as Wal-Mart was not included in the definition (see Appendix A).The rationale for including three age ranges was to determine whether one age group was better at predicting one s fashion and shopping orientation. For example, the younger years of 9-13 may possibly be more formative in developing one s fashion sense than the later years. This can be illustrated by young children 18

displaying their interest in clothing and fashion by playing dress-up, thus showing the influence that the earlier years may have on their fashion sense. Three age ranges were also chosen to reflect whether participants moved during their upbringing, and to determine if this may have affected their fashion and shopping orientation. The distance away from a major shopping area item was included to check the assumption that rural people live further from shopping areas. H1a: Participants rural or urban background will predict their fashion orientation, such that people from more urban backgrounds will score higher on the fashion orientation scale. H1b: Participants rural or urban background will predict their shopping orientation, such that people from more urban backgrounds will score higher on the shopping orientation scale. Research question 2: How does self-monitoring affect participants fashion orientation? Because self-monitoring has the effect of making people more sensitive to the social cues around them and concerned with how they are perceived publicly, it is possible that self-monitoring will moderate the relationship between one s rural or urban background and their fashion orientation. If so, participants high in self-monitoring may have higher fashion orientation if they are from urban backgrounds. Self-monitoring may increase the influence that one s background has on their fashion orientation. H2: Participants from urban backgrounds who are high in self-monitoring may have higher fashion orientation scores than participants from urban backgrounds who are low self-monitors. 19

The following figure illustrates the relationship between self-monitoring, population size and fashion orientation: Self-monitoring Rural vs. Urban Background Fashion Orientation Figure 1. A Model of Path Analysis Research question 3: How does one s background (rural or urban), affect fashion innovativeness? It is believed that students from urban backgrounds have greater convenience and access to shopping malls and are more exposed to frequent advertising from these stores, leading to increased knowledge and involvement with new fashions and trends. Therefore, it is expected that students from urban backgrounds will be more innovative than participants from rural backgrounds. H3: Participants rural or urban background will predict fashion innovativeness, such that people from more urban backgrounds will have higher scores on the fashion innovativeness test. Methodology Participants Participants were selected for this study with the help of the Applied Research Center on the UW-Stout Campus. A total of 2,000 undergraduate students were randomly selected for the study, which is approximately one third of the undergraduate student population. The sample excluded all distance education and graduate students. Of the 2,000 students who were contacted, 256 of them started, and 164 of them completed the survey. 20

Materials The survey included measures of demographics, self-monitoring, fashion and shopping orientation, fashion innovativeness, and additional shopping items. The survey was developed using Qualtrics, an online survey software tool. Demographics. Survey participants were asked to provide the following demographics: where they grew up and the approximate population, how many miles they lived away from a shopping mall, gender, major, age, and ethnicity (see Appendix A). Self-monitoring scale. The Self-monitoring Scale (Snyder, 1974) is a self-report instrument designed to measure individuals need for social approval, ability to express their true feelings, ability to play multiple roles and control their behavior and expressions. Snyder s original scale included 25 items, but was revised by Snyder and Gangestad (1986) into an 18 item measure which was used for this survey. Fashion and shopping orientation. The Statements Associated with Fashion and Shopping Orientation Factors (Gutman & Mills, 1982) measured participants fashion and shopping orientation. Participants completed this multidimensional scale that measured factors related to fashion and shopping orientation. The fashion orientation factors were: fashion leadership ( It is important for me to be a fashion leader ), fashion interest ( I always buy at least one outfit of the latest fashion ), importance of being well-dressed ( If you want to get ahead, you have to dress the part ), and anti-fashion attitude ( I resent being told what to wear by so-called fashion experts ). The shopping orientation factors were: shopping enjoyment ( I like to go to stores to see what s new in clothing), cost consciousness ( I make purchases only when there is a need, not an impulse ), traditionalism ( I prefer traditional styling in my clothing ), practicality ( I avoid high-fashion clothing because it goes out of style so quickly ), 21

planning ( I plan my shopping trips carefully ), and following ( I buy new fashion looks only when they are well accepted ). Fashion innovativeness. Gordon, Infante, & Braun (1986) developed a methodology to assess fashion innovativeness by using photographs to measure an individual s willingness to wear recent fashion and trends by asking whether they would buy and wear fashions assuming they were financially able. The test provided evidence as to how fashionable an individual was through using pictures from the most recent fashion journals as well as pictures of fashions that were popular five years ago. The same methodology was used for this survey, however a modern version was created. This was done by researching current and past fashion trends. Six color photographs were carefully selected for each gender using fashion retailing websites and Google Image. Pictures were selected with the assistance of a UW-Stout Apparel and Communication Technologies professor. Three of the pictures showed styles from the most recent fashions and trends, while the other half of the pictures were of styles that were popular from five to fifteen years ago (see Appendix B). After seeing each picture, participants were asked whether they would wear and buy the fashion trend depicted. The pictures were shuffled so that participants did not know whether the fashions depicted were new or old, thus testing their openness to wear new fashions and ability to identify new trends. Additional items. Additional exploratory questions were asked to find out what a person values most when they buy clothing, where they like to shop (store patronage), and online shopping habits. These questions provided a more detailed picture of consumers (see Appendix C). Procedure The UW-Stout students generated from the random sample were emailed a link to an 22

online survey. The email contained a short letter which explained the study and provided a link to the survey. Upon clicking the link the participant was automatically brought to the statement of implied consent, and if they agreed they continued on to the survey. The first questions dealt with the population size and the student s rural or urban upbringing, followed by the selfmonitoring measure, fashion and shopping orientation scales, additional fashion questions, demographics, and the fashion innovativeness test. To obtain the highest response rate possible, the survey was active for slightly less than three weeks. Students who had not completed the survey after the first two weeks were emailed a survey reminder. Results Data Preparation After the close of the survey, the data was downloaded from Qualtrics and uploaded into a Microsoft Excel spreadsheet for cleaning. Various adjustments were made to the data before it could be analyzed. All of the participants population estimates were checked to ensure the most accurate population information. Only missing population estimates or estimates that were significantly inaccurate were corrected and filled in. To accomplish this, the U.S. Census Bureau American Fact Finder website was used (U.S. Census Bureau, 2010). This website was helpful because it gave population information for the years 1990, 2000, and 2009. This allowed for the ability to account for any major population changes that may have occurred during the time a participant was 9-13 years old from the time they were 13-18 years old, for example. The data indicated that participants current ages ranged from 18-24. Therefore when participants were 9-13 years old, it would have been between the range of years 1996-2006, and when they were 13-18 years old, it would have been between the range of years 2000-2011. The age category of 18-23 years old reflected mainly participants current living area and population information. 23

Another item requiring attention was the question of how many miles a person lived away from a major shopping area. The survey asked for participants to answer the question in miles. However due to the open-ended nature of the question, 31 participants gave answers in minutes instead (e.g., I live 15 minutes from the mall). To address this, the minutes were converted into miles using 45 mph as a guide for the average speed people would be driving. Some participants also indicated their proximity to a mall by stating the name of the mall closest to them instead of how many miles it was away from them. To correct this, Google maps was used to find out the distance between the participant s town to the particular mall. The participants rural or urban background (which was measured using population size) and distance from a shopping area data were separate items in this study, but only the rural vs. urban background was used for the primary analyses. This was done because participants rural or urban background from when they were ages 9-13, and the distance they lived away from a mall during that same time, were significantly correlated, r(157) = -.389, p <.01. This significant negative correlation confirms that the more rural background the participant grew up in, the further they lived away from a shopping mall. In addition, the distance to shopping area variable for the three age ranges were correlated with each other to confirm that participants were not reporting large changes in the accessibility of shopping over time. Not surprisingly, there were strong correlations on this distance variable between the different age categories, all p s <.01. Further, the rural vs. urban background data for participants ages 18-23 (which is synonymous with their current residence), and the distance from a mall for these ages was also strongly correlated, r(157) = -.519, p <.01. After discovering that all of the rural and urban background data was strongly correlated with the distance from the mall data for all age groups, only the rural and urban background data for the analysis was used. 24

Correlations were also run on the rural and urban background variables for the three age categories. Results showed that all of the rural and urban background categories were highly correlated with one another, suggesting that for the most part respondents did not move (or if they did, they moved to similar sized towns), all p s <.01. Therefore, only the 9-13 and current rural and urban background categories were used for the analyses because although they were correlated, we know the majority of the participants did move during this time (only 6.2% of the sample reported growing up in Menomonie, WI). There were 164 completed responses after the data was cleaned. When running a frequency of the populations from participants rural and urban backgrounds, the data was skewed because there were a few responses from participants who had grown up in foreign countries with very large populations (e.g., Zhangjiakou, China had a population of 4.3 million). Only nationwide data was analyzed and surveys from participants who grew up in foreign countries were eliminated, leaving 159 responses. Participants background for the 9-13 age range was neither rural or urban because the mean population size was under 50,000 people, (see Figure 2). Participants background for the 13-18 age range was also neither rural or urban, and participants current background was more rural because most of them live in Menomonie, (see Figure 3). 25

Figure 2: Population Range for Participants Ages 9-13 Figure 3: Population Range for Participants Ages 18-23 26

Analysis After making the modifications for the rural and urban backgrounds and distance from a mall, the data was analyzed using SPSS. The sample was made up of 78% females, 21.4% males, and.6% other. 94.3% of participants identified as being white, 1.9% were Asian, 1.3% were unknown, and.6% each were American Indian or Alaska native, Hispanic or Latino, Multiracial, and other. A variety of apparel shopping information was gathered from participants to learn more about their current shopping habits. It was found that when shopping for clothing, participants valued the following qualities in order of importance: quality, price, comfort, fashionable, versatility, durability, trendy, revealing, and shock value. Table 1: What Participants Value when Shopping for Clothing Variable Mean Standard Deviation Quality 2.60 1.49 Price 2.62 1.75 Comfort 3.44 1.76 Fashionable 4.65 2.16 Versatility 4.65 1.82 Durability 4.94 1.68 Trendy 5.73 1.87 Revealing 7.93 1.11 Shock value 8.45 1.06 27

(Note. For this question, participants were asked to rank the above qualities from the most to least important to them; 1 was most important, and 9 was least important). The five most popular stores that participants chose from a list and said they shopped at the most regularly were Target (N = 91), Kohl s (N = 79), American Eagle (N = 65), Forever 21 (N = 63), and Victoria s Secret (N = 57). The five least popular stores to shop at were Ann Taylor (N = 3), Lands End (N = 3), Tiger Lily (N = 3), Burberry (N = 4), and Eddie Bauer (N = 4). It was not surprising that college students shopped at Target and other discount mass retailers the most frequently because these stores focus a lot of their advertising towards discount home and dormitory supplies, and there are also many Target retail locations in this geographic area. It was also not surprising that college students shopped less at stores like Ann Taylor, Lands End and Burberry, because these retailers sell higher-end clothing and their products and advertising are more targeted at adults. When participants were asked how often they shopped for clothing, nearly half (44.7%) said less than once a month, 25.8% said once a month, and 22% shopped 2-3 times a month. Participants reported spending $83.54 on a typical shopping trip (N = 147, M = 83.54, SD = 75.28). 23.8% of participants typically spent $100, and 17.7% of participants said they spent $50. Only participants that gave responses in exact dollar amounts were included for this question, or if they gave a range, the mean answer was analyzed. When asked about their online shopping habits, approximately half of participants (52.2%) shopped online less than once a month and 21.4% of participants never shopped online. The majority of participants did not shop online more often than in-person; 83% shopped in-person more often, and 17% shopped online more often. 28

Fashion Orientation and Self-Monitoring To create a fashion orientation score, a composite general score for each participant was developed. This was computed using items from the following fashion categories: fashion leadership, fashion interest, and importance of being well-dressed. These three fashion categories had items with good internal consistency, α =.916. The three items from the anti-fashion attitude category were removed because doing so increased the reliability of the scale. Table 2: Means, Standard Deviations and Skew of Main Variables Variable Mean Std. Dev Skewness Fashion Orientation 41.20 10.25 -.215 Shopping Orientation 49.48 7.17 -.750 Rural/Urban 9-13 45,858.11 91,451.5 3.96 13-18 49,691.29 99,212.92 3.73 18-23 25,162.14 44,411.35 5.56 Self-monitoring 8.7 3.18 -.072 Fashion Innovativeness Male 17.03 2.16-1.45 Female 16.99 2.79.028 (Note. The above means, standard deviations, and skew data were calculated for the five main variables of this study. The highest fashion orientation score a participant could receive was 65 and lowest score they could receive was 13. The highest shopping score a participant could receive was 80, and the lowest score they could receive was 16. Rural/Urban background was 29

measured through participants population size from three different age groups. Self-monitoring was scored in the direction of a high self-monitor. The highest self-monitoring score was 18, and lowest was 0. Fashion innovativeness was measured through respondents scores from six pictures. For each picture, a score of 1 was determined to be the most innovative answer (when participants indicated they already owned the item), and 4 was the least innovative answer (when participants indicated they would never buy the item). Lower scores reflected higher fashion innovativeness, and higher scores reflected lower fashion innovativeness. The highest fashion innovativeness score a participant could receive was 6, and lowest was 24. The scores for the past fashion items were reverse coded, as they were deemed not innovative). To test Hypothesis 1a that participants from more urban (larger population) backgrounds would have higher fashion orientation scores and Hypothesis 2 that this relationship would be moderated by self-monitoring, hierarchical multiple regression was used. Following the recommendations of Baron and Kenny (1986) and Frazier, Tix, and Barron (2004), the Population Size of participants Background and Self-Monitoring were entered on the first step and the Population Size of participants Background x Self-Monitoring interaction term was entered on the second step. Participants rural or urban background from the ages 9-13 was not a significant predictor of Fashion Orientation, β = -.035, t(153) = -.433, p =.666. Self-Monitoring acted as a predictor for Fashion Orientation, β =.175, t(153) = 2.188, p =.030, but not a significant moderator, β = -.319, t(152) = -1.640, p =.103. Similarly, participants current rural or urban background (age 18 23) did not predict fashion orientation scores, and self-monitoring was a predictor for fashion orientation, but not a moderator. Table 3: Fashion Orientation and Self-Monitoring Regression 30

Variable β P R 2 9-13 Fashion Orientation -.035.666.032 Self-monitoring.175.030.032 Fashion Orientation x Self-monitoring -.319.103.049 18-23 Fashion Orientation -.070.379.036 Self-monitoring.181.025.036 Fashion Orientation x Self-monitoring -.887.088.054 Shopping Orientation To create a shopping orientation score, a composite general score for each participant was developed. This was computed using items from the following shopping categories: Shopping enjoyment, cost consciousness, traditionalism, practicality, planning, and following. Multiple items were removed to increase reliability; including two items from cost consciousness, one item from traditionalism, and one item from following. After removing these four items, the internal consistency of the measure was acceptable, α =.708. To test Hypothesis 1b that participants from more urban (larger population) backgrounds would have higher shopping orientation scores, regression analysis was used. Participants rural or urban background when participants were ages 9-13 did not have a significant impact on shopping orientation, β = -.147, t(151) = -1.831, p =.069. Participants current rural or urban background was also not a significant predictor of shopping orientation scores, β = -.080, t(151) = -.985, p =.326. 31

Fashion Innovativeness A composite fashion innovativeness score was calculated for males and females by adding up the responses from the questions asking whether participants said they would buy the pictured current and past fashions. Participant s rural or urban background from the ages of 9-13 years old did not have a significant impact on fashion innovativeness for females, β = -.0.36, t(115) = -.391, p =.696, or males, β = -.171, t(31) = -.966, p =.342. Similarly, participant s current rural or urban background did not have a significant impact on fashion innovativeness for females, β = -.033, t(115) = -.353, p =.725, or males, β = -.133, t(31) = -.745, p =.462. Discussion It was predicted that there would be differences in fashion and shopping orientation as a function of one s rural or urban background, however, no differences were found between rural and urban participants. It was also found that self-monitoring did not moderate the relationship between participants rural or urban background and their fashion orientation, though it was related to fashion orientation on its own. Rural vs. urban background did not predict participants fashion innovativeness for either males or females. One potential explanation for these unexpected results is the composition of the sample. The sample lacked diversity, as 94.3% of the participants were white, and 78% were female. Also, the number of respondents who grew up in rural locations greatly outnumbered participants from urban locations. Only 27.7% of participants from the ages of 9-13 years old grew up in urban locations with populations over 50,000, while only 9.4% of participants currently live in an urban area. If there had been equal amounts of participants from urban and rural populations, differences may have been revealed on their fashion and shopping orientations. Future samples would benefit from more racial, ethnic, and gender diversity, as these factors could influence 32

results. For example, people of varying races and ethnicities may have differing interests, attitudes and opinions regarding fashion and shopping. The literature indicated that self-monitoring is a characteristic that has received attention from market researchers because of its association with buying behavior. Interestingly, although self-monitoring was not a moderator, it did slightly predict fashion orientation scores. This provides evidence that because high self-monitors are very concerned with how they are perceived publicly, they are more likely to care about fashion than low self-monitors. This also showed that low self-monitors are less concerned with fashion, perhaps because they want to stay true to themselves and are not interested in changing their behavior to fit in with others or are indifferent to fashion. This finding is consistent with what Sullivan & Harnish (1990) found that high self-monitors are more concerned with physical appearance, and Browne & Kaldenberg s (1997) findings that high self-monitors were more interested in fashion than low self-monitors. Auty & Elliot s (1998) study found that high self-monitors held more negative attitudes towards unbranded jeans than low self-monitors did, which further illustrates high selfmonitors need for social approval and concern over their public image. Previous research on the relationship between rural vs. urban background and fashion attitudes has been mixed. Some research has shown that consumers who scored high in fashion innovativeness were more likely to be from urban rather than rural areas (Gordon, Infante, & Braun, 1986). Although the methodology from their study was used in the present study, the results of this study were not consistent with what Gordon et al. found. On the other hand, Summers, Belleau, and Wozniak (1992) found that rural and urban consumers did not differ on their perceptions of fashion and shopping. Because the composition of the sample in the present study was significantly different from the older and mainly employed sample of Summers et al., 33

it seemed likely that some differences would be found. However, without a larger proportion of participants from a truly urban background it is difficult to say these results are supportive of the Summers et al. finding. Future research on this topic should include several methodological changes. First, several items on the current measure would need to be reworked. In general, less open-ended questions should be used. The questions regarding participants population estimates and distance away from a shopping mall were both asked in open-ended format. This was done with the intention of not limiting people s responses. However, this led to varied answers that needed a lot of adjustment. For example, participants were asked to provide the distance they lived away from a mall in miles, however many reported the distance in minutes. Asking close-ended questions in a range would have provided more accurate data. Also, devising a simpler way to measure whether a participant is from a rural or urban background would be advisable, as using rural vs. urban background and distance away from a mall as separate predictors proved to be challenging. A major limitation of this study was the sample size and unequal number of responses from participants from rural and urban locations. If this study was replicated at UW-Stout, it would be recommended to offer an incentive to achieve a higher response rate. Although this study was sent to one third of the undergraduate population, there was only an 8% response rate. However, even if the response rate or sample size was larger, it is unknown whether differences would be found between urban and rural students, because the majority of UW-Stout students are likely from more rural backgrounds. It is also important to note that the results of this study cannot be generalized to all undergraduate consumers, which is why further research is needed from different college campuses across the country. This same study would likely have different 34

results if administered in a highly populated city or large college town where there would be a more racially and ethnically diversified sample. It is recommended that future studies examine college students in a larger urban area to see if students from more urban backgrounds will have higher fashion orientation scores. It would also be beneficial to identify additional concepts that may influence individuals fashion and shopping orientations, such as; personality traits, selfconcept, fashion involvement, brand loyalty, materialism, media influences, or other demographic variables. Another limitation of this study was the fashion innovativeness test. Fashion is very subjective and difficult to measure. Because there was a relatively small number of pictures included in the fashion innovativeness test, this was not necessarily a representative sample of all the current fashion trends, nor was it necessarily indicative of one s fashion sense. Because only three pictures of current fashion trends were included in this study, there might not have been enough items to determine whether a person is truly innovative or not. An individual could be a fashion innovator, but simply disliked the particular fashions picked out for this survey. For these reasons, just because a participant was deemed as being not innovative on this particular test, it does not exclusively mean they are not a fashion innovator. Recommendations for improving a fashion innovativeness test similar to this one would be to include numerous pictures of past and present fashions, and conduct pilot testing by asking the opinions of a greater number of people, preferably fashion experts. Although differences were not found among the fashion and shopping orientations of rural and urban participants, the information gathered has interesting implications for retailers. The results showed that UW-Stout students found price and quality of clothing to be of highest importance, and fashionable or trendy clothing less important. Because the sample of this study 35

was primarily made up of individuals from rural backgrounds, this was consistent with my hypothesis. It was hypothesized that students from rural backgrounds would have lower fashion orientation scores, and the findings reflected this through the generally low scores on fashion orientation, shopping orientation, and fashion innovativeness. This was also shown through students preference for clothing based on price and quality over fashion. Other results found that the majority of this market segment does not shop online, and prefers to shop at large department store retailers such as Target and Kohl s, rather than more adult-oriented specialty stores like Lands End, Ann Taylor, and Eddie Bauer. If adult-oriented specialty stores want to compete with large discount retailers for the college student market segment, they should focus their efforts on meeting the needs of their customers better. For example, Lands End has come out with a new line recently called Lands End Canvas, which is aimed at younger consumers. However, to compete in the market, they will need to have lower prices and perhaps have more retail stores in this area because it was found that more college students shop in person than online. It is also recommended that future studies ask additional questions about store patronage. For example, it would be beneficial to learn the names of stores where college students actually shop because of their lack of money, versus where they would like to shop if they had available funds. By learning this information, retailers would be provided with information about college students preferences for clothing if money was not a factor, thus affecting their marketing strategies. Still, the information provided by this study can help local retailers and marketers better understand the college consumer market and help them adapt their marketing strategies for the future. 36

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Appendix A Demographics 1. Think about your life between the ages of 9-13. Where did you live for the majority of that time?, (City or town, state) What was the approximate population? Don t know Approximately how many miles away from a major shopping area did you live? (A major shopping area is defined as a shopping mall that includes clothing stores and mid-level department stores such as Boston Store, JCPenney s and Macy s. A shopping mall with discount retailers such as Wal-Mart is not included in this definition). miles 2. Now, think about your life between the ages of 13-18. Where did you live for the majority of that time?, (City or town, state) What was the approximate population? Don t know Approximately how many miles away from a major shopping area did you live? (A major shopping area is defined as a shopping mall that includes clothing stores and mid-level department stores such as Boston Store, JCPenney s and Macy s. A shopping mall with discount retailers such as Wal-Mart is not included in this definition). miles 3. Finally, think about your life between the ages of 18-23 (or currently, if you are in this age range). Where did you/have you lived for the majority of time between the ages of 18-23?, (City or town, state) What was the approximate population? Don t know Approximately how many miles away from a major shopping area did you live? (A major shopping area is defined as a shopping mall that includes clothing stores and mid-level department stores such as Boston Store, JCPenney s and Macy s. A shopping mall with discount retailers such as Wal-Mart is not included in this definition). miles 4. What is your gender? Male Female Other 41

5. What is your major at UW-Stout? 6. What year were you born in? 7. What is your ethnicity? _ White _ African-American _ Asian _ American Indian or Alaska native _ Pacific Islander _ Hispanic or Latino _ Multi-racial _Other Would prefer not to say 42

Appendix B Fashion Innovativeness Test Assuming that you are financially able, indicate whether you would purchase and wear the fashion shown above by checking the time period you would most likely make that selection. Already have it (or something very similar) I would buy it within 1-6 months I would buy it within 6 months to a year I would probably never buy it Current Female Fashions Past Female Fashions 43

Current Men s Fashions Past Men s Fashions 44