FINAL Lululemon Research Report 2 - SP 15 - Elasmar

Branded Athletic Apparel Consumption

College Student Consumption of Branded Athletic Apparel

A Measurement Model for Lululemon

Alexis Feinberg

Boston University

April 30, 2015

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Table of Contents

I. Introduction Page 3

II. Background Page 3

Company History Page 3

Competition Page 5

Industry Analysis Page 9

III. Literature Review Page 11

Scholarly Journals Page 11

Industry and Trade Publications Page 18

Newspapers and Magazines Page 22

IV. Theoretical Framework Page 24

V. Proposed Predictors Page 26

Proposed Predictors from Literature Page 26

Proposed Predictors within Theoretical Framework Page 29

Selected Proposed Predictors Page 32

VI. Development of Measures Page 32

Multiple-Item Measures Page 34

Single Item Measures Page 43

VII. Development of Survey Instrument Page 47

Survey Instrument Page 50

VIII. Analysis of Measures Page 56

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Qualitative Review Page 56

Quantitative Review Page 57

Assessment of Validity and Reliability Page 58

IX. Revisions To Survey Instrument Page 66

X. Conclusions Page 68

XI. References Page 70

XII. Appendix A – Frequency Distributions Page 73

XIII. Appendix B – Inter-Item Correlations Page 102

XIV. Appendix C – Factor Analysis Page 112

XV. Appendix D – Reliability Analysis; Cronbach’s Alpha Page 146

XVI. Appendix E – Inter-Item Correlation Matrix Page 155

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I. INTRODUCTION

Lululemon wants to increase its consumer base of college students who purchase their

athletic apparel. Based on this information, the following study will examine the following

research question:

RQ: What drives college students to purchase branded athletic apparel?

In order to answer this question, it’s essential to determine what factors will influence the

likelihood college students will purchase branded athletic apparel. Using information from past

research about this subject matter and other related topics, recommendations will be able to be

determined to assist Lululemon in reaching its objective.

II. BACKGROUND

Company History

Lululemon Athletica was founded in 1998 in Vancouver, British Columbia by Dennis

“Chip” Wilson in response to a rise in female participation in sports and in conjunction with

Wilson’s own interest in the yoga industry. As a former business owner in the surf, skate and

snowboard industry, Wilson’s interest in yoga as a form of exercise began after he took the first

commercial yoga class offered in Vancouver (Lululemon 2014). During this period in the yoga

industry, cotton was the main fabric used for power yoga, a type of yoga that is considered to be

sweatier than others (Lululemon 2014). The use of cotton for yoga apparel was deemed not

acceptable by Wilson, thus inspiring him to create an alternative solution under the brand name

“lululemon”.

Wilson’s ensuing interest in “technical athletic fabrics” (Lululemon 2014) lead to the

opening of Lululemon’s first store in November 2000, located in the beach area of Vancouver.

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While the product was generally geared towards educated women who lead a healthy and active

lifestyle, the company eventually expanded to include performance apparel for men and young

females (Reuters 2012). The concept of each store was to have a community “hub” where

customers could not only outfit themselves in yoga-inspired athletic gear, but also learn and

discuss healthy living topics (Lululemon 2014). As of 2013, Lululemon stores were primarily

located in the United States, Canada, New Zealand, and Australia with an online presence

through their website (Reuters 2013). Additionally, Lululemon’s finished apparel and accessories

were set for shipping from distribution centers located in Vancouver, Sumner, British Columbia,

Washington, and Melbourne, Australia (Reuters 2012).

Despite Lululemon being touted as one of the fastest growing brands in 2013, that same

year the company faced challenges with production of its yoga pants and brand image due to

public comments made by Wilson. The store had to recall 17% of their stretchy black yoga pants

in March 2013 because of customer complaints related to the product’s level of sheerness

(Isidore 2013). Ultimately, this caused a loss of revenue between $57 million to $67 million

(Wischhover, 2013). In response to this issue, Wilson made statements claiming that some

women’s bodies didn’t work for the company’s yoga pants, alluding to judgmental comments

about female bodies (Lustrin, 2013). This severely hurt the brand’s reputation, especially since

women are considered the largest consumer segment for Lululemon.

As a result of Wilson’s comments, in June 2013, he stepped down as CEO and was

temporarily replaced by Christine Day who worked to repair the damage brought on by Wilson.

Then, in December 2013 Lauren Potdevin, a former executive at TOMS Shoes, took a permanent

position as Lululemon’s CEO (Reuters 2013). With Potdevin at the helm of the Lululemon

brand, the company continued to rebuild its image and became close to reaching almost $1

billion in retail sales (Reuters 2013).

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As the company continued the rebuilding process, Lululemon announced plans in late

2014 to grow the men’s segment of its business; in the Fall of that same year the company

opened its first men’s-only store in New York City and planned to open more by 2016. The idea

to establish a men’s store was an outgrowth of Lululemon’s customized shorts program that was

offered in its Vancouver flagship store (Lieber 2014). In a Racked article by Chavie Lieber, she

explained that Lululemon “can no longer sit back and rest on its female-centric laurels:

Competition is fierce in the fitness industry, with companies like Nike, Reebok, and Adidas

inhaling the money of male shoppers, and Lululemon must expand its offerings if it wants to

compete” (Lieber 2014, par. 9). With management changes in the wake of the company’s first

major communication disaster and new plans to extend the Lululemon brand, the company has

taken the necessary steps to achieve a positive image in its target consumer’s mind to ensure

growth and profitability.

Competition

While Lululemon has been an industry leader for trendy athletic apparel and accessories,

with the brand image issues the company faced in combination with competition from other

companies, Lululemon will have to continue to differentiate itself to stay competitive in the

athletic apparel market. The competitors that are most likened to the Lululemon brand are Lucy

Activewear and Athleta. According to Morningstar analyst Bridget Weishaar, “Lululemon has

long had a loyal following that has helped the company fend off pressures of growing

competition. But the loyalty has begun to erode.” (Peterson 2014, par. 10). This erosion of brand

loyalty is most likely due to the 2013 disaster brought on by poor quality in product and the

negative comments made by Wilson concerning female bodies.

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Weishaar also explained that as an industry, yoga has increased in popularity to the point

of “active-wear commanding some of the best pricing premiums in the apparel space, and

athletic garb increasingly worn for purposes other than exercise, the formerly niche market has

become mainstream, and competition is flooding the space.” (Peterson, 2014, par. 11). This sort

of competition from other athletic apparel companies could push Lululemon to take measures

such as keeping “its prices in a competitive range or to justify higher prices with a technically

differentiated product,” according to Weishaar (Peterson 2014, par. 20).

Like Lululemon, Lucy Activewear was founded during the “dotcom” era in 1999 in

Portland Oregon (Culverwell 2008); it started as an online-only retail venture (Gunderson 2010).

At the time, founder Sue Levin thought that women’s workout wear was “overdue for a

makeover,” (Lucy company website, par. 1) because by the end of the 1980’s the stereotypical

leotards and legwarmers had evolved into “baggy shorts and ill-fitting college t-shirts.” (Lucy

company website, par. 1).

While Lululemon opened its consumer segment to both women’s and men’s, Lucy

focused solely on women’s athletic wear that was versatile in style to be both workout and casual

attire. In developing the Lucy apparel line Levin focused on creating items that were able to

perform well, be “tug-free” and have long-lasting materials; the apparel was also made to

specifically fit women’s bodies with a range of size and shape options (Lucy company website,

par. 3). In comparison to Lululemon, as of 2010 the price points for Lucy (in the pant category)

ranged from $68 to $98, whereas Lululemon’s prices for pants ranged from $74 to $108

(Gunderson 2010). The difference in price points illustrates the previously mentioned comment

by Chavers concerning the need for new business strategies for Lululemon.

Another way that Lucy differs from Lululemon is the way that apparel and accessories

are sold: Lucy sells through corporate-owned stores (the company opened its first store in New

York City in 2001 [Gunderson 2010]) and a website in addition to selling through affiliates and

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as wholesale (Lucy company website). As of 2010, the company had a total of 65 stores with

several locations concentrated in the Portland area—a number much less than Lululemon at the

time (Gunderson 2010).

In 2004 Lucy hired Mike Edwards as CEO to help the company, which was having

severe financial trouble; they were operating only 12 stores. The following year Lucy went

through a round of financing for a total of $20.3 million dollars (Businesswire 2005) where

Chico’s FAS Inc., a pricey apparel retailer for women, invested $10 million of those funds

(Gunderson 2010). During this funding process Edwards said, “This funding and strategic

relationship puts us in a key position to accelerate our company growth and build upon our

highly successful brand and customer relationship. “ (Businesswire 2005).

Two years later, in 2007, Lucy was acquired by VF Corporation, an apparel conglomerate

located in Greensboro, North Caroline for $110 million. Soon after this acquisition, Edwards left

after helping the company expand to 65 stores with annual sales around $60 million dollars. In

2010, Lucy moved its headquarters from Portland to San Leandro, California where VF

Corporation’s outdoors division was located (Gunderson 2010). In response to this move, retail

analyst Jennifer Black put the relocation into perspective as a competitor to Lululemon: “This is

putting Lucy in a groove that makes more sense… It looks as if Lucy has been stepping up its

game with merchandise. They just need to step it up in every way." (Gunderson 2010, par. 7).

This comment by Black shows that as a comparable brand to Lululemon, Lucy needed to make

strategic moves to be able to be a true competitor to the activewear giant.

Another competitor of Lululemon that was developed in the late 90’s was Athleta—now

a division of Gap Inc. Athleta was founded in 1998 as a catalog-based premier fitness apparel

brand for women out of Petaluma, California (Gap, Inc. company website). Athleta’s founder,

Scott Kerslake, started out in the surf business similar to Lululemon’s Wilson, but with a

different purpose for establishing the brand: he often found himself “listening to female friends

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complain about a lack of selection in women's workout wear,” (Warner 2003, par 2). Kerslake

saw that his female friends wanted gear that would hold up for intense exercise, but would also

be “fashionable enough to wear to the office,” (Warner 2003, par 3). At the time, bigger

companies—in particular Nike—were still focused on a male-dominated image that attracted

young men but turned off women (Warner 2003).

By 2003 Kerslake expected Athleta’s sales would be up to $30 million, almost a third

more than its sales of $18 million in 2001. During this period in Athleta’s growth, it was still a

privately owned company, but did go through a round of investment in 2002; Richmond

Financial invested $6 million to help Athleta expand its product lines and revitalize its website

(Warner 2003). Like Lucy that went through a round of funding before being sold to a larger

retail entity, in 2008 Athleta was bought by Gap Inc. for $150 million. For Gap, this was a

strategic move that brought the almost 40-year-old retailer into the activewear market with a

ready-made division (Colliver 2008). During this acquisition, Joe Teno had already taken over as

CEO of Athleta from Kerslake (he was the chief operating officer at Athleta); after Gap took

over the company, Teno stayed with the title of president of Athleta (Rosenbloom 2008).

It wasn’t until 2011 that Athleta opened up its first store in San Francisco and finally

joining the ranks of Lucy and Lululemon who already had many brick-and-mortor locations

(Clifford 2011). In an article by Clifford concerning Athleta’s first store, Lenk was quoted as

saying that this move was necessary even at a time when store sales (compared to online) were

declining because “with this type of product, women’s active athletic product, it is really

important to be able to feel it, touch it, try it on.” (Clifford 2011, par. 3). Clifford also noted that

it seemed to be a strategic move for Athleta’s parent company to seize some of the success that

Lululemon had. Parallel to Athleta’s opening, Lululemon was trading for more than four times

its initial public stock offering price in 2000; it was also operating a website and its 130 or so

stores were driving more than 90 percent of its revenue (Clifford 2011). By 2013 Athleta

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expanded its stores to 65 locations around the United States; Gap had transformed the brand

from a mail order and online entity to a brick-and-mortar retailer (Quackenbush 2014).

Industry Analysis

For both men’s and women’s activewear (“fitness apparel”), this segment of the clothing

business has been on the rise over the last few years (Mintel 2014), more specifically, at a rate

that is four times as fast as the entire apparel industry. On a global level, the fitness category is

expected to hit $126 billion dollars in sales for 2015 (Lieber 2014). According to Mintel, as of

October 2014, “avid exercisers and those who are recently inviting exercise into their lives more

frequently are certainly contributing to the uptick in the market; however, the growth stems more

from consumers’ increased desire for casualization in all forms.” (Mintel 2014, par. 1 “What you

need to know”). This information reveals that there is a rising trend in the fitness apparel

industry where consumers want clothing with a dual function: exercise-friendly and fashionable

enough to be worn casually.

The call for activewear that “multitasks” has cultivated increased competition both from

“core fitness clothing brands” (Mintel 2014) as they create more casual lines and from non-

athletic retailers who are introducing activewear into their clothing lines (Mintel 2014).

Economic drivers are also the reason behind the rise in popularity of fitness apparel.

According to the same Mintel report, consumer confidence is back to highs not seen since the

recession of 2008 while unemployment rates are estimated to be down for the fifth year in a row

(Mintel 2014). These factors feed into consumers having more discretionary income to spend on

health-related items, including fitness apparel (Mintel 2014). However, Mintel cautions, “post-

recession, the US median household income has continued to decline,” (Mintel 2014, par. 4

“Executive Summary”). For the activewear market, this is relevant because fitness clothing

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purchases tend to be discretionary, therefore a change in household income will affect the

purchasing decisions of consumers when it comes to this apparel segment (Mintel 2014).

When consumers are looking to purchase fitness apparel, the outlet of choice is in-store

shopping (95%), mainly at “mass merchandisers”; however, online purchasing comprised a large

portion as well at a rate of 56% (Mintel 2014). This suggests that the overall trend toward

increased online and mobile shopping will continue to rise, especially because heavy consumers

of fitness clothing belong to savvy, younger generations, thus being more likely to shop these

channels (Mintel 2014). Additionally, these results show that consumer purchasing preferences

were directed towards stores such as Target or Walmart (56% combined in-store and online), but

specialty fitness clothing retailers such as Lululemon, Lucy, or Athleta did capture a fair amount

of survey preference (31% combined in-store and online) (Mintel 2014).

An interesting statistic that contrasts with the rise in popularity of fitness apparel is that

as of July 2014, “two thirds of Americans are exercising at rates that are equal to or less than

they did a year ago, or just not exercising at all, “ (Mintel 2014, par. 6, “Executive Summary”).

Perhaps those who fit into this exercise segment are still purchasing fitness apparel for aesthetic

reasons, rather than functional. However, there is still a large percentage of American who do

participate in exercise; in a study conducted by the U.S. Bureau of Labor Statistics, the agency

found that “men spend twice as much time exercising as women do—and therefore more likely

to shop for appropriate attire,” (Lieber 2014, par. 7). Even so, going as far back as 2003,

women’s athletic apparel was a $25 billion annual market was expected to expand by more than

50 percent by 2005 (Warner 2003). What these statistic show is that there is a large market for

women’s fitness apparel, so it’s ideal that companies such as Lululemon put their focus on that

consumer segment. However, these companies can’t forget to appeal to their male shoppers since

their interest in activewear is significant enough to have an impact on the fitness apparel market.

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III. LITERATURE REVIEW

Exploring background information is vital to understanding how to conduct research

within the context of the Lululemon brand and the athletic apparel industry as a whole. In order

to further investigate how to formulate the appropriate independent variables and corresponding

measures and to understand what past researchers in the field have accomplished, it’s important

to also look at previous research about shopping habits and any specific research relating to

athletic apparel. This will be explored in the following sections through the investigation of

scholarly journals, industry and trade publications, and newspapers and magazines.

Scholarly Journals

An important feature of college student populations is the act of seeking peer approval,

including the reliance on peer opinions to make buying decisions. When looking at this decision-

making factor in the setting of fitness and health, Yun & Silk (2011) found that college students

may look for advice from peers or mimic peers’ fitness effort. In using this as a basis for study,

Villard & Moreno (2011) looked at how online media (Facebook) displays may influence the

current generation’s thoughts concerning fitness and nutrition by both peer profiles and online

advertisements. While this study was limited to undergraduate freshman, overall Villard &

Moreno (2011) found that over 70% of evaluated profiles referenced fitness behaviors on

Facebook, mainly discussing physical activity. These results show that physical activity is an

important lifestyle choice for at least one segment (freshmen) of the college student population.

The concept of the importance of physical activity and health was also supported by Ohl

& Taks (2008), but referred to as “sporting.” The researchers found that “sporting goods” (i.e.

apparel, accessories, sports equipment) are spreading outside of the sporting world. This shift in

sports-lifestyle symbolizes the idea of “being cool”, therefore sporting goods consumption

becomes mass consumption (Ohl & Taks 2008). At the core of the sporting goods market is

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young people from ages 12 to 25, who are considered to be “heavy” sporting goods consumers

(Ohl & Taks 2008). In relation, Fowler (1999) also discussed some of the major changes in the

activewear and the sports apparel market, emphasizing differences in sport apparel preferences

between males and females. Fowler (1999) found that both men and women looked for comfort,

quality, durability and style, however, women indicated that “fit” was more important than men.

Additionally, in contrast to females, males have a stronger affiliation with brands and with sport

heroes whom they look to as idols to reflect who he wants to become (Fowler 1999).

Similarly, Roman & Medvedev (2011) investigated peer approval and group acceptance

influence in the apparel (referred to as “sartorial” in the study) purchases of college students and

participation in popular trends on the campus of the University of Georgia. The researchers

observed popular clothing trends on campus such as pairing Nike brand track shorts with UGG

fur boots, causing the researchers to believe that such a disparity in style may suggest another

influence that drives popular clothing trends (Roman & Medvedev 2011). Through the use of a

questionnaire, Roman & Medvedev (2011) found that the most popular items used in the survey

were not nationally recognized and were not often advertised. Additionally, the apparel was

considered to be affordable and ranged in price from $55 to $200.

In more results the majority of respondents indicated that that he or she wear the same

apparel on-and-off campus, suggesting that trends “transcend” the boundaries of the campus or

that the students’ community identity is strong (Roman & Medvedev 2011). While students

deemed the apparel in the questionnaire to be affordable, they were actually more concerned

about style than cost (Roman & Medvedev 2011). This shows that participating in trends on

campus is a larger importance to the students than the actual price of what he or she is

purchasing, so perhaps whether the clothing is affordable or not may be irrelevant.

In regards to peer approval and acceptance, the same study found that respondents felt his

or her purchasing behaviors to be more aligned with conforming rather than of their own

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behavior. As such, respondents were more likely to attribute the purchase of a popular brand-

name item to conformity of peers, but they were also more likely to attribute their own purchases

to logical reasons, such as fit and comfort (Roman & Medvedev 2011), showing that attributes of

apparel are important factors in their shopping habits.

The difference between male and female consumers is an important factor as well

because they share preference similarities, but also display a range of varying characteristics.

Bae & Miller (2009) studied a total of 822 male and female college student enrolled at three

public universities using a consumer shopping styles inventory developed by Sproles & Kendall

(1986). They examined specific shopping styles involved in athletic apparel and analyzed the

shopping pattern differences between genders within the United States (Bae & Miller 2009).

There are eight consumer decision-making characteristics that were used to approach

consumption (Sproles & Kendall, 1986):

1. Value for money/price consciousness;

2. Perfectionist/high-quality consciousness;

3. Brand consciousness;

4. Novelty/fashion consciousness;

5. Habitual/brand-loyal orientation;

6. Recreational shopping consciousness;

7. Impulsiveness/carelessness;

8. Confusion from over-choice

The use of these consumer decision-making characteristics has been shown to be successful as a

tool in other apparel buying studies by Hafstrom, Chae & Chung (1992); Mitchell & Bates

(1998); and Fan & Xiao (1998) (Bae & Miller 2009). The results of the study by Bae & Miller

(2009) showed that “female college-aged consumers manifested a greater tendency toward

quality, recreation, confusion, impulse and brand consciousness than male college-aged

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consumers,” (Bae & Miller 2009, p.43). It seems that female college-aged consumers were more

apt to not only seek out well-known brands with particular quality standards, but also to use

shopping as a recreational and impulse buying tool over their male counterparts.

Koa et al. (2012) conducted a similar study in which the researchers looked at the

usefulness of Global Marketing Segmentation (GMS) in sportswear industry, specifically for

male and female college students. GMS examines the “effectiveness of alternative strategies for

serving markets around the world,” (Koa et al. 2012, p. 1566). While a global outlook isn’t

necessary to the study in this report, the findings of Koa et al. (2012) have relevance for the

shopping habits of male and female college students. However, Alden et al. (1999) noted, “the

existence of a global consumer culture has led to a greater ability to target consumers who have

shared consumption values independent of the country they live in,” (Koa et al. 2012, p. 1567).

As such, global preferences may be relevant after all based on this observation because the

shopping habits of international consumers are considered independent of the country he or she

lives in and is more aligned under the scope of gender.

Results of the study supported the notion of shopping habits being related to gender,

transcending the influence of respondent’s country of origin (this study looked at respondents at

large universities in Austria, China, Korea and the United States). There were significant

differences found among gender segments and money spent on apparel. Female respondents

were somewhat more likely to belong to shopper groups that were Fashion Leaders or Sociable

Followers, whereas male respondents tended to be more towards Sensational Seekers (Koa et al.

2012). Additionally, there were no significant differences found among segments in terms of

nationality, household income or age (Koa et al. 2012).

When respondents were asked about sportswear preferences, results showed that

respondents’ favorite sportswear brand, brand purchased and purchase location were significant;

the most frequently reported sports brands were Nike, Adidas and Puma (Koa et al. 2012). In

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terms of purchase place, department stores and specialty stores represented the majority

preference of the sample. Additionally, there were significant differences found among the

segments when it came to intention to repurchase branded products (Koa et al. 2012).

In yet another related gender study, Moye & Kincade (2003) examined the differences

between store patronage and attitudes toward store environments among female `consumers.

While the purpose of this study was more focused on shopping orientation groups in regards to

their patronage preferences, frequency of patronage, attitude towards stores and demographic

characteristics, it still provided some insight into shopper (female) preferences and attitudes.

After distributing and collecting a mail survey to 900 consumers, the results of the study implied

that women in differing shopping segments varied by store patronage, attitudes and demographic

characteristics (Moye & Kincaid 2003).

Two types of shoppers that the researchers identified, the Confident Apparel Shopper and

Extremely Involved Appearance-Conscious Apparel Shopper, are potential important shopper

profiles to the study conducted in this report. The Confident Apparel Shopper was described as

being:

“…confident in her ability to shop, [choosing] the right clothes for herself, [describing]

herself as a good clothing shopper, and has an up-to-date wardrobe. Women in the

Confident segment can shop independently, they like fashion, and the latest trends will

appeal to this customer. They selected department stores as their first store of choice and

specialty stores as their second store of first choice,” (Moye & Kincaid 2003, p. 69).

The second type, the Extremely Involved Appearance-Conscious Apparel Shopper, was

described as believing:

“…a person’s reputation is affected by how she dresses and that dressing well is an

important part of her life. Appearance is a priority for this shopper,” (Moye & Kincaid

2003, p. 69)

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Both of these shopper profiles are relevant because they seem to embody target characteristics of

the Lululemon, Lucy Activewear, and Athleta consumer; not only is each retailer a “specialty”

store, but the apparel also tends to be fashionable and trendy. Consumer decision-making styles

such as these are important indicators of the college student population, a concept explored by

Cowart & Goldsmith (2007), but in the context of online purchasing.

In the study by Cowart & Goldsmith (2007), demographic variables such as income,

education and age were a relevant factor, but only had a moderate impact on the decision to

purchase online. To expand on the subject matter, seven motivations for online shopping were

measured: social escapism, transaction security and privacy, information, interactive control,

socialization, non-transactional privacy and economic motivation (Cowart & Goldsmith 2007).

Cowart & Goldsmith (2007) noted that in a related study by Silverman (2000), apparel is one of

the most popular types of products that high school and college aged consumers shop for on the

Internet. Similarly, in results found by Cowart & Goldsmith (2007), the “elation associated with

shopping for apparel can transcend the mode of contact and emanate from an in-store encounter

as well as an online experience, “ (Cowart & Goldsmith 2007, p. 645). It seems that this attitude

of towards shopping online is credited to the act of fulfillment, originated from participation in

shopping regardless of the outlet in which the activity happens (Cowart & Goldsmith 2007).

Another important research factor is the importance of brand awareness for the consumer.

In a study by Dew et al. (2010), their first objective was to examine whether apparel brands

recalled by consumers were also recognized by more consumers. Indeed, results indicated that

there is a positive relationship between the brands’ recall and recognition performances (Dew et

al. 2010).

In a secondary exploration conducted in the same study, Dew et al. (2010) used multiple

instruments—including a questionnaire and online survey—to further test brand awareness in

consumers. The outcome of this investigation showed that there was no significant support for

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the idea that brands with higher levels of brands awareness are associated with a more favorable

brand association (Dew et al. 2010).

The combined findings of this study showed that under the context of athletic apparel,

there seems to be a positive relationship concerning brand recall and recognition, but applying

this to a favorable brand association is unknown. While brand association was found no have no

support by Dew et al. (2010), it doesn’t mean that there may not be an association for the specific

testing of athletic apparel preferences of college students.

Industry and Trade Publications

For many athletic apparel brands, they’re not usually limited to one mode of retailing,

rather, the brands participate in what Dorman (2013) refers to as “omni-channel or multichannel

retailing,” (Dorman, 2013, p.11). This method of retailing is used by businesses to capture

different groups of consumers through a combination of different channels such as brick-and-

mortar stores, e-commerce, catalogues, etc. This is especially important with the reliance on

technology, i.e. the Internet to conduct everyday activities. Multi-channel retailing has become a

“business model standard” within the retail industry. For example, “nearly all major firms have

developed online operations to complement their existing stores” (Dorman 2013, p.11).

The omni-channel retailing model also assumes that customers will interact with a

company using differing channels before making a purchase; this changes from the traditional

multi-channel concept because there are no longer separate channel A and channel B consumers

(Dorman 2013). Ann Zimmerman, a writer for the Wall Street Journal, who referred to one

particular omni-channel consumer as “showroomers”, supports this notion. A “showroomer” is

defined as “shoppers who scope out merchandise in stores but buy on rivals’ websites, usually at

a lower price,” (Zimmerman 2012). This trend presents a growing threat to profitability of

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physical stores, which already feel the pressure from online competition. Additionally, Adrianne

Shapria, a retail analyst at Goldman Sachs, predicted that consumer preferences are shifting to

favor online shopping (Zimmerman 2012). This shift in consumer shopping to e-commerce has

been the subject matter of several studies, including those by Cowart & Goldsmith (2007).

Using information about shifting shopping habits, Dorman (2013) conducted a content

analysis that explored brick-and-mortar retail as a still-viable outlet in the context of omni-

channel retailing. In Dorman’s opinion, brick-and-mortar retail “remains a key element in a

competitive multi-channel retail strategy,” (Dorman 2013, p. 16). The list of retailers included in

the analysis had to meet the following criteria:

1. Included in Internet retailer Magazine’s Top 500 Guide

2. Primary industry is consumer goods/consumer discretionary (classified by Capital IQ

database)

3. Physical stores are used to market products in direct-to-consumer channel

4. Public company

5. Enterprise value is $100 million + (thus excluding early stage growth companies)

((Dorman 2013, p. 16).

In order to test the hypothesis, retail operating data was taken from the sample list of retailers

that met these five criteria and were analyzed over a five-year period beginning in 2007 (Dorman

2013); interestingly, Lululemon was one the retailers included in this analysis. The results of the

analysis supported Dorman’s initial hypothesis that “brick-and-mortar retail is highly relevant in

omni-channel retailing,” (Dorman 2013, p.16). Clearly, companies that hold brick-and-mortar

operations, in addition to other retailing outlets (catalogue, e-commerce), are still viable for

consumers.

In the search for previous studies concerning apparel consumption by college students,

there were very few studies available that target this narrow of a subject matter. However, Bae

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(2004) attempted to investigate this very topic of athletic apparel consumption, but for male and

female American students and Korean students that took part in a joint “Life Activity Program.”

The Lifestyle Activity Program (LAP) is an international physical actvitiy program that was

offered at a university in the United States and a University in South Korea.

In general, it can be said that before graduating from a higher education institution,

students are a campus population segment that are more interested than most in “working out

during the course of the school year.” (Bae 2004, p. 34). The habit of such physical activity can

also be observed on many other college campuses, whether it’s in the context of student athletes

or the use of a campus recreational facility.

For the population sample used, those that engaged in the LAP program participated in

activities such as aerobic conditioning, basketball, bowling, golf, volleyball, self-defense, stretch

and relaxation, and softball (Bae, 2004). After surveying the LAP program students in both the

U.S. and South Korea, the results were slightly different from previous studies that contrasted

American and Korean students. Essentially, Bae (2004) found that the shopping habits between

the two student groups were quite different. For example, 66.9% of American students indicated

that they shopped on a Friday, whereas Korean students shopped on a Saturday (62.2%). One

result that may have positive implications is that 47.7% of American students preferred to shop

at a specialty store, whereas 58.1% of Korean students preferred to shop at a discount store (Bae,

2004, p. 49). The data pertaining to store preferences is particularly interesting to this report

because the retailers in this study (Lululemon, Lucy, Athleta) are specialty retailers within the

United States.

Shopping patterns between genders are also important variables explored by Bae (2004)

and other researchers such as Mitchell & Walsh (2004); Sproles & Kendall (1986); and Bae &

Miller 2009 on the topic of consumer buying behavior. For Bae (2004), results showed that

generally females were more quality conscious than males. Within the American student

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population, females were found to be more brand conscious than males, but relatively equal in

price consciousness (Bae, 2004). For the Korean student population females were more price

conscious than males, but did not significantly differ in brand consciousness (Bae, 2004). These

results imply that the brand of athletic apparel supersedes price for American students, two

important factors for measuring college student buying habits. Additionally, research by Bae

(2004) revealed that “male and female college students had statistically significant differences on

quality, confusion, price, and brand consciousness, but there were no statistically significant

differences between male and female college-aged consumers on recreation, fashion, and

impulse consciousness in the two countries” (Bae 2004, p. 36). In a related study by Bae &

Miller (2009) that looked at basic consumer decision-making characteristics, their results also

indicated that there were differences between male and female college-aged consumers, but on

quality, recreation, confusion, impulse and brand consciousness, however, there were no

significant differences relating to fashion and price consciousness.

Additionally, in a study applied to the German shopper, Mitchell & Walsh (2004) found

slightly different results as well: male individuals were less apt to a novelty and fashion

conscious, and less likely to be confused when making purchases than their female counterparts.

Perhaps the contrast in several categories of variables between the two studies, especially

concerning athletic apparel, can be attributed to different economic environments and cultural

backgrounds (Bae 2004). For the purpose of the study in this report, while there were differing

results in studies conducted by Bae (2004); Bae & Miller (2009); and Mitchell & Walsh (2004),

the differences between male and female shopping habits in the context of consumer decision-

making characteristics will be important factors to examine.

Newspapers and Magazines

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While research conducted by Bennett and Lachowetz (2004) didn’t directly explore

athletic apparel, they did investigate the interest of Generation Y in relation to action or extreme

sports. This sports category was designated as a “collection of risky, individualistic, and

alternative sports such as skateboarding, BMX riding, surfing, street luge, wakeboarding and

motocross (Bennett & Lachowetz 2004). While the typical college student is either a student

athlete or most likely participating in physical activity by visiting the campus athletic center or

another off-site fitness facility, it certainly doesn’t mean that a niche genre of sports such as

action sports is not an interest of the college student population, thus influencing their apparel

buying habits.

Bennett and Lachowetz defined Generation Y as consisting of individuals born between

1982 and 2003 and “makes up 25% of the United States population while Generation Y’s

predecessor, Generation X, makes up only 16% of the population (Bennett & Laskowetz, 2004,

p. 240). Within this age group, according to the Institute of Educational Sciences, in Fall 2014

expected college enrollment in the United States was predicted to be 21 million individuals

(Institute of Educational Sciences 2014). The idea that Generation Y makes up such a large

proportion of the population—and that college students are a segment within this age group—

shows that athletic apparel brands have the opportunity to capture a large share of the college

aged consumer market.

Not only does Generation Y make up a large percentage of the U.S. population, but the

consumption habits of this age group has also been partially attributed to the growth of action

sports (Bennett & Lachowetz, 2004). In reference to a study by McCarthy (2001), it was

suggested that action sports contains over 58 million consumers between the ages of 10 and 24

who have $250 billion in buying power, thus generating the formation of a sporting culture and a

whole industry of consumer products, including apparel (Fitzgerald 2000).

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As aforementioned, college students are considered to be a large segment of Generation

Y. In research conducted by Meyer et al. (2001), they examined how college students’

perceptions of marketer-controlled (price, advertising) and non-controlled factors (peers, parents,

perceived quality, personal choice) affected this their initial and current purchase of brand name

athletic shoes and apparel. The researchers sampled 110 undergraduate students that were

skewed female by 65% and two-thirds ages 17 to 19 with more than 85% of ages between 17 and

22 (Meyer et al. 2001).

When examining first time purchases of respondents, results indicated that,

“advertising and price were rated as somewhat influential, but significantly less

influential than personal choice, perceived quality and same-sex friends. For current

purchases, advertising was significantly less influential (as compared to advertising in the

first-purchase condition), while price was significantly more influential (as compared to

first-time purchase again). Again, personal choice and perceived quality were more

influential. Peer influence was also significantly less influential when compared to first-

time purchases.” (Meyer et al. 2001, p. 19).

While these results were geared towards athletic shoes, a similar pattern emerged with athletic

apparel purchases. Another pattern that emerged about athletic apparel purchases was that

advertising and personal choice persisted from first-time purchases to current purchases (Meyer

et al. 2001).

Meyer et al. (2001) also found that in relation to peers, females were significantly more

likely than males to be influenced in their purchasing choices. In sum, the results of the study

indicated that non-marketer controlled factors were perceived by college students to be more

influential than marketer controlled factors for both first-time and current purchases of brand

name athletic shoes and apparel (Meyer et al. 2001). It seems that factors such as peers,

23


perceived quality, and personal preferences have more impact on college students’ shopping

preferences than company designed marketing campaigns.

VI. Theoretical Framework

After reviewing previous research made available through scholarly journals, industry

and trade publications, and newspapers and magazine concerning shopping habits of college

students and motivations to purchase athletic apparel, a number of factors have been identified as

predictors of branded athletic apparel consumer behavior. Due to the diversity of the studies and

articles in the literature review, a theoretical framework is necessary to organize these variables

in a way that is meaningful and to develop other variables that may be important to the study of

college student consumer behavior. Therefore, the framework of this project will be organized

within the context of Icek Azjen’s Theory of Planned Behavior (TpB).

While it’s valuable to be able to accurately predict consumer behavior on behalf of the

client, Lululemon, the process of doing so is actually quite complex and can better be explained

with insight from the TpB into beliefs and attitudes that influence human behavior. One should

note that although this model has been use to map behavior in many different contexts under

fashion and shopping modes, no prior studies specifically concerning branded athletic apparel

consumption are available at the moment. However, many of the proposed predictors of branded

athletic apparel consumption have a place within the TpB framework.

Prior to formulating the TpB, Ajzen’s (1991) basis for the theory’s development lay in

the Theory of Reason Action, which suggested that “a person’s behavior is determined by his/her

intention to perform the behavior and that this intention is, in turn, a function of his/her attitude

toward the behavior and his/her subjective norm,” (Ajzen 1991). He suggests that the best

predictor of behavior is intention, which is determined by three things: a person’s attitude

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towards the specific behavior, their subjective norms and their perceived behavioral control

(Ajzen 1991). The TpB holds that only specific attitudes towards the behavior being evaluated

can be expected to predict that behavior.

According to Ajzen (1991) it’s also important to measure a person’s subjective norms, or

his or her beliefs about how people that are important to him or her will view the behavior in

question (similar to peer approval); knowing these beliefs can be just as important as knowing a

person’s attitudes. Lastly, perceived behavioral control, which refers to people’s perceptions of

their ability to perform a given behavior, influences intentions (Ajzen 1991). In sum, the more

favorable the attitude and the subjective norms and the greater the perceived control, the stronger

a person’s intention to perform the behavior being examined will be (Ajzen 1991).

Figure 1: Conceptual Model of TpB

Source: Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, p. 179-211.

The following sections will provide a comprehensive list of proposed predictors as

organized by behavioral topics discussed in the literature review and then as applied to the TpB.

By doing so, it can be better understood how college student’s beliefs, attitudes, and subjective

norms will influence their behavior in relation to purchasing branded athletic apparel.

V. Proposed Predictors

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Proposed Predictors from Literature

To first understand where proposed predictors from the literature review should stand in

relation to the TpB, they were organized by different factors; those factors were as follows: peer

driven factors, product driven factors, demographic factors, people and interaction factors, and

media driven factors. After using this method of organization as a precursor to using the TpB

model, the proposed predictors were then able to be better identified within the TpB.

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Peer Driven Factors

Peer influence on making choices (Villard & Moreno 2012)

Peer influence on fitness effort (Villard & Moreno 2012)

Perception of peer approval (Roman & Medvedev 2011)

Perception of peer judgment (Roman & Medvedev 2011)

Product Driven Factors

Likelihood that a brand name will come to mind (Dew et al. 2010)

Perceived ease to which a brand name comes to mind (Dew et al. 2010)

Type of name brands (Bae 2004)

Apparel prices (Dew et al. 2010)

Apparel style (Roman & Medvedev 2011)

Apparel fit (Roman & Medvedev 2011)

Apparel comfort (Roman & Medvedev 2011)

Apparel quality (Roman & Medvedev 2011; Bae 2004)

Perceived product value (Cowart & Goldsmith 2007)

Perception of product purchasing convenience (Cowart & Goldsmith 2007)

Type of store (Bae 2004)

Type of Shopping Companions (Bae 2004)

Demographic Factors

Age (Koa et al. 2012)

Gender (Koa et al. 2012)

Student year (researcher developed)

Expenditure on apparel (Koa et al. 2012)

Student’s major (Koa et al. 2012)

People & Interaction Factors

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Attitude towards physical activity (Villard & Moreno 2011)

Frequency of physical activity (researcher developed)

Type of physical activity participation (researcher developed)

Attitude towards clothing brands (Dew et al. 2010)

Brand awareness (Dew et al. 2010; Cowart & Goldsmith 2007; Bae 2004)

Attitude towards brand loyalty (habitual/brand-loyal orientation) (Cowart & Goldsmith

2007)

Attitudes towards shopping (Cowart & Goldsmith 2007)

Time spent shopping (Bae 2004)

Frequency of shopping (Bae 2004)

Motivations for online shopping (Cowart & Goldsmith 2007)

Attitude towards impulsive shopping (Cowart & Goldsmith 2007; Bae 2004)

Attitude towards being trendy/fashionable (Bae 2004)

Attitude towards apparel trends on campus (Roman & Medvedev 2011)

Intention to recommend brand (researcher developed)

Media Driven Factors

Type of shopping information by media medium (Bae 2004)

Attitude towards branded advertisements (researcher developed)

Proposed Predictors within the Theory of Planned Behavior

After organizing the proposed predictors by different factors, each predictor was easier to

transferr into a principle of the TpB. Those principles were as follows: pre-existing conditions,

beliefs about behaviors, attitudes towards behaviors, normative beliefs, subjective norms,

perceived behavioral controls, control beliefs, intentions, and past behaviors.

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Pre-Existing Conditions





Student’s major (Koa et al. 2012)

Beliefs About Behaviors


Likelihood that a brand name will come to mind (Dew et al. 2010)

Attitudes Towards Behavior



Attitude towards brand loyalty (habitual/brand-loyal orientation) (Cowart &

Goldsmith 2007)




Attitude towards being trendy/fashionable (Bae 2004)

Motivations for online shopping (Cowart & Goldsmith 2007)

Attitude towards clothing brands (Dew et al. 2010)

Normative Beliefs


Peer influence on fitness effort (Villard & Moreno 2012)

Subjective Norms

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Perceived Behavioral Controls


Control Beliefs

Perception of product purchasing convenience (Cowart & Goldsmith 2007)







Intention


Past Behavior

Time spent shopping (Bae 2004)








Selected Proposed Predictors

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While the majority of proposed predictors found through the literature review are

valuable to understanding the shopping habits of college students and the purchase of branded

athletic apparel, due to time and monetary constraints of this study, the proposed predictors have

been narrowed to those that will be central to this study’s investigation. The chosen predictors

were selected based on their appearance in multiple sources—increasing the likelihood of being

actual predictors of purchasing behaviors—and they fit well into the framework of the TpB. The

selected proposed predictors are as follows:

Pre-Existing Conditions





Beliefs About Behaviors


Attitudes Towards Behavior



Attitude towards brand loyalty (habitual/brand-loyal orientation) (Cowart &

Goldsmith 2007)




Normative Beliefs


Subjective Norms

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Perceived Behavioral Controls


Control Beliefs







Intention


Past Behavior








VI. Development of Measures

The ultimate goal of this research study is to develop and pre-test a survey questionnaire

that can be used to identify factors that influence college students’ shopping behaviors as well as

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purchasing habits of branded athletic apparel. In order to achieve this goal, several multiple-item

measures were developed for the proposed predictors previously identified. In addition to

multiple-item measures, several single-item measures were used; these single-item measures

contained the dependent variable—likelihood that college students will purchase branded athletic

apparel—and demographic information that captures money spent on all apparel in a typical

month, academic level, gender, and age. For these predictors, multiple-item measures were not

necessary as they are relatively straightforward and easier to assess than attitudes and beliefs.

In regards to the proposed predictors selected for this study used as multiple-item

measures, this was a necessary decision in order to quantify the validity and reliability of each

construct. Also, because they consisted of beliefs and attitudes, using multiple-items measures

would better capture the true score of these constructs. The majority of the proposed predictors

were taken from studies identified in the literature review and then defined with multiple-item

measures from the Marketing Scales Handbook (Bruner 2009). However, some of the literature

review studies provided definitions for the predictors as well as multiple-item measures; these

were reviewed for measurement overlap and either accepted in their original form or changed

according to the principles of measurement.

For all of the multiple-item measures used in the survey instrument, each was reviewed

using the principles of measurement outlined in the following table (Table 1):

Table 1

Language

The language of measures should be simple in nature and have as little syllables as possible. By using these techniques, errors in measurement can be diminished. It’s also important to note that slang should not be used because not every respondent may understand the phrases or terms used.

Length

The length of each measure should be as short as possible in order to prevent respondents from tiring or losing interest. The only exception to this principle is when length clarified meaning or facilitates information retrieval.

Focus The focus of each measure should be limited to a single

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dimension of a concept. For example, in this study apparel price, quality, fit, and comfort were included as proposed predictors that were separated into four constructs. By doing so, the researcher can isolate a respondent’s belief towards a single concept. If any of these predictors had been combined, the researcher would have been unable to differentiate between individual concepts.

Meaning When attempting to convey the meaning of a concept, it’s ideal to provide a frame of reference if possible as well as to avoid using any jargon that the respondent may not be familiar with.

Word Choice In deciding particular word choice of measures, the researcher has to take into account the subtleties of language. This is particularly important when employing a telephone survey as certain words can be misconstrued or mistaken for another. If this were to occur, the meaning of the measures could be confusing to the respondent.

Assumed Knowledge The researcher cannot assume that the respondent is familiar with the topic you are researching; therefore it’s essential to employ other principle of measurement that will help the respondent understand what you’re asking.

Structure The researcher must be careful to not offer response categories prior to stating the question or providing the statement to which they are asked to react. Providing questions or statements before response categories allows the respondent to understand the topic he/she is answering.

Order Order of each concept is crucial to consider because the meaning of almost any question can be altered by a preceding question. As such, this is why it’s important for order question from general to specific.

Neutral Stance It’s important to use a neutral category within response scales to measures because it allows a respondent with no specific opinion to indicate as such. However, using a neutral category also allows respondents to utilize this category as a way to say “I don’t know”, which is a completely different type of response. This can lead to measurement errors.

Recall Recall questions are used to capture a concept that pertains to a time in the past. The accuracy of recall measures are dependent on if the information was mindlessly processed in the first place, the event was trivial and not thoroughly thought about, and/or if the event occurred a long time ago. Despite these accuracy issues, recall questions are frequently used in measurement.

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Multiple-Item Measures

35


In order to conduct a pre-test, multiple-item measures were used for each of the proposed

predictors selected for study, dictated by the previously outlined theoretical framework of TpB.

Within the TpB, predictors were divided into several categories, including pre-existing

conditions, beliefs about behaviors, attitudes towards behaviors, subjective norms, normative

beliefs, perceived behavioral control, control beliefs, intention, and past behavior. For this

research study, beliefs about behaviors, attitudes towards behaviors, subjective norms, normative

beliefs, perceived behavioral control, control beliefs, and some past behavior were comprised of

multiple-item measures. Most of the multiple-item measures were measured using a five-point

Likert scale. Additionally, a construct based on the dependent variable of this study was

developed using multiple-item measures.

Organization of each construct with its definition and measure was an important task

before developing the survey instrument because it helped to determine what each construct was

trying to accomplish as well as investigating the overlap between measures. Each multiple-item

measure was organized in the following manner:

Beliefs About Behaviors (Azjen 1991, 2006)

Construct: Brand awarenessDefinition: Level in which an individual is aware/know about different athletic brandsSource: Dew et al. 2010; Cowart & Goldsmith 2007; Bae 2004Measures: I choose well-known, designer brands; Advertised athletic clothing displayed in window or catalog is usually a good choice* This construct was eliminated

Attitude Towards Behavior (Azjen 1991, 2006)

Construct: Attitude towards physical activityDefinition: An individual’s affect towards physical activity for fitness or athletic purposesSource: Villard & Moreno 2011

36


Construct: Impulsive shoppingDefinition: “A consumer’s tendency to buy spontaneously, unreflectively, immediately, and kinetically” (Rock and Fisher 1995, p.306)Source: Bruner, G. (2009) Marketing Scales Handbook. Vol 5. 486.

Construct: Brand loyalty (“Commitment to the Brand”)Definition: The degree to which a consumer expresses commitment to a brand or set of brands in a product category.Source: Bruner, G. (2009) Marketing Scales Handbook. Vol 5. 293.

Construct: Attitude towards branded advertisementsDefinition: A consumer’s general evaluation of an advertisementSource: Bruner, G. (2009) Marketing Scales Handbook. Vol 5. 89.

If you saw an advertisement of this brand, you would think it is:

37


Construct: Attitude towards apparel trends on campusDefinition: A person’s affect towards apparel trends observed on campusSource: Roman & Medvedev 2011

Construct: Attitudes towards shoppingDefinition: The degree to which a consumer holds a positive attitude about shopping such that it is enjoyable and worth the time and effortSource: Bruner, G. (2009) Marketing Scales Handbook. Vol 5. 867.

Normative Beliefs (Azjen 1991, 2006)

Construct: Peer influence on making choicesDefinition: The influence peers have on a person’s decision making process when it comes to making a choiceSource: Villard & Moreno 2012; Roman & Medvedev 2011

Subjective Norms (Azjen 1991, 2006)

Construct: Perception of peer approvalDefinition: How accepting a person thinks his/her peers are of him/herSource: Roman & Medvedev 2011

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Construct: Perception of peer judgmentDefinition: A person’s perception of his/her peers’ judgment of apparel choicesSource: Roman & Medvedev 2011Measures: Peer approval is important in the purchasing decisions of other students on campus; Many student purchase brand name apparel products in order to feel a sense of belonging to the campus community; If a brand name apparel product is popular on campus, the majority of my peers will purchase it; My peers make judgments about others based on their apparel; If students don’t purchase popular brand name apparel products, they are not considered part of the student community* This construct was eliminated

Perceived Behavioral Controls (Azjen 1991, 2006)

Construct: Perceived ease to which a brand name comes to mindDefinition: A consumer’s ability to retrieve a brand in his/her mind when given the product category (Keller 1993)Source: Dew et al. 2010

Control Beliefs (Azjen 1991, 2006)

Construct: Perceived product valueDefinition: Measure a person’s belief that the goods and services available from a particular vendor are very good value given the prices charged for themSource: Bruner, G. (2009) Marketing Scales Handbook. Vol 5. 983.

Keeping in mind the brand you just indicated, tell us how much you agree or disagree with the following statements…

39


Construct: Apparel pricesDefinition: A person’s attitude regarding a store’s prices, with some emphasis on how they compare it to other storesSource: Bruner, G. (2009) Marketing Scales Handbook. Vol 5. 697.

Construct: Apparel styleDefinition: How important it is for a consumer to have athletic apparel that is stylish/fashionable Source: Roman & Medvedev 2011; Cowart & Goldsmith 2007; Dew et al. 2010)

Construct: Apparel fitDefinition: How important it is for athletic apparel to fit well in relation to a consumer’s bodySource: Roman & Medvedev 2011

40


Construct: Apparel comfortDefinition: How important it is for a consumer to have comfortable athletic apparelSource: Roman & Medvedev 2011

Construct: Apparel qualityDefinition: How important the quality of athletic apparel is to the consumerSource: Roman & Medvedev 2011; Bae 2004; Cowart & Goldsmith 2007; Dew et al. 2010

Past Behavior (Azjen 1991, 2006)

Construct: Type of physical activity participationDefinition: Type of physical activity a person participates in for fitness (health)/athletic purposesSource: Researcher developed

Construct: Type of shopping information by media mediumDefinition: The kinds of information a consumer is perceptive to by different types of media sourcesSource: Bae 2004

Where do you get information about clothing before shopping?

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Construct: Type of name brandsDefinition: Type of athletic apparel brands that a consumer prefers to purchaseSource: Bae 2004Measures: Lululemon; Athleta; Lucy; Nike; Other*This construct was eliminated

Construct: Type of storeDefinition: Category of stores/retail outlets that the consumer prefers to shop atSource: Bae 2004

Which of these types of stores do you shop at?

Construct: Type of shopping companionsDefinition: Individuals that the consumer chooses to shop withSource: Bae 2004

Who do you shop with?

Dependent VariableConstruct: Likelihood college students will purchase branded athletic apparelDefinition: The likeliness that a college student will purchase certain branded athletic apparelSource: Researcher developed

If you were to buy athletic clothing tomorrow, how likely would you be to buy:

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After defining each predictor and including corresponding measures, the researcher

encountered several changes that need to be made regarding construct inclusion and

measurement wording before proceeding to create the survey instrument. For many of the

measures taken from both the literature review and Marketing Scales Handbook, the wording had

to be changed in order to conform to the nature of this study (branded athletic apparel) or to

ensure that the overlap in meaning between in each measure was accurate.

Key changes made between organizing the measures and creating the survey instrument

was the elimination of certain constructs. Type of Name Brands was eliminated because it was

too similar to Favorite Athletic Apparel Brands, therefore it was considered redundant to include.

Brand Awareness was also eliminated because, in comparison to similar constructs about athletic

apparel brands, it’s meaning and ultimate goal was too similar to other constructs concerning

brand and a respondent’s level of awareness. Additionally, Perception of Peer Judgment was

removed because the measures were too similar to Perception of Peer Approval and it contained

measures that did not focus on the beliefs of the respondent. Error in the overlap for Perception

of Peer Judgment was attributed to the study the measures originated from; the researchers did

not accurately distinguish between the two constructs using measures with differing meanings.

During the process of organization, several constructs were added by the researcher in

order to compliment those found in the literature review: Brand Purchased Most Frequently,

Preferred Athletic Apparel Brand, and Previous Purchase of Preferred Athletic Apparel Brand,

Advertising by Athletic Apparel Brand Purchased Most Frequently. Each of these constructs

were single-item measures and will be further discussed in the following section pertaining to

single-item measures.

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Single-Item Measures

In addition to the multiple-item measures selected for this study, there were also several

single-item measures includes in this study. In the context of the TpB, these measures were

limited to the categories of pre-existing conditions, intention, and some past behaviors. Similar to

the multiple-item measures discussed in the previous section, these measures were developed in

adherence to the principles of measures. The organization of the single-item measures were as

follows:

Pre-Existing Conditions (Azjen 1991, 2006)

Construct: AgeDefinition: Numerical age of an individualSource: Researcher developed

Construct: GenderDefinition: Sex of an individualSource: Researcher developed

Construct: Student (academic) yearDefinition: A student’s current academic yearSource: Roman & Medvedev 2011

Construct: Expenditure on apparelDefinition: An individual’s monthly spending on apparel itemsSource: Koa et al. 2012

How old are you? ______________________________________________

What is your gender? (Choose One)

Male Female

What is your current academic year? (Choose One)

Freshman Sophomore Junior Senior Graduate

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Intention (Azjen 1991, 2006)

Construct: Intention to recommend brandDefinition: A person’s intention to positively recommend an athletic apparel brand to peers, family, parents, and acquaintances (roommates, neighbors, etc.)Source: Researcher developed

Past Behavior (Azjen 1991, 2006)

Construct: Frequency of shopping Definition: How often a consumer shops for apparel in a typical monthSource: Bae 2004; Cowart & Goldsmith 2007

Construct: Frequency of physical activityDefinition: How often a person participates in physical activity for health or athletic purposesSource: Researcher developed

Construct: Most frequently purchased athletic apparel brandDefinition: The single athletic apparel brand that an individual purchases most oftenSource: Researcher developed

How much do you spend on any clothing in a typical month? (Choose One)

Less than $100 $101 to $250 $251 to $400 $401 to $550 More than $551

45


Construct: Branded advertisement consumptionDefinition: (In reference to the most frequently purchased athletic apparel brand) Whether an individual has viewed (“consumed”) an advertisement related to the brand he/she purchases most oftenSource: Researcher developed

Construct: Likelihood to purchase comparable branded athletic apparel Definition: An individual’s propensity to purchase athletic apparel brands that reflect comparability in the athletic clothing marketSource: Researcher developed

Construct: Purchasing habits of comparable branded athletic apparelDefinition: (In reference to likelihood to purchase comparable branded athletic apparel) Whether or not an individual has purchased one of the comparable athletic apparel brandsSource: Researcher developed

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VII. Development of Survey Instrument

Once the measures for each predictor was organized within the framework of the TpB

and analyzed using the principles of measurement, the researcher developed the pre-test survey

that would be distributed to a random sampling of students in the student center of Boston

University. Before organizing the measures by question, beginning with the most general and

ending with the most specific, the researcher began the survey with an introductory statement

that explained the nature of the survey and anonymity of the respondents. The purpose of this

introduction was to prime respondents into understanding what he/she would be asked in the

following survey and to satisfy any question as to whether personal information was required to

complete the questionnaire. It’s essential to note that none of the instructional statements

throughout the survey made any indication of the client, Lululemon. By doing so, the researcher

eliminated any bias respondents may have as a reaction to this relationship. Additionally, a

“thank you” statement was added the end of the survey to denote completion of the

questionnaire.

While there were no distinct sections contained within questionnaire, there were certain

stages involved in the flow of the survey design. The first stage of measures (“A”) asked

respondents general questions regarding their attitude, beliefs and behaviors about physical

activity, shopping, and peers. This was developed as the first stage not only to capture

information about respondents’ beliefs and attitudes, but also to divert the respondent’s attention

away from thinking about the main goals of the survey. If a respondent were to go through the

cognitively process to figure out the exact goal of the survey, it could potentially cause the

him/her to answer questions less truthfully and in a way that he/she thinks is the appropriate

answer. This is often seen in examples of survey instruments that ask respondents personal

questions about drug or alcohol use. Respondents feel that he/she would want to portray

47


himself/herself in a particular way because of the taboo nature of the topic. While the topic of

this particular study is not taboo in nature, the same principle applies with regards to respondents

portraying a particular character.

Multiple item measures were employed in stage “A” in order to capture beliefs and

attitudes of respondents while single item measures were used to find out information that wasn’t

necessarily a belief or attitude; such single item measures included Frequency of Physical

Activity and Frequency of Shopping. The multi-item measures for this stage consisted of the

predictors Attitudes Toward Physical Activity, Type of Physical Activity, Attitudes Towards

Shopping, Impulsive Shopping, Perception of Peer Approval, Peer Influence on Making Choices,

Attitude Towards Apparel Trends on Campus, Type of Shopping Information by Media Medium,

Type of Store, and Type of Shopping Companions.

In the second stage of the questionnaire (“B”), predictors and corresponding questions

became increasingly narrowed in topic to focus on branded athletic apparel; this was employed

without specifying a particular athletic apparel brand. The objective of this section was to guide

the respondent into thinking about a specific topic (branded athletic apparel) in order to share

his/her beliefs and attitudes. This was partially facilitated by the first section because it primed

the respondent into thinking about shopping and fitness in order to continue to narrow the focus

to branded athletic apparel; the researcher organized this section to include the predictors of

Brand Loyalty, Apparel Style, Apparel Fit, Apparel Quality, Apparel Comfort, and Perceived

Ease to Which A Brand Name Comes to Mind.

Additionally, in order to ensure that respondents understood what was meant by “Athletic

Apparel” the researcher included a definition at the beginning of this section: clothing items used

for fitness or athletic purposes (i.e. running tights, sports bra, basketball short, etc.). It’s

important to note that the term “athletic clothing” was used instead of “athletic apparel” because

the language would be better understood by respondents as some may not know the meaning of

48


“apparel”. This is an example of the researcher employing the principles of measurement

concerning language.

After capturing general attitudes and beliefs concerning branded athletic apparel, the third

stage (“C”) explored explicitly-named brands. Before asking questions concerning the dependent

variable (Likelihood to Purchase Branded Athletic Apparel), the researcher first used the

predictor Most Frequently Purchased Athletic Apparel Brand in order to guide respondents to

think about specific brand names that he/she currently purchase. Once a brand name was

indicated by the respondent, he/she was asked questions about that brand using the predictors of

Perceived Product Value, Apparel Prices, Attitude Towards Branded Advertisements, and

Branded Advertisement Consumption. Next, the researcher openly asked respondents about the

athletic apparel brands of Lululemon, Lucy, Athleta, and Nike. For example, the researcher

asked which of these four brands would the respondent be most likely to purchase and if he/she

had ever purchased the brand he/she selected. This utilized the predictors of Likelihood to

Purchase Comparable Branded Athletic Apparel and Purchasing Habits of Comparable Branded

Athletic Apparel. Finally, the dependent variable was introduced using the statement “If you

were to buy athletic clothing tomorrow, how likely would you be to buy…”. Respondents were

once again given the choices of Lululemon, Lucy, Athleta and Nike.

In the final stage of the questionnaire (“D”), the researcher utilized predictors that

concerned demographic items. Respondents answered questions regarding their Expenditure on

Apparel, Student Year, Gender and Age. All of these predictors were answered using single-item

measures.

49


Survey Instrument

A

50

Branded Athletic Apparel Consumption 51


C

52


D

53


VIII. Analysis of Measures

Following the development of the survey instrument, the researcher administered the

completed survey in a pre-test that encompassed a random sampling of 94 college students

within Boston University’s student center (George Sherman Union). Once each questionnaire

was completed and collected from respondents, the researcher proceeded to code each survey

according to a code book previously developed in order to input the data into SPSS Statistics.

After the data-input stage of the pre-test collection, the researcher conducted both a qualitative

and quantitative measure analysis.

Qualitative Review

Before administering the pre-test to the chosen sample, the researcher consulted an

advising professor to evaluate the survey. Using feedback provided from this individual, the

questionnaire was re-arranged in the survey development stage. The changes that occurred as a

result of feedback were primarily based on principles of measurement that included order effect,

changing the format of questions in order to better capture respondent’s answers and the

development of questions regarding the dependent variable. For example, questions five, six and

eight were changed from single-item measures to a format that would allow for multiple item

measures for the predictors of Type of Shopping Companion, Type of Store and Type of

Shopping Information by Media Medium. By changing these predictors into multiple item

measures, the researcher would be better able to capture respondents’ thoughts concerning these

constructs using a Likert-type scale.

54


Unexpectedly, the researcher found interesting respondent behaviors while administering

the questionnaire. Many respondents seemed to be distracted by the atmosphere of the student

center or by colleagues who were accompanying the individual. Additionally, respondents would

often receive the questionnaire and then proceed to flip through the pages of the survey to assess

how long it would take him/her to complete the task; often, as a result of this action, the

researcher received feedback regarding the length of the questionnaire. The impact of these

behaviors will be discussed in a following section regarding survey revisions.

Quantitative Review

After reviewing the pre-test administered to the sample population, the researcher found

that several surveys had not been filled out to completion. However, none of the surveys were

incomplete to the point of disregard, thus all 94 surveys were included in the data.

Following data collection, the researcher provided a unique ID number and developed the

aforementioned coding strategy for each construct. For constructs that contained multiple-item

measures, a five-point Likert scale was utilized in a format of one to five, with one representing

the lowest level of agreement with the item and five representing the highest level of satisfaction

with the item. For the measures “When I participate in physical activity I feel annoyed”, “I

carefully plan most of my purchases”, “I don’t like to shop”, “I don’t care about clothing trends

on campus”, “Irritating”, “Not Informative”, “Bad” and “Boring”, each was initially coded in a

similar fashion to the other multiple item measures, but then later recoded in SPSS for accurate

analysis.

Demographic variables were measured on either a ratio or nominal level, so the coding

rules were dissimilar to those that utilized a Likert scale. These are described in the following

table (Table 2):

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Table 2: Coding Rules for Demographic Variables

Q22: How much do you spend on any clothing in a typical month?

Nominal

Less than $100: 1$101 to $250: 2$251 to $400: 3$401 to $550: 4More than $551: 5

Q23: What is your current academic year? Nominal

Freshman: 1Sophomore: 2Junior: 3Senior: 4Graduate: 5

Q24: What is your gender? NominalMale: 1Female: 2

Q25: How old are you? Ratio

Coding was based on the number provided by the respondent. For example, if a respondent indicate that he/she is “20” then the code for this variable was “20”.

56


For data that was considered “missing” the researcher left these measure blank within SPSS.

After completing the data entry phase of the study, the information was then analyzed for

validity and reliability.

Assessment of Validity and Reliability

In order to determine the validity and reliability of the measures—meaning that they

measured what they were intended to measure with accuracy—the researcher began the

assessment process by running individual frequency distributions. By doing so, the researcher

would be able to detect any outliers or possible errors from the data while also reviewing the

distribution of answer categories; the researcher did not detect any outliers or possible errors

within the frequency distributions. These can be found in Appendix A.

After utilizing an outlier detection method (frequency distributions), the researcher ran a

validity analysis of multiple item measures using Pearson’s Correlation formula. First, an inter-

item correlation was ran by construct and then ran again to create an inter-item correlation matrix

amongst all measures. By doing so, the researcher could determine if each measure could

potentially belong to a different construct, which would later be determined in a factor analysis.

57


Overall, the majority of the correlations were found to be satisfactory enough to continue

to the next phase of analysis. Those measures that were found to be unacceptable were

highlighted in red to “flag” their low correlations (below 0.20), but were not eliminated. While in

a regular study these measures would certainly be eliminated from further analysis, the decision

to keep them in the process was due to the idea that perhaps they correlated better with other

measures. This would be determined in the factor analysis stage of assessment and if they still

had an indication of error, those that continued to produce error would be eliminated.

Additionally, because many of the correlation results are relative to the data set, it would have

been too soon in the assessment process to determine concrete elimination as long the

correlations were significant at the p=0.05 level. Correlations that were flagged, but not

eliminated are outlined below. Full data output of these correlations can be found in Appendix B

and Appendix E.

The first correlation to indicate possible measurement error was between measures for the

predictor of Impulsive Shopping. The measure that was determined to be the cause of potential

error was “I carefully plan most of my purchases.” Such a determination was made because the

correlation between this measure and “I often buy things without thinking” was 0.18; “I buy

things according to how I feel at the moment” was 0.11; and “When I go shopping, I buy things

that aren’t on my shopping list” was 0.08. Due to the fact that this measure was a recoded item,

the researcher made sure to re-check that the measure was indeed recoded. Once this was

confirmed the researcher proceeded to indicate that this measure could cause error in further

assessments.

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Inter-Item Correlation for Impulsive Shopping

I carefully plan most of my

purchases (r)

I often buy things without

thinking

I buy things according to how

I feel at the moment

When I go shopping, I buy

things that aren’t on my shopping

listI carefully plan

most of my purchases

1 0.18 0.11 0.08

I often buy things without

thinking0.18 1 0.43 0.34

I buy things according to how

I feel at the moment

0.11 0.43 1 0.36


things that aren’t on my shopping

list

0.08 0.34 0.36 1

59


Outside of this issue, the correlations between the three other measures contained in Impulsive

Shopping proved to be valid.

The next construct to contain a measure with a possible error designation was Peer

Approval; the measure in question was “If I don’t purchase popular brand name clothing

products, I’m not considered part of my peer group.” When this measure identified as another

recoded item to contain error, the researcher made sure to once again re-check that it was indeed

recoded. This measure was determined as such and deemed a potential point of measurement

error by highlighting the correlations that were less than 0.20.

Inter-Item Correlation for Peer Approval

I’m always aware of how my peers on campus

perceive me

It’s important to be accepted by

my peers on campus

Being accepted by my peers as a

part of the campus

community is important to me

If I don’t purchase popular

brand name clothing

products, I’m not considered part

of my peer groupI’m always

aware of how my peers on campus

perceive me

1 0.37 0.41 0.12

It’s important to be accepted by

my peers on campus

0.37 1 0.81 -0.08


part of the campus


0.47 0.81 1 0.46

If I don’t purchase popular

brand name clothing

products, I’m not considered part

of my peer group

0.12 -0.08 0.05 1

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As compared to the measurement error indicated in Impulsive Buying, it seems that “If I don’t

purchase popular brand name clothing products, I’m not considered part of my peer group”

caused significantly less correlation between measures, even resulting a negative correlation with

“It’s important to be accepted by my peers on campus.”

In continuation, potential measurement error was found in the construct of Peer

Influences on Making Choices between “I think having the same clothing products gives me a

sense of belonging to my peers” and “When I see my peer with a particular clothing product I go

buy it.” This type of error is quite different from those previously found because it was contained

between two measures, rather than due to a single measure.

Inter-Item Correlation for Peer Influences on Making ChoicesI purchase

clothing products only because

they’re popular with my peer

group

I think having the same clothing

products gives me a sense of

belonging to my peers

When I see peers with a particular clothing product,

I go buy it

I purchase clothing products

only because they’re popular with my peer

group

1 0.37 0.43

I think having the same clothing

products gives me a sense of

belonging to my peers

0.37 1 0.17

When I see peers with a particular clothing product,

I go buy it

0.43 0.17 1

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Because validity was found in combination with other measures, the researcher determined that

there was little indication that these measures would need to be eliminated in the future; perhaps

they would be reorganized using factor analysis or would be found reliable.

In order to further investigate correlations between measures, an inter-item correlation

matrix was created utilizing all multiple item measures (Appendix E). These correlations

revealed that several measures, when compared to measures outside of their original construct,

seemed to have acceptable correlations. For the researcher, this was an indication that the factor

analysis may result in new constructs.

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Before conducting the first factor analysis, the researcher determined how many

constructs were developed for the pre-test using multiple item measures; this number was

determined to be 15, thus resulting in a starting factor analysis that included 15 factors. The

majority of the measures belonged to one factor—with many organized into their original

groupings. However, there were some measures that showed the propensity to belong to more

than one factor with indication that it was “stretching” to belong to more than one category.

While most that belonged to more than one factor were limited to two in number, “I get value for

my money when I buy this brand” and “Good” belonged to several more, four and three

respectively. The measures that indicated a propensity to belong to more than one factor were “I

think having the same clothing products gives me a sense of belonging to my peers” (factor 11

and factor 15); “I’m always aware of how my peers on campus perceive me” (factor 6 and factor

13); “Purchasing athletic clothing displayed in a window or catalog is usually a good choice

(factor 1 and factor 10); “I usually have one or more outfits of the latest style” (factor 8 and

factor 13); “This brand is excellent value for the money” (factor 1 and factor 5); “I get value for

my money when I buy this brand” (factor 1, factor 5, factor 8 and factor 12); “This brand is

worth every cent” (factor 1 and factor 15); and, “Good” (factor 3, factor 12 and factor 15).

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Knowing that conducting a factor analysis can be quite sensitive to splitting measures

amongst categories, the researcher made the decision to reduce the number of factors by a single

number in order to comb through the analyses with a more critical eye. Therefore, the next factor

analysis contained 14 factors. Like the previous analysis containing 15 factors, there were

measures that belonged to a single factor with a few that belonged to more than one category. A

change that was noted in processing an analysis with less factors is that some measures that

previously belonged to a single category had split to belong into more than one category; the

researcher determined that this could be attributed to reducing the number of factors, therefore

the reorganization cause certain measures to change in assimilation. The measures that had the

propensity to belong to more than one factor, but belonged to a single category in the previous

analysis, were “When I go shopping, I buy things that aren’t on my shopping list” (factor 3 and

factor 10); “I stick with the usual brands of athletic clothing because I know it is best for me”

(factor 1 and factor 4); “Having athletic clothing that fits me well is important to me” (factor 1

and factor 5); and “Informative” (factor 3, factor 4 and factor 14). Additionally, those that

continued to belong to more than one factor were “I usually have one or more outfits of the latest

style” (factor 2, factor 7, factor 9 and factor 11); “This brand is excellent value for the money”

(factor 1 and factor 5); “I get value for my money when I buy this brand” (factor 1, factor 5,

factor 7 and factor 12); “This brand is worth every cent” (factor 1 and factor 13); and, “Good”

(factor 3 and factor 11).

64


Due to the fact that there were several measures that could belong to more than one

factor, the researcher continued using factor analysis to reduce the number of factors to 13.

Additionally, the researcher decided to eliminate measures from the construct Attitude Towards

Branded Advertisements. While conducting a factor analysis the researcher started to see that

these measures were quite different from the other multi item measures that captured beliefs and

attitudes; while these measures are able to capture a belief in conjunction with the priming

question, without the question provided before the measures contained in the pre-test, these

measures no longer represented respondent beliefs; thus, the elimination was necessary.

Factor analysis continuation was also not possible until certain measures were completely

eliminated from assessment; measures that were necessary to eliminate were those that persisted

in measurement error through factor splitting. Measures that were removed from further

assessment were “Having athletic clothing that fits me well is important to me”; “I keep my

wardrobe up-to-date with the changing fashions”; “Fashionable, attractive clothing is important

to me”; “This brand is worth every cent”; “I often buy things without thinking”; “I purchase

clothing products because they are popular with my peer group”; “When I see a certain clothing

trend on campus, I usually like it”; “When I see my peers with a particular clothing product, I go

buy it”; “I stick with my usual clothing brands of athletic clothing because I know it is best for

me”; “Purchasing athletic clothing displayed in a window or catalog is usually a good choice”;

and, “I usually have one or more outfits of the latest style”.

65


Once the data was cleaned of problematic measures, the researcher proceeded to re-run a

13 factor analysis of the measures. This resulted a better set of factors, but contained two factors

that included only one measure, which meant that the number of factors needed to be reduced.

Therefore, the researcher continued the factor analysis process with 11 factors. This new set of

factors once again resulted in having one factor with a single measure. As such, the researcher

reduced the number of factors to 10, which resulted in a clean and final factor analysis. However,

it’s important to note that there were two factor categories (factor 9 and factor 10) that were

viewed as having factors loadings that weren’t very high, but were able to be kept without

further reducing the number of factors; factor 9 contained a loading of 0.53 for the measure “I

carefully plan most of my purchases” and factor 10 contained a loading of 0.55 for “I enjoy

following clothing trends I see on campus”. These results indicate that there may be

measurement error once reliability was calculated.

To conduct a reliability assessment using Cronbach’s Alpha formula, each measure was

designated into the factor grouping as shown through the factor analysis process. For most of the

factor groupings, they had either a very good or excellent calculation; however, there were

several that required the elimination of a single measure in order to increase the reliability to at

least acceptable, or the elimination of an entire grouping all-together. Factor groupings that had

high reliability can be attributed to the fact that many of the measures that were grouped together

using factor analysis had similar meanings and high results in within factor analysis.

The factor groupings that showed very high Cronbach’s Alpha calculations (either

considered “very good” or “excellent” in nature) included factor 1, factor 2, factor 3, factor 4,

factor 5, factor 6, and factor 7. Additionally, factor 10 was accepted as being reliable enough to

be included in results with a good reliability and a Cronbach’s Alpha of 0.62. Factor 8 was

accepted within the final results as well, but only had a mediocre reliability with a Cronbach’s

alpha of 0.54. See Appendix D for full reliability results.

66


In the case of factor 5, while it had a very good reliability, the data showed that if “When

I buy athletic clothing how it fits on me is important” was removed from the factor grouping, the

reliability would increase to 0.90 and change to an excellent reliability. Thus, the researcher

eliminated this measure to increase the reliability of this factor grouping. Another factor

grouping that required an elimination of a measure was factor 9. Initially, this grouping was

comprised of three measures with a Cronbach’s Alpha of 0.37, a result that is considered

unacceptable. In order to increase the reliability and avoid complete categorical elimination, the

researcher was able to eliminate the measure “I carefully plan most of my purchases” to increase

reliability to mediocre—a Cronbach;s Alpha of 0.52; this action was foreshadowed in the

validity stage of the quantitative assessment process.

Interestingly, those factor groupings that had issues with reliability were deemed as such

in the factor analysis phase or even as early as the validity phase. Measures that appeared to have

measurement error resulted in either elimination or a reliability that was not very high. In sum,

indicators of measurement error were well founded in nature and directed the process of the data

assessment for validity and reliability. Overall, many of the measures were kept in groupings that

were similar to their original constructs with a few new groupings that paired different constructs

together, but seemed to align in belief or attitude topic.

Revisions to Survey Instrument

67


At the conclusion of the qualitative and quantitative assessments, the researcher had the

available information to revise the survey instrument to optimize its use through future research.

Based on findings from the assessments, the following changes are proposed for the final version

of the survey instrument:

68


1. Reformat Structure of Problematic Questions: Having the ability to observe respondents

while each person completed the pre-test questionnaire was valuable to the researcher in

order to see where the individual had issues completing the task; this was introduced in

the previous section concerning a qualitative assessment. While the researcher kept

within standard practices of instrument length (principles of measurement), there some

issues that need to be revised for the final questionnaire. Several respondents had trouble

following the provided directions for question three that contained the predictor of Type

of Physical Activity. The primary objective was to have respondents choose the physical

activity item he/she participates in the most, but by employing multiple boxes to mark,

respondents assumed that it was a multiple answer item, often not reviewing the

statement that indicated to choose only one item. Instead, the researcher will ask the same

question, but instead utilize a singular line for respondents to fill in the physical activity

he/she participates in. This will eliminate the result that occurred in the pre-test: many

questionnaires contained a missing item designation for question three due to respondents

marking multiple answers for this construct. Such results most likely created

measurement error that was seen in the analysis phase of the study. Another question that

requires revision is question 12 that contained the predictor of Most Frequently

Purchased Athletic Apparel Brand. The researcher found that by allowing respondents to

answer the question in a “fill in the blank” format, it opened the results to missing data

regarding subsequent corresponding questions. In order to resolve this issue, the research

will eliminate this question completely and replace it with a question that contains the

four brands indicated in the dependent variable construct. By doing so, not only will the

researcher minimize measurement error, but it will also open future results to contain

more beliefs and attitudes concerning the client and its competitors.

69


2. Expand Line of Questioning to Include Unique Respondents: After reviewing the data

from each questionnaire, the researcher found that there was a significant amount of

respondents who indicated that he/she either didn’t exercise or didn’t purchase athletic

apparel. With the current line of questioning contained in the pre-test, having such a

category of respondents created missing data because he/she wasn’t able to answer many

of the questions. As a remedy to this issue, the researcher will include constructs that

investigate this type of respondents and why he/she does not purchase athletic apparel. In

order to complete this task, the researcher will revisit studies indicated in the literature

review and consult the Marketing Scales Handbook (Bruner 2009) for new measures that

will capture the beliefs and attitudes of this category of respondents.

3. Eliminate Redundant Questions: Data taken from completed pre-test surveys revealed

that question 19 and question 22 (dependent variable) were too similar in nature, despite

the fact that question 19 is a single item measure and question 22 is a multiple item

measure. Including both constructs also seemed redundant to the length of the survey.

Because the goal of the study is to capture the true score of respondents’ beliefs and

attitudes, it is best to have more multiple item measures in order to do so. Thus, the

researcher will eliminate question 19 and replace it with the dependent variable.

Subsequent constructs and questions that previously corresponded to question 19 could

be reapplied to the dependent variable. This will create more data concerning the

dependent variable.

70


4. Reword Measures for Clarity: While respondents were completing the pre-test survey,

several individuals asked for clarity concerning the construct Types of Stores. For

example, some individuals didn’t understand what was meant by “discount retailer”; this

indicated that the researcher violated the principles of measurement and the measures

lacked clarity in meaning. To solve this issue, the researcher will provide examples

relevant to each measure in order to clarify what is meant by each type of store.

5. Eliminate Measures with Absolute Error: After employing a quantitative assessment of

the data to determine the validity and reliability of multiple item measures, several

measures were found to contain error that resulted in elimination. Clearly, even though

the researcher reworded many measures to meet the principle of measurement, it was not

enough to minimize or avoid measurement error. For this reason, those measures that

were eliminated as a result of assessment will not be included in the final questionnaire.

Conclusions

In using a strategy to develop to pre-test questionnaire before conducting a full study for

the client and studying branded athletic apparel consumption among college students, the

research was able to evaluate the level of precision of the survey instrument as well as the

measurement error that resulted from the pre-test. While there are several revisions that need to

be made to the survey instrument based on a qualitative and quantitative assessment, the majority

of the measures were strong in validity and reliability with several being re-categorized in a

factor analysis.

Due the results of the pre-test, the researcher recommends utilizing a pre-test for further,

but separate studies and to ensure that the sample population of the final survey instrument is of

substantial size. By doing so, the client can ensure that results are applicable to any final

recommendations based on the research study.

71

Branded Athletic Apparel Consumption 72


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Warner, F. (2003, July 6). Grass-Roots Business; Dressing Women to Sweat Fashionably. Retrieved from: http://www.nytimes.com/2003/07/06/business/grass-roots-business-dressing-women-to-sweat-fashionably.html

Yun D, Silk KJ. (2011) Social norms, self-identity, and attention to social comparison information in the context of exercise and healthy diet behavior. Health Communication. 26:275–285.

Zimmerman, A. Wall Street Journal, "Showdown Over 'Showrooming'." (2012, January 23). January 23, 2012. Accessed April 25, 2013. Retrieved from: http://online.wsj.com/article/SB10001424052970204624204577177242516227440.html

75


Appendix A – Frequency Distributions

I enjoy physical activity

Frequency Percent Valid Percent Cumulative PercentValid 1 1 1.1 1.1 1.1

2 5 5.3 5.3 6.4

3 2 2.1 2.1 8.5

4 43 45.7 45.7 54.3

5 43 45.7 45.7 100.0

Total 94 100.0 100.0

When I participate in physical activity I feel annoyed


2 51 54.3 54.3 86.2

3 4 4.3 4.3 90.4

4 5 5.3 5.3 95.7

5 4 4.3 4.3 100.0

Total 94 100.0 100.0

I like to participate in physical activity


2 5 5.3 5.3 6.4

3 2 2.1 2.1 8.5

4 46 48.9 48.9 57.4

5 40 42.6 42.6 100.0

Total 94 100.0 100.0

I feel good when I'm physically active


2 3 3.2 3.2 4.3

3 1 1.1 1.1 5.3

4 36 38.3 38.3 43.6

5 53 56.4 56.4 100.0

Total 94 100.0 100.0

How often do you participate in physical activity?

76



2 26 27.7 28.0 40.9

3 32 34.0 34.4 75.3

4 23 24.5 24.7 100.0

Total 93 98.9 100.0

Missing System 1 1.1

Total 94 100.0

What type of physical activity do you participate in the most?


2 11 11.7 16.2 35.3

3 6 6.4 8.8 44.1

4 6 6.4 8.8 52.9

5 3 3.2 4.4 57.4

6 17 18.1 25.0 82.4

7 2 2.1 2.9 85.3

8 10 10.6 14.7 100.0

Total 68 72.3 100.0


Total 94 100.0

I enjoy shopping


2 6 6.4 6.4 12.8

3 7 7.4 7.4 20.2

4 38 40.4 40.4 60.6

5 37 39.4 39.4 100.0

Total 94 100.0 100.0

I carefully plan most of purchases


2 19 20.2 20.2 24.5

3 19 20.2 20.2 44.7

4 42 44.7 44.7 89.4

5 10 10.6 10.6 100.0

Total 94 100.0 100.0

77


I don't like to shop


2 47 50.0 50.5 79.6

3 13 13.8 14.0 93.5

4 4 4.3 4.3 97.8

5 2 2.1 2.2 100.0

Total 93 98.9 100.0


Total 94 100.0

I often buy things without thinking


2 46 48.9 49.5 63.4

3 10 10.6 10.8 74.2

4 22 23.4 23.7 97.8

5 2 2.1 2.2 100.0

Total 93 98.9 100.0


Total 94 100.0

I look forward to my shopping trips


2 7 7.4 7.5 12.9

3 17 18.1 18.3 31.2

4 50 53.2 53.8 84.9

5 14 14.9 15.1 100.0

Total 93 98.9 100.0


Total 94 100.0

I buy things according to how I feel at the moment


2 13 13.8 14.8 18.2

3 24 25.5 27.3 45.5

4 42 44.7 47.7 93.2

5 6 6.4 6.8 100.0

Total 88 93.6 100.0

78



Total 94 100.0

When I go shopping, I buy things that aren't on my shopping list


2 13 13.8 14.1 16.3

3 19 20.2 20.7 37.0

4 48 51.1 52.2 89.1

5 10 10.6 10.9 100.0

Total 92 97.9 100.0


Total 94 100.0

It's important to be accepted by my peers on campus


2 22 23.4 23.7 26.9

3 19 20.2 20.4 47.3

4 44 46.8 47.3 94.6

5 5 5.3 5.4 100.0

Total 93 98.9 100.0


Total 94 100.0

Being accepted by my peers as a part of the campus community is important to me


2 14 14.9 15.2 18.5

3 26 27.7 28.3 46.7

4 42 44.7 45.7 92.4

5 7 7.4 7.6 100.0

Total 92 97.9 100.0


Total 94 100.0

I enjoy following clothing trends I see on campus


2 31 33.0 34.1 47.3

79


3 25 26.6 27.5 74.7

4 21 22.3 23.1 97.8

5 2 2.1 2.2 100.0

Total 91 96.8 100.0


Total 94 100.0

If I don't purchase popular brand name clothing products, I'm not considered part of my peer group


2 35 37.2 37.6 87.1

3 8 8.5 8.6 95.7

4 4 4.3 4.3 100.0

Total 93 98.9 100.0


Total 94 100.0

I purchase clothing products only because they're popular with my peer group


2 38 40.4 41.3 93.5

3 4 4.3 4.3 97.8

4 2 2.1 2.2 100.0

Total 92 97.9 100.0


Total 94 100.0

I don't care about clothing trends on campus


2 27 28.7 28.7 35.1

3 15 16.0 16.0 51.1

4 32 34.0 34.0 85.1

5 14 14.9 14.9 100.0

Total 94 100.0 100.0

I think having the same clothing products gives me a sense of belonging to my peers

Frequency Percent Valid Percent Cumulative Percent

80


Valid 1 23 24.5 24.7 24.72 47 50.0 50.5 75.3

3 11 11.7 11.8 87.1

4 11 11.7 11.8 98.9

5 1 1.1 1.1 100.0

Total 93 98.9 100.0


Total 94 100.0

When I see a certain clothing trend on campus, I usually like it


2 25 26.6 26.6 43.6

3 35 37.2 37.2 80.9

4 17 18.1 18.1 98.9

5 1 1.1 1.1 100.0

Total 94 100.0 100.0

I'm always awarw of how my peers on campus perceive me


2 23 24.5 24.7 32.3

3 35 37.2 37.6 69.9

4 25 26.6 26.9 96.8

5 3 3.2 3.2 100.0

Total 93 98.9 100.0


Total 94 100.0

When I see my peers with particular clothing product, I go buy it


2 47 50.0 50.0 86.2

3 7 7.4 7.4 93.6

4 6 6.4 6.4 100.0

Total 94 100.0 100.0

Parent(s)


2 14 14.9 15.1 24.7

81


3 41 43.6 44.1 68.8

4 26 27.7 28.0 96.8

5 3 3.2 3.2 100.0

Total 93 98.9 100.0


Total 94 100.0

By myself


2 8 8.5 8.5 12.8

3 23 24.5 24.5 37.2

4 46 48.9 48.9 86.2

5 13 13.8 13.8 100.0

Total 94 100.0 100.0

Friend(s)


2 10 10.6 10.6 14.9

3 27 28.7 28.7 43.6

4 43 45.7 45.7 89.4

5 10 10.6 10.6 100.0

Total 94 100.0 100.0

Neighbor(s)


2 9 9.6 9.7 97.8

3 1 1.1 1.1 98.9

4 1 1.1 1.1 100.0

Total 93 98.9 100.0


Total 94 100.0

Discount retailer


2 13 13.8 13.8 18.1

3 42 44.7 44.7 62.8

4 31 33.0 33.0 95.7

82


5 4 4.3 4.3 100.0

Total 94 100.0 100.0

Department store


2 4 4.3 4.3 10.6

3 40 42.6 42.6 53.2

4 39 41.5 41.5 94.7

5 5 5.3 5.3 100.0

Total 94 100.0 100.0

Specialty store


2 15 16.0 16.0 24.5

3 31 33.0 33.0 57.4

4 33 35.1 35.1 92.6

5 7 7.4 7.4 100.0

Total 94 100.0 100.0

Boutique


2 34 36.2 36.2 55.3

3 15 16.0 16.0 71.3

4 21 22.3 22.3 93.6

5 6 6.4 6.4 100.0

Total 94 100.0 100.0

How often do you shop for apparel in a typical month?


2 9 9.6 9.8 13.0

3 17 18.1 18.5 31.5

4 63 67.0 68.5 100.0

Total 92 97.9 100.0


Total 94 100.0

Television

83



2 39 41.5 41.5 67.0

3 26 27.7 27.7 94.7

4 4 4.3 4.3 98.9

5 1 1.1 1.1 100.0

Total 94 100.0 100.0

Magazines


2 23 24.5 24.5 45.7

3 29 30.9 30.9 76.6

4 20 21.3 21.3 97.9

5 2 2.1 2.1 100.0

Total 94 100.0 100.0

Online


2 2 2.1 2.1 12.8

3 16 17.0 17.0 29.8

4 48 51.1 51.1 80.9

5 18 19.1 19.1 100.0

Total 94 100.0 100.0

Newspapers


2 23 24.5 24.5 88.3

3 9 9.6 9.6 97.9

4 1 1.1 1.1 98.9

5 1 1.1 1.1 100.0

Total 94 100.0 100.0

Radio


2 14 14.9 14.9 93.6

3 4 4.3 4.3 97.9

84


4 1 1.1 1.1 98.9

5 1 1.1 1.1 100.0

Total 94 100.0 100.0

I choose well known athletic clothing brands


2 15 16.0 16.0 24.5

3 9 9.6 9.6 34.0

4 46 48.9 48.9 83.0

5 16 17.0 17.0 100.0

Total 94 100.0 100.0

I stick with my usual brands of athletic clothing because I know it is best for me


2 13 13.8 14.1 20.7

3 14 14.9 15.2 35.9

4 40 42.6 43.5 79.3

5 19 20.2 20.7 100.0

Total 92 97.9 100.0


Total 94 100.0

I'm really attached to the brands of athletic clothing I use


2 22 23.4 23.9 34.8

3 18 19.1 19.6 54.3

4 30 31.9 32.6 87.0

5 12 12.8 13.0 100.0

Total 92 97.9 100.0


Total 94 100.0

Purchasing athletic clothing displayed in a window or catalog is usually a good choice


2 21 22.3 22.6 30.1

85


3 46 48.9 49.5 79.6

4 15 16.0 16.1 95.7

5 4 4.3 4.3 100.0

Total 93 98.9 100.0


Total 94 100.0

I am committed to my brands of athletic clothing


2 23 24.5 24.7 33.3

3 19 20.2 20.4 53.8

4 33 35.1 35.5 89.2

5 10 10.6 10.8 100.0

Total 93 98.9 100.0


Total 94 100.0

I usually have one or more outfits of the latest style


2 21 22.3 22.6 31.2

3 29 30.9 31.2 62.4

4 27 28.7 29.0 91.4

5 8 8.5 8.6 100.0

Total 93 98.9 100.0


Total 94 100.0

When it comes to purchasing athletic clothing, I try to get the highest quality


2 21 22.3 22.3 28.7

3 17 18.1 18.1 46.8

4 40 42.6 42.6 89.4

5 10 10.6 10.6 100.0

Total 94 100.0 100.0

I make a special effort to choose the best quality athletic apparel


86


Valid 1 8 8.5 8.6 8.62 15 16.0 16.1 24.7

3 23 24.5 24.7 49.5

4 35 37.2 37.6 87.1

5 12 12.8 12.9 100.0

Total 93 98.9 100.0


Total 94 100.0

When I buy athletic clothing how well it fits on me is important


2 2 2.1 2.1 3.2

3 5 5.3 5.3 8.5

4 45 47.9 47.9 56.4

5 41 43.6 43.6 100.0

Total 94 100.0 100.0

Comfort is important to me when choosing athletic clothing


2 1 1.1 1.1 2.2

3 1 1.1 1.1 3.2

4 41 43.6 44.1 47.3

5 49 52.1 52.7 100.0

Total 93 98.9 100.0


Total 94 100.0

When it comes to purchasing athletic apparel, I choose styles that are comfortable on me


3 3 3.2 3.2 5.4

4 44 46.8 47.3 52.7

5 44 46.8 47.3 100.0

Total 93 98.9 100.0


Total 94 100.0

Having athletic clothing that fits me well is important to me

87



3 3 3.2 3.2 6.5

4 41 43.6 44.1 50.5

5 46 48.9 49.5 100.0

Total 93 98.9 100.0


Total 94 100.0

Getting very good quality is very important to me


2 11 11.7 11.8 14.0

3 12 12.8 12.9 26.9

4 49 52.1 52.7 79.6

5 19 20.2 20.4 100.0

Total 93 98.9 100.0


Total 94 100.0

When it comes to purchasing products, I try to get the very best


2 13 13.8 14.3 19.8

3 22 23.4 24.2 44.0

4 33 35.1 36.3 80.2

5 18 19.1 19.8 100.0

Total 91 96.8 100.0


Total 94 100.0

I keep my wardrobe up-to-date with the changing fashions


2 23 24.5 24.5 41.5

3 26 27.7 27.7 69.1

4 21 22.3 22.3 91.5

5 8 8.5 8.5 100.0

Total 94 100.0 100.0

Fashionable, attractive clothing is very important to me

88



2 23 24.5 24.7 36.6

3 23 24.5 24.7 61.3

4 25 26.6 26.9 88.2

5 11 11.7 11.8 100.0

Total 93 98.9 100.0


Total 94 100.0

Nike

Frequency Percent Valid Percent Cumulative PercentValid 1 87 92.6 100.0 100.0Missing System 7 7.4

Total 94 100.0

Adidas


Total 94 100.0

Puma


Total 94 100.0

New Balance


Total 94 100.0

Champion


Total 94 100.0

89


Reebok


Total 94 100.0

Under Armor


Total 94 100.0

Lucy


Total 94 100.0

Athleta


Total 94 100.0

Wilson


Total 94 100.0

Lululemon


Total 94 100.0

Forever 21

90



Total 94 100.0

Victoria's Secret


Total 94 100.0

Danskin


Total 94 100.0

Mizuno


Total 94 100.0

Diadora


Total 94 100.0

Prana


Total 94 100.0

Umbra


91



Total 94 100.0

STX


Total 94 100.0

Warrior


Total 94 100.0

Asics


Total 94 100.0

Patagonia


Total 94 100.0

North Face


Total 94 100.0

REI


Total 94 100.0

92


Saucony


Total 94 100.0

Fila


Total 94 100.0

Marika


Total 94 100.0

Jockey


Total 94 100.0

Alo


Total 94 100.0

Zella


Total 94 100.0

Speedo


93


Valid 1 1 1.1 100.0 100.0Missing System 93 98.9

Total 94 100.0

Columbia


Total 94 100.0

Old Navy


Total 94 100.0

Fabletics


Total 94 100.0

What athletic clothing brand do you purchase the most? Nike


Total 94 100.0

What athletic clothing brand do you purchase the most? Lululemon


Total 94 100.0

What athletic clothing brand do you purchase the most? Under Armor


Total 94 100.0

94


What athletic clothing brand do you purchase the most? Adidas


Total 94 100.0

What athletic clothing brand do you purchase the most? I don't have one


Total 94 100.0

What athletic clothing brand do you purchase the most? I don't know


Total 94 100.0

What athletic clothing brand do you purchase the most? New Balance


Total 94 100.0

What athletic clothing brand do you purchase the most? Fila


Total 94 100.0

What athletic clothing brand do you purchase the most? Flag Nor Fail


Total 94 100.0

What athletic clothing brand do you purchase the most? Zella

95



Total 94 100.0

What athletic clothing brand do you purchase the most? Athleta


Total 94 100.0

What athletic clothing brand do you purchase the most? Forever21


Total 94 100.0

What athletic clothing brand do you purchase the most? Victoria's Secret


Total 94 100.0

What athletic clothing brand do you purchase the most? Old Navy


Total 94 100.0

This brand is excellent value for the money


3 16 17.0 20.5 25.6

4 42 44.7 53.8 79.5

5 16 17.0 20.5 100.0

Total 78 83.0 100.0


Total 94 100.0

96


I get value for my money when I buy this brand


3 15 16.0 19.2 23.1

4 48 51.1 61.5 84.6

5 12 12.8 15.4 100.0

Total 78 83.0 100.0


Total 94 100.0

This brand is worth every cent


2 6 6.4 7.7 9.0

3 27 28.7 34.6 43.6

4 32 34.0 41.0 84.6

5 12 12.8 15.4 100.0

Total 78 83.0 100.0


Total 94 100.0

Keeping in mind the brand you just indicated, how are the overall prices of this brand?


3 13 13.8 16.7 25.6

4 56 59.6 71.8 97.4

5 2 2.1 2.6 100.0

Total 78 83.0 100.0


Total 94 100.0

Keeping in mind the brand you just indicated, relative to other types of brands, how are this brand's prices?


3 36 38.3 46.2 51.3

4 37 39.4 47.4 98.7

5 1 1.1 1.3 100.0

Total 78 83.0 100.0


97


Total 94 100.0

Once again, keeping in mind the brand you just indicated, what do you expect the price level to be of this brand?


3 28 29.8 35.9 42.3

4 42 44.7 53.8 96.2

5 3 3.2 3.8 100.0

Total 78 83.0 100.0


Total 94 100.0

Keeping in mind the brand you just indicated, have you ever seen an advertisement from this brand?


2 14 14.9 17.9 100.0

Total 78 83.0 100.0


Total 94 100.0

Good


2 1 1.1 1.5 3.1

3 3 3.2 4.6 7.7

4 41 43.6 63.1 70.8

5 19 20.2 29.2 100.0

Total 65 69.1 100.0


Total 94 100.0

Irritating


2 34 36.2 54.0 71.4

3 6 6.4 9.5 81.0

4 10 10.6 15.9 96.8

5 2 2.1 3.2 100.0

Total 63 67.0 100.0

98



Total 94 100.0

Interesting


2 2 2.1 3.2 4.8

3 14 14.9 22.2 27.0

4 37 39.4 58.7 85.7

5 9 9.6 14.3 100.0

Total 63 67.0 100.0


Total 94 100.0

Not Informative


2 20 21.3 31.3 45.3

3 23 24.5 35.9 81.3

4 9 9.6 14.1 95.3

5 3 3.2 4.7 100.0

Total 64 68.1 100.0


Total 94 100.0

Bad


2 33 35.1 51.6 78.1

3 5 5.3 7.8 85.9

4 7 7.4 10.9 96.9

5 2 2.1 3.1 100.0

Total 64 68.1 100.0


Total 94 100.0

Boring


2 29 30.9 46.8 74.2

3 5 5.3 8.1 82.3

99


4 7 7.4 11.3 93.5

5 4 4.3 6.5 100.0

Total 62 66.0 100.0


Total 94 100.0

Not Irritating


2 6 6.4 9.7 16.1

3 10 10.6 16.1 32.3

4 32 34.0 51.6 83.9

5 10 10.6 16.1 100.0

Total 62 66.0 100.0


Total 94 100.0

Informative


2 6 6.4 9.5 11.1

3 23 24.5 36.5 47.6

4 23 24.5 36.5 84.1

5 10 10.6 15.9 100.0

Total 63 67.0 100.0


Total 94 100.0

If you were to purchase athletic clothing from one of the following brands, which would you choose?


2 5 5.3 5.6 22.2

3 3 3.2 3.3 25.6

4 64 68.1 71.1 96.7

5 3 3.2 3.3 100.0

Total 90 95.7 100.0


Total 94 100.0

Have you ever purchased athletic apparel from this brand?

100



2 7 7.4 8.0 100.0

Total 88 93.6 100.0


Total 94 100.0

Keeping in mind the brand you chose in the previous question, how likely are you to recommend this brand to others?


2 6 6.4 6.6 12.1

3 15 16.0 16.5 28.6

4 30 31.9 33.0 61.5

5 35 37.2 38.5 100.0

Total 91 96.8 100.0


Total 94 100.0

Lululemon


2 16 17.0 17.4 57.6

3 10 10.6 10.9 68.5

4 18 19.1 19.6 88.0

5 11 11.7 12.0 100.0

Total 92 97.9 100.0


Total 94 100.0

Athleta


2 24 25.5 25.8 63.4

3 20 21.3 21.5 84.9

4 12 12.8 12.9 97.8

5 2 2.1 2.2 100.0

Total 93 98.9 100.0


Total 94 100.0

101


Lucy


2 22 23.4 24.2 68.1

3 21 22.3 23.1 91.2

4 5 5.3 5.5 96.7

5 3 3.2 3.3 100.0

Total 91 96.8 100.0


Total 94 100.0

Nike


2 3 3.2 3.3 6.5

3 9 9.6 9.8 16.3

4 40 42.6 43.5 59.8

5 37 39.4 40.2 100.0

Total 92 97.9 100.0


Total 94 100.0

How much do you spend on any clothing in a typical month?


2 35 37.2 37.2 95.7

3 3 3.2 3.2 98.9

4 1 1.1 1.1 100.0

Total 94 100.0 100.0

What is your current academic year?


2 20 21.3 21.3 46.8

3 21 22.3 22.3 69.1

4 9 9.6 9.6 78.7

5 20 21.3 21.3 100.0

Total 94 100.0 100.0

What is your gender?

102



2 64 68.1 68.1 100.0

Total 94 100.0 100.0

How old are you?


17 2 2.1 2.2 4.3

18 10 10.6 10.9 15.2

19 15 16.0 16.3 31.5

20 22 23.4 23.9 55.4

21 15 16.0 16.3 71.7

22 8 8.5 8.7 80.4

23 9 9.6 9.8 90.2

24 3 3.2 3.3 93.5

25 2 2.1 2.2 95.7

26 2 2.1 2.2 97.8

28 1 1.1 1.1 98.9

52 1 1.1 1.1 100.0

Total 92 97.9 100.0


Total 94 100.0

103


Appendix B – Pearson’s Correlations

Attitude Towards Physical Activity

Correlations

I enjoy

physical

activity

When I participate in

physical activity I feel

annoyed

I like to participate

in physical activity

I feel good when

I'm physically

active

I enjoy physical

activity

Pearson

Correlation1 .531** .855** .766**

Sig. (2-tailed) .000 .000 .000

N 94 94 94 94

When I participate in

physical activity I feel

annoyed

Pearson

Correlation.531** 1 .578** .612**

Sig. (2-tailed) .000 .000 .000

N 94 94 94 94

I like to participate in

physical activity

Pearson

Correlation.855** .578** 1 .814**

Sig. (2-tailed) .000 .000 .000

N 94 94 94 94

I feel good when I'm

physically active

Pearson

Correlation.766** .612** .814** 1

Sig. (2-tailed) .000 .000 .000

N 94 94 94 94

**. Correlation is significant at the 0.01 level (2-tailed).

Attitude Towards Shopping

Correlations

I enjoy shopping

I don't like to

shop

I look forward to

my shopping

trips

I enjoy shopping Pearson Correlation 1 .655** .751**

Sig. (2-tailed) .000 .000

N 94 93 93

I don't like to shop Pearson Correlation .655** 1 .664**

Sig. (2-tailed) .000 .000

N 93 93 92

I look forward to my

shopping trips

Pearson Correlation .751** .664** 1

Sig. (2-tailed) .000 .000

104


N 93 92 93


Impulsive Shopping

Correlations

I carefully plan

most of

purchases

I often buy

things without

thinking

I buy things

according to how I

feel at the moment

When I go shopping, I

buy things that aren't

on my shopping list

I carefully plan most of

purchases

Pearson

Correlation1 .181 .113 .075

Sig. (2-

tailed).083 .295 .478

N 94 93 88 92

I often buy things

without thinking

Pearson

Correlation.181 1 .430** .341**

Sig. (2-

tailed).083 .000 .001

N 93 93 88 91

I buy things according

to how I feel at the

moment

Pearson

Correlation.113 .430** 1 .355**

Sig. (2-

tailed).295 .000 .001

N 88 88 88 86

When I go shopping, I

buy things that aren't

on my shopping list

Pearson

Correlation.075 .341** .355** 1

Sig. (2-

tailed).478 .001 .001

N 92 91 86 92


Peer Approval

105


Correlations

I'm always

aware of how

my peers on

campus

perceive me

It's important

to be accepted

by my peers

on campus

Being accepted by

my peers as a part

of the campus

community is

important to me

If I don't purchase

popular brand name

clothing products, I'm

not considered part of

my peer group

I'm always aware of

how my peers on

campus perceive me

Pearson

Correlation1 .369** .409** .122

Sig. (2-

tailed).000 .000 .245

N 93 92 91 92

It's important to be

accepted by my peers

on campus

Pearson

Correlation.369** 1 .805** -.008

Sig. (2-

tailed).000 .000 .937

N 92 93 92 93

Being accepted by my

peers as a part of the

campus community is

important to me

Pearson

Correlation.409** .805** 1 .045

Sig. (2-

tailed).000 .000 .669

N 91 92 92 92

If I don't purchase

popular brand name

clothing products, I'm

not considered part of

my peer group

Pearson

Correlation.122 -.008 .045 1

Sig. (2-

tailed).245 .937 .669

N 92 93 92 93


Peer Influence On Making Choices

106


Correlations

I purchase

clothing

products only

because they're

popular with my

peer group

I think having

the same

clothing

products gives

me a sense of

belonging to my

peers

When I see my

peers with

particular

clothing product,

I go buy it

I purchase clothing products

only because they're popular

with my peer group

Pearson Correlation 1 .369** .427**

Sig. (2-tailed) .000 .000

N 92 92 92

I think having the same

clothing products gives me a

sense of belonging to my

peers

Pearson Correlation .369** 1 .165

Sig. (2-tailed) .000 .115

N92 93 93

When I see my peers with

particular clothing product, I

go buy it

Pearson Correlation .427** .165 1

Sig. (2-tailed) .000 .115

N 92 93 94


Attitude Towards Apparel Trends

Correlations

I don't care

about clothing

trends on

campus

I enjoy following

clothing trends I

see on campus

When I see a

certain clothing

trend on

campus, I

usually like it

I don't care about clothing

trends on campus


Sig. (2-tailed) .000 .008

N 94 91 94

I enjoy following clothing

trends I see on campus

Pearson Correlation .450** 1 .277**

Sig. (2-tailed) .000 .008

N 91 91 91

When I see a certain clothing

trend on campus, I usually

like it


Sig. (2-tailed) .008 .008

N 94 91 94


107


Brand Awareness

Correlations

I choose well

known athletic

clothing brands

Purchasing

athletic clothing

displayed in a

window or

catalog is

usually a good

choice

I choose well known athletic

clothing brands

Pearson Correlation 1 .449**

Sig. (2-tailed) .000

N 94 93

Purchasing athletic clothing

displayed in a window or

catalog is usually a good

choice

Pearson Correlation .449** 1


N93 93


Brand Loyalty

Correlations

I stick with my

usual brands of

athletic clothing

because I know

it is best for me

I'm really

attached to the

brands of

athletic clothing I

use

I am committed

to my brands of

athletic clothing

I stick with my usual brands

of athletic clothing because I

know it is best for me


Sig. (2-tailed) .000 .000

N 92 91 92

I'm really attached to the

brands of athletic clothing I

use


Sig. (2-tailed) .000 .000

N 91 92 92

I am committed to my brands

of athletic clothing


Sig. (2-tailed) .000 .000

N 92 92 93


Apparel Style

108


Correlations

I usually have

one or more

outfits of the

latest style

I keep my

wardrobe up-to-

date with the

changing

fashions

Fashionable,

attractive

clothing is very

important to me

I usually have one or more

outfits of the latest style


Sig. (2-tailed) .000 .000

N 93 93 92

I keep my wardrobe up-to-

date with the changing

fashions


Sig. (2-tailed) .000 .000

N 93 94 93

Fashionable, attractive

clothing is very important to

me


Sig. (2-tailed) .000 .000

N 92 93 93


Apparel Fit

Correlations

When I buy

athletic clothing

how well it fits

on me is

important

Having athletic

clothing that fits

me well is

important to me

When I buy athletic clothing

how well it fits on me is

important



N 94 93

Having athletic clothing that

fits me well is important to

me



N 93 93


Apparel Comfort

Correlations

109


Comfort is

important to me

when choosing

athletic clothing

When it comes

to purchasing

athletic apparel,

I choose styles

that are

comfortable on

me

Comfort is important to me

when choosing athletic

clothing



N 93 92

When it comes to purchasing

athletic apparel, I choose

styles that are comfortable

on me



N92 93


Apparel Quality

Correlations

When it comes to

purchasing athletic

clothing, I try to get

the highest quality

I make a special

effort to choose

the best quality

athletic apparel

Getting very

good quality is

very important

to me

When it comes to

purchasing

products, I try to

get the very best

When it comes to

purchasing athletic

clothing, I try to get

the highest quality

Pearson

Correlation1 .858** .679** .662**

Sig. (2-

tailed).000 .000 .000

N 94 93 93 91

I make a special

effort to choose the

best quality athletic

apparel

Pearson

Correlation.858** 1 .640** .650**

Sig. (2-

tailed).000 .000 .000

N 93 93 92 90

Getting very good

quality is very

important to me

Pearson

Correlation.679** .640** 1 .789**

Sig. (2-

tailed).000 .000 .000

N 93 92 93 90

When it comes to

purchasing products,

Pearson

Correlation.662** .650** .789** 1

110


I try to get the very

best

Sig. (2-

tailed).000 .000 .000

N 91 90 90 91


Product Value

Correlations

This brand is

excellent value

for the money

I get value for

my money when

I buy this brand

This brand is

worth every cent

This brand is excellent value

for the money


Sig. (2-tailed) .000 .000

N 78 78 78

I get value for my money

when I buy this brand


Sig. (2-tailed) .000 .000

N 78 78 78

This brand is worth every

cent


Sig. (2-tailed) .000 .000

N 78 78 78


Apparel Prices

Correlations

Keeping in mind

the brand you

just indicated,

how are the

overall prices of

this brand?

Keeping in mind

the brand you

just indicated,

relative to other

types of brands,

how are this

brand's prices?

Once again,

keeping in mind

the brand you

just indicated,

what do you

expect the price

level to be of

this brand?

Keeping in mind the brand

you just indicated, how are

the overall prices of this

brand?


Sig. (2-tailed) .000 .000

N78 78 78

Keeping in mind the brand

you just indicated, relative to


Sig. (2-tailed) .000 .000

111


other types of brands, how

are this brand's prices?

N78 78 78

Once again, keeping in mind

the brand you just indicated,

what do you expect the price

level to be of this brand?


Sig. (2-tailed) .000 .000

N78 78 78


Branded Advertisement

Correlations

Good Irritating Interesting

Not

Informative Bad Boring

Not

Irritating Informative

Good Pearson

Correlation1 .454** .657** .294* .390** .468** .543** .449**

Sig. (2-tailed) .000 .000 .018 .001 .000 .000 .000

N 65 63 63 64 64 62 62 63

Irritating Pearson

Correlation.454** 1 .275* .284* .568** .568** .422** .333**

Sig. (2-tailed) .000 .030 .024 .000 .000 .001 .008

N 63 63 62 63 63 61 61 62

Interesting Pearson

Correlation.657** .275* 1 .219 .184 .222 .325* .247

Sig. (2-tailed) .000 .030 .087 .152 .088 .011 .055

N 63 62 63 62 62 60 60 61

Not

Informative

Pearson

Correlation.294* .284* .219 1 .455** .452** .215 .412**

Sig. (2-tailed) .018 .024 .087 .000 .000 .093 .001

N 64 63 62 64 64 62 62 63

Bad Pearson

Correlation.390** .568** .184 .455** 1 .921** .228 .258*

Sig. (2-tailed) .001 .000 .152 .000 .000 .075 .041

N 64 63 62 64 64 62 62 63

Boring Pearson

Correlation.468** .568** .222 .452** .921** 1 .256* .311*

Sig. (2-tailed) .000 .000 .088 .000 .000 .045 .014

N 62 61 60 62 62 62 62 62

Not Irritating Pearson

Correlation.543** .422** .325* .215 .228 .256* 1 .312*

Sig. (2-tailed) .000 .001 .011 .093 .075 .045 .014

112


N 62 61 60 62 62 62 62 62

Informative Pearson

Correlation.449** .333** .247 .412** .258* .311* .312* 1

Sig. (2-tailed) .000 .008 .055 .001 .041 .014 .014

N 63 62 61 63 63 62 62 63


*. Correlation is significant at the 0.05 level (2-tailed).

Appendix D – Factor Analysis

15 Factors - Rotated Component Matrixa

113


Component

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15I enjoy physical activity

.180

-.103

.011

.787

-.086

.004

.277

-.037

.025

-.007

-.020

-.127

.078

-.013

.258


.082

.158

.020

.480

.027

-.093

.257

-.210

-.179

.315

.158

.308

.278

-.029

-.248


.219

-.038

-.174

.811

.028

.055

.190

.082

-.030

.074

-.029

.204

-.055

-.067

-.049


.346

.000

.037

.711

.236

-.105

.024

.096

-.207

.156

.161

.199

-.048

-.060

.034

I enjoy shopping

-.092

.877

.210

-.030

.050

.161

-.053

.122

.115

.014

.091

.039

.090

.037

-.023


-.062

.177

-.003

-.045

-.034

-.024

.170

-.100

.039

.048

.009

-.039

.021

.883

.049


-.051

.828

.180

-.127

.142

-.007

.046

.045

-.110

.047

.126

.175

.112

.177

.006


.398

.104

.173

-.044

-.057

.079

.042

-.167

.684

.232

-.174

-.069

.139

.101

.019


.005

.885

.193

-.018

.084

.129

-.023

.120

.043

-.035

.138

.082

-.008

.079

-.039


-.069

-.064

-.041

-.161

.046

-.021

.159

.247

.790

-.032

.258

.057

-.178

.011

.180


.251

.171

.240

-.472

-.039

-.136

-.137

.140

.339

-.225

-.104

.099

.068

.011

.101


-.039

.198

.029

.070

.034

.884

-.035

.128

.010

.003

-.006

.085

.077

.043

.179


-.129

.170

-.007

-.064

.022

.844

.064

.319

.041

-.031

.033

.018

.040

.008

-.029

114


part of the campus community is important to meI enjoy following clothing trends I see on campus

.130

.360

-.323

.039

.050

.116

-.196

.528

-.077

.139

.412

-.120

.053

.029

-.125


.039

.142

.191

.108

-.047

-.067

.049

.059

-.015

.048

.856

.022

-.095

-.031

-.046


.266

.197

.101

.030

-.324

.167

.006

.357

.005

.287

.537

.107

.065

.151

.031


.038

.169

.145

-.071

.026

.191

.159

.734

.071

.059

-.020

-.200

.128

-.160

.013


.054

.160

-.013

.037

.103

.127

.088

.075

.039

-.091

.678

.034

.087

.057

.598


.056

.568

.017

.055

.144

.026

.085

.158

-.057

-.119

-.198

-.242

.206

-.394

.288

115


I'm always aware of how my peers on campus perceive me

.196

.253

-.206

-.161

.015

.521

.054

-.103

.025

-.174

.255

-.242

.457

-.237

-.075


.283

.595

-.178

.050

-.267

.134

-.096

.311

-.180

.188

.111

-.082

-.142

-.171

.197


.345

-.025

-.033

.108

.110

.001

.090

.164

-.110

.762

-.018

-.003

.103

-.027

-.056


.297

.029

-.018

.260

.116

-.131

.032

.071

.087

.738

.114

.044

-.062

.096

.086


.593

-.060

.143

-.144

.084

-.257

-.049

.098

.094

.311

.337

.254

.282

-.119

-.049


.431

.259

.081

.073

-.096

-.270

.055

.164

.331

.402

.035

.130

-.053

-.477

.056


.738

.114

.081

.101

.075

-.083

.149

.083

.105

.313

.344

.075

.112

-.038

.064


.150

.344

.045

.060

.216

.060

.057

.469

.104

-.134

.264

-.169

.505

.114

.019

When it comes to purchasing athletic clothing, I

.941

.028

-.001

.054

.046

-.014

-.004

.047

.063

-.007

-.008

.053

-.011

-.125

-.008

116


try to get the highest qualityI make a special effort to choose the best quality athletic apparel

.920

-.029

.077

.128

-.036

.000

-.002

.094

.014

.037

-.065

.032

.014

-.021

-.008


.284

.001

-.169

.242

.583

-.121

-.070

.038

-.184

.164

-.139

.365

-.241

-.156

-.223


.043

.014

.031

-.113

.882

.053

.018

-.004

-.123

.141

-.072

.048

.133

.130

.069


.092

.192

.022

.045

.898

.006

.007

-.033

.103

-.041

.047

-.152

.030

-.113

.054


.404

.125

.003

.359

.513

-.105

-.080

.079

-.159

.168

.110

.341

-.185

-.047

-.241


.661

-.031

-.064

.244

.362

-.143

.072

.140

-.062

.285

-.017

.065

-.059

-.009

-.087


.691

-.118

.091

.282

.136

-.011

.069

.088

-.247

.270

.081

.025

-.019

.152

.162

I keep my wardrobe up-to-date with the

.303

.235

-.050

.178

-.212

.152

.123

.636

-.066

.144

.155

.240

.087

-.027

.075

117


changing fashionsFashionable, attractive clothing is very important to me

.378

.117

.174

.123

.118

.068

.236

.568

.042

.246

.119

.200

.136

-.173

-.075


.277

.220

.068

.041

.362

-.536

.090

.126

-.441

.203

.034

.225

-.041

.013

.117


.208

.114

.137

.117

.210

-.486

.029

.207

-.583

.176

.149

.238

-.043

.086

-.013


.418

-.043

.329

-.252

.100

-.196

.130

-.126

-.428

.157

.107

-.049

-.113

.242

.341


-.040

.131

-.114

.061

.053

-.124

.850

.172

-.026

-.007

.062

.089

.072

.189

-.071


.131

-.068

-.040

.160

.090

.080

.833

.274

.045

.090

.056

-.044

.073

.089

.124


.051

-.099

-.032

.157

-.126

.053

.868

-.148

.070

.049

-.024

-.083

-.011

-.072

-.016

Good .133

-.077

.458

-.036

.071

.242

.247

.087

.038

.198

-.040

.390

.275

-.012

.387

Irritating .072

.167

.862

.036

-.180

-.128

-.083

.013

.040

.006

.039

.158

.115

.100

.069

Interesting -.01 .09 .14 .04 .00 .13 .09 .21 -.05 .10 -.12 .13 .82 .01 .15

118


2 6 0 6 8 9 6 9 0 1 2 2 0 7 4Not Informative

-.032

.034

.417

.578

-.165

-.193

-.237

.239

.113

.236

.106

.146

.102

.114

.086

Bad .020

.168

.935

-.064

.057

.009

-.057

.024

-.055

-.053

.098

.025

-.012

.002

.039

Boring .105

.131

.937

-.068

.113

.034

-.045

.044

-.009

-.009

.065

-.025

-.006

-.113

.017

Not Irritating

.189

.144

.179

.208

-.006

-.005

-.060

-.064

-.030

-.003

.053

.848

.064

-.026

.045

Informative

-.016

.067

.440

.295

-.041

.096

-.096

-.069

.229

.061

.010

.046

.187

.007

.644

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a

a. Rotation converged in 11 iterations.

Total Variance Explained

Component

Initial Eigenvalues Rotation Sums of Squared Loadings

Total% of

VarianceCumulativ

e % Total% of

VarianceCumulativ

e %1 10.051 19.707 19.707 5.516 10.815 10.8152 5.932 11.630 31.338 3.953 7.752 18.5673 4.182 8.200 39.538 3.872 7.592 26.1594 3.534 6.930 46.467 3.482 6.827 32.9865 3.073 6.026 52.494 3.104 6.086 39.0726 2.685 5.264 57.758 2.946 5.777 44.8497 2.393 4.692 62.450 2.839 5.566 50.4158 1.804 3.537 65.987 2.810 5.510 55.9259 1.736 3.404 69.392 2.483 4.868 60.79410 1.553 3.045 72.437 2.471 4.845 65.63811 1.535 3.010 75.447 2.466 4.836 70.47412 1.332 2.611 78.058 2.014 3.950 74.42313 1.238 2.428 80.487 1.827 3.583 78.00614 1.061 2.081 82.568 1.677 3.289 81.29515 1.003 1.967 84.535 1.652 3.239 84.53516 .862 1.691 86.22617 .808 1.584 87.80918 .710 1.391 89.20119 .655 1.284 90.48520 .579 1.135 91.62021 .510 1.000 92.62122 .468 .917 93.53723 .381 .747 94.28524 .349 .685 94.97025 .334 .655 95.62526 .313 .613 96.23827 .290 .568 96.80628 .232 .455 97.26129 .222 .434 97.69530 .199 .390 98.08531 .152 .299 98.384

119


32 .133 .262 98.64533 .122 .239 98.88534 .096 .188 99.07235 .087 .170 99.24336 .081 .158 99.40137 .061 .120 99.52038 .059 .115 99.63539 .047 .091 99.72740 .038 .075 99.80241 .036 .070 99.87242 .029 .056 99.92843 .020 .040 99.96844 .010 .020 99.98845 .005 .010 99.99846 .001 .002 100.00047 1.087E-15 2.131E-15 100.00048 5.717E-16 1.121E-15 100.00049 -1.781E-16 -3.492E-16 100.00050 -3.062E-16 -6.005E-16 100.00051 -8.896E-16 -1.744E-15 100.000

Extraction Method: Principal Component Analysis.


Component

1 2 3 4 5 6 7 8 9 10 11 12 13 14I enjoy physical activity

.176-.09

4.019 .770

-.093

.026-.07

7.288 .008 .032 .091

-.101

-.054

.255


.170 .160 .009 .519 .029-.12

4-.14

1.239 .114

-.154

.244 .271 .018-.42

7


.235-.02

8-.17

8.780 .023 .066 .074 .206

-.039

-.029

-.074

.233-.11

2-.01

5


.387 .006 .039 .703 .231-.10

7.098 .030 .152

-.190

-.051

.214-.06

7.012

I enjoy shopping

-.086

.881 .206-.03

4.048 .155 .117

-.052

.090 .116 .072 .041 .041-.04

9I carefully -.09 .182 -.01 -.05 -.02 .015 -.15 .193 .042 .017 .017 -.02 .817 .026

120


plan most of purchases

5 4 9 9 1 1


-.036

.826 .181-.11

5.141

-.020

.044 .042 .126-.10

2.113 .167 .200

-.051


.425 .100 .166-.03

7-.05

8.096

-.155

.031-.17

6.693 .144

-.093

.142-.07

5


-.004

.890 .189-.03

9.078 .130 .099

-.014

.134 .036-.03

7.097 .059

-.022


-.074

-.072

-.031

-.175

.044-.00

4.225 .164 .283 .790

-.145

.065 .021 .180


.179 .178 .236-.54

2-.05

1-.09

4.049

-.114

-.070

.310 .067 .119-.06

3.210


-.039

.194 .048 .095 .040 .870 .147-.04

6.010 .010 .123 .082 .077 .122


-.139

.176-.00

4-.06

5.029 .836 .329 .065 .036 .024 .045 .021

-.003

-.025


.157 .377-.33

9.027 .051 .118 .517

-.183

.411-.08

4.008

-.111

.005-.11

1


.069 .144 .194 .116-.05

8-.07

5.070 .047 .833

-.015

-.148

.024-.03

1-.13

4

I purchase .327 .195 .104 .065 -.32 .150 .397 -.00 .526 .022 .078 .080 .217 -.11

121


clothing products only because they're popular with my peer group

0 7 0


.050 .186 .134-.08

8.034 .190 .715 .168 .000 .070 .143

-.204

-.172

.056


.057 .149 .030 .071 .092 .124 .036 .078 .738 .058 .160 .035 .105 .484


.050 .571 .032 .058 .137 .018 .125 .079-.16

5-.04

0.241

-.240

-.368

.328


.157 .275-.21

3-.19

8-.00

2.543

-.164

.064 .266-.01

4.367

-.236

-.322

-.072


.336 .579-.15

7.103

-.265

.098 .366-.12

2.099

-.141

-.085

-.096

-.039

.167


.529-.04

8-.03

0.252 .137

-.077

.339 .027-.06

7-.02

7.186

-.101

.231-.34

2


.475-.00

1-.00

6.404 .139

-.200

.236-.02

8.074 .176 .036

-.038

.357-.20

7

I'm really attached to the brands of athletic clothing I

.666-.05

9.138

-.123

.080-.26

9.123

-.067

.321 .116 .281 .206-.05

2-.20

0

122


use


.547 .235 .094 .142-.09

3-.31

9.267 .010

-.005

.397 .009 .075-.28

4-.07

9


.805 .111 .085 .121 .067-.08

7.101 .134 .331 .129 .117 .048 .032

-.055


.102 .388 .018-.02

7.204 .114 .333 .102 .323 .060 .433

-.138

-.059

.076


.912 .038-.00

6-.01

4.025 .026

-.017

.013-.01

2.042

-.050

.079-.18

3.097


.894-.01

6.068 .062

-.053

.041 .032 .018-.06

6-.00

6-.01

8.056

-.083

.085


.330-.00

3-.17

4.240 .583

-.141

.080-.07

3-.18

0-.16

9-.24

6.368

-.125

-.179


.082 .014 .034-.08

1.889 .038 .003 .008

-.053

-.108

.166 .025 .168-.00

4

When it comes to purchasing athletic apparel, I choose styles that

.094 .202 .021 .029 .890 .015-.07

2.014 .065 .100 .008

-.139

-.144

.080

123


are comfortable on meHaving athletic clothing that fits me well is important to me

.442 .131-.00

9.335 .507

-.109

.094-.07

1.072

-.156

-.225

.354-.06

1-.21

3


.713-.02

8-.07

3.240 .359

-.142

.153 .071-.03

7-.04

7-.06

5.059 .019

-.091


.735-.12

0.099 .302 .134

-.012

.098 .060 .082-.22

5.018 .015 .204 .101


.332 .242-.04

9.168

-.209

.147 .631 .128 .165-.05

7.122 .238

-.011

.070

Fashionable, attractive clothing is very important to me

.437 .126 .166 .120 .121 .054 .586 .233 .110 .057 .150 .178-.14

2-.11

9


.338 .209 .079 .080 .364-.56

7.153 .074 .031

-.399

.016 .202 .104 .067


.258 .113 .137 .136 .212-.51

0.226 .024 .137

-.556

-.019

.224 .127-.04

4


.451-.06

6.358

-.187

.101-.21

9-.09

8.101 .116

-.392

-.026

-.080

.364 .241


-.040

.137-.11

9.041 .051

-.115

.143 .862 .069-.03

3.062 .093 .151

-.054

Keeping in mind the brand you just indicated,

.154-.06

7-.03

2.167 .092 .081 .263 .832 .073 .054 .109

-.052

.104 .095

124


relative to other types of brands, how are this brand's prices?


.073-.10

8-.02

3.177

-.129

.043-.12

1.854

-.043

.081-.00

6-.09

7-.03

5-.05

6

Good.195

-.096

.490 .036 .082 .204 .136 .210-.01

3.084 .409 .331 .127 .176

Irritating.065 .172 .859 .021

-.182

-.122

-.003

-.079

.045 .042 .124 .150 .082 .004

Interesting.013 .116 .133 .056 .016 .138 .185 .094

-.069

-.043

.855 .091 .003 .003

Not Informative .019 .042 .409 .590

-.156

-.197

.257-.23

5.115 .138 .129 .138 .126

-.025

Bad.007 .173 .935

-.076

.055 .007 .015-.05

6.096

-.058

-.013

.022-.01

4.006

Boring.105 .136 .936

-.074

.111 .027 .045-.04

9.056

-.007

-.007

-.034

-.112

-.021

Not Irritating .198 .135 .192 .194

-.012

-.013

-.061

-.062

.046-.02

1.100 .845

-.014

-.009

Informative.011 .051 .478 .355

-.037

.083-.06

3-.12

2.067 .275 .316 .023 .108 .472

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a. Rotation converged in 20 iterations.


Component


Total% of

VarianceCumulativ

e % Total% of

VarianceCumulativ

e %1 10.051 19.707 19.707 6.520 12.784 12.7842 5.932 11.630 31.338 4.020 7.882 20.6663 4.182 8.200 39.538 3.950 7.746 28.4114 3.534 6.930 46.467 3.707 7.268 35.6795 3.073 6.026 52.494 3.092 6.063 41.7426 2.685 5.264 57.758 3.040 5.961 47.7037 2.393 4.692 62.450 2.906 5.698 53.4018 1.804 3.537 65.987 2.813 5.515 58.9169 1.736 3.404 69.392 2.499 4.900 63.81610 1.553 3.045 72.437 2.427 4.759 68.57511 1.535 3.010 75.447 1.989 3.901 72.475

125


12 1.332 2.611 78.058 1.899 3.723 76.19913 1.238 2.428 80.487 1.789 3.508 79.70614 1.061 2.081 82.568 1.459 2.862 82.56815 1.003 1.967 84.53516 .862 1.691 86.22617 .808 1.584 87.80918 .710 1.391 89.20119 .655 1.284 90.48520 .579 1.135 91.62021 .510 1.000 92.62122 .468 .917 93.53723 .381 .747 94.28524 .349 .685 94.97025 .334 .655 95.62526 .313 .613 96.23827 .290 .568 96.80628 .232 .455 97.26129 .222 .434 97.69530 .199 .390 98.08531 .152 .299 98.38432 .133 .262 98.64533 .122 .239 98.88534 .096 .188 99.07235 .087 .170 99.24336 .081 .158 99.40137 .061 .120 99.52038 .059 .115 99.63539 .047 .091 99.72740 .038 .075 99.80241 .036 .070 99.87242 .029 .056 99.92843 .020 .040 99.96844 .010 .020 99.98845 .005 .010 99.99846 .001 .002 100.00047 8.858E-17 1.737E-16 100.00048 2.736E-17 5.365E-17 100.00049 -3.378E-17 -6.624E-17 100.00050 -1.330E-16 -2.608E-16 100.00051 -1.896E-16 -3.718E-16 100.000



Component

1 2 3 4 5 6 7 8 9 10 11 12 13I enjoy physical .123 .864

-.197

-.031 .098 .003 .000 .035 .020 -.008 .174 .037 .004

126


activityWhen I participate in physical activity I feel annoyed

.221 .740 .188 .060 .108 -.007 .067-.02

3-.083 -.110 -.066 -.016 .343


.202 .898-.10

5.050 .090 .119 -.007 .062 -.013 -.054 -.022 -.061 -.098


.288 .840-.02

9.189 -.045 .090 .201 .018 .082 .016 -.016 -.028 -.107

I enjoy shopping -.043 .004 .916 .041 -.099 .114 -.053 .121 .036 .091 .085 .026 .015

I carefully plan most of purchases -.115 -.026 .145 .080 .195 -.037 .056 .041 .029 .111 -.094 .857 -.016

I don't like to shop .005 -.123 .856 .106 .055 .065 .223 .031 .063 -.018 .057 .110 .133

I often buy things without thinking .374 -.021 .085 .086 .186 -.077 -.367

-.049

-.122 .551 .018 .330 -.075


-.020 -.086 .878 .076 -.055 .138 .020 .124 .200 .041 .087 .028 -.085

I buy things according to how I feel at the moment .078 .143

-.109

-.031 .196 .288 -.229-.03

4.160 .657 .049 .173 .007


.123 -.261 .166 -.001 -.087 -.079 .048 .090 .037 .787 .012 -.038 .044


.047 .070 .181 .008 .006 .099 -.149 .883 -.032 .002 .145 .018 .068

127



-.094 -.001 .145 .110 .032 .346 -.218 .813 .069 .020 .078 .045 -.094


.232 .077 .235 .081 -.116 .685 -.031 .104 .293 -.177 .096 .065 .007


.001 -.126 .163 -.108 .032 .086 -.034-.11

6.839 -.026 -.125 -.066 -.033


.347 .120 .273 -.369 .086 .252 -.005 .166 .594 .065 -.156 .005 .027


-.023 .054 .084 -.012 .063 .764 .053 .177 .045 .137 .218 -.065 .030


.032 .166 .047 .022 .076 .096 .118 .152 .731 .127 .391 .157 .067


.030 .187 .335 .093 .092 .196 .129 .094 -.116 .187 .660 -.205 -.096

I'm always aware of how my peers on campus

.196 -.130 .147 -.092 .034 .230 -.325 .249 .107 -.170 .649 -.002 .070

128


perceive me


.293 .221 .390 -.348 -.226 .423 .113 .084 .097 .021 .209 .022 -.280


.636 .268-.03

1.028 .105 .352 .080

-.015

-.131 -.012 -.130 .156 .224


.581 .414 .064 .022 .033 .218 -.002-.15

3-.062 -.002 -.187 .130 .340


.744 .035 .015 .052 -.021 .160 .095-.12

8.189 .186 -.021 -.114 .381


.475 .249 .178 -.198 .006 .197 .007-.28

2-.161 .416 -.017 -.318 .059


.817 .201 .123 .041 .104 .150 .031-.04

5.248 .165 .054 .039 .175

I usually have one or more outfits of the latest style .086 -.035 .346 .214 .174 .294 .029 .208 .174 .155 .301 -.181 .392


.875 .073-.02

9.082 .015 -.034 .028 .031 .031 .199 .139 -.101 -.227

I make a special effort .885 .146

-.055

-.018 .016 -.004 .071 .053 -.059 .111 .101 -.022 -.169

129


to choose the best quality athletic apparel


.244 .196 .088 .708 -.056 -.021 .115-.04

9-.185 -.140 -.344 -.202 -.121


.030 .027 .021 .895 -.013 .001 .137 .126 -.102 .006 .026 .185 .138


.047 .038 .127 .918 -.014 .014 -.027 .022 -.029 .051 .176 .043 .029


.250 .474 .118 .578 -.164 .079 .117-.05

1.182 .021 -.207 -.077 -.256


.791 .159-.05

2.195 .222 .055 .153

-.049

-.054 -.081 .005 -.048 -.111


.783 .209-.04

7.021 .184 -.064 .253 .098 .104 -.165 .043 -.030 .025


.388 .198 .274 -.212 .135 .504 .151 .288 .104 .118 -.083 -.120 .064

Fashionable, attractive clothing is very important to

.515 .048 .176 .144 .283 .476 .175 .180 .023 .088 -.063 -.198 .095

130


me


.262 .131 .179 .226 .065 .071 .769-.25

9-.020 -.062 .024 -.063 -.042


.219 .131 .096 .045 .036 .160 .807-.22

3.031 -.177 -.081 .023 -.040


.452 -.040-.02

3-.011 -.107 -.194 .583 .094 .061 .074 -.005 .299 .224


.075 .047 .103 .029 .866 .041 .079-.09

4.079 .010 -.014 .141 -.064


.211 .065-.11

8-.122 .807 .198 .057 .124 .123 .063 .094 .046 .029


.130 .079-.10

3-.032 .856 -.138 -.115 .031 -.076 .027 .031 .016 .092

Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.a

a. Rotation converged in 11 iterations.


Component


Total% of

VarianceCumulativ

e % Total% of

VarianceCumulativ

e %1 10.051 19.707 19.707 6.425 12.598 12.5982 5.932 11.630 31.338 4.175 8.186 20.784

131


3 4.182 8.200 39.538 3.982 7.808 28.5914 3.534 6.930 46.467 3.430 6.725 35.3165 3.073 6.026 52.494 3.136 6.149 41.4656 2.685 5.264 57.758 3.101 6.080 47.5457 2.393 4.692 62.450 3.009 5.900 53.4448 1.804 3.537 65.987 2.823 5.535 58.9799 1.736 3.404 69.392 2.511 4.923 63.90210 1.553 3.045 72.437 2.481 4.864 68.76611 1.535 3.010 75.447 2.258 4.427 73.19312 1.332 2.611 78.058 1.936 3.796 76.98813 1.238 2.428 80.487 1.784 3.498 80.48714 1.061 2.081 82.56815 1.003 1.967 84.53516 .862 1.691 86.22617 .808 1.584 87.80918 .710 1.391 89.20119 .655 1.284 90.48520 .579 1.135 91.62021 .510 1.000 92.62122 .468 .917 93.53723 .381 .747 94.28524 .349 .685 94.97025 .334 .655 95.62526 .313 .613 96.23827 .290 .568 96.80628 .232 .455 97.26129 .222 .434 97.69530 .199 .390 98.08531 .152 .299 98.38432 .133 .262 98.64533 .122 .239 98.88534 .096 .188 99.07235 .087 .170 99.24336 .081 .158 99.40137 .061 .120 99.52038 .059 .115 99.63539 .047 .091 99.72740 .038 .075 99.80241 .036 .070 99.87242 .029 .056 99.92843 .020 .040 99.96844 .010 .020 99.98845 .005 .010 99.99846 .001 .002 100.00047 1.087E-15 2.131E-15 100.00048 5.717E-16 1.121E-15 100.00049 -1.781E-16 -3.492E-16 100.00050 -3.062E-16 -6.005E-16 100.000

132


51 -8.896E-16 -1.744E-15 100.000


Rotated Component Matrixa

Component

1 2 3 4 5 6 7 8 9 10 11 12 13I enjoy physical activity

.142 .862 -.133

.106 -.016 -.072 -.036 .044 -.088 .258 -.157 -.049 -.056


.128 .775 .120 .121 .060 .193 .012 -.071

-.019 -.229 .018 .055 .380


.217 .905 -.097

.079 .027 .012 .058 .114 -.066 -.017 -.029 -.035 -.086


.301 .839 -.039

-.060 .162 .232 .048 .075 .009 .040 .064 .005 -.096

I enjoy shopping

-.039 .018 .944 -.090 .051 -.062 .110 .117 .060 .019 -.006 .002 .011


-.103 -.033 .109 .166 .065 .019 .027 -.056

.090 .051 -.010 .948 -.031


-.014 -.114 .861 .057 .099 .291 .068 .031 .039 .019 .033 .104 .122


.002 -.079 .901 -.057 .058 .017 .135 .148 .033 .059 .157 .031 -.027


.103 .163 -.063

.203 -.023 -.240 -.035 .287 .621 .271 .028 .147 -.047


.150 -.250 .163 -.059 -.021 -.024 .072 -.073

.838 -.044 -.002 .026 .002


.045 .069 .168 .012 .026 -.126 .919 .025 .018 .087 -.078 .001 .075

Being accepted by

-.092 .008 .136 .036 .102 -.175 .871 .305 .042 .038 .057 .039 .006

133


my peers as a part of the campus community is important to me


.234 .103 .247 -.099 .048 .031 .154 .704 -.145 .053 .274 .105 .116


-.010 -.110 .149 .040 -.148 .014 -.049 .069 .013 .224 .898 -.018 -.007


-.020 .079 .138 .100 -.027 .070 .188 .831 .165 .065 -.074 -.139 .045


.070 .125 .119 .088 .038 .082 .166 .101 .086 .831 .350 .056 .082


.166 -.132 .151 .054 -.095 -.221 .372 .270 -.144 .275 -.063 -.066 .612


.665 .102 .014 .007 .062 .178 -.160 .127 .293 -.018 .173 -.101 .447


.765 .241 .120 .146 .056 .059 -.053 .133 .211 .041 .251 .037 .303

When it comes to

.916 .071 -.022

.019 .052 .007 .042 -.008

.178 .012 -.009 -.066 -.001

134


purchasing athletic clothing, I try to get the highest qualityI make a special effort to choose the best quality athletic apparel

.921 .161 -.033

.017 -.044 .025 .018 .050 .082 -.035 -.111 .002 -.010


.234 .203 .027 -.074 .704 .168 -.026 -.078

-.114 -.426 .109 -.105 -.185


.012 .029 .020 -.013 .922 .145 .108 -.006

.019 .015 -.103 .145 .045


.057 .033 .158 -.012 .942 -.047 .009 .048 .006 .127 -.083 -.021 .014


.808 .157 -.051

.207 .184 .195 -.037 .068 -.108 -.036 -.061 -.029 -.063


.814 .195 -.024

.169 .006 .232 .044 -.062

-.188 .124 .007 -.022 -.046


.253 .104 .146 .039 .206 .840 -.186 -.016

-.036 .066 -.031 -.053 -.035


.199 .137 .058 .000 .000 .898 -.152 .093 -.109 -.024 .053 .069 -.017

Keeping in .087 .039 .111 .863 .028 .104 -.104 .058 .012 .040 -.023 .166 -.063

135


mind the brand you just indicated, how are the overall prices of this brand?


.202 .062 -.119

.826 -.104 .053 .152 .159 .043 .104 .129 .033 .004


.086 .085 -.107

.886 .000 -.118 .025 -.153

.000 -.033 -.043 -.012 .108



Component


Total% of

VarianceCumulativ

e % Total% of

VarianceCumulativ

e %1 6.507 20.991 20.991 4.565 14.725 14.725

2 4.007 12.926 33.917 3.289 10.609 25.334

3 3.126 10.083 44.000 2.822 9.104 34.439

4 2.578 8.316 52.316 2.469 7.963 42.402

5 2.199 7.094 59.410 2.431 7.841 50.243

6 2.030 6.550 65.960 2.070 6.677 56.920

7 1.398 4.511 70.471 2.019 6.512 63.432

8 1.303 4.203 74.674 1.633 5.269 68.701

9 1.009 3.254 77.928 1.438 4.639 73.339

10 .941 3.036 80.964 1.271 4.100 77.440

11 .749 2.415 83.378 1.241 4.004 81.444

12 .728 2.347 85.725 1.065 3.437 84.880

13 .676 2.179 87.905 .938 3.024 87.905

136


14 .603 1.945 89.849

15 .499 1.610 91.459

16 .459 1.482 92.941

17 .353 1.139 94.080

18 .299 .966 95.046

19 .280 .903 95.950

20 .228 .737 96.686

21 .192 .619 97.305

22 .166 .537 97.842

23 .148 .476 98.318

24 .120 .388 98.706

25 .100 .323 99.030

26 .077 .248 99.277

27 .068 .220 99.497

28 .063 .202 99.699

29 .045 .145 99.843

30 .025 .079 99.923

31 .024 .077 100.000



Component

1 2 3 4 5 6 7 8 9 10 11I enjoy physical activity .107 .885 -.193 .094 -.008 .023 -.031 .036 .023 .006 .074


.192 .768 .206 .118 .062 -.095

.106 -.139 -.026 -.072 .108


.203 .904 -.107 .074 .020 .084 .014 -.011 .112 -.034 -.129


.303 .828 -.039 -.067 .154 .055 .213 .092 .077 .020 -.164

I enjoy shopping -.043

.021 .924 -.092 .053 .131 -.057 .025 .101 .057 .032


-.215

-.004 .133 .245 .085 .216 .310 .061 -.421 .517 .073

I don't like to shop .003 -.109 .874 .060 .107 .045 .275 .042 .016 .032 .096

137



-.003

-.080 .888 -.053 .056 .156 .025 .185 .119 .033 -.015


.116 .146 -.078 .188 -.018 .002 -.203 .170 .239 .702 .039


.246 -.308 .209 -.094 -.026 -.032

-.146 -.082 .027 .690 -.113


.060 .066 .181 .003 .036 .868 -.162 -.029 .051 -.004 .151


-.087

-.002 .158 .037 .105 .851 -.178 .075 .293 .055 .057


.198 .119 .259 -.069 .048 .236 .137 .303 .569 -.030 .107


.027 -.145 .191 .040 -.170 -.105

-.049 .861 .081 -.045 -.138


-.008

.082 .129 .088 -.020 .208 .085 .001 .831 .174 .110

138



.064 .148 .058 .073 .061 .185 .117 .752 .048 .202 .361


.188 -.085 .159 .055 -.061 .331 -.207 .078 .248 -.115 .711


.759 .087 .081 -.010 .063 -.273

.067 .086 .200 .151 .218


.810 .233 .163 .145 .054 -.091

.022 .211 .123 .165 .134


.922 .065 -.038 .014 .044 .058 -.009 -.007 -.001 .114 -.043


.900 .168 -.055 .023 -.049 .076 .060 -.103 .009 .078 -.023


.254 .169 .067 -.072 .673 -.060

.087 -.133 -.011 -.247 -.460


.013 .035 .031 -.005 .929 .112 .162 -.069 -.042 .064 .020

When it comes to purchasing .052 .044 .130 -.015 .947 .029 -.039 .018 .039 .022 .044

139


athletic apparel, I choose styles that are comfortable on me


.779 .170 -.078 .216 .177 .020 .225 -.050 .036 -.109 -.081


.779 .216 -.063 .176 .004 .096 .262 .091 -.098 -.173 .001


.284 .105 .148 .026 .209 -.234

.771 .009 .043 -.104 -.070


.217 .137 .087 .004 .001 -.183

.869 .036 .098 -.124 -.089


.061 .053 .097 .873 .030 -.060

.151 .021 .002 .103 -.023


.207 .063 -.115 .823 -.105 .143 .044 .165 .157 .065 .021

140



.105 .090 -.096 .878 .001 -.021

-.164 -.072 -.108 -.023 .066



Component


Total% of

VarianceCumulativ

e % Total% of

VarianceCumulativ

e %1 6.507 20.991 20.991 4.752 15.329 15.329

2 4.007 12.926 33.917 3.332 10.748 26.077

3 3.126 10.083 44.000 2.904 9.366 35.443

4 2.578 8.316 52.316 2.492 8.037 43.481

5 2.199 7.094 59.410 2.417 7.796 51.277

6 2.030 6.550 65.960 2.074 6.690 57.967

7 1.398 4.511 70.471 2.003 6.463 64.429

8 1.303 4.203 74.674 1.647 5.312 69.741

9 1.009 3.254 77.928 1.569 5.062 74.803

10 .941 3.036 80.964 1.558 5.024 79.827

11 .749 2.415 83.378 1.101 3.552 83.378

12 .728 2.347 85.725

13 .676 2.179 87.905

14 .603 1.945 89.849

15 .499 1.610 91.459

16 .459 1.482 92.941

17 .353 1.139 94.080

18 .299 .966 95.046

19 .280 .903 95.950

20 .228 .737 96.686

21 .192 .619 97.305

22 .166 .537 97.842

23 .148 .476 98.318

24 .120 .388 98.706

25 .100 .323 99.030

26 .077 .248 99.277

27 .068 .220 99.497

28 .063 .202 99.699

29 .045 .145 99.843

141


30 .025 .079 99.923

31 .024 .077 100.000



Component

1 2 3 4 5 6 7 8 9 10I enjoy physical activity .106 .880 -.194 .099 -.01

4.041 -.039 .057 .000 .025


.198 .758 .210 .129 .052 -.054 .087 -.102 -.077 -.012

I like to participate in physical activity .191 .913 -.111 .066 .043 .046 .023 -.031 -.031 .096

I feel good when I'm physically active .291 .839 -.043 -.076 .180 .004 .221 .071 .025 .059

I enjoy shopping -.041 .023 .921 -.094 .054 .154 -.057 .029 .060 .095

I carefully plan most of purchases -.217 -.007 .134 .250 .065 .190 .306 .089 .523 -.423

I don't like to shop .010 -.112 .875 .065 .098 .078 .265 .065 .032 .020

I look forward to my shopping trips -.005 -.075 .883 -.061 .064 .160 .030 .175 .036 .106


.123 .145 -.081 .188 -.034

.015 -.208 .186 .694 .243


.249 -.299 .207 -.100 -.022

-.060 -.142 -.093 .692 .023


.037 .076 .163 -.005 .036 .885 -.125 -.037 .015 .005

142



-.113 .013 .139 .022 .114 .846 -.134 .049 .077 .242


.198 .121 .252 -.073 .047 .280 .145 .307 -.034 .553


.027 -.136 .187 .020 -.150

-.148 -.044 .813 -.061 .067


-.005 .085 .125 .086 -.029

.260 .094 .015 .174 .823


.075 .131 .055 .084 .019 .268 .098 .808 .182 .053


.209 -.117 .157 .086 -.137

.550 -.231 .186 -.134 .270


.779 .073 .085 .006 .039 -.176 .039 .136 .131 .224


.819 .228 .161 .151 .044 -.030 .008 .236 .149 .128

143



.917 .074 -.044 .009 .060 .060 -.001 -.023 .112 -.016


.895 .176 -.061 .020 -.034

.080 .068 -.115 .077 -.006


.228 .190 .061 -.091 .733 -.173 .108 -.205 -.222 -.037


.004 .027 .028 .005 .920 .122 .155 -.042 .082 -.041


.048 .033 .127 -.004 .935 .063 -.050 .046 .035 .045


.770 .179 -.082 .212 .198 .009 .232 -.066 -.106 .022


.772 .222 -.068 .173 .019 .095 .268 .081 -.176 -.114


.288 .105 .155 .030 .219 -.254 .759 .019 -.105 .050

144



.219 .142 .094 .004 .016 -.218 .863 .037 -.126 .099


.062 .055 .099 .872 .030 -.068 .148 .024 .101 .004


.203 .070 -.119 .816 -.103

.143 .054 .157 .061 .145


.106 .087 -.095 .881 -.008

.003 -.168 -.061 -.026 -.102



Component


Total% of

VarianceCumulativ

e % Total% of

VarianceCumulativ

e %1 6.507 20.991 20.991 4.750 15.324 15.324

2 4.007 12.926 33.917 3.353 10.815 26.138

3 3.126 10.083 44.000 2.881 9.292 35.431

4 2.578 8.316 52.316 2.500 8.065 43.495

5 2.199 7.094 59.410 2.484 8.014 51.509

6 2.030 6.550 65.960 2.379 7.674 59.183

7 1.398 4.511 70.471 1.961 6.327 65.510

8 1.303 4.203 74.674 1.728 5.575 71.085

9 1.009 3.254 77.928 1.540 4.967 76.051

145


10 .941 3.036 80.964 1.523 4.912 80.964

11 .749 2.415 83.378

12 .728 2.347 85.725

13 .676 2.179 87.905

14 .603 1.945 89.849

15 .499 1.610 91.459

16 .459 1.482 92.941

17 .353 1.139 94.080

18 .299 .966 95.046

19 .280 .903 95.950

20 .228 .737 96.686

21 .192 .619 97.305

22 .166 .537 97.842

23 .148 .476 98.318

24 .120 .388 98.706

25 .100 .323 99.030

26 .077 .248 99.277

27 .068 .220 99.497

28 .063 .202 99.699

29 .045 .145 99.843

30 .025 .079 99.923

31 .024 .077 100.000


Appendix E – Reliability Analysis; Cronbach’s Alpha

146


Factor 1

Case Processing Summary

N %

Cases

Valid 88 93.6

Excludeda 6 6.4

Total 94 100.0

a. Listwise deletion based on all variables in the

procedure.

Reliability Statistics

Cronbach's Alpha N of Items

.920 6

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

I'm really attached to the

brands of athletic clothing I

use

16.90 23.518 .729 .913

I am committed to my brands

of athletic clothing16.91 23.555 .792 .903


athletic clothing, I try to get the

highest quality

16.75 23.431 .837 .897

I make a special effort to

choose the best quality athletic

apparel

16.73 23.327 .817 .899

Getting very good quality is

very important to me16.24 25.586 .727 .912


products, I try to get the very

best

16.53 24.367 .742 .910

Factor 2

147



N %

Cases

Valid 94 100.0

Excludeda 0 .0

Total 94 100.0


procedure.



.892 4


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

I enjoy physical activity 12.77 5.170 .802 .846

When I participate in physical

activity I feel annoyed13.02 5.225 .612 .928

I like to participate in physical

activity12.80 5.066 .850 .829

I feel good when I'm physically

active12.61 5.381 .829 .841

Factor 3


N %

Cases

Valid 92 97.9

Excludeda 2 2.1

Total 94 100.0


procedure.



.865 3

148



Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

I enjoy shopping 7.64 3.024 .772 .794

I don't like to shop 7.63 4.082 .702 .854

I look forward to my shopping

trips7.97 3.460 .783 .775

Factor 4


N %

Cases

Valid 78 83.0

Excludeda 16 17.0

Total 94 100.0


procedure.



.830 3


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

Keeping in mind the brand you

just indicated, how are the

overall prices of this brand?

7.00 1.351 .696 .758

Keeping in mind the brand you

just indicated, relative to other

types of brands, how are this

brand's prices?

7.23 1.530 .645 .807

Once again, keeping in mind

the brand you just indicated,

what do you expect the price

level to be of this brand?

7.13 1.308 .729 .724

Factor 5

149



N %

Cases

Valid 92 97.9

Excludeda 2 2.1

Total 94 100.0


procedure.



.862 3


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

When I buy athletic clothing

how well it fits on me is

important

8.86 1.661 .645 .903



clothing

8.71 1.638 .805 .744



styles that are comfortable on

me

8.76 1.722 .780 .771

Factor 5 Revised


N %

Cases Valid 92 97.9

Excludeda 2 2.1

150


Total 94 100.0


procedure.



.903 2


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted



clothing

4.40 .441 .823 .



styles that are comfortable on

me

4.46 .471 .823 .

Factor 6


N %

Cases

Valid 92 97.9

Excludeda 2 2.1

Total 94 100.0


procedure.



.892 2


151


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

It's important to be accepted

by my peers on campus3.39 .900 .805 .

Being accepted by my peers

as a part of the campus


3.27 .991 .805 .

Factor 7


N %

Cases

Valid 78 83.0

Excludeda 16 17.0

Total 94 100.0


procedure.



.903 2


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

This brand is excellent value

for the money3.88 .493 .829 .

I get value for my money when

I buy this brand3.90 .613 .829 .

Factor 8


N %

Cases

Valid 93 98.9

Excludeda 1 1.1

Total 94 100.0

152



procedure.



.544 2


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

If I don't purchase popular

brand name clothing products,

I'm not considered part of my

peer group

2.14 .926 .379 .

I think having the same

clothing products gives me a

sense of belonging to my

peers

1.68 .656 .379 .

Factor 9


N %

Cases

Valid 86 91.5

Excludeda 8 8.5

Total 94 100.0


procedure.



.374 3


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

153


I carefully plan most of

purchases6.91 2.415 .101 .524

I buy things according to how I

feel at the moment6.17 2.146 .313 .100


things that aren't on my

shopping list

6.01 2.223 .260 .202

Factor 9 Revised


N %

Cases

Valid 86 91.5

Excludeda 8 8.5

Total 94 100.0


procedure.



.524 2


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

I buy things according to how I

feel at the moment3.53 .911 .355 .


things that aren't on my

shopping list

3.37 .872 .355 .

Factor 10


N %

Cases

Valid 91 96.8

Excludeda 3 3.2

Total 94 100.0

154



procedure.



.617 2


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Cronbach's Alpha

if Item Deleted

I enjoy following clothing

trends I see on campus2.76 1.385 .450 .

I don't care about clothing

trends on campus2.67 1.090 .450 .

155

FINAL Lululemon Research Report 2 - SP 15 - Elasmar

Documents

literature page

reliability page

introduction page

background page

competition page

magazines page

conclusions page

theoreticalframework