FINAL Lululemon Research Report 2 - SP 15 - Elasmar

Branded Athletic Apparel Consumption 1
College Student Consumption of Branded Athletic Apparel
A Measurement Model for Lululemon
Alexis Feinberg
Boston University
April 30, 2015

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. TheoreticalFramework Page 24
V. ProposedPredictors Page 26
Proposed Predictors from Literature Page 26
Proposed Predictors within Theoretical Framework Page 29
Selected ProposedPredictors 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

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 – FactorAnalysis Page 112
XV. Appendix D – Reliability Analysis; Cronbach’s Alpha Page 146
XVI. Appendix E – Inter-Item CorrelationMatrix Page 155

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.

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).

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.

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

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

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

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

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.

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.
ScholarlyJournals
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

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

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

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

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)

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

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

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

(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

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.
Newspapersand Magazines

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).

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,

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

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

ProposedPredictors 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.

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

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)
ProposedPredictors 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.

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 physical activity (Villard & Moreno 2011)
 Attitude towards impulsive shopping (Cowart & Goldsmith 2007; Bae 2004)
 Attitude towards brand loyalty (habitual/brand-loyal orientation) (Cowart &
Goldsmith 2007)
 Attitude towards branded advertisements (researcher developed)
 Attitude towards apparel trends on campus (Roman & Medvedev 2011)
 Attitudes towards shopping (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
Perceived Behavioral Controls
 Perceived ease to which a brand name comes to mind (Dew et al. 2010)
Control Beliefs
 Perception of product purchasing convenience (Cowart & Goldsmith 2007)
 Perceived product value (Cowart & Goldsmith 2007)
 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)
Intention
 Intention to recommend brand (researcher developed)
Past Behavior

 Time spent shopping (Bae 2004)
 Frequency of shopping (Bae 2004)
 Frequency of physical activity (researcher developed)
 Type of physical activity participation (researcher developed)
SelectedProposedPredictors
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 physical activity (Villard & Moreno 2011)
 Attitude towards impulsive shopping (Cowart & Goldsmith 2007; Bae 2004)
 Attitude towards brand loyalty (habitual/brand-loyal orientation) (Cowart &
Goldsmith 2007)
 Attitude towards branded advertisements (researcher developed)
 Attitude towards apparel trends on campus (Roman & Medvedev 2011)
 Attitudes towards shopping (Cowart & Goldsmith 2007)
Normative Beliefs
Subjective Norms
Perceived Behavioral Controls
 Perceived ease to which a brand name comes to mind (Dew et al. 2010)
Control Beliefs
 Perceived product value (Cowart & Goldsmith 2007)
 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)
Intention

 Intention to recommend brand (researcher developed)
Past Behavior
 Frequency of shopping (Bae 2004)
 Frequency of physical activity (researcher developed)
 Type of physical activity participation (researcher developed)
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
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
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.
Multiple-Item Measures
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 awareness
Definition: Level in which an individual is aware/know about different athletic brands
Source: Dew et al. 2010; Cowart & Goldsmith 2007; Bae 2004
Measures: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 activity
Definition: An individual’s affect towards physical activity for fitness or athletic purposes
Source: Villard & Moreno 2011
Construct: Impulsive shopping
Definition: “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.
Construct: Attitude towards branded advertisements
Definition: A consumer’s general evaluation of an advertisement
If you saw an advertisement of this brand, you would think it is:
Construct: Attitude towards apparel trends on campus
Definition: A person’s affect towards apparel trends observed on campus
Source: Roman & Medvedev 2011
Construct: Attitudes towards shopping
Definition: The degree to which a consumer holds a positive attitude about shopping such that it
is enjoyable and worth the time and effort

Normative Beliefs (Azjen 1991, 2006)
Construct: Peer influence on making choices
Definition: The influence peers have on a person’s decision making process when it comes to
making a choice
Source: Villard & Moreno 2012; Roman & Medvedev 2011
Subjective Norms (Azjen 1991, 2006)
Construct: Perception of peer approval
Definition: How accepting a person thinks his/her peers are of him/her
Construct: Perception of peer judgment
Definition: A person’s perception of his/her peers’ judgment of apparel choices
Measures: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 mind
Definition: 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 value
Definition: Measure a person’s belief that the goods and services available from a particular
vendor are very good value given the prices charged for them
Keeping in mind the brand you just indicated, tell us how much you agree or disagree with the
following statements…
Construct: Apparel prices
Definition: A person’s attitude regarding a store’s prices, with some emphasis on how they
compare it to other stores

Construct: Apparel style
Definition: 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 fit
Definition: How important it is for athletic apparel to fit well in relation to a consumer’s body
Construct: Apparel comfort
Definition: How important it is for a consumer to have comfortable athletic apparel

Construct: Apparel quality
Definition: How important the quality of athletic apparel is to the consumer
Source: Roman & Medvedev 2011; Bae 2004; Cowart & Goldsmith 2007; Dew et al. 2010
Past Behavior (Azjen 1991, 2006)
Construct: Type of physical activity participation
Definition: Type of physical activity a person participates in for fitness (health)/athletic purposes
Source: Researcher developed
Construct: Type of shopping information by media medium
Definition: The kinds of information a consumer is perceptive to by different types of media
sources
Source: Bae 2004
Where do you get information about clothing before shopping?

Construct: Type of name brands
Definition: Type of athletic apparel brands that a consumer prefers to purchase
Source: Bae 2004
Measures:Lululemon; Athleta; Lucy; Nike; Other
*This construct was eliminated
Construct: Type of store
Definition: Category of stores/retail outlets that the consumer prefers to shop at
Source: Bae 2004
Which of these types of stores do you shop at?
Construct: Type of shopping companions
Definition: Individuals that the consumer chooses to shop with
Source: Bae 2004
Who do you shop with?
Dependent Variable
Construct: Likelihood college students will purchase branded athletic apparel
Definition: The likeliness that a college student will purchase certain branded athletic apparel
If you were to buy athletic clothing tomorrow, how likely would you be to buy:

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.

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: Age
Definition: Numerical age of an individual
Construct: Gender
Definition: Sex of an individual
Construct: Student (academic) year
Definition: A student’s current academic year
Construct: Expenditure on apparel
Definition: An individual’s monthly spending on apparel items
Source: 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

Intention (Azjen 1991, 2006)
Construct: Intention to recommend brand
Definition: A person’s intention to positively recommend an athletic apparel brand to peers,
family, parents, and acquaintances (roommates, neighbors, etc.)
Past Behavior (Azjen 1991, 2006)
Construct: Frequency of shopping
Definition: How often a consumer shops for apparel in a typical month
Source: Bae 2004; Cowart & Goldsmith 2007
Construct: Frequency of physical activity
Definition: How often a person participates in physical activity for health or athletic purposes
Construct: Most frequently purchased athletic apparel brand
Definition: The single athletic apparel brand that an individual purchases most often
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

Construct: Branded advertisement consumption
Definition: (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
often
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 market
Construct: Purchasing habits of comparable branded athletic apparel
Definition: (In reference to likelihood to purchase comparable branded athletic apparel) Whether
or not an individual has purchased one of the comparable athletic apparel brands

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

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

“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.

Survey Instrument
A

B

C

D

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.
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):
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: 4
More than $551: 5
Q23: What is your current academic year? Nominal
Freshman: 1
Sophomore: 2

Junior: 3
Senior: 4
Graduate: 5
Q24: What is your gender? Nominal
Male: 1
Female: 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”.
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.
Assessmentof 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.
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.
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
list
I carefully plan
most of my
purchases
1 0.18 0.11 0.08
I often buy
things without
thinking
0.18 1 0.43 0.34
I buy things 0.11 0.43 1 0.36

according to how
I feel at the
moment
When I go
shopping, I buy
things that aren’t
on my shopping
list
0.08 0.34 0.36 1
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 group
I’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
Being accepted
by my peers as a
part of the
campus
community is
important to me
0.47 0.81 1 0.46
If I don’t 0.12 -0.08 0.05 1

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

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.
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).
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).
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”.
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.
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.

FINAL Lululemon Research Report 2 - SP 15 - Elasmar

FINAL Lululemon Research Report 2 - SP 15 - Elasmar

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