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2/05 p:/CIRC/Date0ue.indd-p.1

HERE TODAY, GONE TOMORROW:
THE ANTECEDENTS AND CONSEQUENCES OF IN-STORE HOARDING
AT FAST FASHION STORES

By

Sang-Bun Byun

A DISSERTATION

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY

Department of Advertising, Public Relations and Retailing

2006

ABSTRACT

HERE TODAY, GONE TOMORROW:
THE ANTECEDENTS AND CONSEQUENCES OF IN-STORE HOARDING
AT FAST FASHION STORES
By

Sang-Bun Byun

This study raised questions about the role of in-store hoarding and investigated
the antecedents and consequences of hoarding at fast fashion stores. As the market
becomes dynamic and volatile, more retailers are moving toward fast fashion by constantly
delivering new products throughout the season. This fast fashion strategy is a marketing
approach to respond to the latest fashion trends by frequently updating products with a
short renewal cycle and turning the inventory at a rapid rate. As a result, a product life span
is dramatically reduced, thereby increasing fashion perishability. Moreover, in order to
make constant room for new products and minimize markdowns, fast fashion retailers
deliberately limit product availability, creating a sense of scarcity fiom the consumer
perspective.

This study proposed perishability and scarcity as implicit time-limited cues that
influence consumers to take immediate action while in the store. In-store hoarding was
conceptualized as a dominant behavior in response to the implicit time-limited cues.
Prospect Theory was applied as a major theoretical framework.

Based on the empirical investigation of actual shoppers and purchasers in fast

fashion stores, this study supported that perishability and scarcity are central to

understanding in-store hoarding behavior. Anticipated gains of buying and anticipated
losses of not buying were found to be mediators in the effect of perishability and scarcity
on in-store hoarding. The study supported that consumers are more sensitive to
anticipated losses of not buying than anticipated gains of buying, thus selecting an option
that minimizes the risks—hoarding. Furthermore, purchase acceleration and shopping
hedonism were significant consequences of in-store hoarding. Overall, the proposed
model ensured the theoretical soundness and coherence of the conceptual model.

This study made a significant contribution to the consumer and retailing
literature by introducing, defining, and operationalizing new constructs and
measurements including the scales for perishability, anticipated loss of not buying, and
in-store hoarding. This study also provided useful implications for practitioners in

developing and implementing marketing and merchandising management strategies.

Copyright by
SAN G-EUN BYUN
2006

To My Mother

ACKNOWLEDGEMENTS

Completing my doctoral program and dissertation has been a long journey. I
owe so much to the great people around me who have helped me through this time. I
wish to express my deepest appreciation to my esteemed advisor, Dr. Brenda
Stemquist. This dissertation would not have been possible without her expert
guidance. I was lucky to have such a wonderful committee: Dr. Linda Good, Dr.
Kyoung-Nan Kwon, and Dr. Tom Page. I thank you for your guidance and helpful
suggestions. In addition, my sincere gratitude goes to Dr. Mira Lee for her valuable
comments and support during my initial idea development.

I would especially like to thank my mother, brother (Si-Woo), and sisters (J u-
Eun and Sock-Bun) for their encouragement, support and love. My mother’s
unwavering faith in me has been the strongest driving force. Whenever I had a hard
time, I imagined this day and my mother with a big smile on her face. Without her
endless love and dedication, this would have never been possible. I am also sure that
my beloved father, who is in Heaven now, will be very proud of his daughter. I am
sorry that he could not be with us any more to share my great achievement.

I would like to acknowledge my wonderful colleagues, Carol Finnegan, Amy
Chen, and Ying Huang for being great helpers and friends. I am also deeply thankful
to my dear friends, Jung-In, Zeesun, Namjoon and J oonyul for their support and love.
Finally, I truly thank God, who saved me, strengthened my weaknesses and always

led me in His way.

vi

TABLE OF CONTENTS

LIST OF TABLES ............................................................................................................. ix
LIST OF FIGURES ............................................................................................................ x
CHAPTER I
INTRODUCTION ............................................................................................................... 1
J ustifications of the Study ........................................................................................ 1
Research Objectives ................................................................................................. 3
CHAPTER II
LITERATURE REVIEW ..................................................................................................... 5
Fast Fashion Strategy ................................................................................................ 5
Short Renewal Cycle ..................................................................................... 7
Limited Supply .............................................................................................. 8
In-Store Hoarding ................................................................................................... 10
CONCEPTUAL FRAMEWORK ...................................................................................... 15
Prospect Theory ...................................................................................................... 17
Antecedents of In-Store Hoarding .......................................................................... 19
Fashion Perishability .................................................................................. 19
Effect of Fashion Perishability on Anticipated Gains of Buying ............... 20
Effect of Fashion Perishability on Anticipated Losses of Not Buying ...... 21
Scarcity ...................................................................................................... 23
Effect of Scarcity on Anticipated Gains of Buying ................................... 24
Effect of Scarcity on Anticipated Losses of Not Buying ........................... 26
Effect of Anticipated Gains and Losses on In—Store Hoarding .................. 28
Consequences of In-Store Hoarding ...................................................................... 30
Shopping Hedonism .................................................................................. 30
Purchase Acceleration ............................................................................... 33
Cognitive Capacity view of Mood Effects ................................................ 34
Physical Proximity and Purchase Acceleration ......................................... 36
Innovativeness as a Moderator ............................................................................... 37
CHAPTER III
METHOD .......................................................................................................................... 41

vii

Measurement ........................................................................................................... 41

Fashion Innovativeness ............................................................................... 41
Shopping Hedonism .................................................................................... 42
Scale Development Procedures ............................................................................... 43
One-to-One Interview: Stage One ............................................................... 43
Face Validity Tests: Stage Two ................................................................... 45
Pre-Test: Stage Three .................................................................................. 45
Data Collection ....................................................................................................... 50
Sample ......................................................................................................... 50
Data Collection Method .............................................................................. 51
CHAPTER IV
RESULTS ........................................................................................................................... 53
Demographic Characteristics of the Sample .......................................................... 53
Confirmatory Factor Analysis ................................................................................ 55
Analysis of the Structual Model ............................................................................ 60
Path Analysis for Antecedents and Consequences of
In-Store Hoarding ....................................................................................... 60
Comparison of Path Coefficents ................................................................. 65
Moderation Effect: Fashion Innovativeness ............................................... 66
DISCUSSION .................................................................................................................... 72
Antecedents of In-Store Hoarding .......................................................................... 72
Prospect Theory: Path Comparison ........................................................................ 73
Consequences of In-Store Hoarding ....................................................................... 75
Moderation Effect of Innovativeness ...................................................................... 76
CHAPTER V
CONCLUSION AND IMPLICATIONS ............................................................................ 79
Theoretical Implications ......................................................................................... 79
Marketing Implications .......................................................................................... 83
LIMITATIONS OF THE STUDY ..................................................................................... 88
FUTURE RESEARCH ...................................................................................................... 89
APPENDICES
Appendix A: Fast Fashion Retailers’ Product Positioning ..................................... 93
Appendix B: The Comparison of Demographic Variables Between Stores .......... 95
Appendix C: The Results of Path Analysis ............................................................ 99
Appendix D: Summary of Hypotheses Tests ....................................................... 102
Appendix E: The Effect of Prior Store Knowledge ............................................. 104
Appendix F: Survey Consent Form ..................................................................... 106
Appendix G: Survey Instrument .......................................................................... 108
BIBLIOGRAPHY ............................................................................................................ 1 14

viii

LIST OF TABLES

Table 1. Demographic Characteristics of the Sample ........................................................ 54
Table 2. Respondents’ Shopping Information .................................................................... 55
Table 3. Result of Confirmatory Factor Analysis .............................................................. 57
Table 4. Measurement Properties ....................................................................................... 58
Table 5. Path Comparison: Result of Chi-Square Difference Test ..................................... 66
Table 6. Multiple Group Path Comparison Test: Innovativeness ...................................... 69

Table 7. Result of Structural Equation Model Estimation for Innovators
and Non-Innovators ............................................................................................ 71

ix

LIST OF FIGURES

Figure 1. Conceptual Framework of the Study .................................................................. 16
Figure 2. Prospect Theory: Value Function ....................................................................... 17
Figure 3. Result of the Path Analysis ................................................................................. 64
Figure 4. Path Coeflicents for Innovators .......................................................................... 70
Figure 5. Path Coefflcents for Non-Innovators .................................................................. 70

CHAPTER I

INTRODUCTION

“Here today, gone tomorrow.” This is a signal that fast fashion retailers
implicitly send to prompt their customers to take immediate action. To quickly respond to
a fickle and fast-changing world of fashion, retailers such as Zara, H&M, Mango, and
Top Shop defy conventional wisdom (The Economist, June 18, 2005) and have built their
competitiveness by adopting a new strategic concept known as fast fashion. They follow
a mass-boutique approach by introducing high fashion at relatively low prices. Latest
fashion is delivered almost weekly, and productions runs are limited and thus, the product
availability is deliberately restrained (Moore and Fernie, 2003; Dutta, 2002). Such
strategic intention results in increased fashion perishability and the creation of a scarcity
mentality for shoppers, acting as implicit time-limited cues. Consequently, fast fashion
retailers have dramatically changed a conventional business model as well as consumer
shopping and purchase behavior. However, there has been no theoretical approach to
explain how fast fashion strategy influences consumer behavior and how they respond to

such implicit time-limited cues.

Justification of This Study

Despite its growing acknowledgement and strategic implications of fast fashion,
there has been no clear definition and theoretical approach to support its competitiveness

from a consumer decision-making perspective. Along with the changing business

1

environment, a study to explain consumers’ psychological and behavioral responses to
the unique retail offerings of fast fashion retailers is also necessary. Therefore, the major
characteristics of fast fashion and the consequent effects on consumer shopping and
purchase behavior should be identified and investigated.

Fast fashion retailers’ strategic intention on scarcity calls for study. Traditionally,
a retail store’s out-of-stock situation or product unavailability has been perceived as a
‘loss’ due to negative outcomes such as store switching, postponement or cancellation of
purchase (Corsten and Gruen, 2003; Schary and Christopher 1979; Mason and Wilkinson
1976). Previous studies suggested that sure availability of products would provide higher
profits and better values to consumers. As a result, many firms try to minimize the
likelihood of out of stock by stocking enough products in the back or quickly
replenishing the inventory. By contrast, challenging the traditional view, fast fashion
retailers do not consider out of stock as a loss. Rather, they strategically intend to do so to
create a sense of scarcity (Ghemawat and Nueno, 2003). It was not clear how consumers
would behave under the situation where the product quantity is limed and there is an
uncertainty of product availability in the next store visit. Although there have been many
studies on the positive effect of scarcity on product desirability, there has been no
empirical study to explore the theoretical links among perceived scarcity, worries about
product availability, and subsequent in-store hoarding behavior. Likewise, fashion
perishability accelerated by a short renewal cycle will also limit consumers’ freedom to
delay purchase decisions, engendering the same effects as perceived scarcity. This
requires academic attention.

Further more, as the fear of scarcity or unavailability of a product motivates

hoarding (Meagher, and Riskind, 2001; Frost and Gross, 1993, McKinnon, Smith, and

2

Hunt, 1985; Ong, 1999; Verhallen and Robben, 1994; Lynn, 1993), in-store hoarding
often encounters in fast fashion stores. In-store hoarding is expected to play a major role
as a new type of retail entertainment by Simultaneously creating shopping competition
and excitement for consumers. However, the concept of in-store hoarding has received
minimum attention in consumer and retail literature. Is in-store hoarding likely to be
motivated by the same reasons as the general hoarding? Thus, a study to find the major
factors that trigger consumers to hoard products immediately or impulsively and the
resulting outcomes should be conducted to provide new theoretical and strategic insights

for retailers and practitioners working at dramatically changing retail landscape.

Research Objectives

Understanding the diverse aspects of consumer psychology will be the most
important marketing information available to help a company advance one-step further
than competitors. The main purpose of this study is to investigate the antecedents and
consequences of in-store hoarding at fast fashion stores. Consistent with the justifications
of this study, the research objectives are addressed as the following.

First, in order to accomplish these goals, the definition of fast fashion strategy
will be clarified by identifying the key characteristics of this strategy. Second, as an
attempt to find relevant drivers of in-store hoarding, this study will apply the consumers’
psychology of regular permanent hoarding to the context of in-store hoarding. A
definition of in-store hoarding will be provided. It will identify fashion perishability and
scarcity as implict time-limited cues. Next, it will examine how consumers frame these

cues in the dimensions of consmners’ anticipated gains of buying and losses of not

3

buying. In turn, how these dimensions prompt in-store hoarding will be investigated.
Lastly, based on the consumer literature, the consequences of in-store hoarding will be
examined by linking shopping hedonism and purchase acceleration.

The remainder of the dissertation is organized as follows. The first part of
Chapter II introduces the background of a new strategic concept, fast fashion, and the
relevant hoarding literature is reviewed to identify antecedents and consequences of in-
store hoarding. Based on the literature review, the conceptual framework is presented and
the hypotheses are developed. Commodity theory and prospect theory are next reviewed
to explain consumers’ in-store hoarding behavior as responses to the uncertainty about
product availability resulting fiom perceived scarcity and perishability. In the following
section, the consequences of in-store hoarding are discussed. In Chapter 111, research
design, measurement scales, data collection methods are presented. The analysis of
results and the discussion of the findings are provided in Chapter IV. Lastly, Chapter V

covers the conclusion and limitations of this study and directions for future research.

CHAPTER II

LITERATURE REVIEW

In this chapter, the literature relevant to the present study is reviewed. First, this
study discusses the characteristics of a fast fashion strategy and its growing importance in
the retailing and fashion apparel industry. Fast fashion is next redefined. In the following
section, the relevant literature about hoarding behavior is reviewed and its antecedents
are proposed based on a review of the social psychological literature. Lastly, the
consumer purchase behavior and shopping hedonism are reviewed as consequences of in-
store hoarding. For the theoretical explanations among the hypothesized relationships,
commodity theory, prospect theory, uniqueness theory, and the cognitive capacity view of

mood effects are discussed.

FAST FASHION STRATEGY

The speed to market is clearly a critical component for gaining a competitive
advantage for products that have a short life cycle such as clothing. With the increasing
level of competition and market dynamics, the fashion apparel industry has shifted to “the
arena of timing” (Richardson, 1996, p.400) and accordingly, the shortened production
cycle has seemingly become one of the competitive alternatives that fashion apparel firms
can select to respond quickly to changing target markets.

Traditionally, the product development cycle was typically split into

Spring/Summer and Fall/Winter (Moore and Fernie, 2003; Brannon, 2005). By contrast,

5

beyond this traditional basis, fast fashion retailers continuously develop innovations and
introduce new merchandise weekly or bi-weekly, considering fashion as food that spoils
quickly (Dutta, 2002). Accordingly, along with the speedy delivery of products, it has
been referred as, “fast fashion” as in “fast food.”

Although the name of fast fashion has been frequently used, there has been no
clear definition of this strategy. As a result, fast fashion was explained by providing
company examples, rather than based on the given definition. Fast fashion was first
defined by Moore and Femie (2003) as “various strategies to respond commercially to
the latest fashion trends” (p.31). They emphasized the delivery of the latest fashion items
as an essential component in order to retain the sustainability of fast fashion retailers. In
addition, although they explained the goal to respond to the latest fashion trends could be
achieved by various strategies, their definition is too broad and failed to emphasize the
importance of a shortened new product introduction cycle as a key component of fast
fashion strategy.

In a more clear definition, Guercini (2001) used a term called “Quick Fashion
Formulas” and defined it as “a product/service characterized by its potential to supply
retailers with a range renewal service that is produced at short time gaps” (p.69). As
acknowledged in the definition, more fashion retailers derive their competitive advantage
from turning their inventory at a very rapid rate in order to maintain consumer interests.
Despite its emphasis on the growing importance of shortened renewal cycle and rapid
inventory turnover, this defmition does not clearly encompass its goal to deliver the latest
fashion items.

A large assortment of knockoffs and relatively low price are also often identified

in the description of fast fashion (Dutta, 2002; Craig, Jones, and Nieto, 2004; Brannon,

6

2005). However, this study argues that these factors are supplementary to respond to
market dynamics or to increase availability of high fashion to young trend conscious
consumers, but they are not indispensable factors to meet qualifications of fast fashion.
As such, there has been no consensus on the definition of fast fashion. The
previous definitions did not clearly describe the foremost characteristics of fast fashion
strategy. Therefore, synthesizing the definitions of Guercini (2001) and Moore and F emie

(2003), fast fashion strategy is redefined as the following.

Fast fashion strategy (F F S) is a marketing approach to respond to the
latest fashion trends by frequently updating products with a short

renewal cycle and turning the inventory at a rapid rate.

The constant and frequent delivery of latest fashion items and rapid inventory
turnover require retailers to plan on limited supply of products. Therefore, quick
response to dynamic market changes in pursuit of the latest fashion trends are closely
interlinked and interdependent with a shortened renewal cycle and deliberate limited
supply. The following section discusses these two characteristics as a prerequisite to

respond to the latest fashion trends.

Short Renewal Cycle

The nature of high volatility and uncertainty of the market has made forecasting
accurate demands inherently impossible, requiring new capabilities or resources to
quickly respond to the external market changes (Dutta, 2002; Christopher and Towill,

2002; Richardson, 1996). These retailers demonstrated their competitiveness through the

7

fit with the external environment (i.e., market dynamism) and the strategy, evidenced by
their exemplary financial performance.

Fast fashion retailers dramatically reduce the product life cycle to maintain
inventory fi'eshness and frequently introduce new offerings to satisfy consumers’ ever-
changing preferences. They are top leading retailers in the fashion apparel industry and
excel in supply chain management to keep up with an increasing dynamic marketplace
(The Economist, June 18, 2005; Dutta, 2002). The ability to acquire the latest consumer
information and the capacity to quickly respond to market changes (Caro, 2005) allowed
fast fashion retailers to replace the inventory weekly with new designs that better click
new market trends (Economist, June 15, 2005).

As a result, inventory freshness became a norm to maintain consumer interests
and promote frequent store visits (Ghemawat and Nueno, 2003). In turn, a short product
life span and a rapid inventory turnover accelerated fashion perishability. The increased
perishability may be interpreted in two different ways. As mentioned above, the rapid
product turnover and short renewal cycle would encourage frequent store visits with
excitement for new product expectations. At the same time, consumers’ awareness of
fashion perishability would also increase the level of uncertainty about product
availability because products are changing very quickly and thus today’s product may not

be tomorrow’s product.

Limited Supply
Retailers conduct assortment planning by trading off between assortment, variety,
and product availability (Levy and Weitz, 2003). “All fashion end in excess” (Brannon,

2005, p.65). As such, fast fashion retailers are trying to capitalize on exclusivity by

8

limiting product availability. In a consistent line, defying the conventional wisdom, some
researchers suggested different views on the value of out of stock or limited availability
of products in a competitive setting. Balachander and Farquhar (1994) contended that the
out-of-stock situation can be used strategically by ‘stocking less. ’ In other words,
companies can gain more by stocking less when consumers are prone to search elsewhere
upon encountering unavailability. According to them, sure product availability rather
promotes price competition between firms, resulting in less or negative margin for them.
Their study demonstrated that consumers who encountered out of stock of their favorite
items would be willing to visit another store to find the missing item and buy it even if
the product is not on sale or the price is higher. Thus, limited supply enables firms to
avoid price-cutting and obtain higher margins than with assured availability. They
contended that the limited product availability could benefit a firm if the strategic effect
of lower price competition outweighs the direct effect of lost sales.

In a similar vein, Krishnan, Koelemeijer and Rao (2002) argued that although
consistent assortment will enhance value on the part of consumers, not all retailers make
such a commitment. Depending on retail format, consumers have different expectations
for the level of consistency in assortment. For instance, although consumers can find the
lowest price in hard discounters or warehouse clubs, they have lower expectations for a
consistent assortment for such retailers than for grocery stores and department stores
where the assortment is expected to be consistent from one period to another. They found
that retailers adopting a consistent assortment are not profitable since it encourages them
to engage in price-oriented competition, supporting findings by Balachander and
F arquhar (1 994). Likewise, consumers are less likely to expect sure availability of

products for fast moving fashion stores and fast fashion retailers attract them by

9

competing with non-price factors such as quick delivery of high fashion and exclusivity.

The benefits of limited supply are also predicted in the commodity theory.
According to the theory, limited product offerings encourage immediate purchases by
consumers. Along with the rapid product turnover, sparsely stocked shelves reinforce
consumers’ perception that their favorite items will be gone in a moment. Moreover,
being aware of higher likelihood of being out of stock, consumers are often tempted to
purchase non-sale products by willingly paying full price. In fact, the artificial limited
supply strategy, which creates a sense of scarcity, enables fast fashion retailers to increase
sell-through rates, that is, the proportion of a season’s merchandise selling at initial set
price (Senanayake and Little, 2001). This shows another source of a competitive
advantage of FFS.

The following section discusses its major effect on in-store hoarding and
consequent purchase behavior. A more detailed explanation about short renewal cycle and
limited supply will be provided in the development of a conceptual framework and

hypotheses.

IN-STORE HOARDING

Hoarding can be viewed as a type of inventory accumulation. It can be profit
seeking or loss avoiding. However, it often reflects emotional or impulsive buying in that
hoarders tend to associate a high level of perceived risk with being deprived of the
product, and rush to acquire unusual amounts of the product (McKinnon, Smith, and

Hunt, 1985). Hoarding often reflects irnpulsiveness or compulsiveness (Frost and

10

Steketee, 1998). It is often observed for necessity products such as food or natural
resources such as oil and water. Such psychology of hoarding behavior can be extended
and applied to fast fashion marketing.

Despite its interesting and critical implication, there has been minimum attention
given to the process of in-store hoarding and the variables affecting its enactment. In-
store hoarding refers to temporal possession of products during shopping in response to
impulsiveness generated by a certain situational factors (e. g., scarcity, uncertainty, or
competition among shoppers) or appealing product factors.

In-store hoarding is distinguished from regular hoarding or permanent

acquisition of products. Therefore, this study defines in-store hoarding as the following.

In-store hoarding is a behavior that involves rushing to acquire
products in one is possession (in hands or in a shopping basket)
during shopping in a store, in response to various impulsive stimuli

such as situational or promotional factors, or product factors.

Since consumers do not usually have a specific product in mind, in-store
hoarding involves impulsiveness and thus most of apparel purchases are made
impulsively in store. Retailers can substantially influence consumers’ purchase decision
by controlling store variables or situational factors. Impulse buying has been a focal point
for marketing activity and generated substantial research interest (Rook, 1987; Beatty and
Ferrell, 1998). Rook (1987) defined it as when “a consumer experiences a sudden, often
powerful and persistent urge to buy something immediately (p. 191).” In this regard,

theoretical understanding of in-store hoarding will provide important implications.

ll

Similarly, previous literature found that hoarding is mostly driven by scarcity or
time-limited product or promotion availability (Frost and Gross, 1993, McKinnon, Smith,
and Hunt, 1985; Ong, 1999; Verhallen and Robben, 1994; Lynn, 1993). If a product is
always available when needed, there is little motivation for ownership (Frost and Gross,
1993). Accordingly, impulsive hoarding is often observed in factory outlets or discount
stores where scarcity increases due to the relatively intense competition level among
shoppers. More frequently, consumers become more impulsive in hoarding when there
are limited time offers or anticipated product availability in the next store visit is very low
(Verhallen and Robben, 1994). The issue of scarcity becomes more relevant for
psychological and marketing applications.

In fact, retailers ofien use a time-limited promotion strategy by using semantic

9, 66

cues such as “limited release, only while supplies last,” “hurry, limited time only,”
“limit of one per consumer” and “don’t delay” to intentionally convey to the consumer
that the promotion is offered only in a very limited time period (Jung and Kellaris, 2004;
Aggarwal and Vaidyanathan, 2003; Spears, 2001; Lynn, 1993). Putting expiration dates
on deals and notifying customers of the number of products left are also examples of
time-limited promotions (Spears, 2001). As such, scarcity may result from diverse
reasons including deliberate limits on the supply, or limited number of suppliers, costs of
acquiring, keeping, or providing a commodity, restrictions limiting the possession of a
certain commodity, or delays in providing a commodity (Brock, 1968).

Such time-limited promotions prove efiective in that it substantially affects
consumer purchase behavior. For instance, in the study of purchase intention of bonus

packs, Ong (1999) discovered that when consumers assumed there was a limited supply

of bonus packs, the purchase intention was greater than when they did not. In a related

12

study, Park and Kang (2000) found that in addition to the store environment and low
price, the situation of limiting product availability encouraged impulse buying. In
addition, Aggarwal and Vaidyanathan (2003) found that the time-limited nature of
promotions led to a strong purchase intention and a negative effect on intent to search
further for deals.

However, although there have been many studies on the effect of time-limited
promotions on purchase behavior, there has been no known study that examines how
consumers respond to implicit time-limited cues. Fast fashion retailers do not use explicit
signs to promote sales but rather implicitly send a signal to their customers to take
immediate action. Moreover, Aggarwal and Vaidyanathan (2003) also questioned, “If
there is any other way of communicating to the customer the time—limited nature of a
promotion and if yes, how this influences the likelihood of triggering a purchase
decision.” To fill this gap, this study will contribute by expanding the scope of the
boarding literature by finding implicit time-limited cues that give the urge to hoard and
accelerate a purchase.

In this regard, this study proposes that fashion perishability and scarcity send a
message about limited product availability. Further, such time-limited cues are implicit
since they are not indicated clearly in a Sign and perceived subjectively by consumers.
Most important, such cues play vital roles in increasing consumers’ worries about product
unavailability, ultimately creating a store atmosphere built on the customer’s perception
that they must, “buy now, it won’t be here tomorrow.” Thus, these cues prompt
consumers to hoard and buy products immediately and impulsively. In a similar context,
Kwon (2001) proposed that the level of expected future deals could alter purchase

behavior, either accelerating purchase or postponing purchase. As such, it is expected that

13

uncertainty about future product availability or anticipated regrets from delaying a chance
to purchase will influence in-store hoarding and further purchase decision.

Understanding in-store hoarding in fast fashion stores provides critical
marketing implications that can be applied in other stores or industries. This study is
more meaningful in that prior studies have focused on explicit time-limited promotions
that are indicated in a sign with semantic cues and on the effect of price on consumer
hoarding or purchasing behavior. Additionally, little research attention has been devoted
to what makes in-store shopping more fun and enjoyable (Beatty and Ferrell, 1998). In-
store hoarding will create a lot of fun and excitement for shoppers, as does window
browsing. Although it does not always result in an actual purchase, it will be highly
correlated with the decision to buy and will precede the actual purchase. Therefore, it is
very important to examine in-store hoarding behavior and to distinguish it from actual
buying behavior.

Therefore, this study will investigate hoarding behavior in fast fashion stores by
applying the hoarding related literature. This study will explore how consumers will
respond to a situation in which consumers find their favorite products but notice that
there are few products available or anticipate the product will be gone or replaced with
new products very quickly due to a shortened renewal cycle. Furthermore, a question
about how they will feel when they actually board the last item or one from the limited
supply will be investigated by linking shopping hedonism. The theoretical framework
will be presented in the following section. Its antecedents and consequences of in-store

hoarding behavior will be discussed in detail in the next section.

l4

CONCEPTUAL FRAMEWORK

A theoretical framework for this study was developed. Fashion perishability and
scarcity are identified as implicit time-limited cues resulting from a short renewal cycle
and limited product offerings. These are conceptualized as exogenous variables that
frame anticipated gains and losses resulting from the buying decisions. Further, such gain
and loss dimensions are conceptualized as drivers of in-store hoarding. For personal traits,
consumer innovativeness is modeled as a moderator intervening in the effect of
perishability and scarcity on anticipated gains and losses and further on in-store hoarding.
Theoretical discussion is based on Commodity Theory and Uniqueness Theory. For a
major theoretical foundation, Prospect Theory is applied to predict stronger efi’ects of
perishability and scarcity on anticipated losses and a stronger effect of anticipated losses
on in-store hoarding than those of anticipated gains.

Lastly, shopping hedonism and purchase acceleration are proposed as the
consequences of in-store hoarding. The purchase acceleration is modeled as the final
endogenous variable. The conceptual framework is provided in Figure 1. All linkages are

hypothesized to be positive.

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l6

Prospect Theory

Prospect theory, developed by Kahneman and Tversky (1979), provides a
theoretical basis for understanding the effect of perishability and scarcity on consumers’
anticipated gains and losses and finally on in-store hoarding behavior. The theory explains
consumers’ decision making under risks and has been applied to explain consumer decision
making for insurance and gambling. It proposes that value is assigned in the form of a gain
and a loss rather than to absolute final assets, refi-aming the available options in order to
simplify subsequent evaluation and choice (Kahneman and Tversky, 1979). For example,
people perceive stimuli such as brightness, loudness, or temperature in relation to the past or
present context of experience, rather than evaluating absolute magnitudes of such stimuli. In
this case, the subjective experience defines an adaptation level or reference point (Helson,
1964; Kahneman and Tversky, 1979). Or, the same level of wealth may be perceived

differently depending on a person’s current financial status (Kahneman and Tversky, 1979).

Value

b-----

 

Losses v Gains

 

 

[Figure 2] Prospect Theory: Value Fuction

l7

The value ftmction is normally concave for gains, commonly convex for losses
and is generally steeper for losses than for gains (Kahneman and Tversky, 1979). Namely,
losses are perceived more sensitively than gains, causing loss aversion (Kahneman and
Tversky, 1979; Tversky and Kahneman, 1992). For example, in the case of gambling, the
perceived loss of losing $100 is roughly twice as much as the perceived gain of wimiing
$100 (Thaler, Tversky, Kahneman, and Schwartz, 1997). In addition, a price increase is
justified as more unfair than a cancellation of a former price reduction and a cut in wages
is perceived as more unfair than a cancellation of a wage raise (Lieberman, Idson, and
Higgins, 2005). As a result, because loss averters experience losses more intensely than
gains of similar objective magnitude, they tend to select a choice that reduces perceived
risks.

Therefore, based on Prospect Theory, fashion perishability and scarcity are
stimuli and consumers assign values which are captured in the anticipated gains of
buying and the anticipated losses of not buying. Such stimuli (perishability and scarcity)
are expected to be perceived differently depending on consumer-related factors such as
prior store knowledge or experience or fashion innovativeness, etc. that act as reference
points. In turn, the anticipated gains and losses are conceptualized as determinants of in-
store hoarding. Lastly, applying the risk aversion principle, it is proposed that in-store
hoarding is more strongly affected by the anticipated losses from not buying than by the
ancitipated gains of buying. This study also assumes that consumers’ reflections of such
anticipated gains and losses occur spontaneously while they are browsing in store.

The next section discusses fashion perishability, scarcity, and the effect of these

variables on anticipated gains of buying and losses of not buying.

18

Antecedents of In-Store Hoarding
Fashion Perishability

In the past, fashion apparel companies positioned themselves by focusing on low
price or high quality. However, with more sophisticated consumer wants and growing
interests in fashion, the agility needed to guarantee constant delivery of up-to-date
merchandise becomes increasingly critical for business success. In response, more firms
have shifted their focus to providing the latest fashion or high fashion on a fiequent basis
as a differentiation in today’s time-based retail environment. As a result, the life span of
high fashion items has been dramatically reduced, increasing fashion perishability.

In general, perishability refers to an item that has a fixed useful life or looks less
desirable after a certain time (Gupta, Sundaraghavan, and Ahmed, 2003; Voss and Seiders,
2003). For example, a Halloween costume or a Christmas product is useful only before
October 31 or December 25 and becomes less valuable or non-usable after the season
(Gupta, Sundaraghavan, and Ahmed, 2003). Services only have value when they are
produced and consumed, so they are always perishable. For example, airplane or theatre
seats or hotel rooms are valuable only for a limited time and their value ends at a certain
point (Geoffrey, 2000). Namely, they cannot be stored or sold later because they are
offered at particular moments in time. Therefore, such services are also perishable.

Much like food and services, fashion is perishable. In particular, the latest
fashion delivered by fast fashion retailers is highly perishable in that outdated fashion
items cannot be sold later because they look less desirable to their target market.
Moreover, perishability is directly associated with shelf life (Voss and Seiders, 2003).
High fashion items have a high level of perishability in that they do not stay long on

shelves. Perishability increases when the company deliberately shortens a renewal cycle

19

by frequently introducing new products and quickly removing unsold or less popular
items (Al-Zubaidi and Tyler, 2004). As a result, products in a fast fashion store have a
short product life and the value of current products deteriorate quickly when existing
products are replaced with new, more recent products.

Thus, fashion perishability is mainly facilitated by two factors: consumer
demand for newness and marketing strategy. Namely, consumers’ ever-changing
preferences and increased demand for new styles, along with the greater variety in
fashion, has rapidly reduced the product life cycle of fashion apparel (Al-Zubaidi and
Tyler, 2004). In addition, fashion perishability is accelerated by marketing strategies such

as continuous introduction of innovations or a short product renewal cycle.

Efl’ect of Fashion Perishability on Anticipated Gains of Buying

While perishability becomes an essential nature of fashion in today’s time based
market, little attention has been given to the effect of perishability on product valuation.
The strategic importance of perishability for profitability and store image has been
supported in many studies (e.g., Turcsik, 2003; Bemer, 1999; Kerin, Jain, and Howard,
1992). For instance, carrying perishable items drives store traffic and increases purchase
frequency and store loyalty (Tsiros and Heihnan, 2005; Krider and Weinberg, 2000;
Corstjens and Corstjens, 1999). Accordingly, more sophisticated food retailers provide
full-service delis, fresh bakeries, in-store butchers, salad bars and elaborate ranges of
produce to draw more customers into the store and to differentiate them from competitors
(Tsiros and Heilman, 2005; Corstjens and Corstjens, 1999). Hence, perishability attracts

more consumers and motivates them to visit the store more frequently.

20

Moreover, today’s consumers are more trend-driven and demand new things (Al-
Zubaidi and Tyler, 2004; Brannon, 2005). It is expected that perceived perishability
would positively affect consumers’ anticipated gains of buying by making them feel
special or good about the product or themselves wearing the latest fashion. Therefore, it

is hypothesized as follows:

Hypothesis 1: The greater the level of perishability, the greater the level of anticipated
gains of buying.

Eflect of Fashion Perishability on Anticipated Loss of Not Buying

Perishability is closely related to obsolescence of a product. Spoilage in food is
equivalent to obsolescence in fashion. It occurs when a product becomes out of use or out
of date (Cooper, 2004). Since high fashion is the most time sensitive compared to other
apparel items, it has the shortest life span. As a result, the value of merchandise quickly
deteriorates as time passes by in the mind of consumers (Gupta, Sundaraghavan, and
Ahmed, 2003; Voss and Seiders, 2003).

The psychology of obsolescence has been applied in marketing. Companies
deliberately plan on obsolescence in order to encourage continuous purchase of new
items (Bulow, 1986). The concept of planned obsolescence was popularized by Packard
(1960) and is referred to as the deliberate reduction of product life spans. He
distinguished obsolescence of function, quality, and desirability. First, obsolescence of

function was described as a situation in which an existing product becomes outdated

21

when a new product is introduced. In addition, obsolescence of quality arises when a
product breaks down or wears out quickly due to deliberate intent. Lastly, obsolescence
of desirability occurs when a product that still maintains quality or function becomes less
attractive or desirable in consumers’ minds because of changes in design or other
attributes. The latter is also called psychological obsolescence (Packard, 1960). It is
subjective and arises when consumers are no longer attracted to the existing products or
satisfied by them (Cooper, 2004).

In the case of fashion, adoption of new fashion is accelerated by psychological
obsolescence (Voss and Seiders, 2003). It is facilitated by product innovations,
incremental changes in features (i.e., design, or styling), shortened product life cycles,
changes in lifestyle or social status, and peer group pressures (Cooper 2004). Accordingly,
the frequent introduction of new merchandise through a short renewal cycle promotes
psychological obsolescence, significantly reducing the value of existing possessions in
the minds of the consumers. In turn, it will motivate consumers to revisit the stores more
frequently with expectations for new products.

Likewise, on the part of consumers, new styles may look more desirable, while
existing or outdated fashions may appear less attractive or less valuable. “Every
generation laughs at the old fashions but follows religiously the new” (Daniels, 1951,
p.51). Generally, people want to comply with a new trend by continuously updating their
wardrobe to avoid portraying a negative image to other people. For example, in the study
of consumers’ attitude toward an appliance’s life span, Cooper (2004) found that some
consumers replaced the product to avoid giving others a negative impression, regardless
of its functionality. Such psychological obsolescence may significantly promote purchase

of a new item.

22

Simultaneously, fashion perishability is also expected to augment the worries
about product unavailability due to quick changes of merchandise. Certain items are
available only for a certain period of time due to the implementation of short renewal
cycle and the policy to remove ruthlessly slow-selling items with an intention to make
constant room for new merchandise. In addition, fashion conscious consumers’ positive
valuation of fashion perishability or psychological obsolescence about existing clothing
will increase anticipated losses if they do not buy or lose an opportunity to own a trendy

product. Therefore, in a formal term, it is hypothesized as follows:

Hypothesis 2: The greater the level of perishability, the greater the level of anticipated

losses of not buying.

Scarcity

It has been proposed that price and scarcity are two dominant factors that affect
the valuation of a fashion product (Szybillo, 1973). In general, higher price is interpreted
by consumers to indicate the scarcity of the product or the product’s relative
unavailability (Szybillo, 1973). However, Daniels (1951) and Barber and Lobel (1952)
argued that the mass-production (i.e., selling a large number of the same or similar
products in a store) of a medium to high price dress may decrease the perceived value of
the garment. According to them, having only a limited number of a low priced product
may enhance the attractiveness of the product. Their argument implies that actual scarcity,

regardless of price levels, is more critical in affecting the product valuation and even a

23

low priced product can be perceived unique or valuable when the availability of the
product is limited.

Marketers can make their products or services more desirable by manipulating
the number of products offered in a retail store. Producing limited editions of products,
restricting maximum order size for products, having exclusive distribution outlets for
products, and prestige pricing of products are good examples that are commonly used in
marketing practice to limit product availability and to increase desirability of the product
(Brock, 1968; Lynn, 1991) and purchase intent (Jung and Kellaris, 2004).

This scarcity principle is strategically applied by fast fashion retailers. Given the
nature of perishability in fashion, fast fashion retailers deliberately offer limited product
offerings to minimize a portion of unsold products and to make constant room for new
merchandise. In particular, Zara utilizes the scarcity effect very well and thus consumers
may often observe that products are sparsely stocked in the store (Duta, 2003). Due to the
scarcity or limited availability of products in fast fashion stores, products are not easy to
get. Shoppers may have to go through competition among shoppers or hurry to get it
before it is gone. Comodities that are difficult to get are typically more appreciated than
those that are easy to acquire (Lynn, 1992). Therefore, scarcity is expected to affect

shopping behavior by augmenting the desirability of products.

Efl'ect of Scarcity on Anticipated Gains of Buying

From a theoretical standpoint, the valuation of scarcity has been supported by
commodity theory. It has been applied in many contexts and showed a general

applicability to the valuation of consumer products (Szybillo, 1973). Commodity Theory

24

proposes that perceived scarcity has a positive effect on the desirability of an object,
which is known as scarcity effects (Brock, 1968). Namely, it is predicted that people may
desire scarce commodities more strongly than comparable available products.
Commodity means anything including messages, experiences, or objects that meet the
following three criteria. It must be useful, transferable from one person to another, and
have potential to be possessed. A major premise of the commodity theory is that the
valuation of a commodity will increase to the extent that it is perceived as being
unavailable (Brock, 1968; Szybillo, 1973; Verhallen and Robben, 1994). Unavailability
means scarcity or limits on availability. Value refers to a “commodity’s potency for
affecting attitude and behavior” (Brock, 1968, p.246).

To date, a fair amount of research has shown that perceived scarcity has a
positive effect on the attractiveness of a variety of consumer products (Inman, Peter, and
Raghubir, 1997; Simonson, 1992; Ditto and Jemmott, 1989). For instance, Inman, Peter
and Raghubir (1997) found that consumers evaluate more favorably when the temporal
validity of a deal is limited than when it is not limited, especially if the deal offers a
significant saving. In addition, Verhallen and Robben (1994) provided consistent
evidence by examining the effects of four conditions of product availability on
consumers’ preferences for recipe books. These conditions include unlimited availability,
limited availability due to popularity, limited availability due to limited supply and
accidental unavailablity. Interestingly, their study discovered that books of limited
availability due to market circumstances such as popularity and limited supply were
perceived as more costly and more nearly unique than books that were accidentally
unavailable or abundantly available. The positive effect of scarcity on product

attractiveness was most pronounced for books that were of limited availability due to

25

both popularity and limited supply (Robben, 1994).

Furthermore, scarcity may convey the implication of novelty, uniqueness, or
distinctiveness (Fromkin, 1970; Ditto and Jemmott, 1989; Fromkin and Snyder, 1980;
Szybillo, 1973; Framkin, 1972, 1973). According to Uniqueness Theory (Fromkin, 1973),
a desire to possess scarce commodities stems from a need for uniqueness. In the
quantitative review of the commodity theory literature, Lynn (1991) also suggested the
link between possession of a scarce product and anticipated gains of buying. Namely, the
author justified the positive effect of scarcity on desirability of a product in terms that the
possession of scarce products conveys positive feelings of personal distinctiveness or
uniqueness, only if they are desirable and have the potential of being possessed.

Thus, the possession of a scarce product may serve to define the self as different,
unique, or distinctive from others. Consumers will anticipate gains such as uniqueness or
self-enhancement from acquiring or using a product that is perceived as rare or scarce.

Accordingly, it is hypothesized as follows:

Hypothesis 3: The greater the level of perceived scarcity, the greater the level of
anticipated gains of buying.

The Effect of Scarcity on Anticipated Loss of Not Buying

Consumers cope with perceived scarcity by hoarding. The psychology of the
effect of scarcity on hoarding has been studied by many scholars (e.g., Lynn, 1992; Lynn,

1991; Brock, 1968). McKinnon, Smith, and Hunt (1985) suggested that a desire to

26

minimize a perceived risk of loss is a dominant motivation of such behavior. The
valuation of choice alternative is often influenced by the alternatives that are given up
(Loomes and Sugden, 1982). Therefore, when deciding when to make a purchase,
consumers often imagine outcomes that would have occurred had they purchased later
(Cooke, Meyvis, and Schwartz, 2001). Outcomes considered may include regret or a
perceived psychological loss resulting fi'om an opportunity given up to acquire the item
or a delayed purchase decision. Frost and Steketee (1998) found that both compulsive
hoarding and compulsive buying result from a worry about the loss of opportunity. Frost,
Meagher, and Riskind (2001) also provided a consistent finding that the effect of fear of
losing an opportunity on hoarding was pronounced for compulsive hoarders. They argued
that such people tend to perceive that each of their possessions present opportunities
which would be lost if that possession were discarded. Thus, hoarders tend to make the
safe decision to keep or acquire the product. They try to minimize regrets, worries, or
perceived risks resulting from the loss of opportunity.

The time-limited promotion also poses a ‘use-or-lose’ threat to consumers
(Aggawal and Vaidyanathan, 2003, p.393). Inman and McAlister (1994) discovered that
as a coupon’s expiration date approaches, the redemption rate increases. Such behavior
occurs due to the increased anticipated loss of not taking advantage of the promotional
deal when it is valid. Regret is induced by the anticipated loss of an opportunity to save
money. They applied regret theory to explain the effect of an expiration date on coupon
redemption behavior. Regret is defined as “the feeling induced by comparing a given
outcOme or state of events with the state of a forgone alternative” (Bell, 1982). The
theory posits that consumers tend to avoid an alternative that could result in regret. Inman

and McAlister (1994) also discussed the possible application of Prospect Theory as

27

alternative theoretical explanations for the effect of anticipated loss and coupon
redemption behavior.

Thus, under a situation of limited availability, scarce opportunities will affect
consumer perception and behavior more strongly than when they are always available
(Cialdini, 1985). Namely, consumers will respond more sensitively to scarce products or
opportunity by worrying about future product availability and considering not buying as a

loss. Hence, it is hypothesized as the following.

Hypothesis 4: The greater the level of scarcity, the greater the level of anticipated losses
of not buying.

The Effect of Anticipated Gains and Losses on In-Store Hoarding

As discussed previously, hoarding behavior more often takes place in fast
fashion retailers than in other comparable fashion apparel stores. The scarcity by limited
supply is likely to increase consumers’ perceived concerns about future availability of
their favorite items. Given that hoarding occurs not only for future consumption but also
for fear of unavailability (Meagher, and Riskind, 2001; Frost and Gross, 1993, McKinnon,
Smith, and Hunt, 1985; Ong, 1999; Verhallen and Robben, 1994; Lynn, 1993), consumers
encountering a scarcity of a product are likely to be stimulated to hoard the product
immediately before it is taken by other customers. By doing so, consumers try to reduce a
perceived risk about unavailability of a desired product in the expected time period (Tan

and Chua, 2004). In a similar context, Kwon (2001) discovered that as consumers had no

28

or less worry about the product availability in the future, they were less inclined to
purchase a product. Namely, when consumers were sure about product availability, their
purchase intention was weakened or postponed even though there was a perceived value
from receiving a low price or a deal. By contrast, when consumers were highly worried
about future product availability, they showed a strong and immediate purchase intention.

This study proposes that both anticipated gains of buying and losses of not
buying will lead to in-store hoarding. The best senario that supports the logic of this Study
is as follows. The probability of raining influences one’s decision on whether or not to
bring an umbrella. According to the Prospect Theory (Kahneman and Tversky, 1979),
depending on the chances of rain, value is assigned to gain and loss for each decision
choice considered. Applying the risk aversion priciples, it is likely that as the chance of
rain increases, people will anticipate gains of bringing an umbrella. At the same time,
they will anticipate losses of not bringing an umbrella. However, the chance of rain is
expected to influence the anticipated losses more strongly than the anticipated gains.
Thus, the loss dimesion will be steeper than the gain dimension. This is due to the
stronger sensivity to loss resulting from not bringing an umbrella than gains from
bringing an umbrella. Consequently, to minimize the anticipated loss, people decide to
bring an umbrella.

Therefore, it is proposed that perishability and scarcity will influence the
anticipated loss of not buying more strongly than the anticipaged gains of buying. Also,
based on the risk aversion principle, it is reasonable to expect that in-store hoarding is
more strongly affected by the anticipated losses of not buying than by the anticipated

gains of buying. Therefore, it is hypothesized as the following.

29

Hypothesis 5: The eflect of fashion perishability on anticipated losses of not buying will
be stronger than on anticipated gains of buying.

Hypothesis 6: The eflect of scarcity on anticipated losses of not buying will be stronger
than on anticipated gains of buying.

Hypothesis 7: The greater the level of anticipated gains of buying, the greater the level of

in-store hoarding.
Hypothesis 8: The greater the level of anticipated losses of not buying, the greater the

level of in-store hoarding.

Hypothesis 9: The eflect of anticipated losses of not buying on in-store hoarding will be
stronger than that of anticipated gains of buying.

Consequences of In-Store Hoarding

Based on previous literature, shopping hedonism and purchase acceleration are
proposed as main consequences of in-store hoarding. To support the theoretical links, the
cognitive capacity view of mood effect and the psychology of physical proximity are

discussed.

Shopping Hedonism

With the growing acknowledgement of the competitive advantage of experiential
values, more researchers have started to explore the hedonic aspects of shopping,
describing shopping as fun (Babin, Darden, and Griffin, 1994; Griffin, Babin, and
Modianos, 2000; J in and Stemquist, 2004). Hedonic shopping value derives fi'om the

“appreciation of an experience for its own shake” (I-Iolbrook, 1994, p.40). Namely, it is

30

acquired from an interaction with a store environment, product or services, or marketing
activity, and is generated regardless of purchasing or using a product or service (Sweeney
and Soutar, 2001). As a result, the hedonic value is more subjective with emotion-laden
perceptions of shopping experience than extrinsic or utilitarian shopping values (Griffin,
Babin, and Modianos, 2000). It results from pleasure, fun and playfulness through the
shopping experience (Babin, Darden and Griff'm, 1994).

Beyond the traditional mix of price and quality, more retailers are trying to
deliver diverse experiential values as a way to differentiate from their competitors. Pine
and Gilmore (1999) and Mathwick, Malhotra, and Rigdon (2001) redefine retailers as “a
source of memories,” or an “experience stager,” highlighting the critical role of
experiential value as the essential outcome of retailers’ marketing activities. In the study
of consumer shopping values for retail brands, Carpenter, Moore and Fairhurst (2005)
found that there are significant differences in consumer perceptions of hedonic shopping
value across several retail brands. Their study demonstrated the importance of unique in-
store shopping experiences and delivery of hedonic aspects of shopping as an effective
source of differentiation for retail brands.

Many empirical studies found that store-induced hedonic responses or positive
moods have favorable effects on future patronage intention or attitudes toward the retailer
(e.g., Babin and Darden, 1996; Yoo, Park, and Maclnnis, 1998; Sherman and Smith,
1986). Accordingly, extensive research has been conducted to find what factors induce or
increase shopping hedonism, and major attention has been paid to the effect of external
factors such as entertaining retail environment on consumers’ emotional responses and
the consequent purchasing behavior. More recently, however, the topic has been extended

to cover price-related variables such as value or deals (J in and Stemquist, 2004; Jin,

31

Stemquist, and Koh, 2003) and price mavenism (Stemquist and Byun, 2004) as
significant predictors of shopping hedonism. These studies found that financial saving or
perceived value of low price or deals significantly enhance hedonic values. Namely,
when consumers find a discounted product or a lower price than their expectation or
market price, they experience excitement or pleasure. In addition to deals seeking, it was
proposed that sharing price information with others and being price mavens are main
drivers of enjoying shopping activities (Stemquist and Byun, 2004). However, despite the
considerable amount of work in this topic, previous research has not focused on fully
uncovering diverse factors eliciting hedonic shopping values. In this regard, as one of the
first studies, this study proposes that in-store hoarding is a significant driver of shopping
hedonism.

Previous literature shows a possible link between in-store hoarding and shopping
hedonism. When consumers can grab a product of interest before it is sold out or not
available any longer, they may feel as if they win the shopping game or simply interpret
the possession of a scarce product as a potential gain (Tan and Chua, 2004), thereby
experiencing greater hedonic shopping values. Gardner and Rook (1988) found that
shoppers tend to feel excited simply by having something new in their possession.
Moreover, it has been suggested that browsing may be more significant than the actual
acquisition of products and can provide highly pleasurable shopping experiences
(MacInnis and Price, 1987; Sherry, 1990; Beatty and Ferrell, 1998). Similarly, even if
they decide not to buy the item, perhaps due to problems in fit or financial affordability,
in-store hoarding per se is likely to increase hedonic values through enabling shoppers to
imagine owning the scarce item (Desmeules, 2002) or the latest fashion item.

Thus, although it is not permanent acquisition of a product, temporally

32

possessing or hoarding something impulsively in their arms or in a shopping basket is
likely to elicit or enhance hedonic shopping values. Moreover, such values are expected
to be greater at fast fashion stores because consumers would feel accomplished, excited,
and playful when they successfully hoarded hard-to-get items (considering perishability
and scarcity). At the same time, since in-store hoarding is not actual purchasing, hoarding
and trying on a number of items would not cost anything for consumers, but provide a lot
of fun and excitement. It is assumed that in-store hoarding will make shopping activity
more enjoyable and playful, inducing hedonic shopping values. Therefore, it is

hypothesized as the following.

Hypothesis 10: The greater the level of in-store hoarding, the greater the level of
shopping hedonism.

Purchase Acceleration

The main purpose of providing time-limited promotions is to stimulate direct
sales by accelerating consumers’ purchase decision (Aggarwal and Vaidyanathan, 2003).
In today’s promotion driven market, a retailers’ ability to influence consumers’ purchase
decisions is very critical. More importantly, inducing purchase acceleration for its own
brand or in its own store is a more significant part of modern marketing strategy (Neslin,
Henderson, and Quelch, 1985), particularly for product categories that consumers often
consider diverse brands at the same time.

Purchase acceleration has been measured in terms of buying larger quantities of

33

the products and buying at an earlier time (Aggarwal and Vaidyanathan, 2003). For
example, consumers may end up buying more units when they are on promotion, or make
the purchase earlier to benefit from a promotion. In addition, consumers are often forced
to curtail further search in the face of a promotion that is about to close (Aggawal and
Vaidyanathan, 2003).

Rather than relying on price-related promotions, fast fashion retailers expedite
purchase decision—making by using implicit time-limited cues. Similar to the effect of a
limited promotional offer on purchase acceleration, it is likely that consumers may end up
buying more products than they do normally or they expected, or make an immediate
purchase decision without delaying it mostly in response to the fast fashion retailers’ time
limited cues.

Accordingly, the study proposes that purchase acceleration arises from in-store
hoarding and shopping hedonism. First, based on the cognitive capacity view of moOd
effect on information processing, the effect of shopping hedonism on purchase
acceleration will be discussed. In the following section, the psychology of physical
proximity will be discussed for a theoretical support between in-store hoarding and

purchase acceleration.

Cognitive Capacitl View of Mood greets

The information processing model (Bettrnan 1979), which considers the
consumer as a logical thinker who makes purchase decisions in a rational way, was one of
the most popular decision making theories in the past. However, Holbrook and
Hirschman (1982) questioned the traditional information processing perspective on the

point that it neglected important experiential consumption phenomena such as hedonic

34

orientation or emotional and psychological responses in their shopping activity. In this
regard, the cognitive capacity view contends that moods can affect information-
processing (Mackie and Worth, 1989), which may highlight the strategic importance of
retail experiential values in shopping.

According to the cognitive capacity view, people in a happy mood tend to use
heuristic processing strategies, whereas people in a bad mood tend to elaborate
information systematically (Clore, Schwarz and Conway, 1994; Mackie and Worth, 1989).
This is because happy moods activate many positive thoughts in memory and these
thoughts occupy cognitive capacity (Wegener and Petty, 1995). As a result, a happy mood
limits processing capacity and individuals in such a good mood do not have cognitive
resources to process information analytically.

The cognitive capacity view of mood effects on processing gives insights into
consumer accelerated decision making in fast fashion stores. Similar to happy moods in
the cognitive capacity view, hedonic shopping values are an emotion-laden perception of
shopping experience (Griffm, Babin, and Modianos, 2000) and are often influenced by
positive moods or emotions such as excitement, pleasure, or fun experienced during
shopping. Thus, it is expected that hedonic values will influence consumers’ information
processing ability and intention for further product searching. Specifically, since hedonic
shopping values activate positive thoughts about the products and/or the store, shoppers
are likely to evaluate products more favorably. Furthermore, the happy mood and positive
thoughts may motivate them to minimize their information processing efforts by skipping
extensive information searching, such as product comparison across competitors which
may result in the delay of purchase or store switching.

Such logic is also supported by fair amounts of research that have been

35

conducted to discover the effect of hedonic shopping values on purchase behavior.
Consistent evidence showed that hedonic shoppers or consumers who are in a good mood
tend to spend more time and money than originally planned (e. g., Donovan, Rossiter,
Marcoolyn, and Nesdale, 1994; Donovan and Rossiter, 1982). Roehm and Roehm (2005)
and Kahn and Isen (1993) found that in the presence of mild positive mood, variety
seeking behavior increased. Sherman, Mathur, and Smith (1997) found that while
cognitive factors influence store selection and planned purchase within a store, the store
environment and the emotional state of consumers are more significant determinants of
purchase behavior. They also found that emotions such as pleasure and arousal are
associated with the amount of money spent, time spent and the number of products
purchased in the store. Such elevated mood states lead shoppers to evaluate the store and
the products more favorably and make immediate and spontaneous purchases (Heihnan,
Nakarnoto, and Rao, 2002; Sherman and Smith, 1986). Thus, it is hypothesized as the

following.

Hypothesis 11: The greater the level of shopping hedonism, the greater the level of
purchase acceleration.

Physical Proximity and Purchase Acceleration

 

The positive effect of in-store hoarding on purchase acceleration is proposed
based on the psychology of physical proximity. In-store hoarding is a manifestation of

actual purchase behavior. As discussed previously, since in-store hoarding often involves

36

impulsive possession of favorite items, or acquisition of hard-to-get items, the temptation
placed on consumers becomes irresistible, significantly accelerating purchases. Rook
(1987) suggested that consumers could hardly resist their strong urge to purchase when
they physically encounter the object, called the effect of physical proximity. In a study of
impulse buying, Beatty and Ferrell (1998) also explained the logic behind the positive
links between browsing to urge and urge to impulse buying from the idea of physical
proximity. Therefore, consistent with the previous studies, it is hypothesized that in-store

hoarding will prompt purchase acceleration.

Hypothesis 12: The greater the level of in-store hoarding, the greater the level of
purchase acceleration.

Innovativeness as a Moderator

Consumer’s attitude toward a product changes depending on where it stands in
the product life cycle. In general, consumers develop favorable attitudes toward the
product during the introduction, growth, and maturity stages and they change attitudes as
it gets to later stages (Bither, Dolich, and Nell, 1971). Due to the changed attitude,
consumers want to buy a new or competing product. However, such attitude change may
be facilitated by personality traits. For example, innovative consumers may form a more
positive attitude and a stronger interest in a new fashion in the introduction or growth
stage, and start to change their attitude when it gets to the maturity stage or when

products are prevalent. Thus, fashion innovators are among the first adopters of new

37

products when these styles are introduced in the marketplace (Brannon, 2005). They react
very sensitively to the changes of fashion and thus those innovators comprise a crutial
segment for innovation diffusion (Goldsmith, Moore, and Beaudoin, 1999).

Summers (1970) also found that fashion leaders are likely to have more positive
attitudes towards new fashion and enjoy testing and experimenting with new fashions
than non-opinion leaders. In a similar vein, Szybillo (1973) found that there is a
difference in valuation of a scarce fashion product between opinion leaders and non-
opinion leaders. Fashion opinion leaders rated the fashion under the scarce condition as
the most desirable, followed by the fashion under the no scarcity information condition
and under the abundant condition. Among non-opinion leaders, although they showed
least valuation for abundant products, there was no difference in desirability between the
fashion under the scarce condition and the fashion under the no scarcity information
condition.

Although there are distinctions, a number of studies found that opinion leaders
tend to be fashion innovators (e.g., Jacoby, 1971; Summers, 1970). Therefore, Szybillo’s
finding supports the assumption that innovators will respond more sensitively to a
scarcity cue by perceiving it as a way of defining themselves as being different from their
peers. This product life cycle and consumer attitude is also closely related to product
availability. For instance, Swami and Khaimar (2003) found that the perceived scarcity
significantly afl‘ected their decision of adoption of a new product. He proposed a model
testing the effect of limited availability on the adoption of a new product and found that
limited availability induced scarcity effect.

Consistently, innovators are likely to be more responsive to fashion perishability

in the need of uniqueness or as a tool for self-expression. Accordingly, it is assumed that

38

their valuation of such products will significantly increase the anticipated gains of buying
and thus, intensifying the anticipated losses of not buying immediately. Due to the high
level of worries about products being unavailable or psychological obsolescence about
existing products, innovators tend to show great willingness to buy new products even at
a full price (Brannon, 2005). Moreover, once they like it they do not want to delay their
purchase or wait until it is on sale. On the other hand, for non-innovators, that is, those
who are fashion followers or late adopters, fashion perishability and scarcity will be less
meaningful in their product valuation or selection and thus their worry or regret will be
much weaker than that of innovators. Consequently, non-innovators tend to adopt
products later when discounts are offered at the expense of fashion currency and product
availability.

Therefore, it is expected that the previously hypothesized relationships
concerning the antecedents of in-store hoarding will be stronger for innovative consumers.
Such theoretical links can be supported by Uniqueness Theory. Uniqueness Theory
(F romkin, 1973) predicts that people are motivated to maintain a sense of specialness as
they define themselves. It proposes that it is the motivational process of need for
uniqueness that precedes and creates the evaluative differences between common and
rare stimuli (Snyder and Fromkin, 1980). Fromkin and Williams (1972) also supported
that valuation of scarce products relative to abundant products would be greater for
people with a high need for distinictiveness than for people with a low need for
distinctiveness. According to uniqueness theory, innovative consumers tend to feel strong
needs for uniqueness. Such needs are satisfied by acquiring a new product earlier than
others and purchasing scarce products rather than abundant ones.

Taken together, this study proposes that the positive effect of fashion pershability

39

and scarcity on anticipated gains of buying and losSes of not buying will be significantly
pronounced for innovators or those who try to use fashion as a way of asserting their
individuality. For those consumers, high perishability and scarcity driven by the short
renewal cycle and limited supply will give strong urges to hoard products in store.

Given this, the comparison of the model will contribute to understanding both
groups. For example, it is important to investigate where non-innovators show the biggest
difference from innovators and to determine the underlying reasons. Such findings will
provide considerable marketing implications for retailers as to how they implement a
strategy to appeal to innovators because they are the earliest adopter and signficantly
influence the diffusion of new trends to the next adjacent group (Brannon, 2005). In
addition, findings will also provide implications concering how to attract non-innovators
and how to actively engage them in shopping, effectly influencing their purchase decision.

In the formal form, these are hypothesized as the following.

Hypothesis 13: The positive eflect of perishability on anticipated gains of buying will be
stronger for innovators than non-innovators.

Hypothesis 14: The positive eflect of scarcity on anticipated gains of buying will be
stronger for innovators than non-innovators.

Hypothesis 15: The positive eflect of perishability on anticipated losses of not buying will
be stronger for innovators than non-innovators.

Hypothesis 16: The positive eflect of scarcity on anticipated losses of not buying will be
stronger for innovators than non-innovators.

Hypothesis 1 7: The positive eflect of anticipated gains of buying on in-store hoarding
will be stronger for innovators than non-innovators.

Hypothesis 18: The positive efi'ect of anticipated losses of not buying on in-store

hoarding will be stronger for innovators than non-innovators.

40

CHAPTER III

METHOD

The present section discusses measurement, scale development method and data
collection procedures. Next, data analysis methods and procedures to test the proposed

model are presented.

Measurement

Measurements for fashion innovativeness and shopping hedonism were adopted
from existing scales. For the remaining six constructs (scarcity, perishability, anticipated
gain and loss, in-store hoarding and purchase acceleration), since there are no scales
available from previous literature, the scales were modified or newly developed in this
study based on the literature review and one-to-one interviews. The scale development

procedures are discussed in detail in the subsequent sections.

Fitshion Innovativeness

Innovativeness is domain specific. Namely, those who are willing to adopt the
latest merchandise in one product category may be laggard in another (Goldsmith and
Hofacker, 1991; Goldsmith and Goldsmith, 1996; Goldsmith, D’Hauteville, and Flynn,
1998). In this regard, Goldsmith and Hofacker (1991) developed the domain specific
innovativeness scale (DSI) that contains six Likert-type items. The DSI scale showed
psychometric soundness in numerous studies and illustrated its usefulness for both

theoretical consumer research and applied marketing (Goldsmith, d’Hauteville, and Flynn,

41

1998). The scale demonstrated unidimensionality and high internal consistency with
reported alpha coefficients ranging from .73 to .87. The DSI also demonstrated validity of
scale and the measurements were not influenced by social desirability and acquiescence
response biases (Goldsmith and Hofacker, 1991). In this study, to be consistent with other
scales, the 6-point scale was modified to a 7-point Likert scale. Higher scores indicate

greater fashion innovativeness.

Shopping Hedonism

With the growing importance of experiential values in today’s retail environment,
researchers have started to emphasize the hedonic or pleasurable aspects of shopping as a
key competitive weapon (Arnold and Reynold, 2003). For the measures of shopping
hedonism, Babin, Darden, and Griffm (1994) developed a parsimonious, two-dimensional
scale of perceived Personal Shopping Value (PSV) measuring utilitarian and hedonic
values. The original PSV scale was composed of 15 items. Among these, 11 were
measuring hedonic shopping values. The original PSV scale showed strong internal
consistency with Cronbach’s alpha coefficients ranging from .69 to .83. The scale was
validated using diverse samples and measuring theoretical links with other constructs as
well as convergent and discriminant validities. All confirmatory factor loadings
exceeded .60 and were significant. The scale has been extensively used in marketing,
psychology, and consumer behavior studies and provided further evidence in reliability
and validity for US consumers across diverse products although there were some
variations across countries (Griffin, Babin, Modianos, 2000; J in, Stemquist, and Aeran
Koh, 2003; J in and Stemquist, 2004).

In this study, the 11 items of the hedonic shopping value scale were adOpted.

42

However, the tense of the original scales was modified to evaluate hedonic aspect of
shopping experienced “during” shopping at a store, instead of general attitude toward
shopping. The original scale was measured by a nine-point Likert scale ranging from -4
as being strongly disagree to +4 as being strongly agree. This scale was modified to a 7-
point Likert scale to maintain consistency with other measures and to reduce measurement
errors that may be caused by scale differences. The higher the score, the stronger the

hedonic shopping value.

Scale Development Procedures

One-to-One Interview—Stage One

One-to-one interviews were conducted for generating items and a deep
understanding of a phenomenon from the consumers’ perspective (Hudson and Ozanne,
1988). This method has been highly recommended as the first step in a research process
and has proved to be useful for uncovering information that the researcher did not
originally consider (Churchill, 1979). It is also helpful in developing measurements by
allowing the wording of consumers’ use to describe the construct of interest.

For this study, ten female undergraduate and graduate students who shopped at
Zara or H&M stores in the United Sates within the last 6 months were included in this
procedure. The customers for the two retailers are selected since they are the top leading
fast fashion retailers in the world and positioned very closely against each other in terms
of fashionability and price. Such positioning proximity will reduce response errors

resulting from differences in brand image and store positioning. Figure 6 in the appendix

43

shows the positioning of H&M and Zara. Six of the participants shared their experience
at H&M stores and four of them did for Zara. All participants reported that they have
experiences in purchasing in these stores and the majority of the participants expressed
strongly favorable attitudes toward the stores they shopped.

First, the purpose of the in-depth interview was briefly explained to each
participant. Next, each respondent was asked to recall their most recent shopping trip in
one of the fast fashion stores mentioned above and describe shopping experiences in
detail, including their observations or perceptions about the products, store atmosphere,
feelings or psychological responses and purchase decision making processing. If there
was a concept not addressed by the participant, the interviewer asked a series of questions
to provoke thought about the concept missed. For example, the interviewer asked what
caused their urge to grab certain items and carry them around, what made a shopping
experience enjoyable or playful, and how they made a purchase decision.

Mostly, people who shopped at Zara mentioned the scarcity of products when
they described the store environment and atmosphere. Only half of the H&M shoppers
agreed on a sense of scarcity, while half of them disagreed on this construct, showing
some diversity on the degree of scarcity. Mostly, they reported that they had heard of the
stores and the products fi'om their friends, family, class or media before their first visit to
the store. In-store hoarding behavior was identified from most of the interviews and they
confirmed that they had stronger hoarding tendency than in other comparable apparel
stores. At the last stage of the discussion, most of the participants agreed with the
proposed model of in-store hoarding behavior in the fast fashion stores.

For the purpose of scale development for several constructs in this study,

participants’ responses were written down during the interviews. The feedback from the

44

one-to-one interviews was analyzed by three doctoral students majoring in retailing for
representativeness of the scale domain. Each member sorted the participants’ responses
into categories based on similar context. Any disagreement among the three members
was resolved by discussion. Next, the categorized items were incorporated with
preliminary scale items identified through a review of relevant literature to develop the

survey instrument for pre-tests.

Face Validity Tests—Stage Two

The initial items generated from the one-to-one interviews were incorporated
with the measurements that were adapted from the previous studies or derived from the
conceptual discussion in the literature. Two faculty members, six doctoral students and
three master’s students in retailing, consumer behavior, and marketing evaluated the
quality of the measurements in terms of clarity, reliability, and validity of the scales.
Based on their feedback, the items were modified and the process was repeated several
times until they were deemed clear, reliable and valid. Finally, the modified items were

incorporated with the established instruments for the following tests.

Pre- Test—Stage Three

To empirically test the reliability and validity of the scales, a pre-test was
administrated to purify the measures and provide an initial examination of the scale’s
psychometric properties. Assuming a 20% response rate, 500 surveys were distributed to
H&M and Zara shoppers in New York in April 2006. The mall intercept and mail survey

techniques were used for data collection (more detail explanations about these methods

45

are provided in the main test section). Only female shoppers were included to avoid the
potential error variance caused by gender difference. Fifty one responses were returned,
showing approximately 10% of response rate. Among these, forty seven responses were
usable and submitted to a reliability test.

AS recommended by Churchill (1979), items with low reliability were removed
by investigating coefficient alpha. Items with coefficient alpha higher than .7 were
retained. Some revisions were made to improve question clarity, comprehension and
readability. Finally, the confirmed questionnaire was submitted for the main survey. The
next section discusses the six constructs whose scales were developed or modified in this

study.

Scarcity

There have been extensive studies to test scarcity effect. Nevertheless, most of
them have been tested in experimental settings and it has not been empirically explored.
Therefore, this study could be a first step to empirically examine the role of perceived
scarcity in consumer shopping and purchase behavior. Since there were no scales
available to measure scarcity, multiple items were developed.

Scarcity was measured in terms of overall perception about scarcity and product
availability of their favorite items in a Specific size. To measure overall perception about
product scarcity, the description about scarcity provided to respondents in the
experimental study of Jung and Kellaris (2004) was adapted to make continuous variables.
In addition, findings from the one-to-one interviews as well as ideas from theory and
prior literature were incorporated to build scales for scarcity. Thus, the scarcity measure

was composed of the following six items. The higher the score, the greater the perceived

46

scarcity.

 

- My favorite items were often one of the last items lefi on the rack.

- The products that I was interested in were almost out of stock.

- There were only limited number of products per size, style, and color:
- Products of interest were often scarce in my size.

- My favorite styles in this store were mostly available in my size. ®

- I could mostly get my first preference in my size ®.

 

® represents a reversed scale.

Perishability

Fashion perishability represents a fashion item that has a fixed useful life span or
looks less desirable after a certain period of time. It is accelerated by the implementation
of a short renewal cycle or a continuous introduction of the latest fashion items. As a
result, in the fast fashion stores, products are moving very quickly and the same style
does not stay long on the rack. There exists no established scale to measure fashion
perishability. Thus, based on the one-to-one interviews and conceptual discussion in
previous literature, multi-item scales were developed in this study. The currency in
fashion trend, quick movement of products, and frequent introduction of new
merchandise were included as major domains of this construct. The measure for fashion
perishability consisted of eight items in total. The higher the score, the greater the

perceived perishability. It is shown as follows.

 

- This store constantly delivers updated fashion items throughout the season.
- New styles are introduced on a fiequent basis.

- This store rapidly turns over their merchandise.

- Products in this store do not stay on the rack long.

- This store introduces new fashion styles quickly.

- Products in this store are fresh in terms of fashion trend.

47

- Products in this store are moving fast.

- I can mostly find the same merchandise that I saw on my previous store visit. ®
® represents a reversed scale.

 

Anticipated Gains of Buying

Anticipated gains of buying denote expected psychological benefits or hedonic
states that may be achieved through acquiring or using the product. Based on previous
literature and theoretical discussion, anticipated gains of buying were measured in terms
of perceived uniqueness, distinctiveness and the self-image enhancement that a product
generates. The scale consisted of the five items below. The higher the score, the greater

the anticipated gains of buying.

 

When I found a product of interest in this store, I thought that acquiring or
wearing this product would make me...

' Look unique.
- Look fashionable.

Enhance my self-image.

- Feel good about myself

' Feel special.

 

Anticipated Losses of Not Buying

Anticipated losses of not buying denote expected psychological discomfort or
uneasiness that results from not acquiring the product immediately or missing an
opportunity to purchase the favored product. Two items used by Abraham and Sheeran
(2003) to measure anticipated regret from not exercising were adapted to reflect a worry
or physiological loss about missing an opportunity to buy. Since there are no multiple-
item scales available, more items that measure this construct were developed in this study

based on the one-to-one interviews. Based on the literature review and the theoretical

48

discussion, anticipated loss of not buying was measured in terms of concerns about
product availability in the next visit or anticipated psychological loss or regret due to
losing opportunity to acquire their favorite item. The measurement included five items.

The higher the score, the greater the anticipated losses of not buying.

 

When I found a product of interest in this store, I thought that...

 

- If I do not buy it right now, I would regret it later

- I was afi'aid that this item would be out-of-stock in my next visit.

- I thought that it would be a loss if I do not buy it today.

- I was concerned that this item might not be available if I came back later

- If I do not get it immediately, I would lose an opportunity to purchase it
because it will be gone tomorrow.

 

In-Store Hoarding

In-store hoarding refers to a rushed behavior to acquire products into one’s
possession (in hands or in a shopping basket) during shopping in a store, in response to
various impulsive stimuli—implicit time-limited cues in this study. In-store hoarding was
distinguished from permanent hoarding and the concept was limited to the hoarding
occurring in store.

Since there were no in-store hoarding scales available, the scales used for
measuring the urge to buy, developed by Beatty and Ferrell (1998), were adapted to
match the concept of in-store hoarding. In addition, from the conceptual discussion in the
hoarding literature and the one-to-one interviews, additional items were included. In total,

the scale consisted of seven items. The higher the score, the stronger the in-store hoarding.

 

When I found a product of interest in this store,

 

- I spontaneously grabbed the product of interest.
- I had the urge to grab the product immediately.

49

- I was carrying around products while shopping.

- I snapped things up while shopping in this store.

- Once I picked up a product, I did not want to put it down although I
was not sure if I would buy it or not.

- I hurried to grab the products of interest and kept them to myself

- On this trip, I found a number of things I wanted to grab immediately
even though they were not on my shopping list.

 

Purchase Acceleration

 

Purchase acceleration denotes increased rate of purchase which is reflected in
the quantity of products purchased and rapidity in decision making or hastened purchase
decision (Aggarwal and Vaidyanathan, 2003; Krishna and Shoemaker, 1992; Neslin,
Henderson, and Quelch, 1985). Among them, Aggarwal and Vaidyanathan (2003) used
two items to measure purchase acceleration: buying larger quantities of the products and
buying them at an earlier time. These items were modified to measure shoppers’
subjective notion about the quantity of product purchased. With regard to the measure for
buying at an earlier time, Aggarwal and Vaidyanathan (2003) calculated the interpurchase
times. However, since it is not of interest in this study to mathematically compute the
interpurchase times, the item was modified to reflect a notion of rapidity in purchase
decision-making. This notion was supported by one-to-one interviews and the conceptual
discussion in previous literature. Therefore, it was measured by the three items below.

The higher the score, the stronger the purchase acceleration.

 

- I purchased an item that I had not planned to purchase on this trip.

- I purchased more products than I would do normally on this shopping trip.

- I made a purchase decision immediately rather than postponing until the next
visit to this store.

 

50

Data Collection
Sample

The sample for this study was composed of actual female shoppers from H&M
and Zara. These two stores were chosen because they are top leading fast fashion retailers.
The actual shoppers were selected to provide more reliable and realistic marketing
implications for both practitioners and academicians. Both purchasers and non-purchasers

were included.

Data Collection Method

Survey method was selected to test the conceptual framework. New York was
chosen as a survey Site due to the similarity in location of both H&M and Zara. In this
way, response biases due to the location difference or variation in sample characteristics
was reduced. Given that most of the fast fashion stores are located in open shopping
districts, not in a shopping mall, and the questionnaire consisted of many questions, the
combination of mall intercept and mail survey methods was employed to collect the data
for this study. First, the author of this study contacted the stores to receive permission to
distribute the survey in front of their stores. Next, in addition to the researcher, five
students were hired as research assistants to distribute questionnaires.

Following extensive training sessions to ensure consistency of administration
and breadth of distribution of the questionnaire, each pair of data collectors was assigned
to a different location. Each assistant approached female shoppers who had just exited the
store to introduce themselves and briefly explain the purpose of the survey, while

showing the cover letter and flyer that explained the purpose of the study, how to

51

participate in the current survey and how to receive incentives (see Appendices F and G).
If the shopper agreed to participate, the research assistant handed them an envelope
enclosed with a questionnaire and a business return envelope. Chewing gum was also
given to increase interest in the survey and encourage participation. Additionally, in order
to increase the validity of responses, participants were asked to mail out their answers
within 48 hours. This was designed to acquire their fresh Shopping memories. Two
incentive options were provided for those who completed the survey: a gift card and a
donation. Participants who selected the gift card option were entered into a drawing for
several sizable gift certificates (i.e., six $203, two $50s, and one $100). For those who
selected the donation option, the researchers donated $1 per person to the Katrina Funds
by American Red Cross.

In total, 2000 questionnaires —half to each retailer— were distributed in front of
10 different stores from May 19, 2006 to May 26, 2006. Following the recommendations
of Bush and Hair (1985) to increase representativeness of the sample, balanced quota was

imposed with respect to the time of day and which day of the week.

52

CHAPTER IV

RESULTS

This chapter reports the results from statistical analyses and discusses the
findings. It starts with the demographic characteristics of the sample, followed by the
reliability and validity of the measures. Next, the analyses of a path model and group

comparison are reported. Finally, the findings are discussed in this chapter.

Demographic Characteristics of the Sample

A total of 249 surveys were returned out of 2000, yielding a 12.5% response rate.
Of this, 234 responses were usable for this study. Of the sample, 76.6% were between 20
and 29 years old. About 82.1% of the sample had or was currently pursuing a university
degree or higher educational background. The annual income (not family income) of
56.4 % of the sample was reported below $34,999. Caucasians accounted for the largest
portion, 39.3% of the sample, followed by 30.8% Asians. Sample characteristics are
provided in Table 1. There were no significant differences in the demographic
characteristics (age, income, education and ethnicity) between the stores. The result is
reported in Appendix B.

Sixty per cent of the responses came from H&M shoppers and 40% from Zara

shoppers. When asked whether they had previous shopped in the store, 88.5% of the
sample responded that they had, while only 11.5% reported that it was their first visit to

the store (Table 2).

53

Table]. Demographic Characteristics of the Sample (N=234)

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

N Percent (%)
Under 20 l l 4.7%
20-24 96 41.0%
25-29 83 35.5%
Age 30-34 29 12.4%
35-39 2.1%
40 or over 9 3.8%
Missing 0.4%
High school or below 10 4.2%
Some college 32 13.7%
Education Bachelor’s degree 123 52.6%
Master’s degree or higher 64 27.3%
Missing 5 2.1%
Under $20,000 80 34.2%
$20,000 to $34,999 52 22.2%
$35,000 to $49,999 40 17.1%
Income $50,000 to $64,999 30 12.8%
$65,000 to $79,999 12 5.1%
$80,000 to $99,999 3 1.3%
$100,000 more 9 3.8%
Missing 3.4%
African American 36 15.4%
Caucasian 92 39.3%
Asian 72 30.8%
Ethnici ani h, is anic or
W Egan: Orlilgi: 18 7'7%
Other 14 6.0%
Missing 3 0.9%

 

54

 

Table 2. Respondents’ Shopping Information

 

 

 

 

 

 

 

 

 

N Percent (%)
C A 140 . °
Store Visited ompany 59 8 /°
Company B 94 40.2%
Shopping Shopped before 207 88.5%
Experience First visit to this store 27 11.5%

 

 

Confirmatory Factor Analysis

Following the recommendations by Anderson and Gerbing (1988), the two-step
approach was used. First, Confirrnatory Factor Analysis was employed to evaluate the
reliability and validity of the measures, followed by the path analysis. Amos 4.1 with the
maximum likelihood estimation was used. The quality of data was evaluated in terms of
reliability, convergent and discriminant validities (Kline, 1998).

Reliability of each construct Was assessed in terms of Cronbach’s alpha.
Following Nunnally (1978)’s recommendation, an item whose reliability was lower
than .7 was removed from the analysis. In this step, two items were dropped. The
excluded items were “My favorite items were often one of the last items left on the rack”
and “I can mostly find the same merchandise that I saw on my previous store visit
(reversed scale).” The remaining 42 items were submitted to the confirmatory factor
analysis.

The purification of the scales was performed by excluding items with
insignificant t-value and low loading coefficients (Anderson and Gerbing, 1988). Items

with poor loading values (<.60) were eliminated one by one and the analysis was rerun.

55

In total, nine items were dropped in this step (see Table 4). Each construct had a least
three items (Hair, Anderson, Tatham, and Black, 1998). Consequently, 33 items in total—
3 items for anticipated gains of buying and purchase acceleration, 5 items for anticipated
losses of not buying and shopping hedonism, and 6 items for perishability and in-store
hoarding—were submitted to test the path model. The reliabilities of all seven constructs
were between .82 and .92, showing satisfactory reliability (Nunnally 1978). In addition,
unidimensionality was demonstrated for each construct, ensuring international
consistency (Sethi and King, 1994).

Convergent validity was assessed by the significance of the lambda loadings
(Anderson and Gerbing, 1988). The factor loading values for each individual indicator to
its respective latent variable was highly significant (p < .001), and all loading coefficients
were above .63. This provided the evidence that the measured items robustly represented
the underlying constructs, showing strong convergent validity (Bollen 1989; Kline 1998).

Discriminant validity is the degree to which extracted factors measured by
different sets of indicators falling within the same latent construct are distinguished from
one another (Bollen 1989; Kline 1998). All items in seven constructs were loaded,
without cross loading, in the underlying construct. Furthermore, the )(2 difference test
showed that the correlation between two constructs was significantly different from 1,
verifying distriminant validity (Anderson, 1987). Therefore, the results revealed internal
consistency, convergent validity, and discriminant validity of the scales.

Even though the chi-square test result was significant ()(2= 850.232, df=462,
p<0.001), incremental fit index (IFI=.92), Bentler-Bonett non-normed fit index (NNF I
= .91), comparative fit index (CFI = .92), and root mean square error of approximation

(RMSEA =.060) indicated good fits of the CFA models to the data. The results of CFA are

56

shown in Table 3. Selected and excluded items and Cronbach’s alpha for each construct

are reported in Table 4.

Table 3. Result of Confirmatory Factor Analysis

 

 

Parameters Sténdardrzed Standardized T-value P-value
strmates Error

V4 6 Scarcity 0.765 0.124 8.923 0.000
V5 6 Scarcity 0.693 0.109 9.539 0.000
V6 6 Scarcity 0.739 0.114 10.373 0.000
V7 6 Scarcity 0.696 0.065 14.220 0.000
V8 6 Scarcity 0.734 - -
V10 6 Perishability 0.695 - -
V11 6 Perishability 0.862 0.129 10.705 0.000
V12 6 Perishability 0.625 0.115 9.216 0.000
V13 6 Perishability 0.745 0.085 13.319 0.000
V14 6 Perishability 0.832 0.117 10.370 0.000
V15 6 Perishability 0.645 0.108 9.484 0.000
V19 6 Gain 0.885 0.070 15.326 0.000
V20 6 Gain 0.777 0.059 13.372 0.000
V21 6 Gain 0.845 - -
V22 6 Loss 0.735 - -
V23 6 Loss 0.887 0.090 13.625 0.000
V24 6 Loss 0.772 0.070 15.981 0.000
V25 6 Loss 0.901 0.094 13.843 0.000
V26 6 Loss 0.837 0.100 12.827 0.000
V28 6 Hoarding 0.691 - -
V29 6 Hoarding 0.704 0.099 9.497 0.000
V30 6 Hoarding 0.697 0.111 9.413 0.000
V31 6 Hoarding 0.696 0.121 9.235 0.000
V32 6 Hoarding 0.770 0.115 10.287 0.000
V33 6 Hoarding 0.791 0.122 10.549 0.000
V34 6 Hedonism 0.809 - -
V35 6 Hedonism 0.727 0.075 11.767 0.000
V36 6 Hedonism 0.767 0.083 12.555 0.000
V43 6 Hedonism 0.733 0.100 9.736 0.000
V44 6 Hedonism 0.820 0.075 13.613 0.000
V45 6 Purchase 0.811 0.124 8.632 0.000
V46 6 Purchase 0.702 0.110 7.328 0.000
V47 6 Purchase 0.828 - -

 

57

Table 3. (Cont’d)

 

Standardized Standardized

 

Parameters . T-value P-value
Estimates Error
Perishability H Scarcity 0.083 0.055 1.497 0.134
Loss H Scarcity 0.467 0.105 4.442 0.000
Gain H Scarcity 0.007 0.107 0.066 0.948
Hoard H Scarcity 0.264 0.094 2.814 0.005
Hedonism H Scarcity 0.087 0.092 0.950 0.342
Purchase H Scarcity 0.025 0.135 0.187 0.852
Gain H Perishability 0.241 0.069 3.485 0.000
Loss H Perishability 0.369 0.071 5.226 0.000
Hoard H Perishability 0.331 0.067 4.920 0.000
Hedonism H Perishability 0.303 0.065 4.689 0.000
Purchase HPerishability 0.344 0.090 3.807 0.000
Gain H Loss 0.642 0.124 5.169 0.000
Hoard H Loss 0.709 0.120 5.909 0.000
Hedonism H Loss 0.642 0.112 5.715 0.000
Purchase H Loss 0.793 0.159 4.974 0.000
Hedonism H Hoard 0.613 0.110 5.561 0.000
Purchase H Hoard 0.858 0.162 5.291 0.000
Gain H Hoard 0.671 0.125 5.374 0.000
Hedonism H Purchase 0.766 0.155 4.954 0.000
Gain H Purchase 0.701 0.171 4.098 0.000
Gain H Hedonism 0.849 0.131 6.469 0.000

 

,2: 850.232, df=462, p<0.001, f/df=1.84
CFI=.92, NNFI=.91, IFI=.92, RMSEA=.06

 

Table 4. Measurement Properties

 

Std. Cronbach’s

 

Items Loading alpha

Scarcity
V4. The products that I was interested in were almost out of stock. .77
V5. There were only limited number of products per size, style, 70

and color. °
V6. Products of interest were often scarce in my size. .74 .82
V7. My favorite styles in this store were mostly available in my 70

srze.
V8. 1 could mostly get my first preference in my size. .74
Excludid item:

 

V3. My favorite items were oflen one of the lost items lefl on the rack

 

58

 

 

 

 

 

 

 

Items Std. Cronbach’s
Loading alpha
Perishability
V10. New styles are introduced on a frequent basis. .70
V11. This store rapidly turns over their merchandise. .86
V12. Products in this store do not stay on the rack long. .63 88
V13. This store introduces new fashion styles quickly. .75 '
V 14. Products in this store are fresh in terms of fashion trend. .83
V15. Products in this store are moving fast. .65
Excluded items:
V9. This store constantly delivers updated fashion items throughout the
season.
V16. 1 can mostly find the same merchandise that I saw on my previous
store visit.

Anticipated Gain of Buying
V19. This product would enhance my self-image. .78
V20. This product would make me feel good about myself. .89 .87
V21. This product would make me feel special. .85
Excluded Items:
VI 7. This product would make me look unique.
V I 8. This product would make me look fashionable.
Anticipated Loss of Not Buying
V22. If I do not buy it right now, I would regret it later. .74
V23. I was afraid that this item would be out-of-stock in my next 89

visit. '
V24. I thought that it would be a loss if I do not buy it today. .77 .92
V25. I was concerned that this item might not be available if I .90

came back later.
V26. If I do not get it immediately, I would lose an opportunity to 84

purchase it because it will be gone tomorrow. '
In-Store Hoarding
V28. I had the urge to grab the product immediately. .69
V29. I was carrying around products while shopping. .70
V30. I snapped things up while shopping in this store. .70
V31. Once I picked up a product, I did not want to put it down 70

although I was not sure if I would buy it or not. ' .88
V32. I hurried to grab the products of interest and kept them to 77

myself. '
V33. On this trip, I found a number of things I wanted to grab 79

immediately even though they were not on my shopping list.

 

59

 

Std. Cronbach’s

It
ems Loading alpha

 

Excluded item:
V2 7. l spontaneously grabbed the product of interest.

Hedonic Shopping Value

V34. Shopping at this store was truly a joy. .81

V35. I continued to shop, not because I had to, but because I
wanted to.

V36. Shopping at this store truly felt like an escape. .77

V43. While shopping at this store, I felt a sense of adventure. .73

V44. Shopping at this store was a very nice time out. .82

 

.73
.87

Exclutfll Items:

V3 7. Compared to other things 1 could have done, the time spent
shopping at this store was truly enjoyable.

V38. I enjoyed being immersed in exciting new products.

V39. I enjoyed shopping at this store for its own sake, not just for the
items I may have purchased

V40. I enjoyed a time because I was able to act on the “spur-of-the-
moment. ”

V41. During a shopping trip I feel the excitement of hunt.

V42. While shopping at this store, I was able to forget my problems.

 

Purchase Acceleration

V45. On this shopping trip, I purchased an item that I had not 81
planned to purchase. '
V46. I purchased more products than I would do normally on this 70
trip. ' .84

V47. I made a purchase decision immediately rather than 83
postponing until the next visit to this store. °

 

Analysis of the Structural Model

Path Analysis for Antecedents and Consequences of In-Store Hoarding

A latent SEM analysis with Maximum Likelihood Estimation was conducted to

test the causal relationship between constructs. The results of path analysis indicated that

60

the proposed model had a significant chi-square value 0(2= 879.136, df=471, p<0.001,
xz/df=1.86), but other model fit indices showed a satisfactory fit to the data (CF I=.92,
NNFI=.91, IFI=.92, RMSEA=.06). The standardized factor loading estimates and fit
indices are reported in Figure 3 and Appendix C. All of the parameter estimates for the
structural paths were in the hypothesized direction, except for one path for the effect of
scarcity on anticipated gains of buying. The results of hypotheses tests are summarized in
Appendix D.

As proposed, perishability had a significantly positive effect on anticipated gains
of buying (standardized estimates= .29, t= 3.922, p<.001) and anticipated losses of not
buying (standardized estimates= .35, F 5.190, p<.001), supporting Hypotheses 1 and 2.
On the other hand, although perceived scarcity was hypothesized to have a positive effect
on anticipated gains of buying, the result showed that there was no significant effect of
scarcity on this variable (standardized estimates= -.03, t= -.405, p= .686, n.s.). Therefore,
Hypothesis 3 was rejected. In addition, scarcity showed a significant and positive effect
on anticipated losses of not buying (standardized estimates= .32, F 5.030, p<.001),

supporting Hypothesis 4.

Hypothesis 1: The greater the level of perishability, the greater the level of anticipated
gains of buying. (Supported)

Hypothesis 2: The greater the level of perishability, the greater the level of anticipated
losses of not buying. (Supported)

Hypothesis 3: The greater the level of perceived scarcity, the greater the level of
anticipated gains of buying. (Not supported)

Hypothesis 4: The greater the level of scarcity, the greater the level of anticipated losses
of not buying. (Supported)

61

Both anticipated gains of buying (standardized estimates=.27, t= 3.753, p<.001)
and anticipated losses of not buying (standardized estimates= .50, t= 6.078, p< .001)
significantly increased in-store hoarding. Therefore, Hypotheses 7 and 8 were supported.

For the consequences of in-store hoarding, the relationship with shopping
hedonism and purchase acceleration were tested. As hypothesized, there were significant
and positive effects of in-store hoarding on both shopping hedonism (standardized
estimates= .32, t= 4.190, p= <.001) and purchase acceleration (standardized
estimates= .42, F 4.517, p= <.001). Furthermore, shopping hedonism showed a
significant and positive effect on purchase acceleration (standardized estimates= .22, t=

2.643, p= .008). Therefore, Hypotheses 10, 11 and 12 were all supported.

Hypothesis 7: The greater the level of anticipated gains of buying, the greater the level of
in-store hoarding. (Supported)

Hypothesis 8: The greater the level of anticipated losses of not buying, the greater the
level of in-store hoarding. (Supported)

Hypothesis 10: The greater the level of in-store hoarding, the greater the level of
shopping hedonism. (Supported)

Hypothesis 11: The greater the level of shopping hedonism, the greater the level of
purchase acceleration. (Supported)

Hypothesis 12: The greater the level of in-store hoarding, the greater the level of
purchase acceleration. (Supported)

Unexpectedly, the modification indices suggested a significant )(2 improvement
by releasing a path between anticipated gains of buying and shopping hedonism.
Although this link was not proposed in the originally proposed model, it is reasonable to
assume that anticipated gains such as uniqueness and self-image improvement by owning

fast fashion products will induce great shopping excitement and pleasure among shoppers.

62

Therefore, a new path linking these two constructs was allowed and it showed a
significant and positive effect of anticipated gains on shopping hedonism (standardized
estimates= .43, t= 5.714, p<.001).

In addition, there was a significant association between anticipated gains of
buying and losses of not buying. Based on the logical discussions by Inman and
McAlister (1994) and Spears (2001) that consumers’ perceived gains from promotional
offers are transferred to perceived losses as the expiration date to take advantage of such
deals approaches, the model was respecified by allowing a new path between these two
constructs. Anticipated gains of buying showed a significantly positive effect on

anticipated losses of not buying (standardized estimates= .35, t= .058, p<.001).

63

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Comparison of Path Coefficients

To examine differences in the path effect size between the effects of perishability
and scarcity on anticipated gains of buying and anticipated losses of not buying, the chi-
square difference test for the comparison of path coefficients was conducted by
constraining each path to be equal and then releasing the path (Anderson and Gerbing,
1988). The result is shown in Table 5.

As proposed in Hypothesis 6, the result indicated that there was a significant
difference in the path coefficients between the effect of scarcity on anticipated gains of
buying (Gain 6 Scarcity) and its effect on anticipated losses of not buying (Loss 6
Scarcity) (A 12 = 12.031, p<.001). However, although perishability showed a stronger
effect on anticipated losses of not buying (Loss 6 Perishability) than on anticipated gains
of buying (Gain 6 Perishability), there was no statistically significant difference between
the path coefficients (A x’ = .037, p= .8475, n.s.). Therefore, Hypothesis 5 was rejected
and Hypothesis 6 was supported.

Lastly, the effect of anticipated gains of buying on in-store hoarding (Hoarding
6 Gain) and the effect of anticipated losses of not buying on in-store hoarding (Hoarding
6 Loss) were compared. As hypothesized, the result showed that there was a significant
difference in the path coefficients. Namely, the effect of anticipated losses of not buying
on in-store hoarding was significantly greater than that of anticipated gains of buying (A

f = 4.798, p<.05). Therefore, Hypothesis 9 was also supported.

Hypothesis 5: The eflect of fashion perishability on anticipated losses of not buying will
be stronger than on anticipated gains from buying. (Not supported)

65

Hypothesis 6: The eflect of scarcity on anticipated losses of not buying will be stronger
than on anticipated gains from buying. (Supported)

Hypothesis 9: The efi"ect of anticipated losses on in-store hoarding will be stronger than
that of anticipated gains. (Supported)

Table 5. Path Comparison: Result of xz Difference Test

 

 

X2 Df Ax2 p—value
Unconstrained baseline model 879.136 471 - -
Equalin Constrained Path
(Parish —-) Gain) = (Parish —) Loss) 879.173 472 0.037 0.8475
(Scarcity —) Gain) = (Scarcity -> Loss) 891.167 472 12.031 0.0001

(Gain —> Hoarding) = (Loss —) Hoarding) 883.934 472 4.798 0.0285

 

Note: Models were compared between unconstrained baseline model and a model with
a path parameter which was constrained to be equal across the two groups.

Moderation Eflect—Fashion Innovativeness

To test the moderation effect of innovativeness, summed scores for six items
were computed and approximately the top 40% of the sample was divided into fashion
innovators and around 40% of the bottom for non-innovators. The middle 20% was
removed from the analysis to find distinctive differences between the groups.

The comparison of the strength of the path coefficients was conducted by
constraining each path to be equal across the two groups, and the resulting model fit was
compared to a base model in which all paths were freely estimated (Anderson and
Gerbing, 1988). The data showed a partial support for the moderating effect of

innovativeness. The result is shown in Tables 6 and 7 and Figures 4 and 5.

66

As expected, the result of a chi-square difference test indicated that there was a
significant difference in the chi-square change when the equality constraint for
perishability and anticipated gains of buying (Gain 6 Perishability) was released
between the two groups (A f = 4.1, p<.0429). The innovators showed a significantly
stronger relationship than do non-innovators, supporting H13. Interestingly, the path was
highly significant for innovators (standardized estimates= .50, p<.001), whereas it was
not significant for non-innovators (standardized estimates=. 12, p=.294).

For H14, the chi-square difference test indicated that the release of the equality
constraints between the scarcity and the anticipated gains of buying (Gain 6 Scarcity)
did not significantly improve the model fit (A 12 = .2, p=.6547, n.s.). Therefore, H14 was

rejected.

Hypothesis 13: The eflhct of perishability an anticipated gains of buying will be stronger
for innovators than non-innovators. (Supported)

Hypothesis 14: The efect of scarcity on anticipated gains of buying will be stronger for
innovators than non-innovators. (Not supported)

The chi-square difference test showed the release of equality constraint of the
perishability and the anticipated losses of not buying (Loss 6 Perishability) did not
significantly improve the model fit (A 1’ = 3.4, p=.0652, n.s.). Therefore, H15 was not
supported. Also, although it was in the hypothesized direction, there was no significant
differenceibetween the two groups in the effect of scarcity on anticipated losses of not

buying (Loss 6 Scarcity), leading to a rejection of H16 (A 1’ = .1, p=.7518, n.s.).

67

Hypothesis 15: The efiect of perishability on anticipated losses of not buying will be
stronger for innovators than non-innovators. Wot supported)
Hypothesis 16: The efiéct of scarcity on anticipated losses of not buying will be stronger

for innovators than non-innovators. (Not supported)

As proposed, the chi-square difference test showed a significant model fit
improvement when the equality constraint of the anticipated gains of buying and in-store
hoarding (Hoard 6 Gain) was released (A x2 = 4.2, p<.0404). In other words, innovators
showed a stronger relationship between the anticipated gains of buying and in-store
hoarding than did non-innovators, supporting H17. Interestingly, such a significant
relationship was not found for the non-innovative group.

Lastly, the path coefficients for the effect of anticipated losses of not buying on
in-store hoarding (Hoard 6 Loss) did not significantly differ between the groups (A 12 =
1.4, p= .2367, n.s.). Therefore, H18 was not supported. Although not significantly
different, the result showed the opposite prediction to the hypothesis. Namely, non-
innovators showed a stronger effect of the anticipated losses on in-store hoarding than did

innovators.

Hypothesis 1 7: The efiect of anticipated gains of buying on in-store hoarding will be
stronger for innovators than non-innovators. (Supported)
Hypothesis 18: The efi'ect of anticipated losses of not buying on in-store hoarding will be

stronger for innovators than non-innovators. (Not supported)

68

Table 6. Multiple Group Path Comparison Test: Innovativeness

 

 

 

)(2 Df A)? p-value
Unconstrained baseline model 1722.5 942 - -
Constrained Pm
Gain (- Perishability 1726.6 943 4.1* 0.0429
Gain (- Scarcity 1722.7 943 0.2 0.6547
Loss (- Perishability 1725.9 943 3.4 0.0652
Loss (- Scarcity 1722.6 943 0.1 0.7518
Loss (— Gain 1726.2 943 3.7 0.0544
Hoard é- Gain 1726.7 943 4.2* 0.0404 5
Hoard (— Loss 1723.9 943 1.4 0.2367
Hedonism (- Gain 1722.7 943 0.2 0.6547
Hedonism (— Hoard 1722.6 943 0.1 0.7518
Purchase (- Hedonism 1722.6 943 0.1 0.7518
Purchase (— Hoard 1725.6 943 3.1 0.0783

 

Note: Models were compared between unconstrained baseline model and a model with a path
parameter which was constrained to be equal across the two groups.

 

Significance levels: ’p < 0.05
Shaded parts indicate a significant difference in the path coefficient between the groups.

69

Figure 4. Path Coefiicients for Innovators (n=88)

 

 
 
    
 
  
  
    
 

.50“

  

 

Shopping
Hedonism

Perishability

  
 
    

ln-Store
Hoarding

.28“

 

 

Anticipated
Losses of
Not Buying

   
   

Purchase
Acceleratior

  
 

 

 

.39ttt

 

 

 

*** P<.001, ** P<.01, *p<.05
Note: Items for each construct are not shown in the figure.

Figure 5. Path Coefficients for Non-Innovators (n=96)

 

 

Perishability

 

 

Purchase
\cceleration

 

 

 

 

 

*** P<.001, ** P<.01, *p<.05
Note: Items for each construct are not shown in the figure.

70

Table 7. Result of Structural Equation Model Estimation for Innovators and Non-
Innovators

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

lnnovators Non-Innovators
(N =88) (N =96)
Path Standardized Standardized
. P-value . P-value
Estimates Estimates
Gain 6 Perishability .50*** .000 .12 (n.s.) .289
Gain 6 Scarcity -.05 (n.s.) .646 -.15 (n.s.) .199
Loss 6 Perishability .18 (n.s.) .130 .40*** .000
Loss 6 Scarcity .39*** .000 .33" .001
Loss 6 Gain .29" .009 .49*** .000
gm. "ct" Hoard (- Gain .46*** .000 .01 (n.s.) .949
strmates
Hoard 6 Loss .27* .015 .65*** .000
Hedonism 6 Gain .53*** .000 .38“ .002
Hedonism 6 Hoard .23* .038 .34" .005
Purchase 6 Hedonism .28" .007 .29* .031
Purchase 6 Hoard .40*** .000 .30“ .031
. . 12 = 1722.521, as: 942, p< .001, fiat: 1.829
F" Indlces IFI= .33, CFI=.82, RMSEA=.067

 

 

 

 

Significance levels: m p < 0.001; Wp < 0.01 r p < 0.05
Shaded parts indicate a significant difference in the path coefficient between the groups.

71

DISCUSSION

This study raises questions about the role of in-store hoarding and investigates
the antecedents and consequences of hoarding at fast fashion stores. The anticipated gains
of buying and losses of not buying influenced by the two implicit time limited cues
(perishability and scarcity) are proposed as antecedents of in-store hoarding. Furthermore,
based on previous literature review, shopping hedonism and purchase acceleration are
modeled as consequences of in-store hoarding. Prospect theory, commodity theory,
uniqueness theory, mood effect and physical proximity effect provided theoretical
supports. Overall, the findings of this study support the proposed model in explaining and
predicting consumers’ in-store hoarding behavior in fast fashion stores. The following

section discusses frndings more specifically in each criterion.

Antecedents of In-Store Hoarding

First, this study found that scarcity and perishability are significant drivers of in-
store hoarding through anticipated gains of buying and anticipated losses of not buying.
Perishability positively influenced consumers’ anticipated gains of buying. The perceived
benefits from buying include positive feelings about the product or self-image due to
wearing the product. The more consumers are aware of perishability, the greater the
anticipated gains of buying. This implies that fashion perishability resulting from
constant release of the latest fashion increases the valuation of fashion items.

In addition, it was proposed that if consumers perceived scarcity of products,

their anticipated gains of buying would be greater than those who did not, to the point

72

that fashion forward consumers tend to appreciate exclusivity or limited availability of
products (Szybillo, 1973; Brannon, 2005). Unexpectedly, however, while scarcity
significantly influenced consumers’ anticipated losses of not buying, it did not have a
significant effect on the anticipated gains of buying. Put differently, the perceived or
experienced scarcity of products was only related to the anticipated loss of not buying,
while it did not significantly affect—either positively or negatively—the way they
perceive or value a product. Therefore, in this study, the scarcity effect—a positive
valuation of scarcity—proposed by commodity theory (Brock, 1968) was not found.

In addition to scarcity, perishability led to the anticipated losses of not buying.
Namely, both perceived scarcity and perishability increase worries about out of stock
possibility, or regrets or psychological loss that may result if consumers did not get it
right away. In turn, along with the anticipated gains of buying, such anticipated losses of
not buying significantly rendered them eager to grab products immediately and carry
them around while shopping. This is consistent with the notion that general hoarding
occurs in a fear of scarcity or possibility of product unavailability (Meagher, and Riskind,
2001; Frost and Gross, 1993, McKinnon, Smith, and Hunt, 1985; Ong, 1999; Verhallen
and Robben, 1994). This also implies that the hoarding literature can be applied to a

specific in-store hoarding situation.

Prospect Theory—Path Comparison

Prospect theory explains consumer decision-making under uncertainty and
proposes that consumers respond to losses more sensitively. As a result, people tend to

make a decision to minimize losses. Findings in this study were consistent with the

73

concepts in prospect theory. As hypothesized, scarcity was more strongly related to the
anticipated losses of not buying than to the anticipated gains of buying. More importantly,
the anticipated loss of not buying was a stronger determinant of in-store hoarding than the
anticipated gains of buying. This finding is in line with prospect theory’s consumer loss
aversion under uncertainty (Kahneman and Tversky, 1979; Tversky and Kahneman,
1992). Furthermore, Spears (2001) acknowledged that time limited promotions influence
consumer behavior by expediting a purchase because consumers are promoted to advance
promotional gains before the opportunity to participate is lost. Hence, this finding implies
that a strategic use of implicit time-limited cues significantly expedite in-store hoarding
primarily by increasing consumers’ psychological losses or discomfort, such as worries
about out of stock possibility or anticipated regret resulting from a delayed purchase
decision.

Furthermore, there was a significant link between the anticipated gains of buying
and anticipated losses of not buying. This finding supports the arguments by Inman and
McAlister (1994) and Spears (2001) that consumers first perceive gains from promotional
offers (e. g., delay of payment or coupon offered), and then such perceived gains are
reframed as a potential loss with the increasing possibility of losing an opportunity to

take advantage of the promotional offers as the deadline for participation closes.

Prior Store knowledge

Prior store knowledge gained through direct and indirect experience has been
found to be an important determinant of behavior. Therefore, a post hoc analysis was
conducted to examine how prior store knowledge influenced the perception of scarcity

and perishability and further in-store hoarding. The result is reported in Appendix E.

74

The data revealed that prior store knowledge significantly influences the
perception of perishability. Shoppers with a high level of store knowledge tended to be
aware of perishability more strongly compared to people with a low level of store
knowledge. On the other hand, store knowledge did not affect the perception of scarcity.
Finally, store knowledge had a significant and positive effect on in-store hoarding.
Namely, people with high store knowledge tend to hoard products more while shopping.
Taken together, such findings may imply that prior store knowledge is a factor that

partially explains variations in perception of perishability and in-store hoarding behavior.

Consequences of In-store Hoarding

This study empirically supported that in-store hoarding is closely related to
shopping hedonism and purchase acceleration as it significantly increased hedonic
evaluation of shopping activity and accelerated actual purchases. In-store hoarding
activity made Shopping more fun, exciting, and playful, which ultimately led to purchase
acceleration.

There was also an indirect efl’ect of in-store hoarding on purchase acceleration
through shopping hedonism. In other words, the more the consumers experienced
hedonic shopping values, the more likely they were to purchase more than they expected
or normally do. These findings are consistent with the notions proposed by the mood
effect that when they are in a good mood or hedonic state they tend to spend more money
on products (Donovan, Rossiter, Marcoolyn and Nesdale, 1994; Donavan and Roister,
1982; Roehm and Roehm, 2005; Kahn and Isen, 1993). In addition, shopping hedonism

was also significantly induced by the anticipated gains of buying. Given that the

75

anticipated gains mainly come from the perceived perishability in this study, this implies
that anticipated positive thoughts or feelings perceived from constantly delivered latest
fashion make shoppers more excited and pleasant, increasing shopping hedonism.

In addition, it appeared that hoarded products showed a higher possibility of
being purchased. Thus, this study confirmed the psychology of physical proximity that
people tend to purchase more when they are actually close to the products (Rook, 1987;
Beatty and Ferrell, 1998). Fundamentally, the view of accelerated purchase triggered by
the implicit time-limited cues is in line with the studies that the time-limited nature of
promotions strongly encourage customers’ purchase intention, while discouraging the

intent to search further for deals (Aggarwal and Vaidyanathan, 2003; Spears, 2001).

Moderation Effect of Innovativeness

The moderation effect of innovativeness was proposed and tested to examine
individual differences in in-store hoarding behavior. However, from Hypotheses 13
through 18, only two of them supported the theoretically hypothesized moderating effect,
indicating that innovativeness does not fully differentiate consumer in-store hoarding
behavior. Nevertheless, the differences between innovators and non-innovators in the two
supported hypothesized relationships provide vital implications for both theorists and
practitioners.

First, on an interesting note, the role of perishability was different in affecting
consumer perception and behavior depending on the level of innovativeness. Namely,

innovators were more likely to interpret it as a positive cue that increases the desirability

76

of fashion items. For this group, perishability was not significantly associated with the
anticipated losses of not buying. In contrast, non-innovators did not relate perishability
with anticipated gains of buying but with anticipated losses of not buying, encouraging
immediate action.

Secondly, drivers for in-store hoarding varied according to innovativeness. For
innovators, both anticipated gains of buying and loss of not buying were determinants of
in-store hoarding. On the other hand, for non-innovators, only anticipated losses of not
buying motivated them to hoard and anticipated gains of buying had no significant
influence on hoarding. A likely explanation for this is that innovators have a stronger
autonomy in product selection and purchase decision-making than do non-innovators.
They influence people rather than being influenced by them (Brannon, 2005). Innovators
have an autonomy to filter from the many options those that fit their individual aesthetic,
by simply appreciating or enjoying quick movement of products and trying on frequently
updated fashion. This finding is in accordance with previous empirical findings that
innovators tend to prefer products that are new, exclusive, or distinctive in the need of
uniqueness (Fromkin, 1973). However, as evidenced by the significant, positive effect of
scarcity on anticipated loss of not buying, once consumers found their favorite items,
they were influenced by the possibility of it becoming out of stock, therefore hoarding the
items immediately to avoid uncertainty. Non-innovators are trend followers. This group
of people is motivated to follow a new trend and is more likely to be strongly influenced
by time constraints or quantity constraints planned by marketers than are innovators.

In the other four hypotheses, there were no significant differences between
innovators and non-innovators. In both groups, the perceived scarcity did not influence

anticipated gains of buying. In summary, the results showed that innovativeness partially

77

acts as a moderator in some of the antecedents of in-store hoarding. In particular,

perishability is perceived differently depending on innovativeness.

78

CHAPTER V

CONCLUSION AND IMPLICATIONS

As the market becomes dynamic and volatile, more retailers are moving toward
fast fashion by constantly delivering new products throughout the season to maintain
inventory freshness and make the selling floor more inviting for shoppers (Brannon, 2005).
This fast fashion strategy is a marketing approach to respond to the latest fashion trends by
frequently updating products with a short renewal cycle and turning the inventory at a rapid
rate, constantly making room for new offerings. As a result, a product life span is
dramatically reduced, thereby increasing fashion perishability. Moreover, in order to make
constant room for new products and minimize markdowns, fast fashion retailers
deliberately limit product availability, creating a sense of scarcity in the part of consumers.

This study proposed such increased perishability and scarcity as implicit time-
limited cues that influence consumers to take immediate action while in the store. Being
aware of these implicit signals, shoppers are trigged to hoard products spontaneously
with anticipated psychological gains from acquiring or using the product and/or
anticipated losses or regrets from delayed decision-making. In this study, in-store
hoarding was conceptualized as a dominant shopping behavior observed in fast fashion
stores. The conception was introduced and its antecedents and consequences were

investigated. The following section discusses its theoretical and marketing implications.

Theoretical Implications
This study is the first known academic effort to address the competitiveness of

fast fashion strategies from a consumer behavior perspective. Previously, the focus has

79

been placed on supply chain management (Moore and F ernie, 2003). How consumers
respond to the fast fashion strategy has been overlooked in investigating its strategic
competitiveness. More over, since there has been no consensus on the clear definition of
this strategy, it was redefined to cover the major characteristics of the fast fashion
strategy: Constant delivery of latest fashion with a short renewal cycle. With the growing
importance of fast fashion in today’s time-based competition, understanding changing
consumer behavior will contribute to extending the body of the literature and the
theoretical frameworks in the consumer and retailing field.

Second, this study also initiated scholarly inquiry about the role of in-store
hoarding. Based on the empirical investigation of actual shoppers and purchasers in fast
fashion stores, this study supported that perishability and scarcity are central to
understanding in-store hoarding. Anticipated gains of buying and anticipated losses of not
buying were found to be mediators in the effect of perishability and scarcity on in-store
hoarding. Perhaps more importantly, this study provided meaningful implications by
confirming the consequences of in-store hoarding. Namely, the data demonstrated that in-
store hoarding significantly increased purchase acceleration as well as shopping
hedonism. Overall, the proposed model ensured the theoretical soundness and coherence
of the conceptual model.

Third, this study contributed to extending the scope of the literature by
investigating implicit time limited cues. They are implicit because there are no written or
direct signals. Previous studies have focused on the time-limited promotions using
explicit semantic cues. In addition, the messages of such promotions were mostly price-
or sales-oriented which provided only a short-term solution since these were easily

imitated by competitors. Moreover, Aggarwal and Vaidyanathan (2003) acknowledged

80

that it has not been clear how the time constraint or the quantity constraint affects
consumer shopping behavior and purchase decision-making. With this regard, this study
contributed to the literature by identifying and empirically testing the effect of the two
implicit time-limited cues—perishability from a short renewal cycle and scarcity from
deliberately limited product availability. Apparently, these two cues substantially
influenced consumer behavior by increasing the level of anticipated losses of not buying,
ultimately inviting them to take immediate action.

Fourth, by applying prospect theory as a theoretical explanation, this study
extended the understanding of consumer behavior regarding the uncertainty about
product availability accelerated by scarcity and perishability. Such implicit time-limited
cues had a stronger influence on anticipated psychological losses arising from lost
opportunity to purchase it or possible future regret resulting from not taking immediate
action, than on anticipated gains of buying. Additionally, evidenced by the positive effect
of anticipated gains on anticipated loss of not buying, this study confirmed that the
perceived gain was reframed as a loss if consumers do not take advantage of an
opportunity to acquire a product or benefit from a promotional offer (for example, a
coupon or limited big sale) when it is available or valid. Furthermore, this study found
that such anticipated loss of not buying was clearly a more dominant determinant of in-
store hoarding relative to anticipated gains of buying, which evidently demonstrated the
effectiveness of the fast fashion retailers’ implicit message, “Buy now, it won’t be here
tomorrow.” This study increased the validity of prospect theory by applying it to an
empirical study. It supported its major view that consumers are more sensitive to a loss
than a gain and thus people select an option that minimizes the risks. This study

theoretically demonstrated the role of implicit-time limited cues in affecting consumer

81

shopping and purchase behavior.

Fifth, considering the economic significance of fashion goods, it becomes more
important to understand the factors affecting consumer valuation of fashion goods
(Szybillo, 1973). Although the scarcity effect—a positive valuation of scarce products—
was not supported in this study, the findings of the positive effect of perishability on
anticipated gains of buying provided an answer for the question. Namely, a rapid and
constant delivery of the latest fashion throughout the season substantially increases the
valuation of fashion products in today’s time-based market.

Sixth, the moderation effect of innovativeness contributed to explaining the
partial variations of in-store hoarding. For innovators, perishability was primarily
perceived as a gain rather than a loss and both the gains and losses proved to be of crucial
importance in determining in-store hoarding. This anticipated loss of not buying was only
meaningfully affected by perceived scarcity. By contrast, in the case of non-innovators,
perishability was not interpreted as a gain but it exclusively increased their anticipated
loss of not buying. Such differences highlighted individual differences in interpreting
marketing signals and the consequent consumer behavior.

Seventh, this study made a significant contribution to the consumer and retailing
literature by introducing, defining, and operationalizing new constructs or new
measurements. It includes measures for perishability, scarcity, anticipated gain of buying,
anticipated loss of not buying, and in-store hoarding. Scales for purchase acceleration
were modified to fit in the survey method. Following Churchill’s (1979) procedure of
scale development, considerable efforts were made to generate valid and reliable
measurement scales for these constructs. All of the measures proved reliable and

significantly linked with other constructs, showing strong convergent validity and

82

nomological validity. Therefore, this study paved a way to investigate more diverse
aspects of consumer behavior by developing new constructs and measures.

Eighth, this study advanced the shopping hedonism literature, an
underdeveloped but important research area (Stemquist, Byun and J in, 2004). By linking
in-store hoarding, this study contributed to the literature by finding a new but powerful
antecedent that makes shopping more fun, enjoyable, and playfirl. This theoretical linkage
provides insights into in-store hoarding as a new and interesting element of retail
entertainment.

Finally, many consumer researches have been criticized because they mostly
focused on convenience samples such as students. In this regard, the findings of this
study are more meaningful in that it used actual shoppers and investigated actual

purchase activity instead of purchase intention.

Marketing Implications

This study also provides useful implications for practitioners in developing and
implementing marketing and merchandising management strategies. The following
discusses major implications of this study.

First, as today’s retailers increasingly offer comparable merchandise and
compete with strategies that can be easily imitated, they should direct more of their
attention at developing long-term and non-price oriented strategies. The emphasis on
price-led promotion encourages consumers’ store or brand switching (Corstjens and
Corstjens, 1999). This study may suggest that retailers can avoid price-oriented

competition and affect purchase behavior by manipulating implicit time-limited cues such

83

as scarcity and perishability. For example, even retailers who do not have capabilities to
implement fast fashion could also make their products more appealing by utilizing
promotional and display techniques to give the impression that the product is in limited
supply (V erhallen and Robben, 1994; Szybillo, 1973), or provide fresh or different looks
by simply changing product layouts or positions more frequently.

Second, contrasting the conventional norm of sure availability of products to
avoid sales losses, fast fashion retailers intentionally limit supply. Although the scarcity
effect was not found in this study, the findings of this study demonstrated the significant
role of scarcity from a strategic perspective. Namely, perceived scarcity significantly
triggered hoarding by increasing worries about the possibility of an item being out of
stock. This eventually led shoppers to accelerate purchases by making an immediate
purchase or purchasing more than they expected. In addition to scarcity, perishability also
showed the same positive effect on anticipated loss of not buying. Therefore, retailers
would benefit from a greater understanding of whether greater level of perceived scarcity
or perishability lead to a greater level of anticipated loss of not buying.

Third, of perhaps even greater interest to retailers is the consequences of in-store
hoarding. The study found that consumers’ awareness about the implicit time-limited
cues could alter shopping patterns by encouraging immediate hoarding, in turn
powerfully precipitating a purchase. This phenomenon can be understood by the
increased impulsivity or urge to buy when people are physically close to their favorite
products. Hoarded products, in opposition to abundant products that are easily picked up
or without any competition, are more likely to be meaningful to shoppers. As a result,
customers are less likely to delay their purchase or give up the products unless there is a

financial constraint or size problem. As brand or store switching has been a problem for

84

most companies, this holds important implications for retailers. Namely, by manipulating
scarcity and perishability, retailers may create a certain level of shopping competition
among shoppers, thereby encouraging immediate actions in the store.

Additionally, in the increasingly competitive environment, retaining existing
customers and attracting new ones has been a challenge for all companies. Accordingly,
the pursuit of customer loyalty becomes extremely vital (Carpenter and Fairhurst, 2005;
Koufaris, 2002; Pine and Gilmore, 1999). Among many outcome variables that make
shoppers keep coming back, shopping hedonism has recently received most attention in
retailing literature. Many researchers found that hedonic shopping experience is
positively related to repatronage intention and store selection (e. g., Babin and Darden,
1996; Yoo, Park, and MacInnis, 1998; Sherman and Smith, 1986; Kim and Jin, 2001;
Carpenter and Fairhurst, 2005). As proved in the positive effect of in-store hoarding on
shopping hedonism, consumers tend to consider in-store hoarding as a fun activity.
Furthermore, the imagining of psychological gains from acquisition of fast fashion
products greatly increased hedonic shopping values. Apparently, today’s consumers are
easily bored with the same choice options and are not excited with easy-to-get products
or fashion that is abundant or always available. Therefore, this study suggests that
increasing the level of competition among shoppers by utilizing the two implicit time-
limited cues as well as providing unique and distinctive products on a frequent basis
would be an effective way to increase shopping hedonism, which eventually leads them
to spend more time in a store, purchase more, and visit the store more frequently.

Fifth, this finding demonstrates that the moderating role of innovativeness
cannot be neglected. The study found that fashion innovators evaluate perishability of

fashion products as more desirable and appealing than do non-innovators. This implies

85

that innovative consumers appreciate continuous innovation in fashion which motivates
them to frequently visit the stores looking for the latest products. They are willing to try a
new fashion every time new products are introduced and influence fashion opinion
leaders and fashion followers by increasing visibility of new fashion (Goldsmith and
Newell, 1997; Brannon 2005). Irnportantly to note, among other dimensions of Rogers
(1983)’s innovation including compatibility, complexity, observability, trialability and
perceived risk, relative advantage has been identified to be one of the dominant factors
affecting the adoption of innovation (Holak, 1985; Holak, Lehmann and Sultan, 1987;
Olshavsky and Spreng, 1996). Given that, retailers should be able to stimulate
psychological obsolescence by highlighting a positive aspect of perishability of fashion
as a relative advantage for innovators. With new product expectations, such relative
advantages of fashion perishability will provide them with strong motivations to try on
new trends and visit the sore more fi'equently.

On the other hand, non-innovators were more sensitive to the possibility of out
of stock or uncertainty of product availability, increasing anticipated losses when they did
not get it immediately. Accordingly, such anticipated loss was a dominant driver of in-
store hoarding for non-innovators. These findings imply that actual purchase is
influenced by different factors or circumstances depending on innovativeness. Innovators
and non-innovators are both important customers for fast fashion retailers and play
different roles in the diffusion of innovation (Brannon, 2005). Retailers should keep in
mind this difference and strategically use it to appeal to innovators and non-innovators,
respectively. In summary, for the innovative group, retailers should emphasize the
positive aspects of perishability—that is, benefits from the delivery of the latest fashion

on a frequent basis—to motivate them to hoard products and lead to actual purchasing.

86

On the other hand, for the non-innovative group, emphasis on the fact that “here today,
gone tomorrow” or “buy now, it won’t be here tomorrow” could be a significant signal
affecting their behavior. Furthermore, fast fashion retailers may influence non-innovators’
purchase behavior by making them feel confident about their buying decisions or giving a
belief that they can get the most fashionable products in these store and therefore buying
here reduces fashion risks.

Lastly, the results from the additional analysis of the effect of prior store
knowledge indicated that there was a difference between highly knowledgeable shoppers
and less knowledgeable shoppers in the perception of perishability and the resulting
hoarding behavior. In other words, shoppers with high store knowledge tend to be more
aware of a short product renewal cycle and quick movement of products. Moreover, they
tend to show stronger hoarding tendency than people with low or no store knowledge. It
appears that being aware of perishability, knowledgeable shoppers are ready to hoard
products as soon as they step into the store. Therefore, provided that perishability is a
major exogenous variable for in-store hoarding which in turn positively affects shopping
hedonism and purchase acceleration, retailers should try to increase consumers’ store
knowledge level by drawing new customers into the store or increasing their awareness
of their strategy by using promotional strategies such as media or word of mouth.

In conclusion, beyond the traditional mix of price and quality, more retailers are
moving toward unique and diverse experiential values as a way to differentiate from their
competitors (Pine and Gihnore,l999; Mathwick, Malhotra, and Rigdon, 2001). In this
regard, understanding of the antecedents and consequences of in-store hoarding behavior

is expected to provide a number of implications for consumer research and marketing.

87

LIMITATIONS OF THE STUDY

Before drawing generalizations from these results, several limitations of this
study should be taken into account.

First, this study focused on only two fast fashion retailers located in New York
City, although the data were collected from ten different stores. Although these retailers
are the leading companies implementing fast fashion strategy, the findings of this study
should be interpreted considering this limitation. In addition, the data were collected from
only one location, New York City, thus the representativeness of the sample is a major
limitation of this study.

Second, this study attempted to reduce confounding factors by limiting the
response time to 48 hours. Nevertheless, because New Yorkers are exposed to many other
fashion stores, it is likely that respondents who shopped several stores on the same day or
next day may have a less clear memory about their shopping experience about the store
where they were asked to respond to this survey.

Third, the sample size for the group comparison was not large enough for testing
the complex model and consequently some of the model fit indices were slightly below
the desired levels. Therefore, caution is required when the results are interpreted.

Fourth, some bias might have been introduced by the omission of important
variables. In-store hoarding is influenced by many other factors such as price or store
crowdedness. Therefore, additional variables could be included and tested in order to
more fully explain in-store hoarding and its outcome variables.

Fifth, the positive valuation of scarcity was not supported in this study. A

potential bias might be related to the measurement of scarcity. The perception of scarcity

88

might be confounded or conflicted with a variety of products offered by fast fashion
retailers.

Lastly, it is probable that the majority of the respondents who participated in this
survey had a more positive attitude toward the brand or a more pleasant shopping
experience than those who did not participate. Therefore, caution is required when the

results are interpreted and generalized.

FUTURE RESEARCH

In response to the limitations mentioned above, the study suggests several
directions for future research.

First, the proposed model in this study should be extended to include additional
variables. In-store hoarding is a new concept in retail and consumer literature. This study
focused on perishability and scarcity as implicit time-limited cues. As discussed
previously, in-store hoarding may be triggered by a number of reasons. Some of the
examples could be the effect of price, a variety of products, and store crowdedness. When
price is lower than consumers’ internal reference price, a diverse set of products are
offered, or the store is crowded with shoppers, hoarding is likely to be prompted due to
the increased competition among shoppers. Therefore, incorporating additional
exogenous variables of in-store hoarding would increase the exploratory power of this
construct. Thus, further investigation of in-store hoarding is warranted.

Second, in addition to the call for the extended research on the antecedents of in-

store hoarding, future research should especially devote attention to the outcomes of in-

89

store hoarding. In this study, it was limited to cover purchase acceleration and showing
hedonism, but further research should be conducted to investigate how perceived
perishability and scarcity and the resulting in-store hoarding influence store visit
frequency and repatronage intention.

In addition, this study should be replicated in diverse contexts to increase the
validity of the findings. New measurements should be retested multiple times to be
refined. Furthermore, the validity of the findings could be improved with a more
substantial number of respondents from diverse geographical locations. The replicability
of the proposed model also requires inclusion of more varied fast fashion retailers at
different price ranges and merchandising concepts.

Fourth, future research should explore possible ways to reduce response bias. To
minimize the response bias resulting from the confusion of memory, the data should be
acquired as soon as shoppers exit a store or in a shorter time than 48 hours. As mentioned,
the respondents of this study might have a more positive attitude toward the brand, which
may create response bias. Therefore, such confounding factors should be identified and
tested as moderators in future research.

Fifth, an interesting direction for firture research could be an empirical
comparison of fast fashion stores and non-fast fashion stores. It would be interesting to
investigate what the major drivers of in-store hoarding for non-fast fashion stores are and
how they are different from fast fashion retailers. This finding may provide meaningful
implications for both retailers. Another related future research area is men’s hoarding
behavior. Are they also sensitive to perishability and scarcity? Do they hoard in fast
fashion stores in the same way women do? The investigation of such questions would be

a fruitful research direction as male consumers become more conscious about fashion.

90

Lastly, the study on hoarding behavior at offline stores can be extended to online
hoarding behavior. More fashion retailers whose products were available only offline are
opening their online stores to serve their target customers more broadly. In addition to the
information regarding the number of products available, frequent introduction of high
fashion products at online stores is also expected to encourage hoarding in customers’
online shopping baskets. As found in this study, hoarding affected shopping hedonism.
The competition for online retailers is mostly price-oriented. Therefore, the application of
the model proposed in this study would provide meaningful implications in developing
successful online retailing strategy.

To conclude, this study proposes that fast fashion strategy is a new way of
building competitive advantages to respond successfully to environmental pressures such
as market dynamism in the fashion apparel industry. Continuous investigation of fast
fashion strategy by replicating and extending the proposed model guarantees promising
future research and its theoretical and strategic contributions to the fashion and relevant
retailing industry are expected to continue to grow as long as more people aspire to

frequent changes and fashion plays a vital role in our daily life.

91

APPENDICES

92

APPENDIX A

Fast Fashion Retailers’ Product Positioning

93

High Price
A

 

 

Next
Benetton
Cortefiel
Gap
Massimo Dutti
Fashion - 4 5 Fashion +
Vogele Zara Mango
F t
as H&M
Matalan
Pull and Bear
17
Low Price

Fast Fashion Retailers’ Product Positioning, adopted fiom Columbia Business School
Marketing Cases, 2003.

94

APPENDIX B.

The comparison of demographic variables between stores

95

Appendix B-l . The Result of ANOVA: The comparison of Age and Income between stores

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3%: df mean 51:11:; F Sig.
Between 15.748 1 Company A: 15.748
6‘9“!” 3.36
Age gum 4561.291 231 19.746 -798 .373
oups Company B:
Total 4577.039 232 2.83
Between
C A:
Groups 1.173 1 01112123? 1.173
Income gum" 563.327 224 1.515 .467 .495
0g” Company B:
Total 564.500 225 2.59

 

96

 

Appendix B-2. The Result of Crosstab Analysis: The comparison of education between the stores

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

$611331“ Some Bachelor Higher Total
below college degree degree
Count 20 74 36 139
Expected 6.1 19.4 74.7 38.8 139.0
Count
Company % within 6.5% 14.4% 53.2% 25.9% 100.0%
A store
location
% within 90.0% 62.5% 60.2% 56.3% 60.7%
Education
% of Total 3.9% 8.7% 32.3% 15.7% 60.7%
Count 1 12 49 28 90
Expected 3.9 12.6 48.3 25.2 90.0
Count
C % within 1.1% 13.3% 54.4% 31.1% 100.0%
ompany
B store
locatron
% within 10.0% 37.5% 39.8% 43.8% 39.3%
Education
% of Total .4% 5.2% 21.4% 12.2% 39.3%
Count 10 32 123 64 229
Expected 10.0 32.0 123.0 64.0 229.0
Count
% within 4.4% 14.0% 53.7% 27.9% 100.0%
Total store
location
% within 100.0% 100.0% 100.0% 100.0% 100.0%
Education
% of Total 4.4% 14.0% 53.7% 27.9% 100.0%
Chi-Square Result (N=229)
Value df Sig. (2-tailed)
Pearson Chi-Square 4.188 3 .242
Likelihood Ratio 4.936 3 .177
Lrnear-by-Lmear 2.657 1
Assocratron

 

 

 

 

 

 

97

 

Appendix B-3. The Result of Crosstab Analysis: The comparison of ethnicity between the stores

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Afrrcan Caucasian Asian Spanish Other Total
Amencan
Count 18 65 38 9 8 138
Exléegfi 21.4 54.7 42.8 10.7 8.3 138.0
Company % wrthrn
A store 13.0% 47.1% 27.5% 6.5% 5.8% 100.0%
location
% Within 50 0% 70 7% 52 8% 50 0% 57 1% 59 5%
Ethnicity . . . . . .
% of Total 7.8% 28.0% 16.4% 3.9% 3.4% 59.5%
Count 18 27 34 9 6 94
EXPCC‘C" 14.6 37.3 29.2 7.3 5.7 94.0
Count
Company % within
B store 19.1% 28.7% 36.2% 9.6% 6.4% 100.0%
location
% within 50 0% 29 3% 47 2% 50 0% 42 9% 40 5%
Ethnicity ' ' ' ' ° '
% of Total 7.8% 11.6% 14.7% 3.9% 2.6% 40.5%
Count 36 92 72 18 14 232
Blamed 36.0 92.0 72.0 18.0 14.0 232.0
ount
% within
Total store 15.5% 39.7% 31 .0% 7.8% 6.0% 100.0%
location
0 . .
€333; 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%
% of Total 15.5% 39.7% 31.0% 7.8% 6.0% 100.0%
Chi-Square Result (N=232)
Value df Sig. (2-tailed)
Pearson Chi-Square 8.152 4 .086
Likelihood Ratio 8.289 4 .082
Linear-by-Linear
Association '558 1 '455

 

 

 

 

 

 

98

APPENDIX C.

The Results of Path Analysis

99

Results of Path Analysis

 

Standardized Standardized

 

 

Parameters , T-value P-value
Estrmates Error
Gain 6 Perishability 0.29 0.133 3.922 0.000
Gain 6 Scarcity -0.03 0.082 -0.405 0.686
Loss (- Perishability 0.35 0.107 5.190 0.000
Loss 6 Scarcity 0.32 0.065 5.030 0.000
Structure Loss 6 Gain 0.35 0.058 5.290 0.000
Model Hoard 6 Gain 0.27 0.060 3.753 0.000
Hoard 6 Loss 0.50 0.076 6.078 0.000
Hedonism 6 Gain 0.43 0.066 5.714 0.000
Hedonism 6 Hoard 0.32 0.080 4.190 0.000
Purchase (- Hedonism 0.22 0.121 2.643 0.008
Purchase (- Hoard 0.42 0.138 4.517 0.000
Measurement v4 (- Scarcity 0.77 0.124 8.913 0.000
Model V5 (- Scarcity 0.70 0.109 9.511 0.000
V6 6 Scarcity 0.74 0.114 10.373 0.000
V7 6 Scarcity 0.70 0.065 14.177 0.000
V8 6 Scarcity 0.74 - -
V10 6 Perishability 0.70 - - 0.000
V11 6 Perishability 0.86 0.126 10.785 0.000
V12 6 Perishability 0.64 0.114 9.333 0.000
V13 6 Perishability 0.76 0.085 13.304 0.000
v14 (— Perishability 0.81 0.114 10.205 0.000
V15 6 Perishability 0.66 0.107 9.645 0.000
V19 6 Gain 0.88 0.070 15.312 0.000
V20 6 Gain 0.78 0.059 13.369 0.000
V21 6 Gain 0.84 - -
V22 6 Loss 0.74 - -
V23 6 Loss 0.89 0.090 13.599 0.000
v24 (- Loss 0.77 0.070 15.981 0.000
V25 6 Loss 0.90 0.094 13.828 0.000
V26 6 Loss 0.84 0.100 12.852 0.000
V28 6 Hoarding 0.70 - -
V29 6 Hoarding 0.70 0.097 9.526 0.000
V30 6 Hoarding 0.70 0.108 9.426 0.000
V31 6 Hoarding 0.67 0.118 9.168 0.000
V32 6 Hoarding 0.77 0.112 10.361 0.000
V33 6 Hoarding 0.79 0.119 10.674 0.000

 

100

 

Standardized Standardized

 

 

 

Parameters , T-value P-value
Estrmates Error
V34 6 Hedonism 0-31 - -
V35 6 Hedonism 0.73 0.076 11.683 0.000
V36 (- Hedonism 0.77 0.084 12.499 0.000
v43 6 Hedonism 0.74 0.100 9.740 0.000
V44 6 Hedonism 0.82 0.076 13.566 0.000
v45 6 Purchase 0.79 0.122 8.255 0.000
V46 6 Purchase 0.66 0.109 6.723 0.000
V47 6 Purchase 0-35 - -
Covariances Perishability H 0.121 0.057 1.675 0.094
Scarcrty
a1 1.541 0.205 7.533 0.000
Disturbance 0.733 0.121 6.077 0.000
Variances a3 0.646 0.1 19 5.412 0.000
a4 0.732 0.111 6.578 0.000
a5 1.801 0.327 5.509 0.000

 

XZ= 879.136, df=47l, p=0.000, f/df=l.86
CFI=.92, NNFI=.91, IFI=.92, RMSEA=.O6

 

101

APPENDIX D.

Summary of Hypotheses Tests

102

Summary of Hypotheses Tests

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

H Hypotheses Results
H1 The greater the level of penshabrlrty, the greater the level of Supported
antrcrpated garns of buyrng.
H2 The greater the level of perrshabrlrty, the greater the level of Supported
antrcrpated losses of not buyrng.
H3 The greater the level of scarcity, the greater the level of anticipated Not
gains of buying. supported
H4 The greater the level of scarcrty, the greater the level of antrcrpated Supported
losses of not buyrng.
The effect of perishability on anticipated losses of not buying will
H5 be stronger than on anticipated gains of buying. Supported
The effect of scarcity on anticipated losses of not buying will be Not
H6 . . . .
stronger than on antrcrpated garns of buyrng. Supported
H7 The greater the level of antrcrpated garns of buymg, the greater the Supported
level of 1n-store hoardrng.
The greater the level of anticipated losses of not buying, the greater
H8 the level of in-store hoarding. Supported
The effect of anticipated losses of not buying on in-store hoarding
H9 will be stronger than that of anticipated gains of buying. Supported
H10 The greater the level of 1n-store hoardrng, the greater the level of Supported
shopp1ng hedomsm.
H11 The greater the level of shopp1ng hedomsm, the greater the level of Supported
purchase acceleratron.
H12 The greater the level of 1n-store hoardrng, the greater the level of Supported
purchase acceleratron.
H13 The effect of penshabrlrty on antrcrpated garns of buyrng wrll be Supported
stronger for 1nnovators than non-1nnovators.
The effect of scarcity on anticipated gains of buying will be Not
H14 . .
stronger for 1nnovators than non-1nnovators. supported
The effect of perishability on anticipated losses of not buying will Not
H15 . .
be stronger for 1nnovators than non-1nnovators. supported
The effect of scarcity on anticipated losses of not buying will be Not
H16 . .
stronger for 1nnovators than non-1nnovators. supported
H17 The effect of antrcrpated garns of buyrng on 1n-store hoardrng wrll Supported
be stronger for 1nnovators than non-1nnovators.
The effect of anticipated losses of not buying on in-store hoarding Not
H18 . . .
wrll be stronger for 1nnovators than non-1nnovators. supported

 

103

 

APPENDIX E.

The Effect of Prior Store Knowledge

104

Appendix E-l. The Effect of Prior Store Knowledge

 

     
   
 
 
 

       
 

Anti ' ed .44*** .

Gaicrlgabf Sh°PP.“‘8

Bun‘l * Hedonism
.3 5 * * *

 

Perishable

  

 

Store

 

 

 

Knowledge

 

 
 

 

 

 

Anticipated
Losses of
Not Buying

ln-Store
Hoarding

     
   
   

1%: 1051.432, df= 568, p= .000, war: 1.351
IFI= .91, NNFI=.90, c1=1=.91, RMSEA=.O6O

  

 

Appendix E-2. Path Analysis for The Effect of Prior Store Knowledge

 

Standardized Standardized

 

Parameters , T-value P-value
Estimates Error
Perishability (- 0.374 0.059 4.805 0.000
Knowledge
Scarcity (- Knowledge 0.110 0.089 1.488 0.137
Gain (- Perishability 0.284 0.132 3.902 0.000
Gain (- Scarcity -0.029 0.081 -0.404 0.686
Loss (- Perishability 0.350 0.106 5.227 0.000
Loss (- Scarci 0.326 0.065 5.043 0.000
3mm” Loss (— Gain ty 0.350 0.058 5.300 0.000
Model Hoard (— Gain 0.269 0.058 3.836 0.000
Hoard (- Loss 0.429 0.074 5.548 0.000
Hoard (- Knowledge 0.272 0.075 4.113 0.000
Hedonism (- Gain 0.442 0.066 5.846 0.000
Hedonism (- Hoard 0.303 0.079 4.020 0.000
Purchase (- Hedonism 0.235 0.120 2.824 0.005
Purchase (- Hoard 0.393 0.135 4.358 0.000

 

12: 1051.432, df= 568, p= .000, gm: 1.851
IFI= .91, NNFI=.90, CFI=.91, RMSEA=.O6O

 

105

 

APPENDIX F.

Survey Consent Form

106

Consent Form

Shopping Experience in a Fashion Apparel Store

This is a survey to understand how consumers shop in a fashion apparel store. This
research is funded by College of Communication Arts and Sciences at Michigan State
University. Your responses will help us extend the body of knowledge on consumer
behavior.

There are no right or wrong answers. We are interested in your shopping experience in
fashion stores. Your participation is completely voluntary. Members of the research team
will have access to the answers you provide but you personally will not be identified in
any published report. Your recorded responses will be stored in a locked cabinet, and
coded data will be stored in password protected computers in a locked room. After 10
years, the electronic data and the completed questionnaires will be destroyed.

You may choose not to participate in the study, you may decline to answer specific

questions, and you may discontinue participation at any time. All responses will remain
confidential.

Once you complete the questionnaire, you will have a chance to win a store gift card or
we will (that is, you don’t need to pay anything) donate $1 in your name to the National
Disaster Relief Fund of American Red Cross. If you choose the gift card option, you will
then be entered into a drawing for several sizable gift cards for the store you visited (i.e.,
five $20 gift cards, two $50 gifi cards, and one $100 gifi card). However, you must
respond to the survey within 48 hours to be eligible. You will be asked to provide your
email address at the end of the survey. Your e-mail address will be used only to select the
prizewinners or donators and will not be distributed to anyone. The winners will be
contacted by e-mail for mailing address information during June 2006. The names of the
winners will also be posted on the Website for this survey.

If you have any questions about this study, please contact the principal investigator, Dr.

Brenda Stemquist, Professor, 369 Comm Arts Building, Michigan State University, East
Lansing, MI 48824; by phone (517) 355-0256; or by email stemgui@msu.edu. You may
also contact the secondary investigator, Sang-Bun Byun, Ph.D. candidate, at 362 Comm
Arts Building, Michigan State University, East Lansing, MI 48824; by phone (517) 862-

0058; or by email bmsanl @msu.edu.

If you have questions or concerns about your rights as a research participant, please feel
free to contact Peter Vasilenko, Ph.D., Director of the Human Subject Protection
Programs at Michigan State University: (517) 355-2180, email: irb@msu.edu, or regular
mail: 202 Olds Hall, East Lansing, MI 48824.

Thank you very much for your time and consideration. If you agree to participate, please
go to the next page.

107

APPENDIX G.

Survey Instrument

108

Part One
_

1. In which store did you receive this questionnaire?
1) H&M 2) Zara 3) Other

 

2. Have you shopped in this store before (including different locations)?
1) Yes 2) No, this is my first visit to this store.

The following questions refer to your shopping experience in the store chosen in the
guestion 1. For each of the following items, please indicate your level of agreement.

On this shopping trip, I found that... 6% those m Nata W was $3:
3. My favorite items were often one of
the last items left on the rack. 1 2 3 4 5 6 7
4. The products that l was 1nterested m 1 2 3 4 5 6 7
were almost out of stock.
5. There were only limited number of
products per size, style, and color. 1 2 3 4 5 6 7
6. Products of 1nterest were often scarce 1 2 3 4 5 6 7
1n my Size.
7. My favorite styles in this store were 1 2 3 4 5 6 7
mostly ava1lable 1n my s1ze.
8. I could mostly get my first preference 1 2 3 4 5 6 7

in my size.

For each of the following items, please indicate your level of agreement.

Based on my knowledge or sunny on Same 9mm am
experience... Dimes 0"“ W ’°" Mae ‘9‘”
9. This store constantly delivers updated 2 3 4 5 6 7
fashion items throughout the season.
10. New styles are introduced on a 1 2 3 4 5 6 7
frequent bas1s.
11. This storerapldly turns over their 1 2 3 4 5 6 7
merchandise.
12. Products 1n thlS store do not stay on 1 2 3 4 5 6 7
the rack long.
13. This store mtroduces new fashion 1 2 3 4 5 6 7
styles qulckly.
14. Products in this store are fresh in
terms of fashion trend. 1 2 3 4 5 6 7
15. Products in this store are moving fast. 1 2 3 4 5 6 7
16. I can mostly find the same
merchandise that I saw on my 1 2 3 4 5 6 7

previous store visit.

109

Please circle the number, which best describes your reaction to each statement.

When I found a product of interest in
this store, I thought that...

17.

18.

19.

20.

21.

If I do not buy it right now, I would
regret it later.

I was afraid that this item would be
out-of-stock in my next visit.

I thought that it would be a loss if I do
not buy it today.

I was concerned that this item might
not be available if I came back later.

If 1 do not get it immediately, I would
lose an opportunity to purchase it
because it will be gone tomorrow.

When I found a product of interest in
this store, I thought that acquiring or
wearing this product would make me...

22.
23.

24.

25.
26.

Look unique.

Look fashionable.
Enhance my self-image.

Feel good about myself.
Feel special.

When I found a product of interest in
this store,

27.

28.

29.

30.

31.

32.

33.

I spontaneously grabbed the product
of interest.

I had the urge to grab the product
immediately.

I was carrying around products while
shopping.

I snapped things up while shopping in
this store.

Once I picked up a product, I did not
want to put it down although I was
not sure if I would buy it or not.

I hurried to grab the products of
interest and kept them to myself.

On this trip, I found a number of
things I wanted to grab immediately
even though they were not on my
shopping list.

110

Mae Grumman
2 3
2 3
2 3
2 3
2 3
0.1111” an“!
2 3
2 3
2 3
2 3
2 3
Us H Slim
2 3
2 3
2 3
2 3
2 3
2 3
2 3

Part Two
—
Please circle the number, which best describes your reaction to each statement.

On this shopping trip, It; Dime m Natal 39;“ Ages

34. Shopping at this store was truly a joy. 1 2 3 4 5 6

35. I continued to shop, not because 1 had
to, but because I wanted to.

36. Shopping at this store truly felt like an
escape.

37. Compared to other things 1 could have
done, the time spent shopping at this 1 2 3 4 5 6 7
store was truly enjoyable.

38. I enjoyed being immersed in exciting
new products.

39. I enjoyed shopping at this store for its
own sake, not just for the items I may 1 2 3 4 5 6 7
have purchased.

40. I enjoyed a time because I was able to
act on the “spur-of-the-moment.”

41. During a shopping trip I feel the 1 2 3
excitement of hunt.

42. While shopping at this store, I was
able to forget my problems.

43. While shopping at this store, I felt a
sense of adventure.

44. Shopping at this store was a very nice
time out.

Please circle the number, which best describes your reaction to each statement.

For the products of interest I found in this may . m an“ auuy
. usages Nana Pose
store, 063088 New roan Aqua

45. I purchased an item that I had not

planned to purchase on this trip. 1 2 3 4 5 6 7
46. I purchased more products than I
would do normally on this shopping 1 2 3 4 5 6 7
strip.
47. I made a purchase decision
immediately rather than postponing 1 2 3 4 5 6 7

until next visit to this store.

111

Betore today’s Visrt to this store... Dimes ”59039 an... "a“ ,9... M89 Peas
48. I knew pretty much about this store. 1 2 3 4 5 6 7
49. [did not feel very knowledgeable about 1 2 3 4 5 6 7
this store.
50. Among my circle of friends, I was one of
the “experts” about this store. 1 2 3 4 5 6 7
51. Compared to. most other people, I knew 1 2 3 4 5 6 7
less about this store.
52. When it comes to this store, I really did 1 2 3 4 5 6 7

not know a lot.

Part Three

—
Considering your general buying behavior, please respond to the following statements by
circling the number that best represents your degree of agreement or disagreement. Your

responses should reflect your feelings toward apparel shopping.

Ithink... .523, cam 933.? um Sm“ Aces 233’
53. In general, I am among the last in
my circle of friends to buy a new 1 2 3 4 5 6 7
fashion item when it appears.
54. If I heard that a new fashion style
was available in the store, I would 1 2 3 4 5 6 7
be interested enough to buy it.

55. Compared to my friends, I own few
new fashion items.

56. I would consider buying a new
fashion item, even if I have not 1 2 3 4 5 6 7
heard of it yet.

57. In general, I am the last in my circle
of friends to know the latest fashion 1 2 3 4 5 6 7
trends.

58. I know more about new fashion than
other people do.

112

Part Four
_

Finally, please fill in the blank or check the appropriate response for each question.

 

Do you currently live in New York? ( ) Yes ( ) No
If your answer is no, where do you live?
City: State: Country:
Are you
( ) 1. African American ( ) 2. Caucasian
( ) 3. Asian ( )4. Pacific Islander
( ) 5. Native American or Alaskan native ( ) 6. Spanish, Hispanic or Latino origin
Other:
What is your age group?
under 20: years old
. 20—24
. 25-29
. 30-34
. 35-39
. 40 or over: years old
Your degree currently pursued or your highest level of education if you are not currently in
school:
( ) 1. Less than high school ( )2. High school or equivalent
( ) 3. Some college ( ) 4. Bachelor’s degree
( ) 5. Master’s degree ( )6. Doctoral degree

What is your total annual income?
) a. Under $20,000

) b. $20,000 to $34,999

) c. $35,000 to $49,999

) d. $50,000 to $64,999

) e. $65,000 to $79,999

) f. $80,000 to $99,999

( ) g. $100,000 more

 

 

O‘Ui-thr—

AA _. AAA

Now that the survey is completed, please select which option you prefer.
( ) l. Gifi card option: You will be entered into a drawing. Please provide your email address.

E-mail address: @

( ) 2. Donation option: I will donate $1 in your name to the National Disaster Relief fund of
American Red Cross. Please provide your name and your email address.
Name (optional):

E-mail address: @
Your name and your E-mail address will not be distributed to anyone. The winners and

donators will be contacted by e-mail during June 2006. Based on your agreement, the
names will be posted on the Website at www.msu.edu/~byunsa_1fi.

 

Thank you for taking time to respond to this questionnaire.

113

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