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AN EXPLORATORY STUDY OF
THE EFFECT OF SHOPPING CONGRUENCE ON PERCEPTIONS,
ATTITUDES, AND PURCHASE INTENTIONS
IN ONLINE AND OFFLINE STORES

presented by
Eun-Jung Choi ;

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AN EXPLORATORY STUDY OF
THE EFFECT OF SHOPPING CONGRUENCE ON PERCEPTIONS,
ATTITUDES, AND PURCHASE INTENTIONS
IN ONLINE AND OFFLINE STORES

By

Eun-Jung Choi

A DISSERTATION
Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of
DOCTOR OF PHILOSOPHY
Retailing

2008

ABSTRACT

An Exploratory Study of the Effect of Shopping Congruence on
Perceptions, Attitudes, and Purchase Intentions in Online and Offline Stores

By

Eun-Jung Choi

Using the Cognitive Continuum Theory (CCT) as a theoretical framework, this
study initially proposes a new theory, “shopping congruence,” which explains how
people react to shopping environments, based on their shopping tasks. The research
empirically tested the impact of shopping congruence on perceptions, attitudes and
purchase intentions in an actual offline store situation in Study 1. Due to the increasing
importance of multi-channel retail strategies, Study 2 tested the robustness of the impact
of shopping congruence in an actual online store situation. This study found that a
shopping congruence incorporates both cognitive shopping congruence and intuitive
shopping congruence. The results of Study I assure the positive impact of shopping
congruence on both cognitive and intuitive perceptions, on cognitive attitudes, and on
purchase intentions toward stores, though not on affective attitudes toward stores. The
results of Study 2 found that the impact of shopping congruence on perceptions and
purchase intentions is robust across multi-channels (offline and online stores), whereas
the impact of shopping congruence on analytic and affective attitudes toward stores is not
robust across multi-channels. Considering these results, multi-channel retail strategies
and strategic plans for store environments should differentiate between offline stores and
online stores. Managerial implications for multi-channel retailers and further research for

shopping congruence are discussed.

Copyright by
EUN-JUNG CHOI
2008

DEDICATION

I dedicate this dissertation to my family, especially,
to my mother, Chulja Lee who made all of this possible for her endless
encouragement and support
to my husband, Eun-Suk Chun for his patience and warm advice
to my farther in law, Jungho Chun, and my mother in law, Soonja Cho for instilling
their importance of hard work and high motivation

to my lovely son, Young who might be proud of his mom’s this big achievement.

iv

ACKNOWLEDGEMENTS

I would like to express my heart-felt gratitude to my advisor, Dr. Charles Steinfield. I
have been amazingly fortunate to have him. He has guided me go through this long
journey to obtain my doctoral degree. His deep understanding and flexibility helped me

overcome crisis situations and finish this dissertation.

I am also grateful to my committee, Dr. Roger Calantone, Dr. Hairong Li, and Dr. Brenda

Sternquist for their insightful comments and theoretical and practical advices.

TABLE OF CONTENTS

LIST OF TABLES .............................................................

LIST OF FIGURES ...........................................................

CHAPTER 1

INTRODUCTION .............................................................
Research Problem .............................................................
Theoretical Background ....................................................
Purpose of the Research .....................................................
Significance of the Research ...............................................

CHAPTER 2

LITERATURE REVIEW ....................................................
Cognitive Continuum Theory (CCT) .....................................
Depth properties on dynamic task systems in CCT .................
Surface properties on dynamic task systems in CCT ...............
Intuition and analysis in CCT .........................................
Advantage of CCT ......................................................
Shopping Environment Literature .........................................
The Impact of shopping environment on decision-making ........

The Impact of Affect on Decision-Making ...............................
Affect literature ..........................................................

CHAPTER 3

CONCEPTUAL FRAMEWORK ...........................................
Study 1. Exploring Shopping Congruence ...............................

The impact of shopping congruence on perceptions toward stores

The impact of shopping congruence on attitudes toward stores

The impact of shopping congruence on purchase intentions
Study 2. Exploring Shopping Congruence across Multi-channels

CHAPTER 4

RESEARCH METHODOLOGY ...........................................
Research Design .............................................................
Manipulation of retail store ............................................
Manipulation check .....................................................
Procedure for the experiment ..........................................

Page
viii

iv

O‘MNt-‘t-t

10
12
13
15
16
19
20
2O
23

26
29
29
32
34
35

Independent Variables and Dependent Variables ......................... 47

Independent variables .................................................... 47
Dependent variables ...................................................... 48
Control Variables .................................................................................. 49
CHAPTER 5
RESULTS ................................................................................................ 53
Results of Study 1 ............................................................. 54
Results of Study 2 ............................................................. 61
Results of Regression Analysis .............................................. 67
CHAPTER 6
DISCUSSIONS & CONCLUSIONS ......................................... 72
Discussions and Conclusions ................................................ 72
Directions for Future Study ................................................... 79
Contributions and Implications .............................................. 81
APPENDICES
APPENDIX 1. Flow of Online Survey Process ............................. 86
APPENDIX 2. Scenario for Shopping Tasks ................................ 88
APPENDIX 3. Measurement of Survey ......................................... 89
APPENDIX 4. Control Variables ............................................. 9O
BIBLIOGRAPHY ................................................................................... 91

vii

LIST OF TABLES

Page
Table 1. Task Characteristics .................................................... 12
Table 2. Properties of Intuition and Analysis ................................. 15
Table 3. Results of Manipulation Check for Offline Store .................. 44
Table 4. Results of Manipulation Check for Online Store .................. 44
Table 5. Subject Demographic Features ....................................... 53
Table 6. Results of ANOVA Test for H2 ...................................... 59
Table 7. Results of ANOVA Test for H4 ...................................... 60
Table 8. Results of ANOVA Test for H6 ...................................... 60
Table 9. Results of ANOVA Test for H8 ...................................... 65
Table 10. Results of ANOVA Test for H10 ................................... 66
Table 11. Results of ANOVA Test for H12 .................................... 66
Table 12. Result of the Regression for Study 1 .............................. 70
Table 13. Result of Regression for Study 2 ................................... 71

viii

LIST OF FIGURES

Page
Figure 1. Conceptual Model of Study ............................................ 27
Figure 2. MCM Island Store ..................................................... 41
Figure 3. MCM Boutique Store .................................................. 41
Figure 4. SDD Mall ................................................................ 42
Figure 5. mcmworldwide.com ................................................... 42
Figure 6. Email to Ask for Online Survey ..................................... 46

* The Images of Figure 2 to Figure 6 in this dissertation are presented in color.

ix

CHAPTER I. INTRODUCTION
Research Problem

For the past two decades, the shopping environment literature has been the focus of
extensive research. Shoppers perceive shopping environments as providing meaningful
cues for store evaluation, which provide reliable information about product quality, value,
and the overall shopping environment (Bitner, 1992). For example, store layout is a crucial
factor in creating store image, which positively influences merchandise value and loyalty
intentions (Baker, Parasuraman, Grewal, and Voss, 2002). Due to the strong impact of
shopping environments, 3 retail store’s internal and external environments directly
influence store performance, such as store sales and market share (Kumar and Karande,
2000).

Scholars and practitioners try diligently to understand the processes behind a
consumer’s decision-making process for shopping. They work to support their actions
through a better understanding of consumer choice and to allocate marketing resources
wisely. Better business performance can be achieved through a better understanding of
consumer shopping behaviors. However, even though prior literature has reported on how
consumers use multi-channels differently, and how they perceive different shopping
channel environments, few studies have looked at how consumers experience their
shopping environments across multi-retail channels when making their purchase decisions,
and what drives those shopping decisions (e.g., Mathwick, Malhotra, and Rigdon, 2002).

Therefore, when considering dynamic consumer shopping choices in multi-channel
shopping situations, it is crucial to examine how shopping decisions are made through a

multi-channel shopping environment and to identify the factors that effect shopping

behaviors across multi-channels.

Theoretical Background

In the last decades, a pivotal study on consumer shopping decision-making has
focused on cognitive processes and decision-making, by intertwining them with
motivational forces. Research about the interplay between motivation and cognition has
provided us with an understanding of consumer behavior goals, decision-making processes
and judgments (J char, Maheswaran, and Peracchio, 2006). This study will be based on the
Cognitive Continuum Theory (CCT) (Hammond, 1988), for exploring the Shopper’s
decision-making process, because CCT successfully explains the mechanism behind the
interplay between motivation and cognition in consumer-shopping decision-making.

CCT is an adaptive theory of human judgments made by the dynamic interaction
between task characteristics and environmental characteristics. According to the theory,
people perform a task based on that task’s characteristics, and dynamically change their
cognitive decision-making according to task environments. Various cognition modes are
distributed along a continuum, with intuition at one pole and analysis at the other pole. The
two distinctive types of cognition cannot be separated by a sharp boundary. Some
cognitive modes may include a larger number of intuitive elements than analytic elements,
so they may be located closer to the intuitive pole of the cognitive continuum. On the other
hand, one might engage less with ideas that induce intuitive cognition, such as design and
color. As a result, that decision-making would lie closer to the analytical anchor of the
cognitive continuum because people tend to make a connection between task

characteristics and cognitive activities.

A salient advantage of CCT is that it allows us to predict people’s cognitive
activities by looking at the task demands. Due to the task and cognitive continuum, a
person’s cognitive activities can be predicted. Depending on the task demands, the
appropriateness and effectiveness of analysis or intuition are considered. People change
their decision-making strategies less often if they are making good decisions than if they
are making bad decisions. By matching this cognitive mode with the task characteristics a
cognitive continuum is established (Hammond, 2000). Therefore, from a practical
perspective, we can forecast consumer-shopping behaviors based on CCT because CCT
provides an understanding of the pattem-seeking behaviors found between shopping tasks
and shopping environments. In addition, CCT has the potential to provide us with
directions regarding information display, information design, and stimuli of multi-cues for
decision-making (Dunwoody et a1., 2000). Thus, considering today’s multi-channel retail
situations, CCT is a useful theory for explaining and predicting consumers’ shopping
choices in a multi-channel shopping context.

It should be noted, however, that CCT seems to have a serious omission in that the
theory only focuses on cognition. Earlier literature on decision-making documented the
impact of affect on decision-making as it influences consumer decisions and
decision-making processes (e. g., Darden and Babin, 1994). Affect is integrated with
cognition in a heuristic way, so future studies need to investigate the integration between
cognition and affect in decision-making (Zimiles, 1981). When considering shopping
behavior, people might react both cognitively and affectively when they interact with a
shopping environment in their decision-making process. When adapting CCT into the

shopping context, the following research questions emerge from the literature:

0 Does a congruence exist between shopping tasks and shopping environments in an
offline store?

. If the congruence between shopping tasks and shopping environments exists in an
offline store, will the congruence influence people’s decision-making in shopping? In
other words, will various people perceive a shopping environment differently? Will
people have different attitudes toward a store? Will people purchase more when
experiencing congruence in the store?

. How do people react cognitively and affectively to a shopping environment?

These days, with the emergence and growmg popularity of e-commerce, there are a
number of conventional retailers who have worked to integrate e-commerce into their
channel mix. Multi-channel retailing, including e-commerce, has created a “click and
mortar” synergy, which combines physical and virtual competitive advantages with the
avoidance of channel conflicts (Steinfield, Adelaar, and Lai, 2002). Due to the emergence
of multi-channel retailers, consumer-shopping patterns have evolved that take advantage
of the benefits found in multi-channel shopping situations. The population of
multi-channel shoppers is also growing (Choi, 2006).

Multi-channel shoppers combine diverse channels, approach multi-channels, and
search information across multi-channels in various ways: for example, they may search
online but make their purchase in an offline store, based on their online-information search
(Wallace, Giese, and Johnson, 2004). Due to the rapid emergence of multi-channel
retailing, including online shopping, there are various consumer behavioral studies that

compare online shopping and conventional shopping: for instance, searching behaviors

across physical and virtual channels have made people more price-sensitive, so that the
price ranges offered by the two different channels have narrowed (e.g., Bryjolfsson and
Smith, 2000; Degeratu, Rangaswarny and Wu, 2000); and people have found different
levels of satisfaction from dynamic retail experiences and perceptions in multi-channel
shopping situations (e. g., Mathwick et al., 2002; Montoya-Weiss and Voss, 2003).
However, most of these research issues have been investigated only in an online

shopping situation, so we are not sure how different an online channel and an offline
channel are in a shopping decision-making context. The following research questions
emerge:

. Does the congruence between shopping tasks and shopping environments exist in

an online store?

. Are there differences in the congruence between shopping tasks and shopping

environments when comparing online and offline stores?

0 Are cognitive and affective reactions to shopping environments different?

In summary, CCT was chosen as the foundation for this study. While employing
CCT as a theoretical framework, this study explores the existence of shopping congruence
between shopping tasks and shopping environments across multi-channels. It also explores
how shopping congruence influences perceptions, attitudes towards stores, and purchase

intentions while experiencing online and offline stores.

Purpose of the Research

With CCT’s theoretical framework as a basis, this study’s primary purpose lies in

exploring shopping congruence, which deepens the understanding of a Shopper’s
decision-making and its managerial implications. Thus, this study’s purposes are as
follows:
(1) To explore whether congruence between shopping tasks and store environments
affect perceptions toward stores;
(2) To explore whether congruence between shopping tasks and store environments
affect attitudes toward stores;
(3) To investigate whether congruence between shopping tasks and store environments
increases purchase intentions;
(4) To empirically test whether shopping congruence is robust across multi-channels;
(5) To explore any differences in the impact of shopping congruence across
multi-channels;
(6) To explore how affect influence on shopping decision-making process; and
(7) To provide meaningful managerial implications for multi-retail channel strategy to

marketers who operate multi-retail channels.

Significance of the Research

(1) This study explores shopping congruence by empirically testing the shopping
congruence between tasks and store environments and its impact on purchases.

(2) By comparing offline and online stores, we can understand how different multi-retail

channels are from a shopping decision-making perspective.

(3) Based on an understanding of consumers’ shopping choices by their shopping tasks,
this study provides managerial implications such as retail multi-channel strategies and
strategic plans for store environments.

(4) Empirical tests of CCT have rarely been conducted in a consumer shopping choice
context. CCT is an adaptive theory, derived from both theoretical and empirical
considerations on human judgments (Doherty and Kurz, 1996). As Hammond, Hamrn,
Grassia, and Person (1987) mentioned, it is important to empirically test CCT in a
specific context. Therefore, this study is meaningful and brings insight about the
interaction between shopping tasks and shopping environments, by empirically testing
CCT in the retail context.

(5) By testing shopping congruence, which incorporates cognition and affect toward the
shopping environment, this study provides understanding how shoppers analytically

and intuitively react to shopping environments by shopping tasks.

The dissertation is organized as follows: Chapter 2 provides a comprehensive
review of Cogiitive Continumn Theory, shopping environment literature, and the impact
of affect on decision-making. In Chapter 3, I address a conceptual framework consisting of
study 1, which is set to explore the shopping congruence, and study 2, which is designed to
explore the shopping conguence across multi-channels. Research desigr and data
collection methods are provided in Chapter 4, and Chapter 5 contains findings from this
study. Finally, in chapter 6, a discussion, along with conclusions and managerial

implications of the study are addressed, as well as directions for future study.

CHAPTER II. LITERATURE REVIEW
Cognitive Continuum Theory (CCT)

Cogiitive Continuum Theory (CCT) is an adapted theory that explains how people
make decisions in complex and dynamic information environments by analyzing a
person’s cogritive activity and its shift, produced by task circumstances (Hammond, 1988,
1990, 1996). CCT view is that this dynamic relationship, between tasks and the depth- and
surface- properties of obtained information, influences a person’s ability to utilize that
information in order to achieve his/her particular goals (Hammond, 1988). Consequently,
the relationship between tasks and the properties of both intuitive cognition and analytic
cogiition form a cognitive continuum.

CCT provides a theoretical understanding of people’s cogiition when they perform
and make decisions based on complicated information in dynamic tasks. CCT suggests that
people operate under various task conditions and perform in order to complete a given task,
depending on the structure and function of that task. Thus, tasks are important because
people think and act according to the given tasks. Tasks evoke various cogritive activities
and influence a person’s ability to utilize information, in order to achieve his/her particular
goals. Task characteristics are features brought on by task conditions (e. g., number of cues,
measurement of cues, and decomposition of task). Task conditions influence whether a
task is intuitive or analytical. There are two types of task characteristics: those that induce
intuition and those that induce analysis. Tasks can be ordered on a continuum by their
capacity to induce intuitive cogrition, quasi-rationality, and analytic co grition (Hammond,
1988)

CCT suggests that people’s cogrition does not anchor on a certain mental function,

but also moves between intuition and analysis, and there is an all-or-none shift between
them over time. The behavioral consequences of these changes can be foreseen by given
tasks, and thus anticipated, because of the gadual movement between intuition and
analysis, and by the all-or-none shift between them (Hammond, 1988).

There are four premises in CCT (Hammond, 2000):

(1) Various modes of cogrition can be placed on a continuum anchored at two poles.
One is intuitive cogiition and the other is analytical cogrition;

(2) There are three cogritive forms (activities): analysis, intuition, and
quasi-rationality. These lie on a continuum, with quasi-rationality falling between
intuition and analysis. Quasi-rationality is a kind of bounded rationality on
decision-making. It relates to an intermediate level of a given task characteristic,
between an intuition level and an analysis level (Doherty and Kurz, 1996). For
example, assume one lost a purchased item when visiting a very crowded shopping
mall. After arriving home, one would go back to the shopping mall in order to find
the item, even late at night. On the other hand, if one lost the same item, but it had
been obtained by using a free coupon, one’s reaction might vary. Most normal
people are less likely to go back to the shopping mall to find the item they received
for free. This reaction might be irrational, because the value of the item is obviously
equal in either case, but the perceived value of the item is different. As in the
example, human cogrition often settles in the middle of the continuum, in the mode
of quasi-rationality, called common sense. This brings on inconsistency, error, and
so on. However, it emerges as a valuable form of cognition as it tries to avoid the

irrationality of intuition, as well as the fragility of analysis (Harmnond, 1987,

1988)

(3) Cognition is dynamic over time. Cognitive activities move back and forth along the
continuum between one pole and the other pole. In doing so, intuitive and analytical
components, relative to quasi-rationality, will be changed by dynamic cognition.
CCT views cognition as having constancy, with a stable environment as bringing
the best response to tasks. Under destabilized conditions, inconsistent cogrition is
changed on the continuum. In other words, cognition can be changed by
inconsistency in the relative intuitive and analytical components of an environment,
so the cognition that is anchored at a certain pole can move to the other pole on the
continuum.

(4) There can be an order of tasks on the continuum, depending on whether they induce
intuition, quasi-rationality, or analysis. CCT believes that inducing one of the three

modes of cognition is within an individual’s capability.

CCT views decision-making as a dynamic task system, which consists of two
properties: depth properties and surface properties. The surface properties are divided into
intuitive surface properties and analytic surface properties. The following is a detailed

review about depth and surface properties in CCT.

Denth properties of dmamic task systems in CCT

Depth properties refer to the nature of shopping tasks, so they include structures
and functions of the tasks. Hammond, Hamm, Grassia, and Person (1987, p. 756) defined

the depth property of a dynamic task system as “the covert relationships among the

10

variables within the task,” that is, the properties of a task. Suppose, for example, that one
has a task of shopping for a gift for a friend’s birthday, and will attend the birthday party
tonight. He/she decides to purchase a cellular phone that provides high quality at the
cheapest price. In this case, depth properties are a gift shopping task looking for a cellular
phone having a high quality at a cheap price.

Hence, the depth properties can be replaced with other properties of task. In the
retail context, the depth properties of a task system are very important because they
influence store/brand choice and purchase. As depth properties are linked with business
performance, many studies have examined the depth properties of shopping (e. g., Roy,
1993; Suri, Long, and Monroe, 2003). The depth properties of a task system also influence
a consumer's assessment of immediately-displayed information, so they influence the
technical types of decision-making, such as an heuristic technique and an attribute-based
technique, as well as the organizing principles of given information that lead to the
decision processes. In CCT, task characteristics are significant because people tend to
focus on cognitive processing in order to achieve their tasks, whether they are influenced
by intuition or analysis, while ignoring the problems of describing and differentiating
among judgnent tasks.

Table 1 below presents task characteristics that induce either intuition or analysis,
which are summarized by Doherty and Kurz (1996). As can be seen in Table 1, task
characteristics will be induced by either intuition or analysis, so depending on the task
characteristics, tasks will require different information processing and decision-making

styles.

ll

Table 1. Task Characteristics

 

Task characteristics inducing intuition

Task characteristics inducing analysis

 

Familiar task

No prior training or task information
Many cues presented (more than 5)
Contemporaneous cues presentation
Inter-correlated cues

Pictorial cues presentation

Subject measures cue levels

Many alternatives on judgment scales
Rapid response required

Normally distributed criteria

No available organizing principle
Equal cue weight

Linear organizing principle and functions
relating cues

Outcome knowledge available
Minimal or no feedback

Unfamiliar task

Prior training and task information given

Few cues presented

Sequential cue presentation or orthogonal cues
Skewed, peaked, multimodal (bimodal) distribution
and criteria

Quantitative cue presentation

Objective measures, pointer readings

Few alternatives on judgnent scale

Open response time

Nonlinear functions relating cues to criteria
Organizing principle readily available
Unequal cue weights

Nonlinear organizing principle

Outcome knowledge not available

Cognitive feedback

 

Source: Doherty and Kurz (1996) Social judgnent theory. Thinking and Reasoning, 2 (2/3), 109-140.

Surface properties of dypamic task systems in CCT

The surface properties of dynamic task systems are “those which the operator sees”
(Hammond, 198 8, p.5) and “the overt display of the task variables to the subjects,” that is,
information display characteristics (Hammond et al., 1987, p.756). CCT narrows the
influence of the shopping enviromnent into direct stimuli of the information display. That
is, the surface property is comprised of the retail environment, such as the store interior,
music, and service from sales people.

The surface properties are divided into two types of properties: intuitive surface
properties and analytic surface properties. The intuitive surface properties are reflected by
pictorial and simultaneous information presentation and the analytical surface properties
are reflected by organizations of information in a sequential, simplified and self-paced
manner. For example, return to the earlier case in which the shopper wished to purchase a

cellular phone as a birthday present. When the customer visits a mobile service provider’s

12

retail store for gift shopping, he/ she experiences the store interior and product displays,
presenting various designs and colors of cellular phones, which are intuitive surface
properties of the task system, as well as the sales person’s explanations about certain
products’ functions and POP displays about prices and features, which are analytic surface

properties of the task system in the store.

Intuition and Analysis in CCT

There are many modes of cognition, which are cognitive mental processes formed
when humans experience and process knowledge and information. CCT views people as
having two forms of cognitions: intuitive cognition and analytic cognition. In CCT, the
intuitive cognition is defined as a judgnent obtained by an informal and unstructured mode
of reasoning, without using analytic methods or accurate calculations about given
information. Intuitive cognition is a primary intuitive mental function, low in terms of
being cognitively controllable and conscious, so spontaneous and speedy actions may flow.
On the other hand, analytic cognition is defined as a judgnent obtained by a formal and
structured mode of reasoning, using analytical methods or accurate calculations about
given information. Analytic cognition is a mental function, such as the ability to think,
reason, and remind, which is cognitively controllable, conscious, and structured
(Hammond et al., 1987).

However, CCT rejects this simple dichotomous view of analysis and intuition.
Cognition is not strictly rational or intuitive, but often falls between the two extremes,
which is called “quasi-rationality.” Thus, the cognitive mode cannot be purely analytic

cognition or intuitive cognition, and is more often a mixture of analysis and intuition

13

(Hammond et al., 1987). To find the differences between intuitive cognition and analytic
cognition, Hammond, et a1. (1987) tested the efficacy between intuitive and analytic
cognition by the direct comparison of three modes of cognitions: intuitive, analytic, and
quasi-rational cognition. In contrast to prior cognition studies, they found that intuitive and
quasi-rational cognition frequently outperformed analytic cognition in accuracy of
judgnent. CCT initially recognized intuitive cognition as one of the modes of cognition on
the cognitive continuum that may be superior to analytic cognition in terms of judgnent.
As can be seen in Table 2, the properties of intuitive cognition and analytic
cognition are very distinctive in terms of control, awareness, activity speed, memory,
metaphor usage, organizing principle, errors, and confidence. Human judgnent is located
on the cognitive continuum, which has these two different forms of cognitions. To
determine the location of a judgnent on the cognitive continuum, each property for
intuition and analysis is aggegated by its mean value. This measure provides a “Cognitive
Continuum Index,” and the resultant number indicates the location of each person’s
judgments on the cognitive continuum. The CCI provides a means of quantifying a
person’s direction and degee of cognitive movement on the cognitive continuum, given a

certain task circumstance (Hammond, 2000).

14

Table 2. Properties of Intuition and Analysis

 

Intuitive Cognition Analytic Cognition
Cognitive control Low High
Conscious awareness Low High
Amount of shift across High Low
Indicators High Low
Speed of cognitive activity Raw data or events shared Complex principles stored
Memory Pictorial Verbal, quantitative
Metaphors used Weighted average Task specific
Organizing principle Normally distributed Few, but large
Errors High confidence in answer; Low confidence in answer;
Confidence Low confidence in method High confidence in method

 

Source: Hammond (2000). Judgments Under Stress. New York: Oxford University Press;
Hammond et al. (1988). Judgnent and decision making in dynamic tasks. Information and Decision
Technologies, 14(March), 3-14.

Advantages of CCT

CCT has an important advantage in the prediction of a person’s cognition, in that
CCT makes it possible to predict the person’s cognitive activity (Doherty and Kurz, 1996).
Because of the pattern-seeking behaviors, CCT allows us to quantify cognitive movement
and also allows us to predict both the movement of cognition and the interaction between
cognition and task by measuring these pattern behaviors. That is, due to the conguence
between tasks and behavioral consequences, behavioral consequences can be anticipated.

Pattern seeking behaviors can be influenced by three conditions: (1) organization of
information display that offer a high degee of perception such as visual, auditory, or
otherwise; (2) organization of information display that offer a high degee of concepts such
as presentation of a time sequence of events; and (3) situations that require people to
provide coherent explanations about his/her decision-making related with the events
(Hammond, 1988).

In spite of CCT’s advantage over a possibility of behavior prediction, there are few

studies that have applied CCT in a retailing context. Mathwick, Malhotra, and Rigdon’s

15

research (2002) initially adapted CCT into a retail context in order to explain the
relationship between retail display information and shopping tasks. Using CCT, they
examined the effect of the dynamic retail experience from retail display information on
experiential perceptions of value for two different retail formats: online shopping and
catalog shopping. They also examined how consumer shopping tasks and retail
information display properties influenced consumer perceptions of experiential value, and
whether they enhance or undermine consumer perceptions of value associated with their
retail experiences. They found that shopping tasks directly influenced perceptions of
efficiency, economic value, shopping enjoyment, and all active dimensions of value.
Goal-oriented shoppers are associated with higher perceptions of economic value and
efficiency and a lower perception of enjoyment than intuitive shopping oriented shoppers.

They also found that conguent interactions between shopping tasks and retail
information display properties enhanced the impact on reactive dimensions of value.
Inconguent combination between shopping tasks and retail information display properties
are associated with lower levels of retail visual appeal, retail entertainment value, and
perceived service excellence than conguence combination.

Emphasizing the impact of information display on task achievement, they
suggested that retailers should be aware of the challenges found in conveying product
information in different store environments, such as in—store, online, and through print or

broadcast media.

16

Shapping Environment Research Literature

From a theoretical viewpoint, a central tenet of shopping environmental
psychology is gounded in the Theory of Affordances (Gibson, 1979). This theory suggests
that people perceive physical environments related to information as a meaningful entity
and implies that people use a store’s physical environmental cues when they are evaluating
stores, because these cues provide reliable information about product-related attributes like
quality, value and the overall shopping experience (Bitrner, 1992).

People store their perceptions of these physical environmental cues, which directly
convey information to them about stores and products in the stores. Based on the
experience of a shopping environment, people make shopping decisions. Due to the
significant influence of shopping environment on shopping choice, many studies have
intensively investigated the impact of various store environmental cues on store patronage,
store image, or purchase intention: music (e. g., Dube, Chebat, and Morin, 1995); liglnting
(e.g., Golden and Zimmerman, 1986); colors (e.g., Babin, Hardesty, and Suter, 2003);
ambience (e.g., Spangenberg, Crowley, and Henderson, 1996); and store crowding (Hui
and Bateson, 1991).

For example, Babin et al. (2003) treated color (orange vs. blue), lighting (bright vs.
soft), and product item price (US$59.95 vs. US$149.95) and then tested these effects by the
main effect itself, by two-way interactions, and by three-way interactions. They found that
consumers evaluated a blue interior more favorably than orange interior, by showing
geater excitement and higher store patronage intention. The result changed, though, when
the color was combined with lighting changes and controlling for price.

Indeed, it has been found that store environmental cues strongly influence shopping

17

related decisions in the previous shopping environmental studies. Furthermore, a retail
store’s internal and external environment actually influences retailer performance. For
example, Kumar and Karande (2000) reported that retail performance (sales and market
share) was significantly influenced by internal retail store environments such as number of
checkout counters, the number of non-gocery products sold and external retail store
environments such as a type of store neighborhood. .

The shopping environment automatically influences shopping tasks, guides
information processes related to shopping, and can even change shopping behaviors.
Recent evidence shows that environments automatically activate tasks (e. g., goals and
motives), which are frequently associated with the previous experience, and these tasks
then unconsciously influence information processing and behaviors (e. g., Chartrand and
Bargh, 2002).

Due to the powerful impact of the store environment on retail business, retailers are
trying to provide various retail environments for their customers and potential customers.
To date, multi-channel retailers utilize the Internet as their technical tool to build various,
deep relationships with customers. They have tried to provide personalized information,
communities, and proactive interaction tools in order to meet customers’ needs and to
create a competitive advantage in the market. For example, while using the Internet or
navigating on Web sites, consumers experience many banner advertisements designed to
gain the consumers’ attention, either by flashing techniques or with attractive offers for a
certain product (such as coupons and special discounted offers). Due to these efforts,

consumers may come to the Internet with an experiential shopping task.

18

The impact of shopping environment on decision-making

People make choices heuristically, in order to minimize the cognitive efforts of
their decision-making processes, but the heuristics used for store choice are largely
unknown (Mitchell, 2001). Earlier literature seems to have been interested in studying
heuristic processing to find the distinction between analytic processing (data-driven
processing) and intuitive processing (overall evaluation-driven processing) (F iske and
Neuberg, 1990; Mantel and Kardes, 1999; Sujan, 1985). For instance, Mantel and Kardes
(1999) tested analytic and intuitive processing—though they used somewhat different
terms: attribute-based processing (specific attributes) and attitude-based processing
(overall evaluation)——in a consumer preference context. In analytic processing, focal
attributes are heavily weighed, whereas less focal attributes are easily neglected. Weights
for attributes influencing a certain choice are different in not only intuitive processing but
also in analytic processing, depending on the importance of the attributes.

Other studies, on the other hand, tried to exploit consumers’ store choice with the
alternative “gestalt” view of store image (e.g., Lindquist, 1974). They insisted that
consumers simplified analytic processing when constructing complicated store
information because they have limited motivations and cognitive capabilities, which
suggests that store image is more than just “factually-based opinions.” It implies “a
structure of some sort that is tying together the dimensions that are at work” (Lindquist,
1974,p.30)

When people positively perceived store environmental cues, their store patronage,
store image, and purchase intentions were higher than people who had negative or neutral

perceptions. In another words, people who positively perceived store environmental cues

l9

were also satisfied with other factors in the store, such as service and products. For instance,
store layout is an important determinant when creating the store image, which positively
influences merchandise value and patronage intention (Baker et al., 2002). Store layout not
only contributes to a store's image, but also increases value perception and patronage
intention toward the store.

Thus, we can imply from these results that store environmental cues mi ght
influence the store-related decision-making (judgnent) in a heuristic manner. There are
numerous store environmental cues, so people might experience the complexity and
subtlety of cognitive functioning when they make decisions related to shopping, based on

the information from those cues.

The Impact of Affect on Decision-Making
Affect literature

Cognitive psychology affirms the importance of affect in fully accounting for
cognitive schemata of various decision-making concepts and contexts. In the shopping
environment literature, studies examine both positive and negative affect elicited fi'om
store environmental cues. Positive affect induced by pleasant store environmental cues
encourages customers to stay longer and to have more interactions with other sales people
(e. g., Babin and Darden, 1995); to simplify their purchase decision-making; to build
positive store image (Darden and Babin, 1994); and to increase the quality and value
perceptions of the merchandise (Baker et al., 1994). On the other hand, negative affect,
which is induced by loud music or crowding, encourages customers to leave earlier (e. g.,

Eroglu and Machleit, 1990). Therefore, considering the literature, positive affect and

20

negative affect are very important as positive and negative affect elicited flom store
environment eventually influence store performance as well as simply influence shoppers’
store interactions.

In the previous psychological literature, positive affect is found to promote
cognitive flexibility and to generate more elaborate, organized thoughts. This is because
happy feelings become more associated with differentiations among different, multiple
relations of stimuli (similarities or differences) on cognitive decision-making. That is,
consequently, happy feelings influence cognition by promoting cognitive decision
processes (Isen, 1997).

On the other hand, negative affect does not seem to influence the motivational and
methodological complexity of decision-making processes because people in a negative
frame may be more concerned about loss, so they flame behavior conservatively in order to
protect themselves flom loss. Thus, the impact of positive affect cannot be simply extended
into a negative affect flame. Specifically, strong negative affect, not positive affect,
impacts decisions by disrupting the decision process (Isen and Geva, 1987).

Thus, in summary, it is important to consider the impact of affect on cognition to
understand consumers’ decision-making because affect influences cognition’s elaboration.
Positive affect and negative affect seem to differently influence cognitive decision-making
because positive affect diversely influences cognitive decision-making and its process;
whereas negative affect does not influence complexity of decision-making and its process,
but conservatively and passively influences a loss flame of decision-making.

The impact of affect depends on situations or tasks, and is regular, predictable, and

understandable. That is, positive affect seems to increase the ability to interact between

21

task types and encourages a substantive process related to the elaboration and thinking of
decision making, rather than response bias or non-systernatic processes (Isen, 1997).
Positive affect is influential in two kinds of tasks (global and specific tasks) and fosters
intrinsic motivation such as interest in related task and variety seeking (stimulation
seeking) among alternatives. People tend to stay in a positive affect-condition in order to be
fun. In doing so, their global tasks become more specific by the intrinsic motivation
(Estrada, Young, and Isen, 1992).

Positive affect also may influence diverse types of decision-making, depending on
task types, situations, importance or utility of the tasks. It is usually assumed that the
decision influenced by affect is irregular or unusual. For that reason, affect has been
regarded as a negative moderator, having non-conscious and contarninative effects on the
elaboration of cognitive decision-making processes. The intensity of emotions, which are
closely tied to a specifiable behavior, is likely to interrupt ongoing behaviors and
eventually to result in behaviors directed toward different goals (goals that differ flom the
shoppers’ original goals) (Clark and Isen, 1982).

Positive affect (even mild positive affect) could explicitly influence
decision-making and its process in a regular fashion (e.g., Isen, 1997). Positive affect cues
positive material in memory and makes it easier to access that memory, allowing people to
have more accurate and organized information. In doing so, people induced by positive
affect have more efficient (less time) decision-making and at the same time, they put their
efforts on more complex decision-making (Isen and Means, 1983). That is, positive affect
makes information more accessible and eventually helps efficient decision-making.

Furthermore, positive affect may change behavioral orientations. For instance, people tend

22

to have hedonic orientation when they have no specific goals. The hedonic orientation
attributes pre-existing pleasure emotions and, in doing so, people become engaged with
favorable evaluations of novel stimuli (Schwarz, 1986). Thus, as positive emotion
increases the desire to approach more novel stimuli, positive emotion fundamentally
changes psychological orientation.

Due to the impact of the positive affect, previous studies tried to identify positive
affect-inducing properties of retailing environments because they attract shoppers and
influence in-store shopping behaviors. For example, Childers, Carr, Peck, and Carson
(2001) found that entertainment is a very strong predictor of attitude for online shopping in
botln utilitarian and immersive/hedonic online shopping environments. They found that
consumers’ online purchases are motivated not only by utilitarian but also hedonic
motivations. Similarly, Wakefield and Baker (1998) found the importance of affect
(excitement). Consumers who experienced mall shopping excitement have a desire to say
more about the mall and the excitement is the strongest factor influencing re-patronage
intention and shopping involvement with the mall. Regarding to the literature, the affective
quality of shopping experience might have a significant effect on shopping intentions, so

more investigation about the impact of affect on shopping experience is needed.

Interaction between com’tion and affect in decision-making

In the literature, the principle of cognitive-affective interaction was invoked as a
moderator, bothering a process of mental tlninking. The affective system is less amenable to
behavioral analysis and is acknowledged to have an interior apparatus, by pointing to the

linkage between thought and feeling. Due to the features of affective systems,

23

psychologists reasserted the importance of internal states in understanding how people
tlnink. In particular, this cognitive-affective interaction received geat attention in a
learning context. However, the affective system is not merely a necessary condition for
learning, or an independent driving force, but rather, an interactive element that contributes
to its scope and character by steering and monitoring the cognitive performance. Cognitive
style refers to a characteristic mode of cognition that is closely interwoven with aspects of
an individual’s character and personality (Zimiles, 1981).

From a semantic network perspective, which explains people’s mental information
processes, cognitive and affective perceptions are related to each other (e. g., Bower and
Cohen, 1982). The flequency and variance of affective or emotional responses across
different stimuli are indicative of varying affective meaning. People have affect evolving
into important nodes in the semantic network of their brains that could guide future action.
These nodes are naturally linked to more concretely perceived elements, which form
traditional cognitive nodes of information and also associate with the objects and
environments. That is, relevant cognitive information and affective information are highly
interactive with each other in decision-making. Thus, there is a significant need for more
research in this area. Investigation of individuals’ cognitive and affective components will
provide a more in-depth understanding of their respective contribution to one’s general
predisposition toward an object than we gain by looking at either component alone
(Cervellon and Dube, 2002).

Therefore, considering the cognitive and affective decision-making literature,
shoppers might experience store environments in both cognitive and affective aspects.

Also, stores environments are important drivers to shopping decision-making because they

24

induce the affective and cognitive interaction and consequently influence shopping

decision-making.

In summary, this chapter reviews CCT’s significant features such as deptln and
surface properties on dynamic task systems in CCT, intuition and analysis in CCT, and its
advantages. The shopping environmental literature review addresses the significant impact
of the shopping environment on consumer choice and its impact on decision;making. The

impact of affect on decision-making is studied in the later part of the literature review.

25

CHAPTER III. CONCEPTUAL FRAMEWORK

An idea flom the shopping environment literature leads me to a shopping
conguence flamework that interweaves shopping tasks and store environments, and
integates the general flamework of the Cognitive Continuum Theory.

Consumer shopping tasks are categorized in the shopping task literature by various
contexts and aspects due to the strong influence of the shopping task on store-attribute
salience and store choice. For instance, Kenhove and Waterschoot (1999) defined the
shopping tasks as regular purchases, urgent purchases, large quantities, difficult jobs, and
getting ideas. Kau, Tang, and Ghose (2003) proposed a typology of online shoppers, using
six brand criteria: comparison, online shopping, deal proneness, information seeking, ad
orientation, and offline shopping. The typology includes (1) the on-off shopper; (2) the
comparison shopper; (3) the traditional shopper; (4) the dual shopper; (5) the e-Laggard;
and (6) the information surfer. Tauber (1972) classified shopping tasks motivated by
psychological needs into two categories: personal (role playing, diversion,
self-gatification, learning about new trends, physical acuities and sensory stimulation) and
social (social experience, communication with otlners, peer goup attractions, status and
authority, pleasure and bargain). Most Internet studies have seen the online consumer as
engaging in two tasks: experiential-oriented shopping and goal—oriented shopping (e.g.,
Hoffman and Novak, 1996).

It is not surprising that there is an array of shopper types to meet various product
contexts. However, due to the variety presented, it was difficult to draw any significant
generalizations flom the previous studies. As a result, two types of shoppers are identified

for this study, using a simple typology: utilitarian and hedonic. The utilitarian shopper is

26

characterized as task-related and rational, while the hedonic shopper is characterized as
entertainment-seeking and emotional. Westbrook and Black (1985) mentioned that the
classifications do not necessarily have to be sharply delineated flom each other; some tasks
can be more utilitarian or hedonic in nature, but can contain features of both.

In the decision-making literature people might react both cognitively and
affectively during their shopping decision-making process, as they interact witln their
shopping environment. Further research that provides a more in-depth understanding of the
interaction between cognitive and affective components is needed (Cervellon and Dube,
2002). This study, therefore, examines shopping and product choice tlnrough both a
cognitive decision process and an affective decision process. This viewpoint is different
flom CCT, as CCT only focuses on the cognitive continuum in decision-making.

Adapting a general framework flom the Cognitive Continuum Theory, my study
conceptualizes dynamic shopping tasks as being two tasks: (1) a goal-oriented shopping
task; and (2) an experiential-oriented shopping task. Based on the shopping environment
literature, this study also conceptualizes two shopping environments: an analytic shopping
environment and an intuitive shopping environment. I refer to this conguent state between
shopping tasks and shopping environments as “shopping conguence.” The shopping
conguence explains how people, contingent upon their shopping tasks, make decisions
related to shopping choice when they interact with shopping environments cognitively and
affectively.

The overall conceptual model for shopping conguence is shown in Figure 1. The
conceptual model consists of two types of shopping conguence and two types of shopping

inconguence: (1) analytic shopping conguence (goal task-analytic store environment);

27

(2) intuitive shopping conguence (experiential task-intuitive store environment); (3)

shopping inconguence (goal task-intuitive store environment); and (4) shopping

inconguence (experiential task-analytic store environment).

Figure 1. Conceptual Model of Study

Goal-oriented
a shopping task
3
5..
.5
o
.n:
m Experiential
-oriented
shopping task

Store Environment

Analytic store environment

Intuitive store environment

 

Analytic Shopping
Congruence

T

Perceptions toward Stores
Attitude Components toward Stores
Purchase Intention

 

l

Perceptions toward Stores

Attitude Components toward Stores
Purchase Intention

 

 

l

Perceptions toward Stores
Attitude Components toward Stores
Purchase Intention

 

Intuitive Shopping
Congruence

T

Perceptions toward Stores

Attitude Components toward Stores
Purchase Intention

 

28

 

STUDY 1. Exploring Shopping Congruence in Offline Stores

According to the Theory of Reasoned Action, people will consider a given
behavior’s consequences before performing that particular behavior. As a result, people’s
perceptions and attitudes influence behavioral intention, which is an important factor in
determining behaviors and behavioral changes (Ajzen, 1985). To examine the impact of
shopping conguence on the shopping decision, and based on TRA, this study looks at

perceptions, attitudes toward stores and purchase intentions as sequential outputs.

The impact of shopping conggence on perceptions toward stores

The shopping environment supports shoppers’ task completion goals and allows
shoppers to appreciate the store environment. Consumers obtain experiential values in
various ways. When the shopping task requirement is met, value perceptions of a retail
experience are fully appreciated. Mathwick et al. (2002) tested retail experiential values
(visual appeal, retail entertainment and service excellence) as a means of evaluating retail
channel performance. They found that when there is conguence between the shopping
environment and the task, shoppers perceive more retail experiential values than when
inconguence exists. Hence, shopping conguence could lead shoppers to tend toward
favorable or unfavorable evaluations about the stores they experienced.

Shoppers who experience a shopping conguence extend mental energy, driven by
the desire to make the most efficient and the best decision possible, relative to time and
effort. Therefore, shoppers who experience a shopping conguence might more positively
respond about both their analytic and intuitive perceptions toward stores.

Hypothesis 1-1: Shoppers who experience a shopping congruence will show
higher analytic perceptions (a. perceived usefulness and b. ease of use) toward

29

stores than shoppers who experience a shopping incongruence.

Hypothesis 1-2: Shoppers who experience a shopping congruence will show

higher intuitive perceptions (entertainment) toward stores than shoppers who

experience a shopping incongruence.

According to Moye and Kincade’s study (2002), consumer’s shopping orientations
will directly influence responses toward retail store environments. They found differences
in the importance ratings of sensory/layout environments, in environmental dimensions,
among shopping task orientations of decisive-, confident-, bargain- and
appearance-consumer goups. This result indicates that shoppers evaluate store
environments differently, depending on their shopping orientations.

Based on CCT, goal-oriented tasks might have a higher value perception, requiring
analytic cognition correlated with a specific transaction. Goal-oriented shoppers will
associate more with an analytic cognition mode when experiencing store environmental
cues, than with an intuitive cognition mode. Thus, goal-oriented shoppers who prefer a
cognitive experience might look for more novel stimuli and exhibit more curiosity about
analytic store enviromnents and saliently perceived analytic shopping environments than
experiential-oriented shoppers.

Hypothesis 2-1: Shoppers who experience an analytic shopping congruence will

show higher analytic perceptions (a. perceived usefulness and b. ease of use)

toward stores than shoppers who aperience an analytic shopping incongruence.

On the other hand, hedonic task orientation has similar shopping motives to
utilitarian motives, but the hedonic task focuses only on hedonic fulfillment, such as
experiencing finn, amusement, fantasy and stimulation (Babin, Darden, and Griffin, 1994).

As mentioned earlier, there are an intuitive pole and an analytic pole in the CCT continuum.

Highly visual, dynamic information displays might fall on the intuitive pole of the

30

continuum, whereas text-based explicit information displays would fall on the analytic
pole of the continuum. People who are experiential-oriented will minimize the dynamic
qualities of information and will either re-organize or disregard some of the information
they receive when they are interacting with store environments. Irn doing so, their
decision-making becomes an intuitive and spontaneous reaction, rather than an elaborate
analytic decision-making process (Hammond, 2000).

People tend to be influenced by the levels of pleasure and arousal induced by
product characteristics as they experience store environments. Their later shopping
behaviors can be influenced by the previous arousal and pleasure experience, through
carry-over effects (Menon and Kahn, 2002). When associating with experiential goals in
emotionally appealing store experiences, experiential-oriented shoppers may have the flow
experience, which is a mental state of operation in which the shoppers are fully immersed
in what they are doing (e. g., Mathwick and Rigdon, 2004).

When people are in a flow status, they associate with skills and challenges that are
balanced and motivating for some important threshold. The flow experience appears in
association with experiential goals during emotionally appealing store experiences (e. g.,
Mathwick and Rigdon, 2004). Hence, shoppers who are oriented by intuition might be
more associated with intuitive shopping environments than with analytic shopping
environments because of their flow experiences in the store environments.

Hypothesis 2-2: Shoppers who experience an intuitive shopping congruence will

show higher intuitive perceptions (entertainment) toward stores than shoppers
who experience an intuitive shopping incongruence.

31

The impact of shopping congmence on attitudes toward stores

In the consumer behavior literature, many studies sought to distinguish between the
cognitive and affective components of attitudes toward stores (e.g., Crites, Fabrigar, and
Petty, 1994). In general, attitudes consist of three main components: affective (the way we
feel); cognitive (the way we think); and behavioral (the way we act) towards a particular
entity (Petty, Wegener, and Fabri gar). This study only focuses on affective and cognitive
attitudes here because behavioral attitudes will be addressed in the section of purchase
behavior intention.

Cognitive components of attitudes are believed to be more fundamental and
constant over time, and more closely connected to basic values as well, so it is difficult to
change them. They may be changed by new knowledge that is convincing and credible. On
the other hand, affective attitudes reflect emotional reactions and are somehow
spontaneous. Thus, they may be changed by repeated exposure to situations related to the
goal associated with the attitude (Wood, 2000).

In the retailing context, the cognitive components of attitudes toward stores pertain
to diverse attributes and functions attached to a given store, whereas the affective
components of attitudes toward stores contain sensations, feelings and emotions that come
flom experiencing store enviromnents. A large body of literature reports that store
environment is important for enhancing both affective pleasure and cognitive pleasure. An
affective pleasure influences approach responses such as global attitude and willingness to
buy (e.g., Sprangenberg, Crowley, and Henderson, 1996). A store environment also fosters
cognitive pleasure, so that cognitive pleasure influences approach responses as well (e. g.,

Fiore, Yarn, and Yoh, 2000). Thus, store experiences fostered by store atrnospherics might

32

influence both affective and cognitive pleasures. These pleasures then affect approach
responses toward a product, such as attitude and purchase intention. According to the
literature, a store experience associated with a certain shopping task might positively
influence the formulation of both analytic and intuitive attitudes toward a store.

Hypothesis 3-1: Shoppers who experience a shopping congruence will show

higher analytic attitudes toward stores than shoppers who experience a shopping

incongruence.

Hypothesis 3-2: Shoppers who experience a shopping congruence will show

higher intuitive attitudes toward stores than shoppers who experience a shopping

incongruence.

Based on CCT, goal-oriented shoppers under a shopping goal task that requires
analytic cognition will be more associated with an analytic cognition mode, when
experiencing store environmental cues through a pattem-seeking behavior. Thus, an
analytic shopping conguence might seek more novel stimuli and exhibit more curiosity
about analytic store environment factors, compared with an intuitive shopping conguence.

On the other hand, experiential-oriented shoppers who are under an experiential
shopping task requiring intuitive cognition will be more associated with an intuitive
cognition mode, when experiencing store environmental cues through a pattem-seeking
behavior. Thus, intuitive shopping conguence will have more influence than inconguence
on intuitive attitudes toward the experienced store.

Hypothesis 4-1: Shoppers who experience an analytic shopping congruence will

show higher analytic attitudes toward stores than shoppers who experience an

analytic shopping incongruence.

Hypothesis 4-2: Shoppers who experience an intuitive shopping congruence will

show higher intuitive attitudes toward stores than shoppers who experience an
intuitive shopping incongruence.

33

The impact of shopping conggpence on purchase intention

As theoretical and empirical studies indicate, the conguence of the surface
property-depth, combined with tasks, facilitates task performance (e. g., Hammond et al.,
1987; Mathwick et al., 2002). This shopping conguence facilitates purchase decisions
more than inconguence when they are experiencing stores by shopping tasks.

Hypothesis 5: Shoppers who aperience a shopping congruence will show higher

purchase intention than shoppers who experience a shopping incongruence.

Based on CCT, in order to achieve a shopping goal, a dynamic relationship between
a shopping goal and analytic shopping environment might influence shoppers’ abilities to
use the information obtained while experiencing stores. Goal-oriented shoppers will make
more purchase decisions when they experience an analytic shopping conguence because
they go through a flow status, where they are fully immersed in their shopping experience
because they have achieved an analytic conguence between their shopping tasks and the
environment (e.g., Matlnwick and Rigdon, 2004). Thus, goal-oriented shoppers under
shopping goal tasks will make more purchase decisions when they experience analytic
conguence because of their goal driven behaviors, when compared with shoppers who
experience a shopping inconguence or an intuitive shopping conguence.

Hypothesis 6: Shoppers who experience an analytic shopping congruence will

show higher purchase intentions than shoppers who experience an analytic
shopping incongruence.

34

STUDY 2. Exploring the Shopping Congruence across Mufti-Channels

“Webmospherics” is a virtual environment counterpart to the physical
surroundings found in an offline retailing context. It includes structural design attributes
(flames, gaphics, text, pop-up windows and search engine configuration), transaction
attributes (one-click check-out, purchase procedures and hyperlinks), media dimensions
(gaphics, text, audio, color and streaming video), and site layout dimensions (orgarnization
and gouping of merchandising) (Childers et al., 2001).

Analytic surface properties of online stores are normally reflected by the
organization of information into a sequential, simplified and self-paced manner (e. g.,
text-based Websites). The analytic surface properties of a store are related to the quality of
online stores because the analytic surface properties of online stores focus on Website
quality and efficiency. For instance, Wolfinbarger and Gilly (2003) suggest quality
measures for e-tailers (called eTailQual), which include four key dimensions: Web design
(related to depth of information and browsing time issues), finlfillment/reliability,
security/privacy and customer service. They found that an etailer’s quality, which is
determined by these key dimensions, is highly related to customer satisfaction, retention
and loyalty, and consequently leads to an etailer’s success.

“Aesthetic response is a reaction to the symmetry, proportion, and unity of a
physical object, a work of poetry or a performance” (Mathwick et al., 2001 , p. 42). In the
online shopping literature, “aesthetics,” which induces intuitive cognition, has received
much attention. In retail shopping, aesthetics is reflected in two key dimensions: visual
appeal and entertainment (Bellenger, Steinberg, and Stanton, 1976; Mano and Oliver,

1993; Mathwick et al., 2001). The aesthetics of both visual appeal and entertainment offer

35

innnediate intrinsic pleasure for its own sake and eventually increase consumers’ desire to
shop by providing a desirable consumption experience (Deighton and Grayson, 1995;
Matlnwick etal., 2001).

As Internet technology is able to serve as a more powerful medium, it provides
various information and experiences to shoppers beyond traditional non-store retailing
formats (e. g., catalog shopping and TV shopping). Through this, an application of a virtual
experience has emerged into online shopping. By inducing intuitive cognition, the virtual
experience indicates a movement toward more multi-sensory interactions, by integating
high quality visuals, stereo sound, and 3-D imagery. Many previous online studies have
reported that online stores can provide emotional and hedonic experiences like enjoyment
and pleasant or arousing feeling as well as functional and utilitarian experiences such as
ease of use and usefulness (e. g., Menon and Kahn, 2002).

However, there are some key aspects of online store environments that are different
flom traditional retail environments. First, a narrow window—the computer
screen—allows consumers to immediately focus on particular items in online shopping
environments. This immediate focus on particular items means that online store
environments might have more influence than physical stores would on the perception of
those items seen by shoppers (Burke, Harlam, Kahn, and Lodish, 1992). Second, online
shopping environments carmot provide offerings such as a high level of service, highly
trained staff, and entertaining and firm retail environments, which are typical devices used
for obtaining competitive advantages in “brick-and-mortar” environments (e.g., Burke,
1997; Wakefield and Baker, 1998). Tlnird, online shopping environments have a weakness

in that they present low tactile information (they lack a range of sensory experiences). As a

36

result, goods requiring multi-sensory inputs to make product choices are purchased less
often on the Internet (e. g., Citrin, Stem, Spangenberg, and Clark, 2003).

Indeed, in the literature, many previous studies have reported the organizational
and/or theoretical differences between offline and online stores, with regards to store
characteristics and offered values. However, very few empirical studies have compared
consumer behavior in offline and online stores. Even then, most of them have only tested
for the online shopping situation itself; they have not empirically tested differences and
similarities between these two channels, so we don’t know whether differences exist across
multi-channels, or what those differences might be. To assure the robustrness of the
shopping conguence across multi-channels, Study l—which used an offline store
context—is repeated in an online store context for Study 2.

Vrechopoulos et al. (2004) reported that consumers perceived usefulness, ease of
use, entertainment and time toward three major store layout types (gid, fleedom, and
racetrack/boutique) differently, when comparing them between a conventional retailing
context and an online retailing context. Thus, the impact of shopping conguence on
perceptions toward online stores may be different. On the other hand, even if shoppers
perceive shopping environments differently between offline and online stores, the
shopping conguence between shopping environment and task is a very strong, concrete
concept, so its impact on perceptions and attitudes toward stores may be robust. Study 2,
therefore, is mearningful in that it investigates whether the shopping conguence is robust
across multi-channels and, furthermore, if differences exist across multi-channels, how

offline and online retail channels are different or similar. Thus,

37

Hypothesis 7-1: Shoppers who experience a shopping congruence will show
higher analytic perceptions (a. perceived usefulness and b. ease of use) toward
online stores than shoppers who experience a shopping incongruence.
Hypothesis 7-2: Shoppers who experience a shopping congruence will show
higher intuitive perceptions (entertainment) toward online stores than shoppers
who experience a shopping incongruence.

Hypothesis 8-]: Shoppers who experience an analytic shopping congruence will
show higher analytic perceptions (a. perceived usefulness and b. ease of use)
toward online stores than shoppers who experience an analytic shopping
incongruence.

Hypothesis 8-2: Shoppers who experience an intuitive shopping congruence will
show higher intuitive perceptions (entertainment) toward online stores than
shoppers who experience an intuitive shopping incongruence.

Hypothesis 9-1: Shoppers who experience a shopping congruence will show
higher analytic attitudes toward online stores than shoppers who experience a
shopping incongruence.

Hypothesis 9-2: Shoppers who experience a shopping congruence will show
higher intuitive attitudes toward online stores than shoppers who experience a
shopping incongruence.

Hypothesis 10-1: Shoppers who aperience an analytic shopping congruence
will show higher analytic attitudes toward online stores than shoppers who
aperience an analytic shopping incongruence.

Hypothesis 10-2: Shoppers who experience an intuitive shopping congruence
will show higher intuitive attitudes toward online stores than shoppers who
experience an intuitive shopping incongruence.

Hypothesis 11: Shoppers who experience a shopping congruence will show
higher purchase intention than shoppers who experience a shopping
incongruence.

Hypothesis 12: Shoppers who experience an analytic shopping congruence will

show higher purchase intentions than shoppers who experience an analytic
shopping incongruence.

38

CHAPTER IV. RESEARCH METHOD
Research Design

Previous studies have employed different experimental designs to empirically test
CCT. For example, Hammond (1987) utilized a between-subjects experimental design
(decision-making made only under a specific task), with highway construction engineering
experts as his test-subj ects. On the other hand, Dunwoody et al. (2000) applied a
within-subj ects experimental design (decision-making made under all task conditions),
using undergaduate students as the subjects. A review of the literature reveals a possible
problem in the between-subjects experimental design; there is some fluctuation in the
scores of goups, due to different participants providing those scores. A better design of
within-subjects experimental research would be one that controls unwanted participants’
variability through an awareness that shopping conguence tests both cognitive and
affective movements as people experience store environments.

Mathwick et al. (2002) examined how shopping task orientations affect perceptions
toward store environments by asking subjects to recall their shopping task orientations and
their most recent shopping experiences in a real shopping situation. This may present
serious inherent problems, such as a time gap between the shopping experiences and the
testing leading to the subjects having difficulty recalling their shopping task orientations.
Retailing research should be done in an actual retailing setting to meet the realities of
retailing. An experimental design would be better for research into the precise causal
relationships between independent variables and dependent variables.

Therefore, this research employed an experimental research design in offline and

online shOpping situations that utilized a within-subjects experimental design for shopping

39

task assignments in real shopping situations. My experiment employed “a
quasi-experimental design.” Internal validity may be threatened by using this experimental
design (Campbell and Starnley, 1966). It was chosen, however, because this research
examines SC in field settings in which random assignments of goups cannot be
accomplished.

The research consisted of two experimental studies: Study 1 for offline shopping
situation and study 2 for online shopping situation. Thus, the experiment design had 3
shopping tasks (a goal-oriented task, an experiential-oriented shopping task, no task,
within the subjects) x 2 offline stores (offline store with intuitive properties and offline
store with analytic properties, within the subjects) x 2 online stores (online store with
intuitive properties and online store with analytic properties, within the subjects).
Manipulation of retail store

To have the reality of a retailing setting, this study utilized a real retail company
who has both offline stores and online stores, which have appeal intuitively and
analytically for their variety of online and offline retail stores. For this field experiment
execution, research cooperation was ganted by MCM in Korea. MCM is a luxury brand of
German origin leather goods. In 2005, Sungjoo Group, a Korean fashion company, owned
MCM. After the merger with Sungjoo Group, MCM rapidly expanded worldwide. For this
present research, MCM was selected as it currently offers different offline and online store
environments, varied by country and regional situations, in order to meet various overseas
market situations.

In the Korean market, offering two different store environments, most of MCM

offline stores are located in major department stores. There co-exist island style stores,

40

small stores which require less space and show analytic merchandise display by product
categories in department stores, and boutique stores, relatively big stores which are
spacious and show intuitive merchandise displays attractively compelling consumers.
MCM island stores were selected because they organized products by category and it is
case to find specific merchandise. On the other hand, MCM boutique stores were selected
because people perceive more emotional appealing and entertaining by their store
atmosphere. Therefore, MCM island stores were used for the analytic store environment
and MCM boutique stores were used for the intuitive store enviromnent and for this present
study (Refer to Figure 2 and Figure 3).

Figure 2. MCM Island Store

    

 

41

For the online store study, MCM’s official shopping mall, SDD Mall
(www.sddmall.com) was used as the analytic online store environment. An official home
page of the Sungjoo Group (www.mcmworldwide.com) was used as the intuitive online
store environment. The SDD Mall offers conventional online shopping environments such
as product display by product categories and segnnents, commurnity, special events, outlet
mall for old stocks and information about new arrivals. On the other hand, the
mcmworldwide.com offers information about new season collections, company
information, and brand heritage by offering fancy visual information by using a flash
technique and eCatalogue (Refer to Figure 4 and Figure 5).

Figure 4. SDD Mall

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Figure 5. mcmworldwide.com

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42

Manipulation Check

A marnipulation check was executed to ensure how the four MCM stores chosen for
this research are different in terms of analytic and intuitive perceptions. The marnipulation
for four retail stores in this study consist of an MCM boutique store (analytic offline store)
and an MCM island store (intuitive offline store), and SDD Mall (analytic online store) and
mcmworldwide.com (intuitive online store). The respondents rated the four stores, within
subjects, in terms of their ‘perceived usefulness’ and ‘ease of use’ fOr analytic perceptions
toward stores and their ‘entertainment’ for intuitive perceptions toward stores. 139 usable
samples were collected.

The manipulation check results suggested that all four of the store manipulations
were successful. According to the T-test analysis of the offline stores, MCM island stores
showed a higher score of ‘perceived usefulness’ than MSM boutique stores (Island store
mean = 5.22; Boutique store mean = 4.58, t(82) = -2.439, p<.05). MCM island stores
showed a statistically higher score of ‘ease of use’ than MCM boutique stores (Island store
mean = 5.13; Boutique store mean = 4.62, t(82) = -2.258, p<.05 ). On the other hand, MCM
boutique stores showed a statistically higher score of ‘entertainment’ than MCM island
stores (Boutique store mean = 4.64; Island store mean = 4.21, t(82) = 2.366, p<.05). Thus,
people perceived the MCM island stores to be more analytic than MCM boutique stores.
People also perceived MCM boutique stores as being more intuitive than MCM island
stores (Table 3).

T-test analyses were also executed for online stores. SDD Mall showed a higher
score of ‘perceived usefulness’ than mcmworldwide.com (SDD Mall mean = 5.87;

mcmworldwide mean = 4.32, t(53) = -5.705, p<.001). SDD Mall showed a statistically

43

higher ‘ease of use’ than mcmworldwide.com (SDD Mall mean = 4.89; mcmworldwide

mean = 4.14, t(53) = -3.352, p<.001). Mcmworldwide.com showed a statistically higher

score of ‘entertainment’ than SDD Mall (mcmworldwide mean = 5.02; SDD Mall mean =

3.16, t(53) = 5.989, p<.001). Thus, the results of the manipulation check confirmed that

people perceive the SDD Mall as being more analytic than mcmworldwide.com. People

perceive mcmworldwide.com as being more intuitive than SDD Mall (Refer to Table 4).

Table 3. Results of Manipulation Check for Offline Store

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Offline
Shopping environment ' MCM Island Store MCM t - value Sig.
perceptron . (Analytic Store) Boutnque Store
n=41 (Intuitive Store)
n=43
Analytic Perception 5 3
-Perceived Usefulness 5.22 4.58 -2439 .017
- Ease oste 5.13 f 4.62 -2.258 .027
Intuitive Perception
_ Entertainment 4.21 4.64 2.366 .020
Table 4. Results of Manipulation Check for Online Store
Online
Shopping engronment SDD Mall mcmworldwide.com t - value Sig.
percep on (Analytic Store) (Intuitive Store)
=27 n=28
Analytic Perception l ;
-Perceived Usefulness . 5.87 4.32 -5705 .001
— Ease oste 4.89 9 4.14 -3352 .001
Intuitive Perception '5
_ Entertainment 3.16 5.02 5.989 .001

 

 

44

In summary, all of the t-test results demonstrated that the experimental
manipulation worked as expected. The four MCM stores were perceived differently, in
terms of their shopping environments. It was confirmed that the four MCM stores would
work as actual stimuli for this experimental research. Therefore, these four retail stores
were used as an experimental manipulation for this study.

Procedure for the experiment
(Study 1_Oflline store research)

For this field experiment execution, research cooperation was ganted by MCM in
Korea. Most MCM offline stores are located in department stores, so for the offline
research, a store intercept survey in Korea was executed in department stores. For the
analytic store environment I used two offline island style stores. For the intuitive store
environment, I used two offline boutique stores.

According to MCM customer research data, more than 50% of MCM store visitors
are goal-oriented shoppers, so even if scenarios are allocated to MCM visitors, it might not
be possible to control their original purpose for visiting the stores. Thus, research recruiters
asked only department store shoppers who just passed in flont of the MCM offline stores
(and did not enter into the store) to participate in this survey.

First, subjects completed the pre-survey questionnaire before their store experience.
After finishing the pre-survey, they received one of the three scenario cards (a
goal-oriented task, an experiential-oriented task, and no task) and they read the given card
before entering the store with this scenario in mind. After experiencing the store, they were
stepped on their way out to fill out the main survey and demo gaphic survey. MCM diaries

were ganted thank-you-gifts to all respondents after they finished their responses. To

45

increase a response rate, respondents were told of the gifts when being asked for the
research participation.
(Study 2_Online store research)

To obtain a balanced sample for the online research, I used the online customer
database of the department store in which we conducted the offline store. The department
store customers received a web mail asking for their participation in the online research.
The web mail included an online survey button linked with the online survey (Refer to
Figure 6)

Figure 6. Email to Ask for Online Survey

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When they click the “start button in the email, the first page of the Internet survey
asked respondents to fill out a screening survey and then asked subjects to click “next
page,” which randomly assigned one out of the six conditions: goal task with analytical
online store, goal task with intuitive onlirne store, experiential task with analytical online
store, experiential task with intuitive online store, no task with analytical online store, and
no task with intuitive online store. Subjects were asked to read the given scenario. Keeping

the scenario in their mind, they visited the linked MCM web store. Alter experiencing the

46

online store, they were asked to fill out the main survey and demogaphic survey (Refer to
Appendix 1). To increase the response rate, the last part of the online survey asked
respondents’ mail addresses. MCM diaries were sent by using the mail addresses to the all
of the online participants.

Finally, after the experiment for both offline and online research, backgound
questions were asked of the participants. The backgound questions covered demogaphic

factors including gender, age, income, occupation, and the control variables.

Independent and Dependent Variables
Indemndent variables

Shopping tasks are the independent variables in this study. Previous studies have
examined shopping task orientations, through self-reporting, after shoppers interacted with
store environments (e.g., Mathwick et al., 2002). Given the large body of evidence
suggesting that people categorize things based on characteristics derived flom their
previous experiences, such as cognitive concepts and schema-based affects (e.g., F iske and
Neuberg, 1990), there may have been a validity problem if the shopping task orientations
were not controlled. To handle the possible errors, a scenario approach was used to execute
this experimental research. While a scenario technique is open to criticism for its lack of
realism, it was useful for this study because the scenario technique allowed a valid
examination of consumer reactions to store environments (Babin, Hardesty, and Suter,
2003). Thus, two scenarios—one for the goal-oriented shopping task and one for the
experiential shopping task—were prepared, to instill shopping task orientations in the

subjects.

47

Several scenarios each were developed for the goal-oriented shopping task and the
experiential shopping task. In-depth interviews were executed to confirm which scenarios
would provide clear shopping tasks to the interviewees. Finally, two scenarios were
selected for use in this research. Each scenario is described in Appendix 2.

Dependent variables

The perception of store environments was tested using measures set by
Vrechopoulos et al. (2004): perceived usefulness and ease of use for the analytic
perception of stores, and entertainment for the intuitive perception of stores. All three of
these dependent variable measures of perception are highly reliable. The reliability scores
are: four items for ‘perceived usefulness’ (.98); three items for ‘ease of use’ (.95); and three
items for ‘entertainment’ (.94).

The original measures were developed for online shopping environments only, on a
7-point Likert scale. To meet both offline and online retail store contexts for this study,
they were modified or elirrninated. Thus, tlnree items were used to test for the perceived
usefulness and three items were used to test for ease of use and entertainment (Refer to
Appendix 3).

The components for identifying attitudes toward stores were tested using semantic
differential cognitive attitude component- and affective attitude component- items.
Respondents were asked to indicate how they thought about stores, when visiting the stores,
for the cognitive attitude components, and how they felt about stores, when visiting the
stores, for the affective attitude components, by using a semantic differential scale on a
7-point Likert scale. The scale has been used in various contexts and has reported scores

with high reliability.

48

Purchase intention was measured by Ajzen and F ishbein’s scale (1980), which
originally consisted of six items that measured the semantic differentiation of an
inclination, with each item associated with a specific behavior. The scale has been broadly
accepted in various contexts in the literature because it is considered sufficient for
explaining both purchase- and non-purchase- behaviors. Taking time constraints into

consideration, only three of the six items were used for this study (refer to Appendix 3).

Control

When examining cognition and affect in decision-making, various external
variables and individual characteristics can have an influence on the interaction with
shopping environment, shopping choice, and its process. Prior studies focused on external
variables such as experience/knowledge, mood, and Internet efficacy as moderators.
Therefore, when exannining the shopping continuum, I eliminated the possible impact of
the three external variables on the experiment’s result. Measurernnent items of the control
variables are listed in detail in Appendix 4.
Brand experience/knowledge

When an experiment uses existing retail stores, it would be difficult to interpret the
results because many exogenous variables that cannot be controlled, such as experience or
knowledge of particular retail stores, are expected. To ensure the validity of the results of
the experiment, experience/knowledge about shopping are expected to influence this
research because prior experiences are used as a guideline to evaluation, called
“schema-driven affect” (Fiske, 1982).

For instance, Mandel and Johnson’s online experiment (2002) reported that

49

experience and krnowledge induces a different formation of decision-making, called
“priming,” about preference toward Websites. They tested the impact of priming on
preference by manipulating Websites’ pictures and colors between experts and novices.
Novices were strongly influenced by the priming in external search in the Websites, but
experts were not mediated by the priming. Mantel and Kardes (1999) also found a similar
result in decision-making processing. Their results showed that when high levels of
involvement are moderated by experience and influence motivations, analytic processing
is made more thoroughly and systematically. On the other hand, previous bad experiences
with online stores such as difficulties or failures of transactions flom certain online stores
can affect value perceptions toward the online stores in this experiment.
M

Many psychologists suggested that pre-existing pleasure emotions might increase
favorable evaluations of novel stimuli more than familiar stimuli in evaluating information.
In doing so, people tend to seek novel stimuli (e.g., Schwarz, 1986). This strong impact of
mood on consumer evaluation is called the “carry-over effect.” For instance, Gardner
(1985) reported that mood states have direct and indirect effects on evaluation, recall, and
behavior through reviewing influences of mood states on consumer behavior. Swinyard’s
investigation of shopping intention (1993) found a similar result: that mood did not exert
significant main effects on shopping intentions, but it played an important role in
interaction effects. Similarly, Li et al. (1999) also found that shopper types were more
influential than mood on impulsive purchase behavior. However, the interesting finding in
this research is that both recreational shoppers who entered the mall in a positive mood and

economic shoppers who entered the mall in a negative mood revealed relatively higher

50

impulse purchase rates. That is, mood was a significant mediator on interactions between
shopper type and purchase.

However, it is important to distinguish between moods that shoppers have and
emotions toward online stores. Researchers have generally distinguished two types of
affect: emotions (e.g., relatively intense feeling states) and moods (e.g., more generalized
mild and long lasting feeling states). However, the previous studies do not provide a clear
distinction between mood and emotion. The possible influence of shoppers’ moods before
interaction with the Web stores might influence the interaction of cognitive and affective
perceptions toward online stores, influencing the shopping decision-making (process) in
this research. Thus, subjects’ moods before experiencing the Web stores in this research
were controlled to examine the interaction between cognition and affect flom Web stores’
properties. Subjects who are in an extremely high or low mood (a pleasurable state or a
unpleasurable state) were eliminated flom the analysis.

Internet efficacy

For the online store experiment, a high level of Internet experience/knowledge
might cause one to engage with more thorough systematic analysis, so that people who
have high Internet experience and knowledge might be associated with higher cognition
than affect flom the surface- and depth-properties of online stores. That is, responses flom
the subjects who have had too many experiences and who have a high degee of knowledge
about online shopping may show distorted reactions to the Websites for this research.

In particular, Internet self-efficacy is important because it reflects a capability with
regards to Internet usage. It is a belief in one’s ability to organize and execute a particular

course of action related to the Internet. Many prior studies reported that as Internet

51

self-efficacy increases, Internet users become well able to use it to achieve desired
outcomes. Eventually, Internet usage and related activities on the Internet increase through
high Internet self-efficacy (e.g., Choi, LaRose, and Lee, 2003). The Internet
experience/knowledge measurement was measured using measures for Internet
self-efficacy. The three items of Internet efficacy were modified to fit into an online

shopping context.

52

V. RESULTS

495 usable response samples were collected. To manage the control variables
discussed in the literature, subjects who showed high scores (6 and 7 out of 7 on the Likert
scale) on the three control variables (brand knowledge/experience, mood, and Internet
efficacy) were eliminated. Thus, 368 response samples were used for fiirther analysis. 188
samples were used for Study 1 (offline store) and 180 samples were used for Study 2
(online store).

As can be seen in Table 5, subject demogaphic features are very similar to the
MCM customer profile data (which is regulatory reported). Therefore, the subjects in this

research are representative of MCM customers.

Table 5. Subject Demographic Features

 

 

Subjects in Present Research MCM Customers*
Gender Gender
Male 20.2% Male 18.5%
Female 79.8% Female 81.5%
Marriage Marriage
Unmarried 78.7% Unmarried 70%
Married 21.3% Married 30%
Age Age
20-25 30.8% 20-25 25.1%
26-30 46.6% 26-30 41.9%
31-35 14.4% 31-35 17.8%
Over 36 6% Over 36 11.7%
Income Income
(Monthly Household Income) (Montlnly Household Income)
Under 2 Million 36.4% Under 2 Million 40.0
2-3 Million 29.3% 2-3 Million 30-4
3-4 Million 12.3% 3—4 Million 8.5
4-5 Million 6.0% 4-5 Million 8.1
5-6 Million 5.8% 5-6 Million 5.0
6-7 Million 4.7% 6-7 Million 4.3
Over 7 Million 5.5% Over 7 Million 3.7

 

 

 

 

 

* The MCM customer features were compiled from the MCM customer profile data (2008. 1.).

After checking that a subject was suitable, a reliability test was executed. In
analytic perceptions toward store environments, the reliability score of the three items for
‘perceived usefulness’ showed .94, and the reliability score of the three items for ‘ease of
use’ showed .90. Regarding intuitive perceptions toward store environments, the reliability
score of the three items for ‘entertainment’ showed .93. The reliability score for the five
items measuring cognitive attitude components toward stores showed .93 and that for the
five items measuring affective attitude components toward stores showed .94. The
reliability score of the three items for purchase intention showed .95. Overall, all measures

showed .90 or higher, and so were highly reliable (Refer to Appendix 2).

Results of Study 1

Study I examined the effect of both shopping conguence (on perceptions and
attitudes toward stores) and purchase intentions, in the context of an offline retail channel.
ANOVA tests were conducted to test the hypotheses, examining the effect of shopping
conguence. Post-hoc tests were performed following the ANOVA tests.

The result of the ANOVA for Hl-l a showed that shoppers who experienced
shopping conguence between their shopping tasks and the store environments showed a
higher ‘perceived usefulness’ toward the stores than shoppers who experienced shopping
inconguence (perceived usefulness_congruence = 5.67 ; perceived usefulness_incongruence =
4.33, F(186) = 72.987, p<.001). Hl-lb revealed that shoppers who experienced shopping
conguence between their shopping tasks and the store environments also showed a higher
‘ease of use’ toward the stores than shoppers who experienced shopping inconguence

(ease of use_congruence = 5.86; ease of use_incongruence = 4.43, F(186) = 79.562, p<.001).

54

Consistent with Hl-2, it was apparent that shoppers who experienced shopping
conguence between their shopping tasks and the store environments showed a higher
‘entertainment’ measurement toward the stores than shoppers who experienced shopping
inconguence (entertainment_congruence = 5.02; entertainment_incongruence = 4.48, F(186)
= 8.726, p<.05). Therefore, Hl-l and H1-2 were statistically supported.

The result of the ANOVA test for H2-1 a indicated that the scores for perception
toward stores were statistically different between the four goups. Analytic shopping
conguence showed the highest score on ‘perceived usefulness’ (perceived
usefulness_analytic congruence = 5.85; perceived usefulness_irntuitive congruence = 5.47;
perceived usefulnessjnconguence (goal task-intuitive store) = 4.88; perceived
usefillness_incongruence (experiential task-analytic store) = 3.75, F(184) = 40.218, p<.001).

The result of the ANOVA test for H2-1b indicated that the scores for perception
toward stores were also statistically different between the four goups. Intuitive shopping
conguence showed the highest score on ‘ease of use’ (ease of _analytic congruence = 5.61;
ease of use_intuitive congruence = 6.14; ease of use_inconguence (goal task-intuitive store) =
5.32; ease of use_incongruence (experiential task-analytic store) = 3.49, F(184) = 82.164,
p<.001).

The result of the ANOVA test for H2-2 indicated that the scores for perception
toward stores were statistically different between the four goups. Intuitive shopping
conguence showed the highest score on ‘entertainment’ (entertainment_analytic conguence
= 4.02; entertainment _intuitive conguence = 6.14; entertainment_inconguence (goal
task-intuitive store) = 5.14; entertainment_incongruence (experiential task-analytic store) = 3.79,

F(184) = 68.537, p<.001). Therefore, H2-1 and 2-2 were statistically supported, as

55

hypothesized (Refer to Table 6).

Consistent with H3-1, shoppers who experienced shopping congruence between
their shopping tasks and the store environments showed a higher score on cognitive
attitude components toward the stores (CAC) than shoppers who experienced shopping
incongruence (CAC_congruence = 5.41; CAC_incongruence = 4.99, F(186) = 10.424, p<.05).
That is, shoppers who experienced shopping congruence thought that the stores offered
higher quality (more cognitive attitudes toward stores) than shoppers who experienced
shopping incongruence.

Not consistent with H3-2, however, shoppers who experienced shopping
congruence between their shopping tasks and the store environments did not show higher
affective attitude components toward the stores (AAC) than shoppers who experienced
shopping incongruence (AAC_congruence = 5.01; AAC_incongruence = 5.07, F(186) = 0.146,
p=.703). That is, shoppers who experienced shopping congruence did not like the store
more (more affective attitudes toward stores) than shoppers who experienced shopping
incongruence. Thus, H3 was only partially supported.

The result of the ANOVA test for H4-1 indicated that scores for cognitive attitude
components toward stores were statistically different between the four groups. Analytic
shopping congruence indicated the highest score on the ‘cognitive attitude component’
toward stores (CAC_analytic congruence = 5.45; CAC _intuitive congruence = 5.37; CAC
_incongruence (goal task-intuitive store) = 5.10; CAC _incongruence (experiential task-analytic store)
= 4.88, F(184) = 3.995, p<.05). Interestingly, the result of the ANOVA test for H4-2
showed that scores for the affective attitude components toward stores were statistically

different between the four groups. In contrast with the hypothesis, intuitive shopping

56

congruence did not show the highest score on an ‘affective attitude component’ toward
stores (AAC_analytic congruence = 4.79; AAC _intuitive congruence = 5.25; AAC_incongruence
(goal task-intuitive store) = 5.34; CAC _incongruence (experiential task-analytic store) = 4.77,
F(184) = 4.579, p<.05). Therefore, H4 was only partially supported.

Shoppers who experienced shopping congruence showed a higher cognitive
attitude component toward the stores than shoppers who experienced shopping
incongruence (H4-1). On the other hand, shoppers who experienced shopping congruence
did not show a higher affective attitude component toward the stores than shoppers who
experienced shopping incongruence (H4-2). Thus, the results revealed that shopping
congruence did not influence affective attitude components toward stores (Refer to Table
7).

The result for H5 showed that participants who had a shopping congruence
between their shopping tasks and their shopping environments showed higher purchase
intentions than participants who did not (purchase intention_congruence = 6.08; purchase
intention_incongruence: 4.53, F (186) = 77.709, p<.001). Hence, H5 was statistically
supported.

The result of the ANOVA test for H6 showed that scores for purchase intention
were statistically different between the four groups. Analytic shopping congruence showed
the highest score on ‘purchase intention’ (purchase intention_analytic congruence = 6.28;
purchase intention _intuitive congruence = 5.86; purchase intention_incongruence (goal
task-intuitive store) = 5.42; purchase intention _incongruence (experiential task-analytic store) =

3.57, F(184) = 66.126, p<.001) (Refer to Table 8).

57

In summary, all of the hypotheses in Study 1, except H3-2 and H4-2, were
statistically supported. As hypothesized, the results found that shoppers who experienced
shopping congruence had higher analytic and intuitive perceptions toward offline stores
and purchase intentions than shoppers who experienced shopping incongruence. One thing
that was not supported is the effect of shopping congruence on affective attitude
components toward stores. Shoppers who experienced shopping congruence had higher
cognitive attitude components toward stores than shoppers who experienced shopping
incongruence, but shoppers who experienced shopping congruence did not have higher
affective attitude components toward stores than shoppers who experienced shopping
incongruence.

The results for Study 1 also found that when experiencing analytic shopping
congruence, shoppers showed higher analytic perceptions and attitudes than shoppers did
when experiencing an intuitive shopping congruence. Shoppers experiencing an intuitive
shopping congruence showed higher intuitive perceptions and attitudes than shoppers
experiencing an analytic shopping congruence. Consequently, shoppers experiencing
analytic shopping congruence showed the highest purchase intentions when compared with
shoppers experiencing analytic shopping incongruence, intuitive shopping congruence or

intuitive shopping incongruence.

58

Table 6. Results of ANOVA Test for H2

 

in Task

Sho

in Task

Sho

in Task

Sho

 

H2-la. Analytic Perception - Perceived Usefulness

Offline Store Environment

 

 

Analytic store Intuitive store
environment environment
Goal-oriented 5.85 4.88
shopping task (n=48) (n=50)
Experiential-oriented 3.75 5.47
shopping task (n=47) (n=43)

 

Goal-oriented
shopping task

Experiential-oriented
shopping task

Goal-oriented
shopping task

Experiential-oriented
shopping task

 

 

H2-1b. Analytic Perception - Ease of Use

Offline Store Environment

 

 

 

 

Analytic store Intuitive store
environment environment
5.61 5.32
(n=48) (n=50)
3.49 6.14
(n=47) (n=43)

 

112-2. Intuitive Perception - Entertainment

Offline Store Environment

 

 

 

Analytic store Intuitive store
environment environment
4.02 5.14
(n=48) (n=50)
3.79 6.14
(n=47) (n=43)

 

 

 

59

 

 

 

 

Table 7. Results of ANOVA Test for H4

 

in Task

Sho

in Task

Sho

 

Goal-oriented
shopping task

Experiential-oriented
shopping task

Goal-oriented
shopping task

Experiential-oriented
shopping task

H4-l. Cognitive Attitude Component

Offline Store Environment

 

 

 

Analytic store Intuitive store
environment environment
5.45 5.10

(n=48) (n=50)

4.88 5.37

(n=47) (n=43)

 

 

H4-2. Affective Attitude Component

Offline Store Environment

 

 

 

Analytic store Intuitive store
environment environment
4.79 5.34

(n=48) (n=50)

4.77 5.25

(n=47) (n=43)

 

 

 

Table 8. Results of ANOVA Test for H6

 

 

H6. Purchase Intention
.:
8
[fl Goal-oriented
.5. shopping task
,2 Experiential-oriented
02 shopping task

Offline Store Environment

 

 

 

Analytic store Intuitive store
environment environment
6.28 5.42
(n=48) (n=50)
3.57 5.86
(n=47) (n=43)

 

 

 

60

 

 

 

 

 

Results of Study 2

Study 2 was designed to test both the influence of shopping congruence (on
perceptions and attitudes toward stores) and purchase intentions, in an online retail channel
context. That is, Study 2 tested the robustness of shopping congruence in online stores. To
analyze Study 2, ANOVA tests were administered, as in Study 1.

On the whole, in an online store context, the results of Study 2 were only partially
consistent with Study 1, which used an offline store context. Some of the results from
Study 2 showed different results than those from Study 1. First, it was apparent that
shopping tasks affected each shopping environment’s perceptions and attitude
components, whether in an online or an offline context.

Consistent with H7-1a, it was apparent that shoppers who experienced shopping
congruence between their shopping tasks and store environments showed higher
‘perceived usefulness’ toward online stores than shoppers who experienced shopping
incongruence (perceived usefulness_congruence = 5.59; perceived usefulness_incongruence =
4.16, F(17 8) = 75.460, p<.001). The result of the ANOVA test showed that shoppers who
experienced shopping congruence had higher intuitive perceptions toward stores than
shoppers who experienced shopping incongruence.

Consistent with H7-1b, shoppers who experienced shopping congruence between
shopping tasks and store environments showed a higher ‘ease of use’ toward online stores
than shoppers who experienced shopping incongruence (ease of use_congruence = 5.48;
ease of use_incongruence = 3.98, F(178) = 67.200, p<.001).

Consistent with H7-2, it was apparent that shoppers who experienced shopping

congruence between their shopping tasks and store environments showed a higher

61

‘entertainment’ perception toward online stores than shoppers who experienced shopping
incongruence (entertainment_congruence = 5.56; entertainment_incongruence = 4.54, F (178)
= 41.088, p<.001). Therefore, H7-1 and H7-2 were statistically supported.

The result of the ANOVA test for H8-1 a indicates that the scores for perception
toward online stores are statistically different between the four groups. Analytic shopping
congruence indicates the highest score on ‘perceived usefulness’ toward online stores
(perceived usefulness_analytic congruence = 5.89; perceived usefulness _intuitive congruence =
5.26; perceived usefirlness_incongruence (goal task-intuitive store) = 5.00; perceived usefulness
_incongruence (experiential task-analytic store) = 3.46, F(176) = 58.572, p<.001).

The result of the ANOVA test for H8-1b indicates that the scores for perception
toward online stores are statistically different between the four groups. Analytic shopping
congruence indicates the highest score on ‘ease of use’ toward online stores (ease of use
analytic congruence = 5.38; ease of use _intuitive congruence = 5.59; ease of use_incongruence (goal
task-intuitive store) = 5.02; ease of use_incongruence (experiential task-analytic store) = 3.10,
F(176) = 58.284, p<.001).

The result of the ANOVA test for H8-2 indicates that the scores for perception
toward online stores are statistically different between the four groups. Analytic shopping
congruence indicates the highest score on ‘entertainment’ perception toward online stores
(entertainment_analytic congruence = 5.18; entertainment _intuitive congruence = 5.98;
entertainment_incongruence (goal task-intuitive store) = 4.81; entertainment_incongruence
(experiential task-analytic store) = 4.30, F (176) = 21.499, p<.001). Therefore, H8-1 and H8-2
were statistically supported (See Table 9).

Not consistent with H9-1, shoppers who experienced shopping congruence

62

between their shopping tasks and the store environments did not show higher cognitive
attitude components toward online stores (CAC) than shoppers who experienced shopping
incongruence. In other words, there is no difference in the cognitive attitude components
toward online stores, between shopping congruence and shopping incongruence
(CAC_congruence = 5.93; CAC_incongruence = 5.91, F(178) = 0.026, p=.873).

Also, not consistent with H9-2, shoppers who experienced shopping congruence
between their shopping tasks and the store environments did not show higher affective
attitude components toward online stores (AAC) than shoppers who experienced shopping
incongruence (AAC_congruence = 5.81; AAC_incongruence = 5.90, F(178) = 0.443, p=.507).
Thus, neither H9-l nor H9-2 was supported.

The result of the ANOVA test for H10-1 indicates that the scores for cognitive
attitude components toward online stores were not statistically different between the four
groups (CAC_analytic congruence = 5.95; CAC _intuitive congruence = 5.91;
CAC_incongruence (goal task-intuitive store) = 6.04; CAC_incongruence (experiential task-analytic
store) = 5.80, F(176) = .545, p=.652). The result of the AN OVA test for H10-2 also
indicates that the scores for affective attitude components toward online stores were not
statistically different between the four groups. (AAC_analytic congruence = 5.76; AAC
_intuitive congruence = 5.87; AAC_incongruence (goal task-intuitive store) = 6.00;
CAC_incongruence (experiential task-analytic store) = 5.86, F(1 76) = .327, p=.806). Therefore,
H10-1 and H10-2 were not supported (Refer to Table 10).

The result for H11 showed that participants who had a shopping congruence
between their shopping tasks and their shopping environments showed higher purchase

intentions than participants who did not (purchase intention_congruence = 6.30; purchase

63

intention_incongruence = 5.96, F(178) = 5.643, p<.05). Hence, H11 was statistically
supported.

The result of the ANOVA test for H12 indicated that the scores for purchase
intention were statistically different between the four groups. Analytic shopping
congruence showed the highest score on ‘purchase intention’ (purchase intention_analytic
congruence = 6.37; purchase intention _intuitive congruence = 6.22; purchase
intention_incongruence (goal task-intuitive store) = 6.12; purchase intention_incongruence
(experiential task-analytic store) = 5.83, F (1 76) = 2.788, p<.05). Thus, H12 was also supported

(Refer to Table l 1).

In summary, the results for Study 2 revealed that shopping congruence was
partially robust in an online retail channel context. Shopping congruence influenced
analytic and intuitive perceptions toward online stores, as well as purchase intentions in an
online retail channel context. On the other hand, shopping congruence did not affect either
cognitive attitude components or affective attitude components toward online stores. In
particular, shopping congruence did not influence affective attitude components toward

online stores, the same result as found with offline stores.

64

Table 9. Results of AN OVA Test for H8

 

in Task

Sho

in Task

Sho

in Task

Sho

 

Goal-oriented
shopping task

Experiential-oriented

shopping task

Goal-oriented
shopping task

Experiential-oriented

shopping task

Goal-oriented
shopping task

Experiential-oriented
shopping task

H8-1 a. Analytic Perception - Perceived Usefulness

Online Store Environment

 

 

 

Analytic store Intuitive store
environment environment
5.89 5.00
(n=47) (n=41)
3.46 5.26
(n=49) (n=43)

 

 

H8-1b. Analytic Perception -_Ease of Use

Online Store Environment

 

 

 

Analytic store Intuitive store
environment environment
5.38 5.02
(n=47) (n=4l)
3.10 5.59
(n=49) (n=43)

 

 

H8-2. Intuitive Perception - Entertainment

Online Store Environment

 

 

 

Analytic store Intuitive store
environment environment
5.18 4.81
(n=47) (n=41)
4.30 5.98
(n=49) (n=43)

 

 

 

65

 

 

 

 

Table 10. Results of AN OVA Test for H11

 

H10-1. Cognitive Attitude Component
Online Store Environment

 

 

.1 Analytic store Intuitive store
3 environment environment
‘" Goal-oriented 5.95 6.04

.5 shopping task (n=47) (n=41)

.2 Experiential-oriented 5.80 5.91

02 shopping task (n=49) (n=43)

 

 

 

 

H10-2. Affective Attitude Component
Online Store Environment

 

 

.: Analytic store Intuitive store
3 environment environment
5‘ Goal-oriented 5.76 6.00

.5 shopping task (n=47) (n=41)

.2 Experiential-oriented 5.86 5.87

”3 shopping task (n=49) (n=43)

 

 

 

 

 

 

 

Table 11. Results of AN OVA Test for H12

 

 

 

H12. Purchase Intention
Online Store Environment
Analytic store Intuitive store
:53 environment environment
[" Goal-oriented 6.37 6.12
,5 shopping task (n=47) (n=41)
5% Experiential-oriented 5.83 6.22
shopping task (n=49) (n=43)

 

 

 

 

 

 

 

66

Results of Regression Analysis

In addition to the other tests, a regression analysis was done to investigate how
shopping congruence influences purchase intention, along with perceptions and attitudes
toward stores, and control variables. As mentioned earlier, respondents who showed high
scores (6 and 7 on 7- point Likert scale) on the three control variables (brand
experience/knowledge, mood and Internet efficacy) were eliminated in Study 1 and Study
2. For this additional analysis, however, the total data—including respondents who. showed
high scores on the three control variables (brand experience/knowledge, mood and Internet
efficacy)——were used. The purpose of this analysis is to investigate whether shopping
congruence influences purchase intention and whether perceptions and attitudes toward
stores influence purchase intention under a statistically un-controlling situation.

A dummy-coding scheme was used in the regression analysis to observe the impact
of shopping congruence on purchase intention; shopping congruence was assigned value 1
and shopping incongruence was assigned value 0. The estimation result for the regression

model is shown below:

 

 

Model for Regression Analysis:

Purchase Intention = Bo + 50 Shopping Congruence + [31 Analytic Perception
+ [32 Intuitive Perception + B3 Analytic Attitude
+ [34 Intuitive Attitude + B5 Brand Experience + B6 Mood
+ B7 Internet Efficacy + u

 

The result of the regression analysis for Study 1 is shown in Table 12. This model,

which looked at seven variables (two types of perceptions, two types of attitude

67

 

components toward stores, and three control variables) and shopping congruence, fit
perfectly and explained a large percentage of the variance with R2 = .588. The coefficient
for shopping congruence was B=.213 (p<.01). Thus, the positive impact of shopping
congruence on purchase intention was found.

The results also found positive impacts for ‘perceived usefulness’ (B=.255, p<.01)
and ‘ease of use’ (B=.090, p<.05), as well as for cognitive perception and ‘entertainment’
(B=.144, p<.01), on purchase intentions. On the other hand, the results for cognitive and
affective attitude components toward stores were not significant. Among the three control
variables, the results found that only ‘brand experience’ positively influenced purchase
intentions (B=.194, p<.01) and that the others were not significant.

The result of the regression analysis for Study 2 is shown in Table 13. The result of
this regression analysis was different from that for Study 1. The results for Study 2 found
the positive impact of ‘shopping congruence’ on purchase intentions (B=.194, p<.05) as
expected. However, both ‘cognitive attitude components’ toward stores (B=.098, p<.05)
and ‘affective attitude components’ toward stores were statistically significant ([3=.111,
p<.01), whereas those for ‘cognitive perception’ and ‘affective perception’ toward stores
were not statistically significant in the online context. The results also found positive
impacts for ‘brand experience’ (B=.181, p<.05) and ‘Internet efficacy’ (B=.085, p<.01) on
purchase intentions. On the other hand, ‘mood’ was not significant. The model for Study 2
explained a large percentage of the variance with R2 = .597.

Consequently, the regression results showed that a shopping congruence, indeed,
influences purchase intentions in both offline and online store situations. Aside from the

impact of shopping congruence, the impacts of perceptions and attitudes toward stores on

68

purchase intentions are totally different, depending on the retail channels. Cognitive and
affective perceptions toward stores positively influence purchase intentions in online stores,
whereas cognitive and affective attitude components do not influence purchase intentions
in offline stores. In online stores, cognitive and affective attitude components toward stores
positively influence purchase intentions, whereas cognitive and affective perception do not
influence purchase intentions in offline stores.

Lastly, the three control variables influence purchase intentions differently,
depending on offline and online situations. Brand experience influences purchase
intentions in both offline and online stores, whereas mood does not have an influence in
either retail channel. Internet efficacy does not influence purchase intentions in offline

stores, but does influence purchase intentions in online stores.

69

Table 12. Result of the Regression for Study 1

 

 

. Standardized
Study 1. Offline Store Unstandardrzed Coefficients T-value F R2
Coefficrents (B)
(Beta)
(““5““) .121 .241
5"”ng “’“gmm .320 .213 2.067 **
Cognitive perception
' Ezgeg‘fiiszsefi’mess .394 .255 2.521 **
' .170 .090 1.010*
Intuitive perception
- Entertainment "
.231 .144 1.337 39.127 .588
“gum“ “mm“ .111 .094 1.293
component
Affective attitude component '001 '00] '012
. .642 .194 2.097 **
Brand experience
Mood .070 .020 .009
.009 .004 .014

Internet efficacy

 

* p<.05 ** p<.01

70

Table 13. Result of Regression for Study 2

 

Unstandardized Standardized

 

Study 2. Online Store Coefficients Coefficients T-value F R2
(B) (Beta)

(Constant) .524 1.252 *
Shopping congruence .441 .194 2.429 *
Cognitive perception

- Perceived usefulness -.001 -.001 -.009

- Ease of use -.005 -.014 -.l37
Intuitive perception

- Entertainment .059 .084 .999 45-575 .597
Cognitive attitude component .191 .098 1.001 *
Affective attitude component .254 .111 1.904 **
Brand experience .242 .181 1.929 *
Mood . 170 .197 .259
Internet efficacy .152 .085 1.870 **

 

* p<.05 ** p<.01

71

VI. DISCUSSIONS AND CONCLUSIONS

Discussion and Conclusion

This study contributes to the development of a new shopping theory. Based on CCT,
this study initially conceptualizes a shopping congruence formed by reviewing CCT, and
literature on shopping environments and the impact of affect on decision-making. People
have a tendency toward pattern-seeking shopping behavior, made by the dynamic
interaction of shopping task characteristics and shopping environment characteristics
(Hammond, 2000). Shopping congruence is an adaptive theory about pattern-seeking
shopping behaviors. As shopping congruence explains how people focus more attention on
those aspects of stores that are salient to their shopping goals, we can forecast people’s
reactions toward stores based on their shopping goals.

This study empirically tested shopping congruence across multi-retail channels.
Study 1 examined whether the shopping congruence influenced perceptions and attitudes
toward stores, and, furthermore, whether the shopping congruence influenced purchase
intentions in an offline store situation. Later, to assure the robustness of the shopping
congruence across two retail channels (offline and online stores), Study 2 tested shopping
congruence in online stores.

The results for H1 and H2 strongly supported the impact of a shopping congruence
between shopping tasks and shopping environments on perceptions toward stores.
Goal-oriented shoppers were more associated with analytic perceptions of shopping
environments, whereas experiential-oriented shoppers were more associated with intuitive
perceptions of shopping environments, as hypothesized. This result is consistent with the

previous studies that reported the influence of consumers’ shopping orientations on their

72

responses to retail store environments (e.g., Babin, et al., 1994; Mathwick et al., 2002;
Moye and Kincade, 2002). In particular, the results found that analytic shopping
congruence influenced shoppers to look for more novel analytic information to meet their
analytic shopping goals. The result also found that intuitive shopping congruence
influenced shoppers to prefer more intuitive shopping experience and saliently perceived
intuitive shopping environments. These results strongly support the previous shopping
literature (e.g., Babin et al., 1994; Mathwick etal., 2002).

Thus, by testing shopping congruence in terms of cognition and affect, the result of
Study I initially assured that shopping congruence incorporates analytic shopping
congruence and intuitive shopping congruence. Goal-oriented shopping seems to drive
more cognitive decision-processes. In doing so, goal-oriented shoppers prefer cognitive
experiences and focus more attention on the cognitive aspects of stores because those
aspects are salient to their cognitive shopping goals. It can be called an “analytic shopping
congruence.” On the other hand, an experiential shopping orientation seems to drive more
intuitive decision-processes and, in doing so, shows that experiential-oriented shoppers
prefer intuitive experiences and focus more attention on intuitive aspects of stores because
those aspects are salient to their intuitive cognitive shopping goals. It can be called an
“intuitive shopping congruence.”

The results for H3 and H4 revealed that shopping congruence only partially
influences attitudes toward stores. Shopping congruence positively influences on cognitive
attitude components, but it does not influence on affective attitude components toward
store. Moreover, an analytic shopping congruence influences cognitive attitudes toward

stores, but an intuitive shopping congruence does not influence affective attitudes toward

73

stores, likewise.

Regarding the results, goal shoppers might easily form analytic perceptions
through their analytic shopping experiences. In doing so, it can be seen that shopping
experiences under specific shopping goals also seem to change cognitive attitudes toward
stores. Thus, this study supported the previous studies that insisted that shopping
congruence impacted cognitive attitudes toward stores (e. g., Fiore et al., 2000; Wood,
2000). This shares a similar idea with the study by Mathwick et al. (2002), which
investigated the impact of shopping tasks on retail experiential values.

On the other hand, intuitive shopping congruence is built by an experiential
shopping task, which is not specific. Even if shoppers are more engaged with intuitive
shopping environmental cues in flow experience, their attitudes toward the store are
unlikely to be changed by their store experience. In general, attitudes can be changed by
store experiences under a certain task situation. However, this study presented a new
finding—affective attitudes do not seem to be changed easily by store experiences under
shopping tasks. This is different from the previous studies reporting that affective attitudes
can be changed by experiences under certain goals (e. g., Sprangenber et al., 1996; Wood,
2000)

In H5, the research showed that there is an impact of shopping congruence between
tasks and environments on a purchase intention. The results strongly suggest that both
analytic-and intuitive-shopping congruencies in shopping tasks and shopping
environments will increase purchase intentions. In particular, it is important to note that
experiential sh0ppers under non-specific shopping goals may make purchases if they

experience intuitive shopping congruence through their shopping experience. This result

74

supported the previous studies, which reported that the congruence of combinations
between task and environment would facilitate a task’s performance (e.g., Hammond et al.,
1987; Mathwick et al., 2002).

The result of H6 found the importance of the impact of shopping goal tasks on
purchase intentions and assured that either an analytic- or an intuitive- store experience
with a store enviromnent is an important factor, as its congruence increases purchase
intentions. Thus, retail stores should provide both analytic and intuitive store environments
in various ways, to meet to various shoppers’ shopping goals. For example, for shoppers
whose shopping goals are lower prices, a retail store environment should provide an easy
search for prices, either by a price device or by providing price information by POP, to
allow for price comparison. For shoppers with a shopping goal of looking for diverse
merchandise assortments, retailers should provide VMD, such as an easy layout for finding
diverse colors and sizes, in order to meet their customers’ shopping goals.

Study 2 tested whether the impact of shopping congruence on perceptions, attitudes,
and purchase intentions toward stores is robust across multi-channels. The results for H7
and H8 showed that the impact of shopping congruence on perceptions toward stores is
robust in an online store context. According to Vrechopoulos et al. (2004)’s finding, people
differently perceive shopping environment such as store layout when comparing between
offline and online stores. Thus, different impact of shopping congruence on perceptions
toward online stores was expected. However, the result revealed that the impact of
shopping congruence on perceptions toward stores is robust.

The results for H9 and H10 about the impact of shopping congruence on analytic

and affective attitude were not the same for online store research as for offline store

75

research. Online shopping experiences under shopping tasks do not influence both the
analytic attitude and the affective attitude toward online stores. This result seems to be
caused by the limitations of online store environments, as the existing online shopping
literature has insisted (e.g., Burke, 1997; Burke et al., 1992; Citrin et al., 2003; Wakefield
and Baker, 1998).

As many previous studies have indicated, online shopping environments have big
weaknesses, such as the lack of a range of sensory experiences and a low level of service.
Due to the poor online shopping environments, in comparison with offline shopping
environments, online shoppers seem to be unable to go through the flow experience, which
is a mental state of operation in which people focus on what they are doing. As online
shopping experiences do not provide this powerfirl flow experience, people might find it
difficult to associate emotionally with intuitive online shopping environments. As a result,
their online shopping congruence does not seem to influence their attitudes toward stores,
which is a subsequent behavior of the perception toward stores.

Thus, as Childers et al. (2001) pointed out, online stores should provide
entertaining shopping environments because online shopping purchases are influenced by
both utilitarian and hedonic motivations. The flow experience of a hedonic shopping
environment in online stores might also influence online shoppers’ analytic and affective
attitudes toward online stores. As hypothesized, the results of H11 and H12 found that the
impact of shopping congruence on purchases is robust across multi-channels. Shopping
congruence might be a powerful theory for explaining pattern-seeking behaviors. Thus,
retailers need to focus their efforts on ideas that will instill concrete shopping goals to

consumers before they enter stores, and then provide better shopping environments for

76

attracting analytic goal-oriented shoppers and intuitive goal-oriented shoppers in various
ways.

Finally, the result of a regression analysis showed that shopping congruence, along
with perceptions, attitudes toward both offline and online stores, and three control
variables, strongly influences purchase intentions. In a regression model of purchase
intentions for offline stores, cognitive and affective attitudes toward stores did not
influence purchase intention. On the other hand, in a regression model of purchase
intention for online stores, cognitive and affective perceptions toward stores did not
influence purchase intention. These contrasting results reveal that purchase models for
offline stores and online stores are different. For example, shoppers are facilitated in
achieving their goals by the more effective navigating offered by online stores than
conventional stores (V rechopoulos et al., 2004).

Considering the results, retailers should develop multi-channel strategies carefully
due to the differences between offline and online retail store situations. According to Shim
et al. (2000), online shopping involves a higher search quality for cognitive information,
whereas offline shopping (traditional shopping) involves a higher experience quality for
experiential information. These two retail channels might provide different shopping
experiences by the different quality of search. Thus, offline and online retailers should
complement their relative weaknesses in comparison with the other. For example, offline
stores should provide a higher quality of search for cognitive information in offline
shopping environments. On the other hands, online stores should provide a higher quality
of experience through improved affective store environments.

Therefore, considering the weaknesses and the differences found between offline

77

and online retail channels, multi-channel retailers should investigate their limitations in
each retail channel and then try to overcome them. Retailers should develop, and then
implement, multi-channel strategies that will increase the advantages of both physical and
virtual synergy (e. g., Steinfield et al., 2002; Tate, Hope, and Coker, 2005). For example,
Steinfield et al. (2002) suggested that the benefits of synergy in multi-channels are lower
costs, greater differentiation through value-added services, an improvement in trust, and
geographic- and product-extensions. Tate et al. (2005) pointed out success factors for
multi-channel retailers: functional integration, channel synergy, brand management,
information management, logistics management, customer management and active

scanning.

In summary, the results of Study 1 assure the positive impact of shopping
congruence on both cognitive and intuitive perceptions, on cognitive attitudes, and on
purchase intentions toward stores, except for affective attitudes toward stores. The initial
results found that a shopping congruence incorporates both cognitive shopping congruence
and intuitive shopping congruence. The results of Study 2 found that the impact of
shopping congruence on perceptions and purchase intentions is robust across
multi-channels (offline and online stores), whereas the impact of shopping congruence on
analytic and affective attitudes toward stores is not robust across multi-channels. Analytic
attitudes (the way to think) and affective attitudes (the way to feel) toward stores do not
seem to be easily changed by a short store experience, which this study used in its testing.
Additionally, the results of a regression analysis supported that shopping congruence,

indeed, strongly influences purchase intentions in both offline and online store situations.

78

Considering the differences between offline and online retail channels, retailers should

develop elaborate multi-channel strategies for success.

Directions for Future Study

First, shopping time may influence shopping congruence, as well as its impact on
attitudes toward stores and on purchase intentions. In the offline store research, subjects
normally took 5-20 minutes while they looked around the offline stores. In the online store
research, shopping time could not be measured because participation was conducted by
email, so it is difficult to say how online subjects spent their time experiencing the online
stores. Thus, a future study should measure shopping time in order to control its impact on
the results. Otherwise, it would be interesting for a future study to focus on shopping time
in order to investigate how the impact of shopping congruence may differ by shopping
time.

Second, this study used the MCM brand of leather goods. This product is of a
touch-and-feel type, and is an expensive brand that targets a luxury market. Due to the
product type and price, the results may not be generalized for other industries or products.
Thus, there are opportunities for further research using diverse product types (experiential
goods vs search goods vs credence goods) and different prices (low vs middle vs high
prices) to discover whether shopping congruence is dependent on product types and/or
prices.

Third, for decades, there have been many studies that have reported on the impact
of environmental cues on product evaluation and brand choice. For example, Berger and

Fitzsimons (2008) found that conceptual priming that is built by the surrounding

79

environmental cues influences product evaluation and choice. In particular, there have
been few studies that have investigated the impact of shopping tasks on perceptions and
attitudes toward products. It would be interesting to see a future study focus on the impact
of shopping congruence on perceptions and attitudes toward product brands in stores.

Fourth, in regards to the result that the purchase model for an online store situation
was not the same as that for an offline store situation, a future study needs to develop a
purchase model for online stores. In general, the objectives in Internet sites can be divided
into three areas: goal orientation (buy), hedonic orientation (browse), and search
orientation (search) (Bucklin et al., 2002). In particular, a searching behavior is one of the
most important factors used in online shopping, so that many online studies have focused
their attention on shoppers’ behaviors in searching for better deals; on search attributes and
search costs (e.g., Bakos, 1997; Degeratu et al., 2000); and on online search efficiencies,
experiences, and processes (e.g., Biswas, 2004; Mathwick and Rigdon, 2004). This
searching for low-cost online shopping might have an influence on the impact of shopping
congruence on multi-channel purchase behaviors. Thus, it would be interesting to see a
future study that focuses on the three orientations for the online purchase model by
including a searching task.

Lastly, the online stores use non-Asian models on their pages, which did not appear
to be the casein the offline stores. This difference in offline and online store environments
may influence shoppers’ reaction to stores. The use of actual retail settings in this study
means that the environmental differences cannot be controlled, which may be a limitation.

In addition, mcmworldwide.com allowed site visitors to actually complete a

purchase when collecting the research data. The site visitors who are participated in this

80

study were able to complete their purchases after looking around both mcmworldwide.com
and the SDD Mall. However, it should be noted that mcmworldwide.com has been
changed after the data collection, so that the site do not allow site visitors to complete a

purchase these days.

Contributions and Implications

This study contributes in three ways. First, this exploratory study contributes in that
it initially proposes shopping congruence. This exploratory study brings new insights
about the interaction between shopping tasks and shopping environments. This is
meaningful in that this research initially conceptualizes shopping congruence and then
empirically tests its impact on shopping decision-making processes in actual retail settings.
Furthermore, this study finds that shopping congruence consists of both an analytic
shopping congruence and an intuitive shopping congruence.

Second, the existence of both an analytic shopping congruence and an affective
shopping congruence has been supported by this study. This study provides an
understanding of how shoppers cognitively and affectively react to their shopping
environments, based on their specific shopping tasks. In the literature on decision-making,
most of the studies have only focused on cognition, as CCT does, with affect being treated
as a mediator to decision-making. This study views cognition and affect as being equal in
value and has found that shopping congruence consists of both cognitive shopping
congruence and intuitive shopping congruence.

Thus, retailers should try to provide both analytic and intuitive shopping

environments, so as to satisfy shoppers with varying shopping goals. When focusing on the

81

results of intuitive shopping congruence on purchase intention, retailers should create a
variety of store environments through visual merchandising and POP. When developing a
store environment plan, a “shot gun strategy” might be an appropriate option. As different
shoppers have a variety of shopping tasks, retailers should strategically provide both
analytic and affective store environments, rather than focus strictly on either analytic or
affective store environments.

In particular, e-tailers should provide those various types of information (i.e.,
multi-sensory cues) that would meet different online consumers’ characteristics for the
future success of e-commerce, as online store environments are a crucial factor to business
effectiveness, traffic and sales in online retailing (Citrin et al., 2003). Hence, multi-channel
retailers should develop interface designs that provide interactions between customers and
Web sites. An online store’s navigation structure and information content can positively
influence online service quality, and eventually influences overall satisfaction moderated
by the service quality perception (Montoya-Weiss and Grewal, 2003). Thus, retailers
should try to increase the quality of online service, which is perceived as being inferior to
that of offline. In addition, retailers should try to enhance affective shopping environments
because shoppers react to shopping environments and then build their attitudes toward
stores both analytically and affectively. To build strong affective shopping environment,
retailers should provide rich multi-sensory information and build strong relationships with
their customers.

Third, it was meaningful to find some robustness and differences, by investigating
shopping congruence between offline and online stores. This study found that shopping

congruence is a concrete concept across multi-channels, but provided an understanding of

82

how multi-retail channels differ from decision-making perspective of a shopper.
Considering these results, there are apparent differences in offline-and online- retail
channels, so retail multi-channel strategies and strategic plans for store environments
should differentiate between offline- and online- stores. For example, in an online store
situation, shopping congruence does not influence a Shopper’s attitude toward the stores,
neither is there an impact on purchase intention, whereas there is an influence in an offline
store situation. The findings emphasize that multi-channel retailers need to understand
channel synergy, in which multi-channels consistently provide seamless communication
and experiences to customers (Steinfield et al., 2002; Kaufman-Scarborough et al., 2002).

Therefore, retailers offering multi-channels to consumers should understand what
differences drive charmel choices and create a consistent image across their multi-channels.
For example, an offline store should promote its own Web multi-channel advantage for
two-way synergy (Schoenbachler and Gordon, 2002). Considering the results of this
research, it would be appropriate to employ a “click & mortar strategy” to multi-channel
situations. As found in this research, an online retail channel and an offline retail channel
are viewed differently in a Shopper’s decision-making process. Thus, when juggling these
two different retail channels, this hybrid approach might be the way to enlarge the synergy
opportunities (Steinfield et al., 2002). For example, online stores should provide more
analytical information to induce online shoppers to shop in offline stores. The online
shoppers will purchase more through their interactions with offline shopping
environments.

Consumers acquire richer information about products and retailers through the

search process. For example, online interactions such as bulletin boards, news goups,

83

chatting, and user evaluation provide e-tailers with the ability to seek out potential
customers and to give more information to their existing customers. In doing so, consumers
seem to be more involved with a number of third parties in their pursuit of good deals in
their online transactions (Coupey, 2001). In reality, most retailers operate their offline- and
online- business systems separately, so it is difficult to achieve synergy between the two
retail channels. Hence, to increase the synergy, multi-channel retailers should have a deep
understanding of these two retail channels and set up integrated operation and marketing
systems.

Finally, from a CCT perspective, this research contributes in that CCT has rarely
been empirically tested in a retail context (Doherty and Kurz, 1996), so this study assured
that theoretical idea of CCT provides an explanation about consumers’ decision-making

when shopping.

In conclusion, based on CCT, this study conceptualizes a shopping congruence,
which explains how people react to shopping environments, based on their shopping tasks.
The empirical results found that a shopping congruence incorporates both cognitive
shopping congruence and intuitive shopping congruence, and is robust across
multi-channels. This study also found that the shopping congruence influences perceptions
and attitudes toward stores and that it influences purchase intentions. The impact of
shopping congruence on attitudes toward stores works in an offline store situation, but does
not apply in an online store situation. Finally, this research found that the impact of

shopping congruence on purchase intentions is robust across multi-retail channels, so

84

shopping congruence is a very important factor to retailers. Retailers should understand

how it works and try to build synergistic multi-channel marketing strategies.

85

APPENDIX 1. Flow of Online Survey Process

  

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APPENDIX 2. Scenario for Shopping Tasks

 

Scenario of goal-oriented shopping tasks

Suppose that you have a need to buy MCM leather good in this MCM store today.
You recently received an MCM gifi coupon, with a $500 value, from a friend. The
coupon will expire in two days.

You therefore visit this MCM store. You will buy a MCM handbag or small
leather goods in this store.

 

 

Scenario of experiential-oriented shopping tasks

Suppose that you have an appointment to meet your friend in this department
store, but you arrived early.

For fun and to kill time, you visit this MCM store. You are just looking around to
see the latest styles in handbags and purses.

 

88

 

APPENDIX 3. Measurement of Survey

 

Items Reliability

 

Analytic Perceptions toward Stores

Perceived usefulness
The store that I have just visited is useful for searching and buying products. .94
The store that I have just visited enhances my effectiveness in product
searching and buying.

The store that I have just visited makes it easier to search for and purchase
products.

Ease of use

The store that I have just visited is easy to use.

The store that I have just visited is flexible to interact with. .90
It is easy to interact with the store I have just visited

 

Intuitive Perceptions toward Stores
Entertainment _
The store I have just visited was lots of fun to browse. 93
The store I have just visited was not just selling—it was entertaining me and °
I appreciated that.

I like the look and feel of the store I just visited.

 

Attitude Components toward Stores

Cogr_rjtive commnents of attitude toward the product/brand
: How do you think about this MCM store after you have visited it?

Poor 1 2 3 4 5 6 7 Excellent -93
Worthless l 2 3 4 5 6 7 Valuable
Unfriendly l 2 3 4 5 6 7 Friendly
Not credible l 2 3 4 5 6 7 Credible
Bad 1 2 3 4 5 6 7 Good

Affective components of attitude toward the product/brand
: How do you feel about this MCM store after you have visited it?

 

 

 

Unsatisfied 1 2 3 4 5 6 7 Satisfied
Do not concentrate fully 1 2 3 4 5 6 7 Concentrate fully '94
Unhappy 1 2 3 4 5 6 7 Happy
Pleasant 1 2 3 4 5 6 7 Unpleasant
Like 1 2 3 4 5 6 7 Dislike
Purchase Intention
Unlikely 1 2 3 4 5 6 7 Likely .95
Improbable l 2 3 4 5 6 7 Probable
Uncertain 1 2 3 4 5 6 7 Certain

 

89

 

APPENDIX 4. Control Variables

 

Items Reliability

 

Experience/Knowledge about Store Brand

Store brand familiarity
Regarding MCM brand, are you? : .94
Unfamiliar l 2 4 5 6 7 Familiar
Inexperienced l 2 4 5 6 7 Experienced
Not knowledgeable l 2 4 5 6 7 Knowledgeable

WWW

 

Knowledge of store brand
I feel quite knowledgeable about MCM.

Among my circle of friends, I’m one of the “experts” on MCM. _93
I know much about MCM.

Strongly disagree 1—2—3—4—5—6—7 Strongly agree

 

 

 

Mood (global)
Are you. . .?
Sad1234567Happy 84

Badmood 1 2 3 4 5 6 7 Goodmood '
Irritable l 2 3 4 5 6 7 Pleased
Depressed 1 2 3 4 5 6 7 Cheerful

Internet Self-efficacy (modified for online shopping)

I feel confident about Internet shopping.

I feel confident describing the functions of Internet hardware when shopping .92

on-line.
I feel confident understanding terms/words related to Internet shopping.

 

 

9O

 

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