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11m mammu

CUSTOMERS’ USE OF ELECTRONIC COMMERCE IN STOCK TRADING
AND EFFECTS ON RELATIONSHIP OUTCOMES

By

Seongcheol Kim

A DISSERTATION

Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of

DOCTOR OF PHILOSOPHY

Mass Media Ph.D. Program
Department of Telecommunication
College of Communication Arts and Sciences

2000

ABSTRACT

CUSTOMERS’ USE OF ELECTRONIC COMMERCE IN STOCK TRADING
AND EFFECTS ON RELATIONSHIP OUTCOMES

By

Seongcheol Kim

In recognizing the significance of linking the power of electronic commerce
to relationship marketing in continuously provided service context, this
dissertation investigates the role of electronic commerce in determining desirable
customer relationship outcomes. In particular, this dissertation proposes the
positive associations between customers’ use of electronic commerce in stock
trading and four customer relationship outcomes such as perceived service
quality, overall satisfaction, attitudinal loyalty, and actual retention. In addition, in
order to control for other factors that could make electronic commerce useful, this
dissertation also examines several conditions under which customers use
electronic stock trading more.

Empirical data were collected from an intercept field survey of 170
customers of two major Korean stock trading brokers. Our results show that
customer knowledge, price sensitivity, access capacity, and frequency of order
are crucial conditions for customers’ use of electronic commerce in stock trading.
Moreover, customer knowledge and price sensitivity appear to predict use of
electronic commerce most significantly, controlling some demographic variables.
Our main findings from a series of hierarchical regression analyses indicate that

use of electronic commerce in stock trading is strongly associated with high

levels of service quality, overall satisfaction, attitudinal loyalty, and actual
retention. In addition, the existence of offline experience is negatively related to
the level of overall satisfaction and attitudinal loyalty. Interestingly, the greater
use of the Internet appears to be associated with lower level of service quality.

Partially consistent with the results of regression analyses, the results of a
structural equation analysis confirm only two of four direct linkages between use
of electronic commerce and relationship outcomes: 1) use of electronic
commerce and service quality, and 2) use of electronic commerce and actual
retention. Service quality turns out to play a significant mediating role in
predicting the impacts of use of electronic commerce on overall satisfaction and
customers’ actual retention. Overall satisfaction also appears to play a mediating
role with service quality in predicting the impacts of use of electronic commerce
on customers’ attitudinal loyalty.

Despite several limitations, including cross-sectional design conducted only
in Korea, this study advances the understanding of the powerful role of electronic
commerce in creating some desirable customer relationship outcomes. The
limitations of this study suggest that the strengths of the findings will be
enhanced through future longitudinal research efforts replicated under different
cultures with larger sample. At the practical level, findings in this study mainly
suggest that the service providers really need to shift a transaction orientation to
relationship building in their efforts on electronic commerce and utilize electronic
commerce arrangements as a strategic vehicle of relationship marketing as well

as a simple driver of cost savings.

Copyright by
SEONGCHEOL KIM

2000

This dissertation is dedicated to my beloved mother who has prayed for me even
in her fight against a brain tumor during my absence.

ACKNOWLEDGEMENTS

This dissertation is a final output ending my long academic journey as a
student and it is also a corner stone enabling further intellectual career as a
proven independent professional.

First of all, I would like to thank SK Corporation and SK C&C Co., Ltd. for
awarding generous funding for my six-year study (1995 - 2000) in the Michigan
State University as a part of their global training program. Their support has
helped me to successfully pursue the second master degree and a doctoral
degree in the field of telecommunication policy and management.

I should acknowledge several faculties who have sincerely guided my
academic life. First, I am extremely grateful to Dr. Charles Steinfield, a MSU
distinguished faculty, who has been my academic advisor as well as dissertation
director. He encouraged me to enter the Ph.D. program and has always guided
my academic efforts toward a proper direction. Without his insights and help, this
dissertation could not be completed in such a short period. I am also greatly
indebted to Dr. Thomas Muth. He is not only my professor but also my intimate
friend and spiritual father. He always prays for me and the magnitude of my debt
to him is inestimable. I am particularly grateful to Dr. Johannes Bauer for his
steady guidance and counsel during my study in the Michigan State University.
Equal thanks must go to Dr. Michael Moch who made significant contributions to
this dissertation through very careful and passionate suggestions. I have also

much benefited from the valuable and useful suggestions of Dr. Hairong Li.

vi

Needless to say, a note to gratitude is owed to the Department of
Telecommunication in the Michigan State University. This department has
provided me with various wonderful opportunities for research, teaching and
professional activities. I am grateful to the chairperson Dr. Mark Levy, the office
manager Ms. Denise Mahoney, my good friend Ms. Vanessa Pollok, my office
mate Mr. Minsuk Kwon and all ITEP people for their kindness and help.

Special credits go to my best friends Mr. Jang Huh and Mr. Yeonwoo Nam,
my professional mentor Mr. Jaemo Park, and my junior in school Mr. Soowook
Kim for their great support as well as their unchanged trust on me.

None other than my family provided the impetus to develop and complete
this dissertation. My father Kijeon Kim, my mother Youngja Han, my father-in-law
Myoungho Kim and my mother-in—law Seonjoo Kim have forgiven me for leaving
them for six years. Without their consistent prayer and support, this dissertation
would not have been possible. I wish to thank my sister Seonmee and
Youngmee, and my brother Kwangchul for their big help and love. Above all, I
owe a great deal to my wife Youngmi. I am really indebted to her for putting up
with all with great patience, sacrifice and loving care over the years. My son
Jiwhan and my daughter Yuwhan, who are the most precious gift for my life,
have always encouraged me to progress further through their shining smiles.

Ultimately, when I consider how this dissertation ever came to be, I
recognize that each and every time, it was only made possible by the promised
invisible hands of God. Thus, I wish to thank my beloved God, who has guarded

my whole academic journey as well as all the days and every step of my life.

vii

TABLES OF CONTENTS

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

LIST OF FIGURES... ..

CHAPTER 1

INTRODUCTION. . ..
Importance of Customer RelatIonshIp
Role of Electronic Commerce... .
Goals and Organization of Dissertation

CHAPTER 2

REVIEW OF THEORETICAL AND EMPIRICAL LITERATURE.
Conditions for Use of Electronic Commerce... ..
Impacts of Use of Electronic Commerce...

Impacts on Service Quality...
Impacts on Overall Satisfaction... .. ..

Impacts on Loyalty...
Impacts of Other Dimensions of Use
Dynamic Links among Relationship Outcomes

Service Quality—Satisfaction......

Service Quality- Loyalty...
Satisfaction- Loyalty...

Indirect Impacts of Use of Electronic Commerce.

CHAPTER 3

METHODS AND PROCEDURES......

Measurement.
Variables for Conditions of Use

Variables for Use of Electronic Commerce In .

Stock Trading...
Variables for Relationship Outcomes
Control Variables...

Research Context: Korean StockTradIng Industry...'..'......

Growth Drivers of Electronic Stock Trading.

Current Market Conditions............
Comparison with the US Situation...

Data Collection... ..
Population Selection
Sampling Procedure...
Field Survey... .
Sample CharacterIstIcs

viii

...xi

’o-I'wL-sL-x

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...30

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...53

CHAPTER 4
RESULTS... ..
General Data Preparation
Data Integrity Checks
Missing data Treatment
Construction of Index Measures

Construct Validity and Factor AnalySIs

Reliability...
Hypothesis Testing...

Differences between ElectronIc Traders ...
and Non-electronic Traders... ..
Hierarchical Regression Analysis... .. ..

Regression Results Predicting Use of
Electronic Trading.

Regression Results Predicting Customer

Relationship Outcomes...

Results of Path Model...

CHAPTER 5

DISCUSSION.
Use of Electronic Commerce In Stock Trading
Conditions for Use of Electronic Commerce...

Impacts of Use of Electronic Commerce... ..
Direct Impacts on Relationship Outcomes...
Indirect impacts on Relationship Outcomes...
Impacts of Other Dimensions of Use...

Electronic Customers vs. Non-electronic
Customers...

CHAPTER 6

SUMMARYAND CONCLUSIONS.............
Summary ofFindings..................

Limitations... .
Managerial ImplIcatIons

Implications for Future Research

APPENDICES...
Appendix A Informed Consent Form...
Appendix B Survey Instrument...

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...82

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. .......94

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...105

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LIST OF TABLES

Table 1 - Electronic Trading Penetration in Korea... ..

Table 2 -— Stock Brokerage Accounts in Korea...

Table 3—Major Brokers in Korea....................

Table4—Age
Table 5-Gender

Table6- Education... ..

Table 7 — lncome.................

Table 8-Job

Table 9— Primary Channel

Table 10 - Electronic Traders vs. Non-electronic Traders... ..
Table 11 - Offline Experience...
Table12-lnternetUsage
Table 13 — Indices Resulting from Factor Analysis... .. ..
Table 14 - Indices Resulting from Factor Analysis (continued)
Table 15 - Internal Consistency Reliability of Index Measures... ....
Table 16 — Comparison of Electronic Trading Users and Non-users.
Table 17 - Comparison of Traders with Different Channels...
Table 18 - Pearson Correlations among Variables... .. .....
Table 19 - Predicting Use of Electronic Trading...
Table 20 — Predicting Service Quality... .... ....
Table 21 - Predicting Overall Satisfaction...
Table 22 - Predicting Attitudinal Loyalty...
Table 23 -— Predicting Actual Retention... ..
Table 24 - Summary of Expected and Obtained Relationships. ..

...45
...46
...47
...54
...54
...55
...55
....55
...56
....56
...56
...56
.....61
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...63
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...70
...74
...76
...78
...80
....81

LIST OF FIGURES

Figure 1 -The Proposed Main Model for the Study...
Figure 2 —The Proposed Path Model for the Study...

Figure 3—The Results of Path Model......

xi

...33

....34

...84

CHAPTER 1

INTRODUCTION

Importance of Customer Relationship

For many years, continuously provided service providers such as financial
service and telecommunication firms have focused on infrastructure operation or
product/service innovation rather than customer relationship management (Hagel
& Singer, 1999). However, as competition has become more intense, many
business organizations have realized that the customer is the foundation of the
business and keeps it in existence (Cook, 1997; Timm, 1998; Bowen &
Schneider, 1999). In particular, in the financial service sector where customer
acquisition costs are significantly higher than retention costs (Ennew & Binks,
1996), an organization’s ability to remain in business and produce superior
organizational performance is becoming a function of its competitiveness and its
ability to win customers from the competition and retain them (Appiah-Adu, 1999;
Ozment & Keller, 1999; Pritchard et al., 1999; Vandermerwe, 1999; Woo & Fock,
1999)

The growing interest in the building and maintenance of strong customer
relationship is primary concern for continuously provided service providers for
several reasons. First of all, some business analysts have suggested that the
cost of recruiting a new customer is five times more than the cost of retaining an

existing customer (Holmlund & Kock, 1996; Ennew & Binks, 1996; Davis, 1997;

Mittal & Lassar, 1998; Kotler, 1999; Krishnan et al., 1999; Zeithaml, 2000). Doing
business with existing customers saves money on a variety of acquisition costs
associated with advertising, personal selling pitch to new prospects, setting up
new accounts, explaining business procedures to new customers, and inefficient
dealings during the customer’s learning process (Stewart, 1996; Mittal & Lassar,
1998; Parthasarathy & Bhattacherjee, 1998). Furthermore, loyal existing
customers are likely cost less to service because costs can be amortized over a
longer customer lifetime (Gwinner et al., 1998; Zeithaml, 2000). Second, existing
loyal customers profit a company more than by saving on costs. Loyal customers
can lead to increased revenues for a company, result in predictable sales and
profit streams, and are more likely to purchase additional goods and services or
pay more for them (Rust & Zahorik, 1993; Gwinner et al., 1998). In addition, they
often generate referrals for a company via word—of-mouth recommendations
(Appiah-Adu, 1999; Methlie & Nysveen, 1999). For example, they may share
their experience with their family and friends, encouraging them to try the service
(Chen-YU et al., 1999). In fact, Reichheld (1996) showed that the longer a
customer stay with a company, the more the customer is worth. He found that
reducing customer defections by as little as five points — from fifteen to ten
percent - can double profits.

Since existing customer base has the superior financial value over new
customers, it is understandable that a greater emphasis is being placed on
relationship marketing or defensive marketing strategies which are new business

approaches focused on building and maintaining long-term and highly integrated

relationships with customers (Parker & Funkhouser, 1997; Rylander et al., 1997;

Gordon, 1998; Dubé & Maute, 1998).

Role of Electronic Commerce

According to Kalakota and Whinston (1997), the importance of attracting,
pleasing, and keeping customers is as old as the notion of marketing itself. What
is new is the use of technology for the active management of a firm’s current
customer base as a strategic asset. Gordon (1998) also recognizes the role of
information technology for relationship marketing by arguing that relationship
marketing and its key components, including mass customization, is simply not
possible without the enabling effects of technology to store, retrieve, process,
communicate and analyze customer data.

As a matter of fact, information technology has always played a role in
attracting and keeping customers (Duncan & Moriarty, 1998) and in shaping firm-
customer interaction (Wayland & Cole, 1997; Bitner et al., 2000; Parasuraman &
Grewal, 2000). Over the last decade, information technology has been adopted
to create, enhance and transform customer relationships (Gordon, 1998) and has
aided firms in forming, enhancing and even terminating relationships with
customers (Davis, 1997). Information technology has really revolutionized the
concept of delivering services and redefined the customer interface (Jones &
Sasser, 1995; Roth & Jackson, 1995; Heskett et al., 1997). Thus, in all
businesses — from a relatively slow-moving business to rapidly changing

businesses - the information technology infrastructure has become critical asset

to create superior linkage or interface with customers (Prahalad & Krishnan,
1999). For example, in the information intensive banking industry, information
technology plays an increasingly significant role. Information technology was
primarily employed to automate the back—office of banks in the 1960sThis
situation had been changed by a move of information technology into the front
office and thus the beginning of management information systems. This
extension has made the banking industry enter a new era, where the customer is
integrated with the technology of the firm (Liao et al., 1999).

Recently, use of electronic commerce as a means of enacting transactions
and relationships with customers is increasing exponentially (Hoffman & Novak,
1996; Davis et al., 1999). Since inherent opportunities of electronic commerce for
conducting business online are driving the development of a new customer
relationship paradigm, development of new products and pursuit of low cost ‘self—
service’ strategies (Costello & Tuchen, 1998; Dunn & Varano, 1999), most
organizations, large and small, are making major electronic commerce-related
investments (Berthon et al., 1999).

In particular, the development of the Internet has made it possible for
service providers to deliver service directly through the Internet with little or no
human intervention (Voss, 2000). Thanks largely to the Internet a lot of service
companies employ the new electronic channels (Ghosh, 1998; Prahalad &
Ramaswamy, 2000). The Internet offers a primary communications channel with
customers (Hoffman et al., 1996; Peterson et al., 1997; Evan & Wurster, 1999), a

whole new way to establish rapport or direct link with customers (Sterne, 1996;

Chou & Chou, 2000), and benefits that can help customers be more efficient and
effective in their interactions with companies (McGaughey & Mason, 1998). The
Internet has made it possible for companies to focus on building relationships
with individual customers and to make direct, intimate and personalized contact
with each customer (Dutta & Segev,1999; Prahalad & Krishnan, 1999; Walsh &
Godfrey, 2000). The World Wide Web (WWW) provides companies with a
powerful means to interact with its customers on one-to-one basis (Hoffman et
al., 1996; Vessen, 1998; Wells et al., 1999) and presents important business
opportunities (Dunn & Varano, 1999). Thus many organizations are leveraging
the World Wide Web to create superior linkages with customers (Venkatraman &

Henderson, 1998).

Goals and Organization of Dissertation

In recognizing the significance of electronic commerce, a lot of previous
studies have concentrated mainly on the adoption of this new technological
innovation in the context of business-to-business transactions or one-time
business-to-consumer interactions. However, little academic research has
investigated electronic commerce in the context of continuously provided service
where the customer typically enters into a formal relationship with the service
provider and, subsequently, consumes or uses the service (continuously or
intermittently) for an extended time period. In addition, there has been little
research focusing on the subsequent outcomes or post-adoption consequences

of electronic commerce. Moreover, in spite of the importance of relationship

marketing, there is little research linking the use of electronic commerce to the
customer relationship outcomes.

The goal of this dissertation is to examine explicitly the effects that
customers’ use of electronic commerce in stock trading service context on key
customer relationship outcomes such as perceived service quality, overall
satisfaction, attitudinal loyalty and actual retention. In addition, in order to control
for other factors that could make electronic commerce useful, this dissertation
also examine several conditions under which customers use electronic stock
trading more.

The relevance of examining these conditions and effects in stock trading
context is apparent. First, electronic commerce is causing fundamental changes
in stock trading market where the value of the products offered is shaped by the
channel of delivery. In addition, the dynamic nature of customer relationships is
especially important in this competitive market (Parthasarathy & Bhattacherjee,

1998; Bolton & Lemon, 1999).

Several research questions can be summarized as follows.
1) What are the conditions influencing the increased use of electronic commerce
in stock trading?
2) What effects does use of electronic commerce in stock trading have on each
of customer relationship outcomes such as perceived service quality, overall

satisfaction, attitudinal loyalty and actual retention? Is there any significant

difference between electronic traders and non-electronic traders in those
relationship outcomes? '

3) What are the dynamic relationships among customer relationship outcomes?
How are the effects of use of electronic commerce in stock trading mediated

by service quality-o verall satisfaction-customer loyalty links?

This dissertation is expected to contribute to electronic commerce and
information technology research as well as marketing research as follows. First,
this dissertation would examine the power of electronic commerce to create
some desirable customer relationship outcomes for continuously provided
service providers. Since most firms today face great pressure to maximize the
benefits from their investments in electronic commerce, it should be helpful to
provide a framework to assess the role of IT (information technology) enabled
electronic commerce in terms of customer relationship outcomes for continuously
provided service providers, in particular stock brokerage firms. Second, this
dissertation may emphasize the need for firms to utilize electronic commerce
implementation as not only a simple driver of cost savings but also a vehicle of
defensive marketing strategies.

This dissertation is organized into six chapters. Chapter one introduces the
importance of customer relationship and the role of electronic commerce.
Chapter two reviews the previous theoretical and empirical literature on
conditions for use of electronic commerce and impacts of electronic commerce

on customer relationship outcomes. Chapter three describes the methods and

procedures of this study. Chapter four presents the results of this study. Chapter
five discusses the findings in detail. Chapter six presents the summary and
conclusions with the limitations and implications of this study. The appendix
includes informed consent form and the survey instrument. The references for

this study are presented at the end of this dissertation.

CHAPTER 2

REVIEW OF THEORETICAL AND EMPIRICAL LITERATURE

Conditions for Use of Electronic Commerce

The main focus of this research is to understand how use of electronic
commerce impacts on customer relationship outcomes in continuously provided
service context, in particular stock trading industry. However, to examine the
influences of electronic commerce, it is necessary to control for other factors that
could make electronic commerce useful (Kraut et al., 1999). Thus it is important
to examine the conditions under which customers use electronic commerce
more.

Whereas breakthroughs in electronic commerce have led to wide
acceptance by customers, customers differ in how and how often they use
electronic commerce (Barczak et al., 1997). Why do customers differ in their
usage of new information technologies such as electronic commerce? This
problem has persisted in the information systems literature for several decades
within various contexts and with diverse variables tailored to the specificity of
information technologies (Agarwal & Prasad, 1998). As a matter of fact, most
previous research had emphasized the use of individual difference variables as
critical factors that influence user acceptance of new systems and, hence, the

actual use of these systems.

0

First of all, personal innovativeness may be associated with customers’ use
of electronic commerce. Rogers (1995) defined innovativeness in terms of the
degree to which a person is relatively earlier in adopting an innovation than other
members of his or her social system. Parthasarathy and Bhattacherjee (1998)
defined the notion of innovativeness as the degree to which an individual is
receptive to new ideas and makes innovation decision independently of the
communicated experience of others. In addition, Steenkamp, Hofstede and
Wedel (1999) defined consumer innovativeness as the predisposition to buy new
and different products and brands rather than remain with previous choices and
consumption patterns. Considering these definitions, Lin and Jeffres (1998)
developed the measure assessing innovativeness traits associated with new
communication technologies and found that the intent to keep up with the
technology was a strong predictor for interest in adoption and use of multimedia
cable technology. Agarwal and Prasad (1998) also developed a specific scale
assessing personal innovativeness in the domain of information technology (PIIT)
and validated the construct in the context of the innovation represented by the
World Wide Web (WWW). Therefore, we may expect that customers would be
willing to use electronic commerce more when they have personal
innovativeness relevant to information technology usage.

Second, according to Li, Kuo and Russell (1999), actual use of electronic
commerce requires knowledge about the Internet or “Internet literacy.” Konana et
al. (2000) also argue that investors’ knowledge of the stock market and trading

as well as knowledge of the Internet is crucial in the online setting. Sharma and

10

Bingi (2000) confirmed this argument by stating that online investors tend to
make the decisions based on independent research rather than relying on a
broker’s advice, whereas traditional investors are individuals who need constant
advise and “hand holding.” Therefore, we may expect that customers would use
electronic commerce in stock trading more when they have enough knowledge of
electronic channel, stock market and stock transaction itself.

Buzzell and Ortmeyer (1995) acknowledged that just-in-time
communication technologies help lower costs and improve service to the
customer. Tax and Brown (1998) also found that deploying technology has
lowered the cost of complaining and enhanced customer perceptions of a firm’s
responsiveness. Based on transaction cost theories, Bakos (1998) argues that
Internet-based electronic marketplaces leverage information technology to match
buyers and sellers with increased effectiveness and lower transaction costs,
leading to more efficient, friction-free markets. The virtual value chain theory also
suggests that electronic commerce reduce transaction costs and lowers product
pricing (Benjamin & Wigand, 1995). These developments are commonly
attributed to the efficiency of friction-free electronic markets that lower transaction
and information processing costs by reducing human intermediation (Konana et
al., 2000). In fact, electronic stock trading has dramatically reduced the direct
transaction costs including brokerage commission. For example, unlike the US.
system in which the brokers charge a fixed amount per trade, the stock
brokerage commission is determined in proportion to the transaction amount in

Korea. Moreover, as expected, the commission rate for electronic trading is only

11

one-third or one-fourth of the commission rate for offline trading (see Table 3).
Thus we expect that service price-sensitive customers are likely to use electronic
commerce in stock trading in order to enjoy the lower commission.

In the meantime, actual behavior of a person may be dependent on the
interaction that occurs between that person and his/her situational environment.
Previous research has demonstrated that the situational variables such as time
pressure may influence a consumer’s conduct and attitude. Linneman et al.
(1995) studied the effect of time pressure on the use of home shopping and
concluded that significant and growing numbers of time pressured consumers
were prepared to do their food shopping from home. Barczak et al. (1997) also
found that individuals might use online grocery shopping because of lacking time
to visit the store. According to Kenhove and Wulf (2000), many consumers are
becoming more concerned about the efficiency of their shopping patterns
because of time pressure, and efficiency can refer to the use of home shopping.
Therefore, we may expect that customers would use electronic commerce more
when they perceive time pressure or have little discretionary time.

In addition to time pressure, social pressure may influence individual
behavior. In a study of microcomputer usage, lgbaria (1993) argued that social
norms had a significant effect on the extent of technology usage. Rogers (1995)
also suggests that an important motivation for any individual to adopt an
innovation is the desire to gain social status. Individuals may use new information
technologies for obtaining a higher social status or a more important position in

their society. In their cross-cultural study on cellular phone usage, Kwon and

12

Chidambaram (1998) found that users were motivated to use information
technology more from social pressure including peer pressure and concern for
social status and current trends. Thus we may expect that customers would use
electronic commerce more when they are under greater social pressure.

The capacity or speed to access to electronic channels including the
Internet may be another situational condition for influencing use of electronic
commerce. Hoag (1996) studied the relationship between access capacity and
media use and indicated that users with high-speed access are more likely to
spend time online, use more parts of the lntemet, and tend to be more frequent
users of data-intense applications. In support of her study, Emmanouilides and
Hammond (2000) found that those users with a high data transfer speed were
more likely to be active users of the Internet. Therefore, we may expect that
customers would use electronic commerce in stock trading more when they have
high-speed access to the electronic networks.

Finally, besides personal and situational conditions, transaction
characteristics such as transaction frequency and volume may be associated
with customers’ use of electronic commerce in stock trading. When customers
are involved in many transactions frequently and they have a sufficient volume of
transactions, electronic commerce would be more effective and convenient way

for them to handle those transactions.

13

Hypothesis 1: In summary, we expect that customers would use electronic
commerce more in stock trading, when they

a) have greater personal innovativeness in the domain of information technology
b) have greater knowledge of electronic channel and stock market

c) are concerned about pn'ce for stock trading service

d) are under greater time pressure

e) are under greater social pressure

0 have higher access capacity

9) place more orders in a week

h) have greater value of investment in stocks

According to Kenhove and Wulf (2000), the motivations, intentions, and
actual behavior of a person are dependent on the interaction that occurs between
that person and his/her situational environments. Considering this possible
interaction, it is expected that key situational factors such as time pressure and
social pressure not only have a direct effect on use of electronic commerce in
stock trading but also moderate the effect of personal factors on use of electronic
commerce. Moreover, it is also expected that key situational factors moderate the
effect of transaction characteristics on use of electronic commerce. Thus we
assume the effects of the interactions between personal factors and situational
factors as well as between transaction characteristics and situational factors on

use of electronic commerce in stock trading.

14

Hypothesis 2: The higher the level of time pressure, the stronger the positive
association between

a) personal innovativeness and use of electronic commerce in stock trading
b) customer knowledge and use of electronic commerce in stock trading

0) pn’ce sensitivity and use of electronic commerce in stock trading

d) frequency of order and use of electronic commerce in stock trading

9) transaction volume and use of electronic commerce in stock trading

The higher the level of social pressure, the stronger the positive association
between

0 personal innovativeness and use of electronic commerce in stock trading
9) customer knowledge and use of electronic commerce in stock trading

h) price sensitivity and use of electronic commerce in stock trading

i) frequency of order and use of electronic commerce in stock trading

j) transaction volume and use of electronic commerce in stock trading

Impacts of Use of Electronic Commerce

The relevant stream of research in marketing has examined the impact of
specific marketing efforts on perceptual dependent variables such as perceived
service quality, overall satisfaction, attitudinal loyalty and actual retention
(Zeithaml, 2000). These customer relationship outcomes have been the focus of
relationship marketing theory and practice since delivering high quality service

and having satisfied and loyal customers are viewed as indispensable for gaining

15

a sustainable advantage (Shemwell et al., 1998).

Recently, as electronic commerce proliferates, all organizations, large and
small, are making major electronic commerce-related investment decisions to
stay in business and be competitive (Berthon et al., 1999). Electronic commerce
investments are expected to improve the execution of business transactions over
various electronic networks. These improvements may result in greater economic
efficiency such as lower costs, and more rapid exchange (Kalakota & Whinston,
1997). In the meantime, according to Coulter and Ligas (2000), new
technological improvements such as electronic commerce can play important
roles in how customers view service relationship. Thus electronic commerce
investments are also expected to result in enhanced service quality and
customer satisfaction but, ultimately, these two success factors are likely to lead
to customer loyalty (Appiah-Adu, 1999; Krishnan et al., 1999).

Given the importance of customer relationship outcomes to service
businesses and the recent development of electronic commerce, a critical
research agenda that requires attention is whether there is a significant link
between electronic commerce-related investments and customer relationship
outcomes (Appiah-Adu, 1999). A better understanding of the relationship
between electronic commerce and customer relationship outcomes may provide
service firms with information to decide the future of electronic commerce
arrangements.

This research assesses the importance of electronic commerce as a

strategic driver of customer relationship outcomes for a continuously provided

16

service firm, in particular stock brokerage firm. This part explains the underlying

processes through which customers’ use of electronic commerce in stock trading
leads to improvements in service quality and higher levels of overall satisfaction,
and ultimately to such desired outcomes as attitudinal loyalty and actual

behavioral retention.

Impacts on Service Quality

Measure of service quality has become a widely used barometer of
business performance over the past decade. It is because that this is indicator of
other critical measures of business performance such as customer satisfaction
and loyalty, profit, market share, and growth (Rust & Zahorik, 1993).

Service quality issues can be spilt into technical quality and functional
quality (Gronroos, 1984 & 1990; Parasuraman et al., 1985; Lovelock, 1996; Mittal
& Lassar, 1998; Sharma 8. Patterson, 1999). Technical quality is the quality of
what is delivered and functional quality is the quality of how the service is
delivered. The technical dimension refers to what the customer receives, or what
he has left after the interaction is over. The functional dimension refers to how
the service is received, or social fit, systems, atmosphere and so on (Holmlund &
Kock, 1996). Similarly, Stank et al. (1999) suggest that service quality consist of
two dimensions including quality relative to operational elements and quality
relative to relational elements. Operational elements are those activities
performed by service providers that contribute to product availability, product

condition, delivery reliability, and delivery speed, consistent quality, productivity

17

and efficiency. Relational quality that went beyond core capabilities focuses on
those activities that enhance service firms’ closeness to customers - frequent
communications, willingness to special deliver in emergencies, accommodation
of special requests, and friendly and knowledgeable staff (Stank et al., 1999).
They also differentiate between the quality associated with the process of service
delivery and the quality associated with the outcome of the service (Lehtinen &
Lehtinen, 1991 ). Therefore, it is usual to distinguish between the outcome of the
service (technical quality) and the process by which the service delivered
(functional quality) in the service sector (Ennew & Binks, 1996).

As explained above, technical quality relates to actual outcomes or the core
service as perceived by the customer (Lovelock, 1996). Since electronic
commerce means the systematic elimination of time, space and form barriers
(Costello & Tuchen, 1998), it may result in more effective performance such as
better quality, greater customer satisfaction, and better corporate decision-
making (Kalakota & Whinston, 1997). The real value of electronic commerce is
that it allows a company to provide customers with a range of actual benefits
including convenience, information, and interactivity (Ghosh, 1998). According to
Roth and Jackson (1995), technology leadership in services firms is a significant
variable that enhances organizational knowledge and improves overall service
quality in the eye of the customer. Bitner and his colleagues (2000) argue that
customers may improve the efficiency and effectiveness of their own service
encounter experience through effective use of information technology across

service encounters. Since electronic commerce may offer timeliness of

18

transaction execution, wide product range, ease of use, value-added services,
quality of research, lower commission rates (Konana et al., 2000), we expect that
use of electronic commerce in stock trading would enhance the customers’
perception of technical dimension of service quality.

Quality evaluations are not made solely on the outcome of a service; they
also involve evaluations of the process or manner of service delivery
(Parasuraman et al., 1985). Roth and Jackson (1995) empirically examined that
the quality of customer interface positively influences service quality in the
banking industry. Since functional quality is concerned with the interaction
between the provider and customer, it may be more affected by the use of
electronic commerce. One of key benefits of implementing electronic commerce
into the delivery of services is the ability to customize (Bitner et al., 2000).
Electronic commerce allows customers to remotely experience products and
services, actively participate in dynamic customization, and create mutually
enforcing customer communities (Venkatraman & Henderson, 1998; Zott et al.,
2000). In particular, Internet-based electronic commerce provides organizations
with a powerful means to interact with its customers on one-to-one basis (Wells
et al., 1999) and make direct and personalized contact with each customer (Dutta
& Segev, 1999; Prahalad & Ramaswamy, 2000), and a personal feeling to the
interface (Chou & Chou, 2000). In addition, the ability of electronic commerce to
carry out an immediate and constant dialogue for 24 hours, 7 days a week is an
integral part of the personalization process (Griffith & Krampf, 1998; Walsh &

Godfrey, 2000). Furthermore, since electronic commerce enables customers to

19

participate in more active relationship with service provider or other customers,
we expect that use of electronic commerce in stock trading would also enhance
the customers’ perception of functional dimension of service quality. The

argument above leads to the following hypothesis.

Hypothesis 3: Customers’ use of electronic commerce in stock trading is

positively associated with their perceived service quality.

In addition, it is expected that conditions for use of electronic commerce in
stock trading may moderate the effect of use of electronic commerce in stock
trading on service quality. Thus we assume the effects of the interactions
between conditions for use and use of electronic commerce in stock trading on

service quality.

Hypothesis 4: The higher level of a) personal innovativeness, b) customer
knowledge, c) price sensitivity, d) time pressure, e) social pressure, 0 access
capacity, g) frequency of order, and h) transaction volume, the stronger the
positive association between use of electronic commerce in stock trading and

perceived service quality.

Impacts on Overall Satisfaction

Overall customer satisfaction has long been a subject of research in

consumer psychology. Customer satisfaction is often seen as the key to a

20

company’s success and long-term competitiveness. In the context of relationship
marketing, customer satisfaction is often viewed as a central determinant of
customer retention (Hennig-Thurau & Klee, 1997; Szymanski & Hise, 2000). Rust
and Zahorik (1993) defined satisfaction as a summary affective evaluation of a
relationship experience. Selnes (1993) defined customer satisfaction as a post-
choice evaluation of a specific transaction. In particular, overall satisfaction was
defined as an overall evaluation based on the total purchase and consumption
experience with a good or service over time (Anderson et al., 1994). A more
comprehensive definition was provided by Oliver (1997), who defined satisfaction
as the consumer’s fulfillment response. Satisfaction was also defined as a
judgment that a product or service feature, or the service itself, provided a
pleasurable level of consumption-related fulfillment, including levels of under- or
over-fulfillment (Woo & Fock, 1999). According to Garbarino and Johnson
(1999), rather than capturing the transient and encounter-specific evaluations
and emotions, applied market research tends to measure customer satisfaction
as the consumer’s general level of satisfaction based on all experiences with the
firm.

Greater interaction between the service provider and the customer
generally enhances satisfaction with the service experience (Zeithaml et al.,
1988). An exploratory analysis by Ennew and Binks (1999) suggests that
participation have a positive impact on customer satisfaction. Since electronic
commerce offer both greater interaction and participation, companies employ it to

improve overall customer satisfaction (Kalakota & Whinston, 1997). According to

21

Strader and Shaw (1997), electronic commerce enables a wide range of new
seller and customer relationships to be set up, for instance increased and
customized product information, customer participation in product development
and reduction in order fulfillment time. All this may improve customers’ overall
satisfaction. Krishnan et al. (1999) also found that new channels of delivery such
as information technology enabled call centers and the Internet are important in
determining overall satisfaction. Thus, we expect that use of electronic
commerce in stock trading would enhance the customers’ overall satisfaction

with their stock brokerage firm.

Hypothesis 5: Customers’ use of electronic commerce in stock trading is
positively associated with their perceived overall satisfaction with their stock

brokerage firm.

. In addition, it is expected that conditions for use of electronic commerce in
stock trading may moderate the effect of use of electronic commerce in stock
trading on overall satisfaction. Thus we assume the effects of the interactions
between conditions for use and use of electronic commerce in stock trading on

overall satisfaction.

22

 

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Hypothesis 6: The higher level of a) personal innovativeness, b) customer
knowledge, 0) price sensitivity, d) time pressure, e) social pressure, 0 access
capacity, 9) frequency of order, and h) transaction volume, the stronger the
positive association between use of electronic commerce in stock trading and

perceived overall satisfaction.

Impacts on Loyalty

Many definitions of loyalty have been presented in the literature. Loyalty
has been defined as a long-term commitment to repurchase involving both
repeated patronage and a favorable attitude (Dick & Basu, 1994). Oliver (1997)
defined customers’ loyalty as a deeply held commitment to rebuy or repatronize a
preferred product or service consistently in the future, despite situational
influences and marketing efforts having the potential to cause switching behavior.
Ellinger et al. (1999) analyzed the two dimensions of customer loyalty —
repurchase intentions (repeat patronage) and commitment to the relationship
(favorable attitude). However, customer loyalty is generally defined in service
contexts as continued patronage of the same provider based on dedication or
pragmatic constraints (Bendapudi & Berry, 1997). Loyalty is conceptualized as a
behavioral intention to maintain an ongoing relationship with a service provider
(Singh & Sirdeshmukh, 2000). Customer loyalty includes the willingness to
recommend the company (Parasuraman et al., 1988; Zeithaml et al., 1996) and
intention to buy additional lines of service or product from the company. In the

meantime, these attitudinal definitions of customer loyalty can be supplemented

23

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with the concept of the behavioral dimension of loyalty such as actual retention
behavior (Dick & Basu, 1994; Ruyter et al., 1998).

Most previous research in the domain of relationship marketing has
focused on the antecedents of loyalty (Sharma 8. Patterson, 1999) because
customer loyalty is increasingly being recognized by businesses as a path to
long-term business profitability.

According to Walsh and Godfrey (2000), electronic commerce provider
develops a relationship with the customer to build customer loyalty through
accessibility, relevancy and constant dialogue with him. It may be possible for a
company to build and nurture relationships with customers and thus claim their
loyalty by engaging in electronic commerce efforts (Kalakota & Whinston, 1997).
The result of interactive relationships through electronic commerce may be a
higher retention of customers (Duncan 8. Moriarty, 1998). This is the process of
‘Iocking’ customers ‘in’. Lock-in is achieved largely because new interactive
technologies enable offerings to be more proactive and precise as service
providers can obtain more and more knowledge about the customers in terms of
their needs, likes, dislikes, and habits. This ‘lock-in’, which means the provider
becomes the customer’s first dominant or sole choice, builds the prospects for
huge growth potential (Vandermerwe, 1999). Thus, we expect that use of
electronic commerce in stock trading would enhance the customers’ loyalty in

terms of not only attitudinal loyalty but also actual behavioral retention.

24

Hypothesis 7: Customers’ use of electronic commerce in stock trading is

positively associated with their attitudinal loyalty.

Hypothesis 8: Customers’ use of electronic commerce in stock trading is

positively associated with their actual behavioral retention.

In addition, it is expected that conditions for use of electronic commerce in
stock trading may moderate the effect of use of electronic commerce in stock
trading on attitudinal loyalty and actual retention. Thus we assume the effects of
the interactions between conditions for use and use of electronic commerce in

stock trading on attitudinal loyalty and actual retention.

Hypothesis 9: The higher level of a) personal innovativeness, b) customer
knowledge, c) pn'ce sensitivity, d) time pressure, e) social pressure, 0 access
capacity, 9) frequency of order, and h) transaction volume, the stronger the
positive association between use of electronic commerce in stock trading and

attitudinal loyalty.

Hypothesis 10: The higher level of a) personal innovativeness, b) customer
knowledge, 0) price sensitivity, d) time pressure, e) social pressure, 0 access
capacity, 9) frequency of order, and h) transaction volume, the stronger the
positive association between use of electronic commerce in stock trading and

actual retention.

25

Impacts of Other Dimensions of Use

In addition to the simple extent of use of electronic commerce in stock
trading, other dimensions of use of electronic trading may have an effect on
customer relationship outcomes. For example, customers, who have been
involved in stock trading through offline channels before they started to use
electronic trading, tend to compare the previous experience in offline channels
and that of new electronic channels (Leszczyc et al., 2000). Thus we may expect
that customers with offline stock trading experience show higher relationship
outcomes because it is easy for them to recognize the relative advantage of
electronic commerce comparing with traditional ways of trading stocks. On the
other hand, customers starting stock trading through electronic commerce are

not subject to this comparison. This argument leads to the following hypothesis.

Hypothesis 11: Electronic traders who don’t have any offline stock trading
experience are more likely to have a lower level of a) service quality, b) overall
satisfaction with their brokers, c) attitudinal loyalty, and d) actual behavioral

retention than others.

In the meantime, among several means for doing electronic commerce, the
Internet is becoming the most dominant infrastructure for electronic commerce in
stock trading. Considering the potential of the Internet, we would expect that
electronic customers using only the Internet have higher relationship outcomes

than other customers using non-Internet channels.

26

Hypothesis 12: Electronic traders using only the Internet channel for stock trading
are more likely to have a higher level of a) service quality, b) overall satisfaction
with their brokers, c) attitudinal loyalty, and d) actual behavioral retention than

others using non-Internet channels.

Dynamic Links among Relationship Outcomes

The dynamic relationships among service quality, customer satisfaction and
customer behavior (loyalty, switching or repurchasing) constitutes the research
area of particular interest (Athanassopoulos, 2000). The cumulative insights from
previous studies support the general notion that service quality enhances
customer satisfaction, which, in turn, contributes to customer loyalty
(Parasuraman & Grewal, 2000). The quality-satisfaction-onalty linkage is also
consistent with Heskett, Sasser, and Schlesinger’s work (1997) on the service-
profit chain. In terms of retaining customers, previous research shows that
service quality and overall service satisfaction can improve customers’ intentions

to stay with a firm (Keaveney, 1995).

Service Quality - Satisfaction

Service quality and customer satisfaction have been the focus of much
marketing theory and practice. In today’s competitive markets, delivering high
quality service and having satisfied customers are viewed as indispensable for
gaining a sustainable advantage. Previous research has suggested that service

quality and customer satisfaction are distinct constructs (Oliver, 1997; Taylor &

27

Baker, 1994), and that there is a causal relationship between the two constructs
(Spreng & Mackoy, 1996). In support of this causal relationship, Shemwell et al.
(1998) showed that service quality has a strong direct effect on patient
satisfaction. Recently Athanassopoulos (2000) attempted to assess the
antecedent of customer satisfaction in the context of the emerging competitive
financial market of Greece. The empirical results of the study have confirmed
previous research contentions that customer satisfaction is a function of service

quality.

Hypothesis 13: Customers’ perceived service quality is positively associated with

their overall satisfaction.

Service Quality — Loyalty

The service management literature suggests that service quality usually
lead to stronger customer loyalty. Offering a superior service provides consumers
with a reason for selecting and remaining with a particular provider (Ennew &
Binks, 1996). Rust and Zahorik (1993) related service quality perceptions to
customer loyalty in banking industry. Parasuraman et al. (1988) found a positive
and significant relationship between customers’ perceptions of service quality
and loyalty including their willingness to recommend the company and their
purchase intention. Boulding et al. (1993) also found a positive correlation
between service quality and behavioral intention including saying positive things

about a university and planing to contribute money to the class. In addition,

28

Zeithaml et al. (1996) offered strong empirical support for the notion that
improving service quality could increase favorable behavioral intentions on the
part of a customer and decrease unfavorable ones. Using data relating to banks
in the UK, Ennew and Binks (1996) has provided preliminary evidence
concerning the positive impact of aspects of functional and technical service
quality on loyalty and retention. Another study showed that customer service is
more important than perceived product value in determining customer loyalty

(Leung et al., 1998).

Hypothesis 14a: Customers’ perceived service quality is positively associated

with their attitudinal loyalty.

Hypothesis 14b: Customers’ perceived service quality is positively associated

with their actual behavioral retention.

Satisfaction - Loyalty

Customer satisfaction is an important outcome for service operations and is
one of the most viable means of influencing customer loyalty (Reichheld &
Sasser, 1990; Anderson et al., 1994). Except in a few rare instances, complete
customer satisfaction is the key to securing customer loyalty and generating
superior long-term financial performance (Jones and Sasser, 1995).

Previous research suggests a strong linkage between customer satisfaction

and customer loyalty. Woodside et al. (1989) found a significant association

29

between overall patient satisfaction and intent to choose a focal hospital again.
Fornell (1992) found a significant relationship between customer satisfaction and
increased loyalty of customers in a Swedish study. Hallowell (1996) documented
that customer satisfaction is significantly related to customer loyalty in a bank
setting and that loyalty is related to profitability. Ellinger et al. (1999) showed that
highly satisfied customers are more loyal than less satisfied customers.
Satisfying customers should therefore be of great importance to service providers

in keeping their customers loyal (Methlie & Nysveen, 1999).

Hypothesis 15a: Customers’ overall satisfaction is positively associated with their

attitudinal loyalty.

Hypothesis 15b: Customers’ overall satisfaction is positively associated with their

actual behavioral retention.

Indirect Impacts of Use of Electronic Commerce

We assume that use of electronic commerce in stock trading will have a
positive effect on each of four key relationship outcomes such as perceived
service quality, overall satisfaction, attitudinal loyalty and actual retention. In the
meantime, the literature review on the dynamic relationships among those
outcomes suggest that service quality can be a more central constructs which

mediates the effect of use of electronic commerce in stock trading on overall

30

satisfaction, attitudinal loyalty and actual retention. This leads to the following

hypotheses:

Hypothesis 16a: The effect of use of electronic commerce in stock trading on

overall satisfaction is mediated by service quality.

Hypothesis 16b: The effect of use of electronic commerce in stock trading on

attitudinal loyalty is mediated by service quality.

Hypothesis 160: The effect of use of electronic commerce in stock trading on

actual behavioral retention is mediated by service quality.

The previous literature review also suggests that overall satisfaction, which
is an antecedent of customer loyalty, can mediate the effect of use of electronic
trading on attitudinal loyalty and actual retention. This leads to the following

hypotheses:

Hypothesis 17a: The effect of use of electronic commerce in stock trading on

attitudinal loyalty is mediated by overall satisfaction

Hypothesis 17b: The effect of use of electronic commerce in stock trading on

actual behavioral retention is mediated by overall satisfaction.

31

As was discussed earlier, if use of electronic trading is the sole exogenous
variable, its effect on customer loyalty outcomes can be mediated by the link
between service quality and overall satisfaction. This leads to the following

hypotheses:

Hypothesis 18a: The effect of use of electronic commerce in stock trading on

attitudinal loyalty is mediated by service quality and overall satisfaction.

Hypothesis 18b: The effect of use of electronic commerce in stock trading on

actual behavioral retention is mediated by service quality and overall satisfaction.

The proposed main research model for this study is presented in Figure 1.

This main model is a hypothesized model composed with hypotheses H1 to H12.

In addition, Figure 2 shows the proposed path model representing hypotheses

H13 to H18.

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34

CHAPTER 3

METHODS AND PROCEDURES

The current chapter reports the research methods and procedures used in
this study. Presentation in this chapter is organized under three major sections:

measurement, research context and data collection.

Measurement

To address the research hypotheses described in the last chapter, multi-
item indices to measure concepts related to personal innovativeness, customer
knowledge, price sensitivity, service quality, overall satisfaction, and attitudinal
loyalty were adopted from previous studies such as service marketing and
innovation research. This is an attempt to minimize unanticipated problems
associated with design and execution of measurement procedures. Some key
measures including use of electronic trading and actual retention were
specifically developed for this dissertation because there are few appropriate
instruments in the previous empirical studies.

Measurement of most constructed variables in this study was executed by
having respondents evaluate numerically a series of survey questions intended
for the construction of Likert-type response scales. A five-point scale from
strongly disagree (1) to strongly agree (5) indicates different levels of agreement

with a question or statement. The validity of individual items was first examined

35

using principal components factor analyses. Items that loaded as expected were
included in creating multi-items indices. According to the method of average
ratings, scores on items written to measure the same construct are summed and
then averaged to create composite measures. All multi-item composite measures
were subjected to reliability analyses. Single-item measures were used when
there was sufficient theoretical justification for including a construct.

The four sets of variables included in our analyses are described below:

Variables for Conditions of Use

In order to test the effects of use of electronic commerce in stock trading on
relationship outcomes, we controlled for important conditions that enable or
encourage individual investors to use electronic trading. These conditions
included several personal factors, situational factors, and transaction
characteristics. We reasoned that individual investors would depend on
electronic trading more when they were personally innovative, when they knew
well about stock trading and electronic channels, when they were sensitive to
service price, when they were under time pressure and social pressure, when
they had greater access capacity, when they ordered frequently, and when they

had larger transaction volume.

Personal innovativeness

Personal innovativeness was defined in the domain of information

technology as the willingness of an individual to try out any new information

36

technology. We used the construct developed and validated by Agarwal and
Prasad (1998). This two-item index assessed personality traits relevant to
customers’ use of electronic trading (e.g., “Among my peers, I am usually the first

to try out new information technologies”)

Customer knowledge
This two-item construct was operationalized as the respondents’ knowledge

about stock trading and electronic channels used for electronic stock trading.

Price sensitivity
Price sensitivity is a two-item construct measuring the extent to which the
respondents were concerned about low prices for stock trading services. This

measure was drawn from previous work (Degeratu et al., 2000).

Time pressure

This is the respondent’s judgment of the extent to which stock trading was
subject to time availability. We used only an item, which is one of the multi-item
indices developed by Kraut and his colleagues (1999) and Kenhove and Wulf

(2000), because their items cross-loaded and did not exhibit validity in this study.

Social pressure

This measure is the respondent’s judgment of the extent to which the

respondent used electronic trading because of motivation from his or her peers’

37

use. Since multiple items cross-loaded and did not exhibit validity and reliability,

we used a single item measure developed by Kwon and Chidambaram (1998).

Acces__s capacity

This five-point item was framed as statement about respondent’s primary
access capacity for electronic commerce in stock trading including: 1) 9600 bps
or less; 2) 14400 bps; 3) 28800 — 33600 bps; 4) 56000 bps; 5) high speed (cable

modem, ADSL or LAN).

Frequency of order
This five-point single item was framed as statement about how many orders

the respondent makes in a week through his/her broker.

Transaction volume
This five-point single item was framed as statement about the total value of

the respondent’s current investments in stocks.

Variable for Use of Electronic Commerce In Stock Trading

Use of Electronic Trading

In order to measure actual use of electronic commerce in stock trading, the
survey asked about the extent or degree to which stock transactions was made

through electronic trading during last six months. This is a composite index with

38

two items, including: 1) portion of total number of orders that was made through
electronic trading during last six months; and 2) portion of total $value of orders
that was made through electronic trading during last six months. The portion was
estimated by the percentage, which was stated on a five-point scale ranging from
1 to 5: 1) 1% to 20%; 2) 21% to 40%; 3) 41% to 60%; 4) 61% to 80%; and 5)
81% to 100%. It was because raw percentages needed to be collapsed into more
managerial and interpretable number of scale categories. In addition, 0 was

assigned to the cases that never used electronic trading.

Dummy Variables for Use of Electronic Trading

In order to differentiate for several types of electronic trading usage, two
dummy variables were introduced. At first, offline experience is a dummy variable
coded as 0 if the respondent has no offline stock trading experience and 1
otherwise, in other words the respondent is a previous or current offline trader.

In addition, lnternet usage, the second dummy variable represents the
respondent’s dependence on the Internet for stock trading purpose. It is coded as

1 if the respondent uses only Internet channel and 0 otherwise.

Variables for Relationship Outcomes

In order to measure impacts of use of electronic trading on customer
relationship outcomes, the survey asked respondents to evaluate the perceived
levels of service quality, overall satisfaction and attitudinal loyalty at the time of

survey. Besides, the survey asked about actual behavioral retention by asking

39

the extent to which stock transactions was made through the primary broker

during last six months.

Service guality

Instead of using the classical twenty—two-item SERVQUAL instrument
(Parasuraman et al., 1988) to measure service quality, a nine-item composite
index representing two sub-dimensions such as technical quality and functional
quality was originally developed. Each item consisting of the composite index
was adopted from previous literature on perceived service quality (Parasuraman
et al., 1985; Ennew & Binks, 1996; Tax et al., 1998; Ennew & Binks, 1999;
Madden et al., 1999; Ozment & Keller, 1999; Athanassopoulos, 2000; Lee et al.,
2000). After the first run of a factor analysis, three items were removed because
they loaded a different factor significantly. These eliminated items include 1)
customer education, 2) interactions among customers and 3) level of service
commission. The remaining six items were examined again using a factor
analysis and included in composing an index. The items measuring service
quality includes 1) consistency and dependability; 2) timeliness; 3) reliability and
accuracy; 4) accessibility; 5) responsiveness and 6) personalization and

customization.

Overgfl satisfaction

This three-item composite index was operationaized based on previous

literature focusing on customer satisfaction, in particular satisfaction with financial

40

service providers (Loh & Ong, 1998; Ellinger et al., 1999; Krishnan et al., 1999;
Methlie & Nysveen, 1999). The index consists of three statements including 1) I
feel that this broker manages my account in a good way; 2) I am delighted with

my overall relationship with this broker; and 3) I wish more of my suppliers were

like this broker.

Attitudinal Loyalty

To measure the respondent’s attitudinal loyalty, a seven-item composite
index, representing all of three sub-dimensions such as relationship commitment,
willingness to recommend and willingness for one-stop shopping, was
operationalized based on previous marketing literature (Jones & Sasser, 1995;
Loh & Ong, 1998; Ellinger et al., 1999; Krishnan et al., 1999; Methlie & Nysveen,
1999; Pritchard et al., 1999; Price & Arnould, 1999). The items representing
relationship commitment includes 1) commitment to continue the relationship with
the broker; and 2) importance of maintaining a long-term relationship with the
broker. To measure willingness to recommend, another aspect of the
respondent’s attitudinal loyalty, three items were adopted among widely used
items for word-of-mouth study. This sub-dimension consists of 1) willingness to
recommend the broker to someone who seeks advice; 2) willingness to say
positive things about the broker to other people; and 3) willingness to gladly
encourage friends and relatives to do business with the broker. To measure
willingness for one-stop shopping, the third aspect of attitudinal loyalty, two more

items were drawn from previous empirical works (Zeithaml et al., 1996; Ruyter et

41

al., 1998). This measure includes 1) willingness to consider the broker as the
first choice in case of buying new financial service or product; and 2) willingness

to do buy other financial services through the broker.

Actual retention

In order to measure the actual behavioral loyalty, the survey asked about
the extent to which stock transactions was made through the respondent’s
primary broker during last six months. This is a composite index with two items,
including: 1) portion of total number of orders that was made through the
respondent’s primary broker during last six months; and 2) portion of total $value
of orders that was made through the respondent’s primary broker during last six
months. The portion was estimated by the percentage, which was stated on a

five-point scale ranging from1 (1% to 20%) to 5 (81% to 100%).

Control Variables

It was noted that conclusions regarding differences in use of electronic
trading and relationship outcome should not be confounded by differences in
demographic variables. Thus we included some demographic variables in our
analyses to hold the respondents’ demographic differences constant. A dummy
variable was used to represent each of the two genders. Female was coded as O
and 1 was assigned to male. The other demographic variables were categorized
on a five-point scale because these variables normally spread out in large

ranges.

42

Research context: Korean Stock Trading Industry

Electronic commerce is causing fundamental changes in the stock trading
market. Inherent opportunities of this innovative channel are driving the
development of a new customer relationship, development of new products,

pursuit of low cost ‘self-service’ strategies, and emergence of ‘virtual brokers’.

Growth drivers of electronic stock trading

Globally, electronic stock trading is the fast-growing financial service and
the situation in Korea is not different. The growing popularity of electronic stock
trading services in Korea is attributable to several reasons.

First of all, as most electronic stock trading services are offered through the
Internet, the surging number of Internet users is recognized as a key growth
driver of electronic stock trading. The number of Internet users in Korea has
surged over the last few years, reaching 15.34 million users as May 2000.
Especially, the number of high-speed Internet users is estimated as 2.3 million
and this number is increasing rapidly every day.

Second, in April 1997, the Securities and Exchange Act was amended,
allowing electronic stock trading services in Korea. Moreover, after the financial
crisis in late 1997, the Korean government has deregulated a variety of restrictive
securities transaction practices to promote the capital market development. For
example, daily stock exchange trading hours were recently extended to five
hours to six hours. Thus Korea became the first country in Asia to keep its stock

exchange open during the lunch hour (USA Today, 2000).

43

Third, the brokerage commission rates for electronic stock trading are one-
third or one-fourth of the commission rates for offline broking. As a result, price-

sensitive customers are switching to electronic stock trading.

Current market conditions

The development of electronic stock trading services has been faster-than-
expected. In spite of the short history of electronic stock trading in Korea, most
local brokers offer electronic stock trading services as at August 2000.

The electronic trading value of stocks surged to nearly 63.1% of total
trading value in August 2000. Excluding foreign investors and institutional
investors, the electronic trading value of stocks reached a high of 74.9% (see
Table 1). This is remarkable when compared to the US online brokerage
penetration ratio of 35% for retail investors in June 2000 (Wall Street Journal,
2000). The number of electronic accounts totaled 3.5 million in August 2000 up
191.8% from 1.2 million in August 1999, representing 38.6% of total active
customer accounts of about 9 million (see Table 2).

Korean electronic stock trading services were initially set up with indirect
access through the Home trading System (HTS), which was linked to Internet
Service Providers (ISPs). Direct access through the Web sites of each broker
has recently developed, and has significant growth potential. Electronic stock
trading via the Internet represents over 92% of total electronic stock trading

value.

44

Table 1 - Electronic Trading Penetration in Korea

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Month Monthly Monthly Stock Monthly Monthly
Electronic Stock Trading Value Electronic Electronic
Trading Value Stock Trading Stock Trading
Penetration Penetration
(only for
individual
investors)
01/1998 $440.7 m $34,717.5 m 1.3% 1.5%
02/1998 $561.1 m $37,431.4 m 1.5% 1.8%
03/1998 $608.0 m $29,020.5 m 2.1% 2.5%
04/1998 $446.4 m $17,425.4 m 2.6% 3.0%
05/1998 $418.2 m $14,741.1 m 2.8% 3.4%
06/1998 $461.1 m $15,377.2 m 3.0% 3.6%
07/1998 $707.0 m $22,562.6 m 3.1% 3.7%
08/1998 $591.9 m $16,119.0 m 3.7% 4.4%
09l1998 $629.9 m $17,246.1 m 3.7% 4.3%
10/1998 $938.0 m $26,718.1 m 3.5% 4.2%
11/1998 $1,604.2 m $42,210.4 m 3.8% 4.5%
12/1998 $2,970.3 m $79,980.5 m 3.7% 4.4%
01l1999 $3,869.4 m $82,911.1 m 4.7% 5.5%
02/1999 $2,248.2 m $36,027.5 m 6.2% 7.4%
03/1999 $4,303.7 m $69,008.4 m 6.2% 7.4%
04/1999 $10,267.2 m $139,252] m 7.4% 8.8%
05/1999 $13,460.6 m $111,842] m 12.0% 14.3%
06/1999 $225841 m $144,243.8 m 15.7% 18.6%
07/1999 $50,603.2 m $229,228.3 m 22.1% 26.2%
08/1999 $51 ,472.6 m $177,390.6 m 29.0% 34.5%
09/1999 $46,380.9 m $143,985.6 m 32.2% 38.3%
10/1999 $51,500] m $134,550.6 m 38.3% 45.5%
11/1999 $95,574.9 m $258,398.5 m 37.0% 43.9%
12l1999 $97,966.5 m $243,579.1 m 40.2% 47.8%
01/2000 $92,724.3 m $207,769.5 m 44.6% 53.0%
02l2000 $145,491.0 m $296,805.5 m 49.0% 58.2%
03/2000 $158,412.0 m $310,696.2 m 51.0% 60.6%
04l2000 $84,907.8 m $157,285.6 m 54.0% 64.1%
0512000 $106,605.6 m $187,484.3 m 56.9% 67.6%
06l2000 $149,117] m $252,612.6 m 59.0% 70.1%
07l2000 $115,450.5 m $189,371.4 m 61.0% 72.4%
08l2000 $97,322.5 m $154,339.4 m 63.1% 74.9%

 

 

 

 

 

(Source: Korea Securities Dealers Association, September 2000)

45

 

Table2 - Stock Brokerage Accounts in Korea

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Month Number of Number of Percentage of

Electronic Accounts Total Accounts Electronic Accounts
01l1998 78,340 3,824,633 2.0%
02/1998 92,622 3,917,214 2.4%
03l1998 102,744 3,684,487 2.8%
04l1998 114,733 3,746,275 3.1%
05l1998 119,687 3,796,657 3.2%
06l1998 125,601 3,619,797 3.5%
07/1998 132,311 3,675,395 3.6%
08/1998 138,974 3,700,503 3.8%
09l1998 144,808 3,552,025 4.1%
10l1998 157,453 3,617,724 4.4%
11/1998 179,041 3,705,017 4.8%
12l1998 227,350 3,792,456 6.0%
01l1999 295,269 4,023,230 7.3%
02/1999 333,451 4,227,422 7.9%
03/1999 389,993 4,368,412 8.9%
O4l1999 493,908 4,741,576 10.4%
05/1999 617,794 5,151,459 12.0%
06l1999 788,284 5,343,567 14.8%
0711999 971,215 5,980,284 16.2%
08/1999 1,188,293 6,223,664 19.1%
09l1999 1,310,265 6,710,440 19.5%
10/1999 1,408,061 6,882,173 20.5%
11l1999 1,788,937 7,264,570 24.6%
12l1 999 1 ,887,245 7,572,839 24.9%
01/2000 2,157,630 7,996,244 27.0%
02l2000 2,402,105 8,344,006 28.8%
03/2000 2,707,528 8,591,021 31 .5%
04l2000 2,943,224 8,707,534 33.8%
05l2000 3,058,332 8,891 ,442 34.4%
06l2000 3,244,083 8,769,332 37.0%
07l2000 3,376,857 8,864,191 38.1%
08l2000 3,467,814 8,975,446 38.6%
(Source: Korea Securities Dealers Association, September 2000)

46

 

Comparison with the US situation

In the US, owing to a slow start on the part of the full-service brokers
including Merrill Lynch in electronic stock broking, the original online brokers
have successfully established a leading market position in the electronic stock
trading market. However, the full-service brokers have recently started to provide
electronic stock trading services, which could pose a significant threat to the
existing online brokers.

Unlike the US. traditional full-service brokers, the leading Korean brokers’
dominance in offline trading has successfully been transferred electronic stock
trading services, given their extensive online networks and technology
infrastructure. As a result, the electronic stock trading market has become even
more concentrated than the traditional offline market (see the Table 3). The
Table 3 shows that the aggregate market share of the five largest brokers is
above 57%. It also indicates that their market shares are positively associated

with the portions of electronic trading in their services.

Table 3: Major brokers in Korea

 

 

 

Brokers Daishin Samsung LG Daewoo Hyundae Total
Market Share 13.8% 13.0% 11.2% 10.1% 9.5% 57.6%
Portion of 80.9% 74.1% 71.1% 69.6% 65.4%

Electronic Trading
in their Service

 

 

 

 

 

 

 

 

 

Commission for .136% .135% .12% .13 .155%
Electronic Trading

Commission for .40% .43% .40% .50 .43%
Traditional

Trading

 

 

47

Since the Korean electronic stock trading service industry is already
crowded and very competitive, most new entrants are likely to focus on niche
markets such as bonds, mutual funds, and unlisted stocks.

The stock trading service is the fittest business to electronic commerce in
Korea and the brokers are the best positioned to benefit from the development of
e-finance services. On the back of a higher number of electronic customers and
advanced information systems, Korean brokers are expected to not only expand
their operations to offer more diversified and advanced online services but also
provide non-financial services such as Internet advertising for banners in future.

The focus of this research is to apply electronic commerce to a specific
financial sector (stock trading), in a specific geographical region (Korea), for a

specific purpose (customer relationship outcomes).

Data collection

Population (Industry) Selection

As discussed previously, the Korean stock trading industry was selected for
the empirical study. The stock trading industry has been selected because it is
well positioned to take advantage of electronic commerce. Furthermore, the
Korean stock trading industry has been selected for the remarkable growth of

electronic trading through the Internet in Korea.

48

Sampling Procedures

This study is an attempt to assess the conditions encouraging individual
investors’ use of electronic commerce in stock trading and subsequent effects on
customer relationship outcomes in the context of the Korean stock trading
industry.

The individual customers (retail investors) of two major Korean stock
trading brokers, that sponsored this empirical study, were selected for the sample
population for this study. However, it was impossible to get a complete list of
customers from these firms because of the strict regulation and concerns on
privacy issues. Since the sampling frame for telephone or mail survey was not
available, a field survey at the branches was selected for the alternative way of
collecting empirical data for hypotheses testing. Thus a list of branches in the
great Seoul area was obtained from these firms and a total of 138 traditional
physical branches and 4 new cyber branches were identified.

Sampling of branches for the field survey was conducted based on the type
(traditional physical branch versus cyber branch) and geographical dispersion to
avoid local bias and to better represent the population. First, in order to ensure
variability in the measure of use of electronic trading, all of 4 cyber branches
were selected. Then the great Seoul area was divided into 6 geographical blocks
and one physical branch in each block was selected after contacting the branch
managers by telephone to check their willingness to help. In summary, a total of

10 branches including 4 cyber branches were selected for a field survey.

49

Through a customer intercept survey at the selected 10 branches, a total of

220 retail investors were contacted.

Field Survey

A customer intercept field survey was chosen for the empirical portion of this
study because there was not an available customer lists for telephone survey or
mail survey. Besides, we would expect that field survey yielded timely results and
higher response rates through real-time interviewing. This method is often used
and justified in marketing research, in particular in service marketing studies
(Gwinner et al., 1998; Ruyter et al., 1998; Pritchard et al., 1999). In spite of the
possibility of missing the investors conducting electronic trading from their office
or home, we assumed that significant portion of them use the cyber branches
near their office or home during lunch or other break time. It is because that they
may not be allowed to do electronic trading at work or they may prefer high-
speed access to the Internet and better IT (information technology) environment
available at the cyber branches to plain dial-up connection at home. In addition,
even the investors conducting electronic trading from their office or home need to
be supplemented by some periodic or irregular visits to offline branches, where
they can monitor real market atmosphere, discuss investment strategy with other

investors or catch up informal information.

Interviewers

50

Four interviewers were hired to help with conducting the field survey. Each
of them was given a two-hour training to introduce them to the survey and
explain the content and flow of questions. The administrative plan including the
compensation issue and the survey schedule was also covered in the training

session.

Pretesting

Fifteen retail investors were arbitrarily selected among the employees of a
major public company in Korea, and then contacted for pretesting the survey
instrument. The original survey instrument was prepared in English and then was
translated into Korean by the main researcher. Considering the result of
pretesting, some ambiguous words or items were revised. In addition, in order to
make the field survey more effective, some redundant items were combined and
the length of survey instrument was reduced. It was because customers are
unwilling to participate in a lengthy intercepted survey administered in a real field

(Szymanski & Hise, 2000).

Actual Field Survey

The field survey took place during two weeks of mid September 2000.
During the first week of the survey, we visited each of selected branches to
check the exact locations, branch facilities, branch atmosphere, and average
traffic of investors. In addition, we met the branch managers not only to get

general information about their customers but also get the final permission to

51

 

conduct a field survey in their branches and set a specific date and time for the
survey. After pre-visiting, the schedule for the field survey was set and the
specific target branches were assigned to each interviewer. Considering our
observation and the suggestions of branch managers, the time zone around the
lunch hour (between 11:00 am. to 2:00 pm.) was selected as a convenient time
to intercept the investors. It was because that most investors were busy around
the market opening or closing time and many employed investors or
homemakers seemed to visit the brokers’ branches near their office or home
during lunch break. Each branch was visited once according to the schedule and
about twenty responses were planned for each branch.

Under the help and cooperation of branch managers, respondents were
intercepted randomly at the selected branches. Then the interviewer introduced
the survey project, promised anonymity and confidentiality, and asked to
participate in the survey. In order to encourage the investors’ participation, the
gift incentives including a computer CD-Rom case and a mouse pad were
offered.

A primary screening question was used to ensure that potential
respondents had been involved in stock trading through the two sponsoring
brokers for at least six months. Then a self-administered questionnaire was given
to the respondent with some explanation about how to fill out it. The interviewer
collected the completed questionnaires after twenty minutes and provided the
respondents with the offered gifts. Of 220 contacted respondents, 33 refused to

participate and 17 respondents did not finish the survey. One of the provided

52

reasons for refusal to participate was their unhappiness with the portfolio
performance. The main reason for incompletion was their tight schedule. Upon
termination of data collection, a total of 170 retail investors were surveyed

successfully, representing a response rate of 77%.

Sample Characteristics

The following tables list simple descriptive statistics for the complete
sample.

First of all, according to Table 4, about half of respondents were in the age
between 30 and 39. Table 5 showed that more than 80% of respondents were
male investors. In addition, about 60% of them turned out to graduate four-year
college (see Table 6) and have full—time jobs (see Table 8). This is consistent
with the common sense that typical individual investors in Korea are normally
male investors who are in the age of 30’s, hold bachelor’s degree, and are fully
employed. Table 7 also showed that the respondents were well distributed in
terms of annual income.

According to Table 9, the sample of this study was also divided into three
groups including traders whose primary channel was electronic trading (71.2%),
traders with telephone channel (14.7%), and traders with face-to-face channel
(14.1%). Thus it would be said that this sample might represent all groups of
individual investors regardless of a field survey at the selected branches.

Furthermore, since about 60% of respondents turned out to have full-time jobs

53

(see Table 8), it would also be said that this sample might include the group of
investors doing stock trading at their office.

Table 10 shows that 13.5% of respondents never use electronic trading for
stock transactions. This fact implies that even a part of non-electronic investors,
whose primary channel was non-electronic channel, uses electronic trading as a
complementary channel for stock transactions.

According to Table 11, about 40% of respondents appeared to have no
experience in offline stock trading. Table 12 shows that about 60% of
respondents use only Internet for electronic stock transactions. Considering only
electronic traders, this rate will go up to 73%. This fact is consistent with the
general notion that the Internet has become a dominant platform for electronic

stock trading in Korea.

 

 

 

 

 

Table 4 - Age
Original Figures Frequency Percentage of Grouped Categories
Cases and Scores
Younger than 25 9 5.3% 1
Between 25 and 29 18 10.6% 2
Between 30 and 39 80 47.1% 3
Between 40 and 49 41 24.1% 4
50 or order 22 12.9% 5
Total 170 100.0%
Table 5 - Gender
Original Figures Frequency Percentage of Grouped Categories
Cases and Scores
Female 31 18.2% 0
Male 139 81.8% 1

Total 170 100.0%

54

Table 6 - Education

 

 

 

 

 

 

 

 

Original Figures Frequency Percentage Grouped
of Cases Categories and
Scores
High school graduate or less 18 10.6% 1
Attended college 23 13.5% 2
Graduate 2-year college 20 11.8% 3
Graduate 4-year college 97 57.1% 4
Postgraduate work degree 12 7.1% 5
Total 170 100.0%
Table 7 - Income
Original Figures Frequency Percentage Grouped
of Cases Categories and
Scores
Under $15,000 33 19.4% 1
Between $15,000 and $29,999 58 34.1% 2
Between $30,000 and $44,999 43 25.3% 3
Between $45,000 and $59,999 17 10.0% 4
$60,000 or more 19 11.2% 5
Total 1 70 1 00. 0%
Table 8 - Job
Original Figures Frequency Percentage Grouped
of Cases Categories and
Scores
Unemployed 30 17.6% 1
Homemaker 13 7.6% 2
Student 1 0 5.9% 3
Part-time employee 17 10.0% 4
(Less than 30 hours per week)
Full-time employee 100 58.8% 5
(30 or more hours per week)
Total 170 100.0%

 

55

Table 9 - Primary channel

 

 

Original Figures Frequency Percentage Grouped
of Cases Categories
and Scores
By electronic trading 121 71.2% 0
By telephone 25 14.7% 1
By face-to-face at branch office 24 14.1% 2
Total 170 100. 0%

 

Table 10 - Electronic traders vs. Non-electronic traders

 

 

 

 

 

 

 

 

Original Figures Frequency Percentage Grouped
of Cases Categories and
Scores
Non-electronic traders 23 13.5% 0
(Never use electronic trading)
Electronic traders 147 86.5% 1
- 1% to 20% 17
- 21 % to 40% 8 Mean of the
- 41 % to 60% 7 degree of use
-61%to 80% 16 “371%
- 81% to 100% 99
Total 170 100.0%

 

Table 11 — Offline experience

 

 

Original Figures Frequency Percentage Grouped
of Cases Categories
and Scores
Investors without offline experience 67 39.4% 0
Investors with offline experience 103 60.6% 1
Total 170 100.0%

 

Table 12 - lnternet Usage

 

 

Original Figures Frequency Percentage of Grouped
Cases Categories and
Scores
Other investors 63 37.1 % 0
Investors using only Internet 107 62.9% 1
Total 170 100.0%

 

56

 

CHAPTER 4

RESULTS

The current chapter reports the results obtained from the field survey.
Included in the presentation are procedures employed to interpret the data
collected and to evaluate their fit with what has been conceptualized and
proposed. Empirical evidence is furnished to reveal the presence (absence) of

significant relationship between the variables.

General Data Preparation

Data Integrity Checks

To detect entry errors and ensure accuracy of entered data, seventeen
cases (10%) in the data set were randomly selected and compared with the
original responses recorded in the paper questionnaire. In addition, as another
statistical technique for detecting errors, univariate frequency tables were
generated to check if any unusual scores were revealed. As a result of

conducting data checks, several errors were identified and properly corrected.

Missing Data Treatment

As discussed in the previous chapter, 17 of 187 respondents in the sample

abandoned the survey instrument before it was completed. Their willingness to

57

complete the questionnaire might be eliminated by a little lengthy questionnaire
or their time pressure at the time of the survey.

For 170 completed responses, due to the advantage of real-time on-site
field survey, there were hardly any missing values in the items measuring
variables for conditions of use, variable for use of electronic commerce in stock
trading, and variables for relationship outcomes. However, there were a few
missing values in the control variables, in particular income, which might be
considered too sensitive or private by some respondents. The missing values

were replaced by the mean scores of the variables.

Construction of Index Measures

Construct Validity and Factor Analysis

It is important that survey questions represent the intended constructs. To
establish construct validity such as convergent and discriminant validity, as well
as unidimensionality, the survey items constructed for the variables of this study
were factor analyzed using SPSS 9.0 for Windows. The principal components
method was used to extract factors sequentially.

Survey items measuring the five constructs for conditions of use, including
personal innovativeness, customer knowledge, price sensitivity, time pressure,
social pressure, were factor analyzed (see Table 13 and 14 for factor loading).
Items with low factor loadings and/or high cross loadings were eliminated. Items

with high factors loadings were summed and then averaged for the construction

58

of indices. As a result, both the indices for time pressure and social pressure
excluded in appropriate items and became single-item measures.

In addition, survey items measuring the construct of use of electronic
trading was also factor analyzed. As expected, the two items loaded on single
factor and showed a very consistent pattern.

Finally, survey items measuring the four constructs for relationship
outcomes, including service quality, overall satisfaction, attitudinal loyalty and
actual retention, were factor analyzed. Regarding service quality, three items
with low factor loadings and/or high cross loadings were eliminated and six
remaining items with high factors loadings were summed and then averaged for
the construction of index. In line with our expectation, the items measuring
overall satisfaction, attitudinal loyalty and actual retention loaded on a single

factor respectively and also showed a consistent pattern respectively.

Reliability

Aside from validity concerns, it is also important to check the reliability of
the index measures constructed. As a commonly used measure of internal
consistency reliability, Cronbach’s Alpha was computed using SPSS 9.0 for
Windows. Table 15 shows the standardized item alpha scores for all the indices
in this study. As seen in Table 15, all index measures of the survey were reliable
enough not to worry about the lack of reliability in measurement. The reliability
level for all index measures exceeded the often-cited benchmark of .70

recommended by Nunnally (1978). The index for use of electronic trading has

59

been measured most reliably. The index measures for relationship outcome
variables have been measured more reliably than those for variables for

conditions of use.

60

Table 13 — Indices Resulting from Factor Analysis

 

 

 

 

 

 

Factor (variance explained) Factor
Loadings

Personal Innovativeness (84.5%)

If I heard a new information technology, I would look for ways to .91

experiment with it

Among my peers, I am usually the first to try out new information .91

technologies

Customer Knowledge (79.2%)

To what extent do you know about stock trading .89

To what extent do you know about electronic channels including

the Internet .89

Price Sensitivity (79.5%)

I use electronic trading to take advantage of low prices .89

I am very concerned about low prices .89

Use of Electronic Trading (97.0%)

The percentage of your total number of orders made through .99

electronic trading during last six months

The percentage of your total $value of orders was made through .99

electronic trading during last six months

Service Quality (60.3%)

This broker provides consistent and dependable performance .79

This broker provides its services at the time it promises to do so .83

This broker insists on error-free records and accurate bills .71

This broker’s service is easily and conveniently accessible .79

This broker responds promptly to customers’ requests .84

This broker gives customers personalized and individualized .69

ahenfion

 

 

61

Table 14 -- Indices Resulting from Factor Analysis (continued)

 

 

 

 

Factor (variance explained) Factor
Loadings

Overall Satisfaction (74.8%)

I feel that this broker manages my account in a good way .84

I am delighted with my overall relationship with this broker .90

I wish more of my suppliers were like this broker .85

Attitudinal Loyalty (73.6%)

I am very committed to continue the relationship with this broker .81

Maintaining a long-term relationship with this broker is very .84

important to me

I would recommend this broker to someone who seeks my advice .93

I say positive things about this broker to other people .88

l gladly encourage friends and relatives to do business with this .93

broker

I will consider this broker as the first choice if I buy new financial .84

service or product

I am willing to do buy other financial services through this broker .76

Actual Retention (95.3%)

The percentage of your total number of orders was made through .98

this broker during last six months

The percentage of your total $value of orders was made through .98

this broker during last six months

 

 

62

Table 15 — Internal Consistency Reliability of Index Measures

 

 

Index Cronbach’s Alpha (a)
Personal Innovativeness .78
Customer Knowledge .73
Price Sensitivity .74
Use of Electronic Trading .97
Service Quality .87
Overall Satisfaction .83
Attitudinal Loyalty .94
Actual Retention .95

 

 

63

Hypothesis Testing

Differences between Electronic traders and Non-electronic traders

The penetration of electronic trading via the Internet in Korea has been
increased significantly, and the electronic linkage with customers through the
Internet has become a strategic necessity for competition in the Korean stock
trading industry. Accordingly, this study suggests that most investors in the
sample (71.2%) have already recognized the power of electronic channels as a
primary means for stock trading. This is consistent with the statistical result
reported by Korea Securities Dealers Association (see Table 1). According to
Table 10, on average, the extent of electronic trading use is quite high. Only a
small part of sample investors (13.5%) have never adopted electronic trading.
More than half of sample investors heavily rely on electronic trading. Thus the
average extent of electronic trading use was found to be as high as 71% in terms
of number of orders and $ amount of orders. It implies that electronic trading is in
the pretty mature stage in the Korean stock trading industry.

The result of this study also indicated that most electronic traders show
higher customer relationship outcomes than non-electronic traders, enjoying the
unique potential of electronic trading. In this respect, there were very significant
differences in the average levels of perceived service quality, overall satisfaction,
and attitudinal loyalty outcomes between electronic trading users and non-users
(see the t-test results in Table 16). On average, according to another t-test,

investors using electronic trading as a primary channel showed significantly

64

Table 16 - Comparison of Electronic Trading Users and Non-Users

 

Variables

Age

Gender

Educafion

Income

Personal Innovativeness
Customer Knowledge
Price Sensitivity
Time Pressure

Social Pressure
Access Capacity
Frequency of Order
Transaction Volume
Service Quality
Overall Satisfaction
Attitudinal Loyalty

Actual Retention

 

Mean Scores Difference
(Std. Deviation)

Users Non-Users t-score Probability
(N=147) (N=23) Level
3.19 (.99) 3.91 (.85) 3.32 .00“
.86 (.35) .57 (.51) -3.47 .00**

3.43 (1.08) 2.96 (1.40) -1.87 .06“
2.46 (1.17) 3.48 (1.24) 3.86 .00***
3.19 (.86) 2.59 (.75) -3.20 .00“
3.40 (.83) 2.46 (.69) -5.15 .00***
3.78 (.98) 2.61 (.66) -5.53 .00***
3.48 (1.04) 3.09 (.10) -1.71 .091-
3.28 (1.22) 2.13 (.69) -4.40 00*“
4.69 (.68) 3.70 (.93) -6.22 .00***

2.80 (1.62) 2.52 (1.28) -.79 .43
3.12 (1.51) 4.35 (1.23) 3.70 00*“
3.63 (.72) 3.21 (.64) -2.66 .01**
3.44 (.80) 2.86 (.82) -3.25 .00**
3.44 (.80) 2.64 (.75) -4.49 .00***
4.34 (1.23) 4.48 (.90) .50 .62

 

1': p < .10, *2 p < .05, **: p < .01, “*2 p < .001
Notes: Standard deviation is shown in parentheses. Probability level is 2-tailed

significance.

65

Table 17 - Comparison of Traders with Different Channels

 

Variables

Age

Gender

Educafion

Income

Personal Innovativeness
Customer Knowledge
Price Sensitivity
Time Pressure

Social Pressure
Access Capacity
Frequency of Order
Transaction Volume
Service Quality
Overall Satisfaction
Attitudinal Loyalty

Actual Retention

Mean Scores
J Std. Deviation)

electronic
channel
(N=121)

3.15 (1.01)
.39 (.31)
3.42 (1.07)
2.37 (1.16)
3.24 (.35)
3.43 (.32)
3.33 (.97)
3.56 (1.04)
3.27 (1.25)
4.70 (.68)
2.97 (1.61)
3.07 (1.55)
3.73 (.69)
3.53 (.76)
3.53 (.76)

4.51 (1.05)

Traders with Traders with
traditional

channels
(N=49)

3.63 (.91)
.63 (.49)
3.22 (1 .23)
3.14 (1 .23)
2.31 (.33)
2.76 (.30)
2.99 (.33)
3.12 (.97)
2.76 (1.09)
4.20 (.94)
2.27 (1.33)
3.32 (1.40)
3.21 (.67)
2.95 (.33)
2.33 (.31)

4.00 (1.44)

Difference

t-score Probability

2.92
-4.15
-1.03
3.86
-2.99
-5.24
-5.56
-2.49
-2.53
~3.87
-2.67
2.94
-4.46
-4.40
-5.37

-2.56

Level

.00"

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.OO***

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.00“

. OO***

.01*

.01*

. 00***

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.OO***

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1':p< .10, *: p< .05, **: p< .01, ***: p< .001

Notes: Standard deviation is shown in parentheses. Probability level is 2-tailed
significance. Traditional channels include telephone and face-to-face channels.

66

higher scores in all relationship outcomes than investors with traditional channels
such as telephone channel and face-to-face channel (see Table 17).

In addition, t-test also was used to test whether demographic variables
were associated with use of electronic trading. The results showed significant
differences in age, where electronic investors are younger, gender, where
electronic investors are more likely males, and income, where electronic
investors have lower than non-electronic investors. It might be noted that little
significant differences were observed with regard to education (no significant
differences if equal variances are not assumed). Therefore, it became necessary
to control for age, gender and income in all analyses to ensure that conclusions
regarding use of electronic trading and relationship outcome differences were not

confounded by differences in demographic variables.

Hierarchical Regression Analysis

Hierarchical regression analysis served as the primary tool for testing
research hypotheses. It involved the comparison of two regression models. The
complete model contained four terms: the dependent variable, control variables,
the hypothesized predictors and the interaction terms of predictors. The reduced
model omitted the interaction terms. With respect to the interaction hypotheses,
the significance of the interaction terms was tested by calculating the F ratio
corresponding to the difference between the reduced model and the complete

model. If the F ratio was not significant, the complete model did not have better

67

explanatory power than the reduced model and the interaction hypotheses are
rejected.

To check the problem of collinearity among the various predictors, the
Pearson correlations were examined (Thong, 1999). It was because high
intercorrelations or redundancies among the independent variables might
introduce unwanted multicollinearity in the regression models (Louadi, 1998).
There is no evidence of significant possibility that collinearity among the
predicting variables might be a problem (see Table 18).

However, to address the possible multicollinearity problem, we followed
Cronbach’s (1987) and Lance’s (1988) suggestions to center the component
variables prior to forming the interaction terms. Centering method is a procedure
whereby the mean of each independent variable is subtracted from its score
(Louadi, 1998). This method provides a means of controlling for collinearity and
avoids confounding the main effects in moderated regressions (Speed &

Thompson, 2000).

Regression results predicting use of electronic trading

Separate two-step, hierarchical regression analysis was performed to test
hypotheses H1 and H2 predicting use of electronic trading. At step 1, we entered
three demographic variables, including age, gender and income, as control
variables. In addition, eight predictors were entered. At step 2, ten interaction
terms of personal factors and situational factors were entered all in once into the

model.

68

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69

Table 19 - Predicting Use of Electronic Trading

 

 

Variables Reduced Model Complete Model
Standardized Probability Standardized Probability
Beta ((3) Level Beta ((3) Level
Control Variables
Age -.09 .20 -.10 .16
Gender .16 .01* .16 .02 *
Income -.26 .00*** -.28 .00 ***
Personal factors
Personal .06 .33 .05 .43
Innovativeness (Pl)
Customer Knowledge .27 .00*** .26 .00 ***
(CK)
Price Sensitivity (PS) .23 .00*** .23 .00 ***
Situational factors
Time Pressure (TP) .04 .59 .04 .54
Social Pressure (SP) .08 .21 .11 .11
Access Capacity (AC) .1 3 .04* .1 3 .051'

Transaction
characteristics

Frequency of Order .14 .03* .17 .01 *
(F0)
Transaction Volume -.04 .55 -.04 .55
(TV)

Interactions
TP x PI -.04 .60
TP x CK -.06 .33
TP x PS .00 .97
TP x F0 -.12 .061-
TP x TV .05 .46
SP x PI -.04 .59
SP x CK -.03 .70
SP x PS .00 .99
SP x F0 -.06 .39
SP x TV .01 .92

Multiple R .71 .73

R-square .50 . .53

Adjusted R-square .47 .47

F 1443*” 802*"

A R-square .03

A F .99

 

1: p< .10, *: p< .05, **:p< .01, ***:p< .001

70

 

As shown, Table 19 represents the regression results of the models with
use of electronic trading as the dependent variable. The result of the reduced
regression model generally turned out as hypothesized. The percentage of
variance explained (R-square) was 50%, implying a statistically significant (p <
.001) and satisfactory model. The F-statistics is also statistically significant.

Two control variables, gender ([3 = .16, p < .05) and income (8 = -.26, p <
.001) were significantly related to use of electronic trading. The effect of the
control variables was almost unchanged when the interactions were entered in
the regression model.

In the model, the predictors such as customer knowledge, price sensitivity,
access capacity and frequency of order appeared to be significant in terms of
both the signs and the significance of the beta weights when determining use of
electronic trading. Thus hypotheses H1b, H1c, H1f, and H19 were supported.
First, as for the situational factors, one of them was found statistically significant
predictor, namely access capacity ((3 = .13, p < .05). Two personal factors
including customer knowledge (8 = .27, p < .001) and price sensitivity ([3 = .23, p
< .001) were also found statistically very significant. In addition, one of two
transaction characteristics, frequency of order (8 = .14, p < .05) appeared to be
significant at the alpha level of .05. The result showed that customer knowledge
and price sensitivity are the most significant predictors.

When we included 10 interaction terms altogether, the increase in R—square
was only .03, which was not significant. This means that the introduction of the

interaction terms in regression did not add significantly to the variance explained.

71

Only the interaction effect between time pressure and frequency of order were
significant but showed negative coefficient. The other interactions were found not
to be significant. Thus hypothesis H2 predicting the interaction relationship
between personal factors and situational factors on use of electronic trading

totally was rejected.

Regression results predicting customer relationship outcomes

A series of hierarchical regression analyses were also performed to test
hypotheses H3 through H12 predicting customer relationship outcomes. At step
1, we entered three demographic variables and eight variables for conditions of
use as control variables. In addition, three predictors including use of electronic
trading and two dummy variables were entered. At step 2, eight interaction terms
between conditions of use and use of electronic trading were entered altogether
into the model.

All of four regression equations were run for the entire sample using use of
electronic trading as a main predictor. It was because there did not seem to be a
strong artifact from including non-electronic traders in predicting the effects of
use of electronic trading. In other words, there was no significant difference

between regressions for the entire sample and those for only electronic traders.

Predicting service quality

As expected by the predictions of hypothesis 3, the coefficient of use of

electronic trading ([3 = .36, p < .01) is significantly different from zero at the alpha

72

level of .01 (see table 20). Thus hypothesis H3 was supported. It means that use
of electronic trading is very positively related to customers’ perceived service
quality. The more they use electronic trading for stock transaction, the higher
service quality the individual investors may enjoy. Surprisingly, the effect of a
dummy variable for Internet usage ([3 = —.18, p < .05) was found to be significant
but negative. This result shows that electronic traders using only the Internet
have lower service quality than other investors using non-Internet channels.
Among the control variables, time pressure appeared to be statistically
significant predictor (B = .21, p < .05), indicating that the perceived service quality

be higher when the investors were under time pressure. Gender ((3 = .14, p < .10)

and social pressure ((3 = -.14, p < .10) showed only marginal significance.

The reduced model turned out to have relatively low (R—square is .26, while
Adjusted R-square is .20) but statistically significant (p < .001) predictive power.
As for the complete model, it did not provide additional significant power of
prediction. The increase in R-square was only .05, which was not statistically
significant. The increase in F-statistics was also not statistically significant.
Therefore, though the interaction between price sensitivity and use of electronic
trading on service quality was significant ([3 = .20, p < .05), the whole interactions
between conditions of use and use of electronic trading appeared to be not
significant when determining customers’ perceived service quality. Accordingly,

against our expectation, the hypothesis H4 was rejected.

73

Table 20 - Predicting Service Quality

 

 

Variables Reduced Model Complete Model
Standardized Probability Standardized Probability
Beta ([3) Level Beta ((3) Level
Control Variables
Age -.02 .82 -.03 .71
Gender .14 .071' .11 .18
Income .03 .74 .00 .98
Personal .10 .20 .11 .15
Innovativeness (Pl)
Customer Knowledge .09 .30 .13 .17
(CK)
Price Sensitivity (PS) -.11 .17 -.14 .071‘
Time Pressure (TP) .21 .01* .18 .03*
Social Pressure (SP) -. 14 .06'r -.09 .30
Access Capacity (AC) .01 .91 -.02 .80
Frequency of Order -.07 .38 -.09 .31
(F0)
Transaction Volume -.05 .56 -.04 .67
(TV)
Independent Variables
Use of Electronic .36 .00" .39 .00**
Trading (UE)
Offline Experience -.03 .75 -.02 .83
Internet Usage -.18 .05* -.18 .05*
Interactions
Pl x UE -.10 .26
CK x UE -.09 .33
PS x UE .20 .02*
TP x UE -.02 .86
SP x UE -.14 .12
AC x UE .07 .45
OF x UE .04 .66
TV x UE -.03 .75
Multiple R .51 .56
R-square .26 .31
Adjusted R-square .20 .21
F 3.95*** 300*“
A R-square .05
A F 1.25

 

1: p< .10, *: p< .05, **:p< .01, ***:p< .001

74

Predicting overall satisfaction

As shown in Table 21, in the reduced model predicting overall satisfaction,
use of electronic trading (8 = .41, p < .001) was found to have a positive and very
strong relationship with overall satisfaction. Thus hypothesis H5 was supported.

Interestingly, a dummy variable, existence of offline experience (8 = -.15, p
< .10) was found to be a marginally significant but negative predictor of overall
satisfaction. Against our expectation, electronic traders who don’t have any
offline stock trading experience were found to have a higher level of overall
satisfaction with their brokers. Thus hypothesis H11b was rejected.

Among control variables, price sensitivity ([3 = -.16, p < .05) and social

pressure (8 = -.22, p < .01) were found to be negatively associated with overall
satisfaction. This implies that investors with higher price sensitivity and social
pressure tend to have lower overall satisfaction. The model also indicates that
older and male investors tend to have higher overall satisfaction with their
brokers. The regression model shows an overall fit at the magnitude of R-square
= .27 (Adjusted R-square of .20).

As for the complete model including eight interactions, it showed similar
results as the reduced model. In other words, the increase R-square was only
.04, which was not significant. The increase in F-statistics was also not
statistically significant. Therefore, in spite of the positive interaction between
price sensitivity and use of electronic trading ((3 = .15, p < .10), the hypotheses

H6 regarding eight interactions were rejected.

75

 

Table 21 — Predicting Overall Satisfaction

 

 

Variables Reduced Model Complete Model
Standardized Probability Standardized Probability
Beta ([3) Level Beta (8) Level
Control Variables
Age .19 .03* .17 061*
Gender .14 .081' . 12 .17
Income -.05 .53 -.06 .47
Personal .06 .41 .08 .33
Innovativeness (PI)
Customer Knowledge .08 .37 .09 .32
(CK)
Price Sensitivity (PS) -.16 .04* -.19 .02*
Time Pressure (TP) .09 .29 .06 .47
Social Pressure (SP) —.22 .00** -.16 .061
Access Capacity (AC) .02 .83 -.01 .94
Frequency of Order -.07 .41 -.04 .65
(F0)
Transaction Volume —.07 .42 -.08 .38
(TV)
Independent Variables
Use of Electronic .41 .00*** .38 .00**
Trading (UE)
Offline Experience -.15 .071' -.14 .091-
Internet Usage -.15 .10 -.15 .091'
Interactions
PI x UE -.03 .72
CK x UE -.13 .14
PS x UE .15 .081
TP x UE .08 .38
SP x UE -.14 .12
AC x UE .05 .59
OF x UE -.08 .36
TV x UE .02 .86
Multiple R .56
R-square .31
Adjusted R-square .20
F 4.01 *** 297*“
A R-square .04
A F 1.10

 

1': p< .10, *: p< .05, **: p< .01, ***:p< .001

Predictin attitudinal lo alt

ln predicting attitudinal loyalty, use of electronic trading ([3 = .42, p < .001)
is statistically significant and has very strong effect (see the reduced model in
Table 22). Thus hypothesis H7 was supported. It means that use of electronic
trading is very strongly related to customers’ attitudinal loyalty to their broker. It
suggests that customers heavily using electronic trading are more likely to show
relationship commitment to their brokers, willingness to recommend, and
willingness for one-stop shopping through their brokers. The dummy variable for
offline experience (8 = -.16, p < .10) was found to be a marginally significant but
negative predictor of attitudinal loyalty. Electronic traders who don’t have any
offline stock trading experience seems to be more likely to show a higher level of
attitudinal loyalty to their brokers. Thus hypothesis H11c was rejected. Another
dummy variable for Internet usage was found not to be significant. Older
investors expressing higher overall satisfaction were again found to show higher
attitudinal loyalty. In addition, investors with higher social pressure (8 = -.20, p <
.01) tend to have lower attitudinal loyalty. The overall fit of the regression model
was good at the magnitude of R-square = .28 (Adjusted R-square = .22).

Considering interactions, the complete model did not provide better
prediction in spite of the existence of two significant interaction effects. Increase
R—square was only .06, which didn’t reach significance. The increase in F-
statistics was also not statistically significant. Thus hypotheses H9 regarding

interaction effects were rejected.

77

Table 22 - Predicting Attitudinal Loyalty

 

Variables

 

Control Variables
Age
Gender
Income
Personal
Innovativeness (Pl)
Customer Knowledge
(CK)

Price Sensitivity (PS)
Time Pressure (TP)
Social Pressure (SP)
Access Capacity (AC)
Frequency of Order
(F 0)

Transaction Volume
(TV)

Independent Variables
Use of Electronic
Trading (UE)

Offline Experience
lnternet Usage

Interactions

Pl x UE
CK x UE
PS x UE
TP x UE
SP x UE
AC x UE
OF x UE
TV x UE

Multiple R
R-square
Adjusted R-square
F

A R-square

A F

Reduced Model Complete Model
Standardized Probability Standardized Probability
Beta ((3) Level Beta ((3) Level
.17 .05* .16 .071
.02 .79 -.04 .65
.03 .76 .04 .66
.03 .68 .04 .62
.06 .54 .07 .45
-.11 .17 -.15 .051
.10 .21 .07 .37
-.20 .01** -.13 .11
.1 1 .16 .04 .61
.05 .57 .03 .69
-.05 .56 -.04 .63
.42 .00*”** .50 .00***
-.16 .061 -.15 .081
-.07 .40 -.09 .30
-.06 .50
-.06 .52
.19 .02*
.06 .52
-.19 .03*
-.04 .69
.05 .62
-.07 .41
.58
.34
.24
3.42“”
.06
1 .53

 

1':p< .10, *:p< .05, **: p< .01, ***: p< .001

Predicting actual retention

According to the reduced model in Table 23, not surprisingly, use of
electronic trading (8 = .48, p < .001) turned out to be a very significant predictor
of actual retention. This indicates that there is a very strong positive relationship
between these two composite indices measuring investors’ actual behaviors.
Thus hypothesis H8 was supported. Among control variables, price sensitivity
and social pressure were found to be negatively associated with actual retention.
Under the interaction effects, these associations became stronger. The
regression model shows an overall fit at the magnitude of R-square = .18
(Adjusted R-square = .10).

As for the complete regression model considering eight interaction effects,
it showed better explanatory power. Increase in R-square was .12, which was
statistically significant at the alpha level of .01. Accordingly, three coefficients of
interactions appeared to be significant. The interactions between social pressure
and use of electronic trading (8 = .28, p < .01), between personal innovativeness
and use of electronic trading ((3 = .17, p < .05), and between price sensitivity and
use of electronic trading ([3 = .14, p < .10) were positive and significant. Thus it is
expected that, in the presence of high personal innovativeness, high price
sensitivity, and high social pressure, use of electronic trading will be more likely
to enhance actual retention. Thus hypotheses H10a, H100 and H10e were
supponed.

In addition, under the interaction effects, the predictive power of main effect

(use of electronic trading) was much enhanced (B was increased from .48 to .62).

79

Table 23 - Predicting Actual Retention

 

Variables

 

Control Variables
Age
Gender
Income
Personal
Innovativeness (PI)
Customer Knowledge
(CK)

Price Sensitivity (PS)
Time Pressure (TP)
Social Pressure (SP)
Access Capacity (AC)
Frequency of Order
(F0)

Transaction Volume
(TV)

Independent Variables
Use of Electronic
Trading (UE)

Offline Experience
lnternet Usage

Interactions

PI x UE
CKxUE
PS x UE
TP x UE
SP x UE
AC x UE
OF x UE
TV x UE

Multiple R
R-square
Adjusted R-square
F

A R-square

A F

Reduced Model Complete Model
Standardized Probability Standardized Probability
Beta ((3) Level Beta ([3) Level
-.06 .53 -.07 .44
.06 .50 .07 .44
. 12 .19 .1 1 .23
-.13 .1 1 —.1 1 .17
-.01 .95 -.02 .81
-.14 .091 -.15 .061
.04 .66 .07 .43
-.14 .091 -.23 .01**
-.12 .15 -.05 .59
-.00 .99 -.03 .72
-.00 .99 .04 .64
.48 .00*** .62 .00***
-.02 .85 -.00 .99
-.09 .32 -.06 .49
.17 .05*
-.07 .46
.14 .091
.01 .95
.28 .00**
-.13 .19
-.01 .91
-.06 .47
.54
.30
.19
2.81 ***
.12
3.15“

 

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81

Considering research hypotheses from H1 to H12, Table 24 provides a

summary of expected and obtained relationships.

Results of Path Model

As Figure 2 indicates, in the path model (structural equation model) use of
electronic trading is specified as an exogenous variable, and service quality,
overall satisfaction, attitudinal loyalty, and actual retention are specified as
endogenous variables. Figure 3 contains the overall goodness of fit indices and
the standardized parameter estimates for the hypothesized model (H13 to H18).
This model was tested using the LlSREL 8.2, which is a program for structural
equation modeling.

The overall goodness of fit indices suggests a suitable and acceptable fit of
the model to the data. The value of Chi-square statistic of 357.37 with 161
degrees of freedom indicated that the LlSREL model would not be rejected by a
test. This Chi-square statistic is statistically significant because our sample size
(N = 170) is within the range recommended (100 < N < 200) for this statistic
(Szymanski & Hise, 2000). The goodness of fit index (GFI) value turned out to be
.82. Though it is a little smaller than the general cutoff value of .90, this may be
acceptable fit value because the sample size of this study is smaller than 250
(Hu & Bentler, 1995). The comparative fit index (CFI) value appeared to be .93,
which is greater than the cutoff value of .90. This acceptable value of the CFI

suggests that the hypothesized path model represented an adequate fit to the

data (Byme, 1994).

82

As shown in Figure 3, five of nine standardized path coefficients are
positive and statistically significant at the alpha level of .05 (t value is greater
than 1.96). The five supports are found for relationships between use of
electronic trading and service quality (y = .31, p < .05), between service quality
and overall satisfaction ([3 = .76, p < .05), between overall satisfaction and
attitudinal loyalty (B = 1.12, p < .05), between use of electronic trading and actual
retention (v = .22, p < .05), and finally between service quality and actual
retention ((3 = .49, p < .05). Thus hypotheses H13, H14b, and H15a were
supported and H3 and H9 were confirmed again. However, inconsistent with the
previous multiple regression results, the relationships between use of electronic
trading and overall satisfaction (H5) and between use of electronic trading and
attitudinal loyalty (H7) were not confirmed here.

Instead of a direct effect, use of electronic trading appeared to have an
indirect effect through service quality on overall satisfaction. Thus hypothesis
H16a was supported. However, use of electronic trading appeared not to have an
indirect effect through service quality on attitudinal loyalty. Thus hypothesis H16b
was rejected. In the meantime, use of electronic trading appeared to have not
only a direct effect but also an indirect effect through service quality on actual
retention. Thus hypothesis H16c was also supported.

In addition, according to Figure 3, use of electronic trading turned out to
have an indirect effect through overall satisfaction as well as service quality on

customers’ attitudinal loyalty. Thus H18a was also supported.

83

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84

CHAPTER 5

DISCUSSION

This chapter extends the discussion of the results reported in Chapter 4,
linking the findings back to existing literature. Interpretation would be sought to
explain why empirical evidences are or are not consistent with the research

hypotheses of the study.

Use of Electronic Commerce in Stock Trading

The current study provides evidence suggesting that electronic commerce
for stock transaction is prevalent in Korea and most individual Korean investors
are users of electronic stock trading. These findings are consistent with a lot of
trade publications reporting that the number of investors who engage in
electronic stock trading has increased considerably in the past several years.
The growing popularity of electronic stock trading services in Korea is attributable
to several reasons including the surging number of Internet users, deregulation,
and lower commission rates for electronic stock trading. However, considering
the unique circumstances of Korean stock market, there might be another main
reason behind the robust growth of electronic stock trading in Korea.

As a matter of fact, the shift from traditional high churn broker managed
accounts to more transparent and direct management by individual investors has

helped fuel the growth of electronic trading. Traditionally, Korean individual

85

investors have given brokers autonomy in running their personal accounts. For
example, an individual investor would give a broker a sum of money and ask him
or her to manage the money, acting as a pseudo-fund manager. Brokers,
meanwhile, did not always act in the best interests of the investors and often
turned the account over frequently, generating large transaction commissions for
the broker and excess costs for the investors. Needless to say, individual
investors were frustrated by this practice, but their options were limited.
However, the advent of electronic stock trading has changed the dynamics of the
individual investor/broker relationship and provided the mechanism by which
individual investors are able to avoid the indiscretion of broker managed
accounts while exercising greater freedom in their investment decisions.

This fact indicates that electronic commerce can play an important role in
solving the problem of moral hazard or opportunism, which has been inherent in
a market-mediated exchange between a customer as a principal and a service
provider occupying the role of an agent (Singh & Sirdeshmukh, 2000). With the
virtue of transparency, defined as the degree of openness about how, where, and
when individual investors’ transactions are executed (Konana et al, 2000),
electronic stock trading provides a countervailing force to constrain the traditional

opportunism in Korean stock market.
Conditions for Use of Electronic Commerce

The first part of empirical results of this dissertation has confirmed previous

research contentions that use of electronic commerce in stock trading is a

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function of several conditions such as personal factors, situational factors and
transaction characteristics.

First of all, inconsistent with prior research, personal innovativeness didn’t
predict use of electronic commerce in stock trading. This finding confirms that
electronic stock trading has already entered into the matured adoption stage in
Korea. Personal innovativeness might be a strong predictor for any new
innovation in early stage of adoption and use.

Second, this study found that investors’ knowledge of the stock market, of
trading, and of electronic channels is crucial for use of electronic commerce in
stock trading. This result confirms previous literature on customer knowledge as
a predictor of customers’ usage of new information technologies (Li et al., 1999;
Konana et al, 2000; Sharma & Bingi, 2000). If customers lack knowledge about
stock market and stock trading, they would prefer indirect investment such as
mutual fund to direct investment based on their own decision-making. Also, they
would like to depend on the recommendation or advice of broker’s fund manager.
Thus there is less motivation to use electronic stock trading in this case.
Furthermore, knowledge about electronic channels including the Internet,
computer and other devices are essential for use of electronic commerce in stock
trading. Here, the importance of customer education comes out. In order to
make computer or Internet illiterate people be electronic traders, the brokers may
need to focus on extensive customer education or training programs, in particular

through their cyber branches. Othewvise non-electronic investors will be behind

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the trend of electronic commerce and experience so—called digital gap in their
economic activities.

As expected by the media reports of increasing volumes of electronic
commerce, our results suggest that price-sensitive customers are more likely to
use electronic commerce for stock transaction. In fact, price sensitivity appeared
to be most significant predictor. The most plausible interpretation of this finding is
that investors generally use electronic stock trading in order to save the
commissions for stock trading in Korea. Thus it can be argued that considerable
price difference between online and offline can be a strong driver of customers’
use of electronic commerce.

Contrary to expectation, the direct effect of time pressure on use of
electronic commerce in stock trading was not supported. This result might come
from the characteristics of Seoul area, where the survey was conducted. Unlike
small cities or rural areas, the physical branches of stock brokerage firms are
almost ubiquitous in Seoul area. People can easily visit the branches, in
particular during lunch break. In addition, since most people are users of the
wireless telephone service, they can make a call whenever time matters. Thus
this result may be changed if the study is replicated in a different setting.

Inconsistent with our hypothesis, our results show that social pressure is
not an effective mechanism to influence use of electronic commerce in stock
trading. It is because use of electronic trading is beyond the early stage of
adoption and most people use it now. Thus social pressure as a predictor of use

of electronic trading doesn’t work well any more.

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One important finding of the study concerns the capacity or speed to
access to electronic channels. Our result confirmed the previous studies (Hoag,
1996; Emmanouilides & Hammond, 2000) by indicating that heavy users of
electronic trading depend on high-speed access. This finding implies that high
usage of electronic commerce is almost impossible without access to a good and
fast infrastructure. Investors suffering from a special condition commonly referred
to as speed poverty, may be eliminated substantially from the benefits of
electronic commerce. Thus the issue of access to electronic channels has
become the critical condition for using electronic commerce. Considering the
problem of speed poverty or speed gap between information rich and information
poor, the establishments of cyber branches in Korea are regarded as very
important strategic moves. Investment on cyber branches will make people
access to high-speed information infrastructure in spite of their limited personal
resources. In addition, the brokerage firms may need to make traditional physical
branches be equipped with complementary cyber facilities. The other service
providers moving toward electronic commerce should remember that access
capacity available to their potential customers is a key condition for rapid
adoption and extended usage of their planned electronic commerce system.

The results of the study provide empirical support for our expectation that
electronic trading is more used by customers placing orders more frequently. It is
because that electronic trading is more convenient and efficient as well as

provides bigger savings in terms of transaction commissions. In addition, it is

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very hard for the customers placing orders less frequently to justify some
personal investments regarding computer equipment or access to the Internet.
Inconsistent with our expectation, there is no evidence supporting that
transaction volume in terms of money value can predict use of electronic trading.
The control variables of the study also show that demographic variables
predict use of electronic trading according to common sense. Matching with our
expectations, use of electronic trading is negatively associated with income. The
possible explanation is that high-income people prefer more prestige service or a
special treatment by a human contact. In addition, in Korea, male investors

appeared to be dominant in using electronic trading.

Impacts of Use of Electronic Commerce

Direct Impacts on Relationship outcomes

The findings of this study are consistent with the much of trade press
reports that stock trading is an information intensive business and information
technology plays an increasingly significant role in it. Confirming the prior
research (Bowen & Hedges, 1993; Heskett et al., 1997), this study showed that
electronic stock trading through information technology has emerged as an
important attribute in achieving customer relationship outcomes. Indeed, our
second set of findings is consistent with the hypotheses that use of electronic
commerce in stock trading leads to higher service quality, higher overall

satisfaction, higher attitudinal loyalty and higher actual behavioral retention.

90

First of all, our findings suggest that greater use of electronic trading be
associated with higher perceived service quality, which is an important
relationship outcome for the brokerage firms. The implication of this evidence on
service marketing strategies of continuously provided service providers is that
information technology initiatives including electronic commerce efforts should be
at the at the heart of customer relationship management. In other words,
electronic commerce arrangements should be managed not only from a technical
or economic perspective but a defensive perspective of customer relationship
management. Moreover, our findings in Korean stock trading industry indicate
that use of electronic trading can improve the functional aspects of service quality
(how it is done) as well as the technical aspects of service quality (what is done).
Thus it is suggested that service providers should try to raise customers’
perceptions of service quality by encouraging customers to use new electronic
channel of service more. When they initiate electronic commerce movement,
they should explore how customers’ use of electronic commerce influences on
several dimensions of service quality.

Our regression results further provide empirical support for the contribution
of customers” use of electronic commerce to overall satisfaction with a service
provider. This support is consistent with recent growing service literature showing
that electronic commerce leads to higher customer satisfaction (Strader & Shaw,
1997; Ennew & Binks, 1999; Krishnan et al., 1999). This empirical support was

significant in Korean stock trading industry because electronic commerce is a

91

mechanism by which individual investors are able to avoid the broker’s
indiscretion while exercising greater control in their investment decisions.

According to our regression results, there is also strong evidence
supporting the positive relationship between customers’ use of electronic
commerce in stock trading and their attitudinal loyalty. Since attitudinal loyalty is
very important outcome in continuously provided service contexts, this finding
has significant implications for managing attitudinal loyalty in service industries.
First, based on the strong relationship between use of electronic commerce and
attitudinal loyalty, service providers may use electronic customers, who have
intentions to maintain ongoing relationship with their service providers, as
valuable assets for new marketing strategies. For instance, our results show that
heavy users of electronic commerce are more likely to recommend their broker to
others. This implies that electronic customers can be more active and powerful
opinion leaders for other electronic laggards. Furthermore, it is easier to fuel
lucrative word-of-mouth on online world. Considering that it involves no direct
costs, this strategy may be the most powerful source for acquiring new
customers (Hoffman & Novak, 2000).

In addition, according to our results, there is high possibility for electronic
customers to show higher willingness for one-stop shopping. It means that they
are more likely to buy additional lines of services or products from the same
service provider. This is a positive signal for most service providers coping with
the challenge of convergence and diversification. In particular, this is a very

significant finding for Korean stock brokerage firms, which are trying to offer more

92

diversified and advanced online services as well as provide non-financial
services such as Internet advertising for banners in future.

Our findings also confirmed a previous research arguing that the result of
interactive relationships through electronic commerce may be a higher retention
of customers (Duncan & Moriarty, 1998). When it comes to actual behavioral
retention, use of electronic commerce again appeared to have strong predicting
power. The issue, however, is not whether electronic customers have showed
high retention but how long they will show the same outcomes. Thus electronic
commerce should be an ongoing process of locking customers in (Vandermenlve,
1999). One possible way for lock-in or so-called electronic stickiness is to
increase switching costs on electronic commerce system. Electronic customers
may regard the investment in an electronic relationship as a sunk cost, thus
disregarding the negative effects of being locked in. Building switching barriers
online world make them being locked in to a firm. For example, if customers are
accustomed to a unique and original online environment, it is hard to learn and
transfer to another system. In addition, other complementary offline strategies
such as loyalty program will be needed to lock in electronic customers for long. In
summary, electronic commerce strategies accompanying by some good
complementary offline strategies can lead to not only reduction in costs but also
profitable and sustainable revenue growth (Brown, 2000).

Our regression results also provide empirical support for the interaction
effects between social pressure and use of electronic trading, between personal

innovativeness and use of electronic trading, and between price sensitivity and

93

 

 

use of electronic trading in predicting actual retention. In particular, in the
presence of high social pressure, use of electronic trading appears to enhance
actual retention more. This implies that the power of electronic commerce in
increasing actual retention may be enhanced when customers are much
influenced by social pressure including peer pressure. Thus, in order to achieve
higher retention through electronic commerce strategies, it may be important for
service providers to make online and/or offline customer community and let
customers create word-of-mouth and be influenced by each other (McWiliam,

2000; Zott et al., 2000).

Indirect Impacts on Relationship Outcomes

The results of a structural equation model provide evidence with regard to
the mediating role of service quality and overall satisfaction on the effects of use
of electronic commerce on customer attitudinal and behavioral loyalty. These
findings enhanced our understanding of the different mechanisms underlying the
effect of use of electronic commerce and confirmed an important role of
electronic commerce in the well-proven service quality-satisfaction-loyalty chain.

The path model revealed service quality had the greatest mediating effect
on overall satisfaction and actual retention, which highlights its importance when
service providers strive to seek competitive advantage by offering superior
service quality and when they adopt a defensive strategy in an attempt to retain
customers. The findings indicate that service quality appeared to be a key

intervening variable linking the exogenous variable with other outcomes. Thus it

94

is suggested that electronic commerce efforts should be focused on increasing
service quality.

The model also suggested that increased use of electronic commerce leads
to increased service quality, to overall satisfaction, and ultimately to attitudinal
loyalty. Overall satisfaction appeared to have a limited but still important role to
link service quality and attitudinal loyalty.

Our results add to the mounting evidence showing that attitudinal loyalty
and actual behavioral retention represent two distinct but related managerial
objectives, each requiring a different kind of intervention through use of electronic
commerce, service quality and overall satisfaction. In order to achieve loyal
customer base, service providers must know how to increase service quality and
how to satisfy their customers through electronic commerce efforts. This is a
strategic process of moving from use of information technology to use of
relationship technology and a process of accelerating customer relationship

through information technology including electronic commerce (Swift, 2000).

Impacts of Other Dimensions of Use

Our regression results also showed that offline experience is negatively
related to the level of overall satisfaction and attitudinal loyalty. This result implies
that the customer base can be thought to comprise two groups of customers: (1)
electronic customers who have not switched from traditional channel (the first-
time adopters of electronic trading) and (2) those have switched from or stayed

with traditional channel. It also implies that differences exist in the sets of

95

 

"m—

 

expectations used by two groups and accordingly implies difference in the levels
of subsequent outcomes. An understanding of how these customer groups differ
in their relationship outcomes might provide crucial insights for designing and
implementing effective electronic commerce strategies. For example, the service
providers should give the switchers an opportunity to abandon their previous
channels based on better benefits from new electronic commerce technologies.

We were surprised to see that greater dependence on the Internet was
associated with poorer service quality when execute stock trading. This finding
runs counter to the expectation that the use of the Internet is associated with
increased service quality. It might because lnternet stock trading is still a work in
progress in spite of its popularity. It is also possible for many customers to feel
still comfortable with proprietary and ad hoc electronic channels. Another reason
that use of the Internet was associated with poorer service quality may be
problems with security and system failures. While it would be wrong to evaluate
the power or potential of the Internet solely on the basis of these results, this
study suggests that there should be a lot of room for further exploiting the
Internet at least within Korean stock trading industry. However, it is expected
that, as more customers become comfortable with the Internet and the Internet-
based electronic trading system improves, this medium will lead to higher

relationship outcomes.

Electronic customers versus Non-electronic customers

It is clear that electronic commerce in stock trading has already taken off
and electronic customers became main streams. Moreover, electronic customers
seem to be receptive to the potential benefits offered by electronic commerce.
However, it should be noted that there are still some remaining people who need
to overcome their reluctance to enter the electronic commerce area including
electronic stock trading.

From the firm’s perspective, the costs of maintaining electronic commerce
system may be lower over long run than using human assistance in the physical
environments. Hence, improving customer relationship outcomes with electronic
commerce features can be the first priority for the firm’s revenue growth and its
long-term profits as well (Newell, 2000). However, in moving toward to electronic
commerce world, it is important to retain the traditional low-tech, high-touch
approach as a viable option for customers (Bitner et al., 2000). In case of the
benefits and investments discrimination between online and offline, offline
customers are getting less and less served. The danger to the firm of failing to
manage the relationships with non-electronic customers proactively is that they
may stop using the service, which implies that they have exited from the
relationship with the firm, possibly to use another firm’s services.

Thus the service providers should be proactive in balancing electronic
customers and non-electronic customers. To do so, they need to adopt a
segmentation approach, depending upon customers’ extent of using electronic

commerce because use of electronic commerce is a matter of degree (Newell,

97

2000). First, according to our results, non-electronic customers or light users of
electronic commerce are those with a relatively smaller proportion of their
investments with the their service provider. Thus service mangers should
examine why they are still reluctant to electronic commerce and need to inform
and educate them about the benefits of electronic commerce. Switching offline
customers to electronic users can yield attractive prospects for increasing share
of portfolio and total revenues. Of course, the minimum investment on the offline
branch network should be well maintained. In contrast, electronic customers are
those with a relatively larger proportion of their resources already invested with
the service provider. It seems that it is a priority to foster increased loyalty among
electronic customers and delivering appropriate levels of electronic services for
this customer segment. Thus service managers should recognize the explicit
attributes of electronic commerce such as features, functions, interfaces,
contents or infrastructures that need to be improved to enhance customer
relationship outcomes, in particular one-stop shopping outcome. In addition,
electronic commerce efforts should be integrated with proper loyalty program.
Finally, the focus on the customers (not technology itself) must be given
highest priority in electronic commerce. However, critical issue follows: how can
service providers remain in control of customers’ access to the channel? From
having a dedicated link to their customers through their physical branch
networks, service providers are pushed backwards in the online value chain with
the danger of losing control of customer access. It is because electronic services

are mediated through several intermediaries such as network operators and

98

Internet access providers or portals. Thus service managers should recognize
that disintermediation and loss of control mean reduced opportunities for
fostering customer relationship outcomes, and develop proper coping strategies

such as strategic alliances with other intermediaries.

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Chapter 6

SUMMARY AND CONCLUSIONS

Summary of Findings

This study contributes to the literature on electronic commerce and service
relationship marketing by empirically examining the conditions for customers’ use
of electronic commerce in stock trading and subsequent impacts on customer
relationship outcomes in the context of Korean stock trading industry, where
electronic trading has already taken off. Through the formulation of research
models and testing of hypotheses, this dissertation expands the knowledge
regarding the significant conditions in which customers use electronic commerce
more, and the influence of customers’ use of B-to-C (business-to-consumer)
electronic commerce on several important relationship outcomes such as
perceived service quality, overall satisfaction, attitudinal loyalty and actual
behavioral retention.

With regard to the first goal of this study, we found that customer
knowledge, price sensitivity, access capacity, and frequency of order are crucial
conditions for customers’ use of electronic commerce in stock trading. In
particular, customer knowledge and price sensitivity appeared to predict use of
electronic commerce most significantly, controlling some demographic variables.

The second goal of this study was achieved by a series of hierarchical

regression analyses confirming the positive links between customers’ use of

100

electronic commerce and all of four subsequent relationship outcomes. To a
great extent, these links are reinforced by our findings. These findings were in
line with the common contention that electronic commerce arrangements might
have an important role in increasing customer relationship outcomes.

It was the third goal of this study to identify the dynamic relationships
among use of electronic commerce and four relationship outcomes. Partially
consistent with the results of regression analyses, the results of a structural
equation analysis confirm two of four direct associations between use of
electronic commerce and relationship outcomes: 1) use of electronic commerce
and service quality, and 2) use of electronic commerce and actual retention.
Service quality turns out to play a significant mediating role in predicting the
impacts of use of electronic commerce on overall satisfaction and customers’
actual retention. Overall satisfaction also appears to play a mediating role with
service quality in predicting the impacts of use of electronic commerce on

customers’ attitudinal loyalty.

Limitations

While it has successfully addressed the proposed hypotheses, as with most
research efforts, this study is not without limitations.

One limitation relates to the sampling procedure. This study adopted a
customer intercept field survey at the selected branch offices because it was
impossible to get a complete customer list for random sampling. Though the

sample of this study may not represent the targeted population well, regulation

101

on customer privacy information necessitated and justified the use of this method
as an alternative way of sampling and data collection. Moreover, considering the
relatively large number of research variables, the sample size of this study may
be a little small and need to be increased.

Another limitation is regarding the cross-sectional design of this study.
Since the data was collected at a single point of time, it is only possible to make
conclusions regarding the associations among the research variables. Causal
linkages and implications could be strengthened with multiple years of
measurement of the research variables. This longitudinal approach may be
adequate to capture changes in some variables or establish a direction. For
example, the strengths of the findings will be enhanced by the use of longitudinal
data in trying to predict actual behavioral retention.

The findings of this study are based on a survey of customers, thus
measures of use of electronic trading and actual retention are based on their
perceptions rather than actual behaviors. In spite of serving as useful proxies,
these subjective perceptions are subject to bias and judgment errors. Thus the
utilization of objective data would improve the quality of this study.

Finally, the Korean context of this study places limitations on the
generalizability of our findings to stock brokerage firms in other national cultures.
Nonetheless, this investigation in a non-US scenario does enhance our
understanding of the contribution of electronic stock trading to relationship
outcomes in other national contexts, and sheds further light on the general

benefits of electronic commerce.

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Managerial Implications

Several managerial implications emerge from this study. These implications
should be of interest to managers in service industries including stock brokerage
services industry.

For managers, it is evident that the time has arrived where no brokerage
firm can ignore the benefits that a sound electronic commerce system can
provide. Thus, it is essential that managers should recognize that electronic
commerce strategies lie at the heart of the capability for survival.

In addition, before they commit adequate resources, managers should
make sure that electronic commerce strategies produce the desirable outcomes
to justify their required investments. They should assess the potential of
electronic commerce in producing favorable outcomes when they implement and
operate an electronic commerce system. Moreover, managers need to shift a
transaction orientation to relationship building in their efforts on electronic
commerce strategies. They really need to utilize electronic commerce strategies
as a vehicle of relationship marketing as well as a driver of cost savings. The
investments for electronic commerce can be only important to service firms to the
extent that they influence certain relationship outcomes, such as service quality,
overall satisfaction, attitudinal loyalty and actual retention.

In the meantime, managers should integrate high-potential electronic
channel within the general channel policy, even if the full content of such a policy
will be reorganized by cannibalization of electronic commerce. Instead of taking

an extreme position such as pure offline or pure online, offline and online should

103

not be dissociated. For example, this ‘clicks and mortar’ hybrid model (Dussart,
2000) can be a winning formula in Korean stock trading industry, where the
efforts for building and running cyber branches are prominent. Korean stock
brokerage firms provide cyber branches to facilitate customers’ use of electronic
commerce. These facilities are specifically designed for individual investors who
don’t have high-speed access to the Internet.

Of course, managers should drastically reconsider their allocation of
resources across electronic channel and traditional offline channels based on the
relative contributions to various outcomes. Possibly, their allocation of resources
may be based on a new market segmentation using use of electronic commerce
as a main criterion. For example, if they want to focus on a segment of electronic
customers, considering the higher relationship outcomes of electronic customers,
more emphases would be placed on electronic channel.

Finally, managers may need to think about how to make their customers
adopt and use electronic commerce as a main channel. This study suggests that
managers should focus initially on greater customer education or training aiming
to increase awareness of electronic commerce and emphasize the benefits of
using it. Furthermore, they should create proper strategies to encourage and
reward customers for their loyalty through electronic commerce. Loyalty program,
which has been long popular in the ‘bricks and mortar’ world, can be a

complementary way of driving higher relationship outcomes.

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Implications for Future Research

The results of this study have some implications for the scholars in the field
of information technology, electronic commerce and service relationship
marketing.

First, they need to put more emphasis on the research evaluating
consequences or outcomes of electronic commerce investments. In particular,
they need to focus on the powerful role of electronic commerce in creating some
desirable customer relationship outcomes in the context of continuously provided
services, where customers have repeated interactions with the same service
providers.

The attention for further study should be given to identify which specific
components or dimensions of electronic commerce, including features, functions,
interfaces, contents, and infrastructure, have the strongest impact on relationship
outcomes favorable to the service firms. We need a new study examining the
appropriate balance to be placed on individual facets of electronic commerce.

In addition, since this study focuses on only Korean stock trading industry
in which electronic commerce is prevalent, it would be interesting to compare this
study with another study of an industry or country in which electronic commerce
is less prevalent or in its infancy. In particular, it would be interesting to see the
impacts of electronic commerce on relationship outcomes in the context of
continuously provided but hard service sectors, including telecommunication and

utility industries, where the main service itself can’t be delivered through

105

electronic networks. Replications to validate the findings of this study under
different cultures would be illuminating.

Another possible study will be one examining the asymmetric impacts of
use of poor system versus excellent system. It is because electronic commerce
system implemented excellently is expected to raise outcomes more than if such
is not in place.

Future research may also examine the relationship between customers’
use of electronic commerce and the actual benefits to them. For instance, in
stock trading context, it will be possible to see the impact of use of electronic
trading on the investment performances such as rate of returns and saving in
commissions, controlling the influence of market variations.

Finally, it would be interesting to examine the possible reverse association
between use of electronic commerce and customer relationship outcomes. It is
because increased relationship outcomes may encourage more use of electronic
commerce. Moreover, this possible causal linkage should be studied by a

longitudinal approach.

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APPENDICES

107

F

APPENDIX A

INFORMED CONSENT FORM

This research project will be conducted to meet the requirements for a
doctorate in Philosophy in the Mass Media Ph.D. program at the Michigan State
University. This project is to examine the impact of electronic commerce on key
customer relationship outcomes in the financial service industry.

Your responses to the questionnaire will remain anonymous and will be
kept confidential under policies of the Michigan State University. In addition, your
privacy will be protected to the maximum extent allowable by law. All reports of
research will be aggregated.

You indicate your voluntary consent to participate in this study by
completing the questionnaire and returning it. Should you have any questions
about the study or the questionnaire, please contact Seongcheol Kim (phone: 1-
517-353-1749, E-mail: kimseon1Qmsggdg) or Dr. Charles Steinfield, Professor
of the Department of Telecommunication, Michigan State University (phone: 1-
517-355-4451, E-mail: steinfie@3g.edu).

If you have questions about your rights as a human subject of research,
please contact Dr. David Wright, Chair of the University Committee on Research
Involving Human Subjects (Phone: 1-517-355-2180, E-mail: UCRIHS@msu.edu).

Thank you. We greatly appreciate your voluntary assistance in this study.

108

APPENDIX B

SURVEY INSTRUMENT

Questionnaire #

 

(SECTION ONE: Use of Electronic Trading)
Instruction: This survey is to examine the impact of electronic trading on
customer relationship outcomes. This questionnaire is targeted for the current
customers of S-Securities Corp. and L-Secun'ties Corp. There are no right or
wrong answers. Please answer for each question.
1. Are you a current customer of this broker?

Yes . No

If you answer “no” in question 1, please quit. Otherwise please go ahead.

2. How long have you been a customer of this broker?
1) Less than 6 months
2) More than 6 months and less than 1 year
3) More than I year and less than 2 years
4) More than 2 years and less than 3 years
5) More than 3 years
3. It this company your primary broker?

Yes , No

4. Do you have additional accounts in other brokers?

Yes , No

If yes, how many additional brokers do you use?

 

109

 

5. On average, how many orders do you make in a week?

1) 1 or less

2) Between 2 and 3
3) Between 4 to 6
4) Between 7 to 10
5) 11 and more

6. How many times do you make non—order interactions with your broker in a
week through Web site, phone or branch office?

 

1) 1 or less

2) Between 2 and 3
3) Between 4 to 6
4) Between 7 to 10
5) 11 and more

7. How many stocks you do have in your portfolio?

 

1) Only1

2) 2

3) 3

4) 4

5) 5and more

8. What is the total value of your current investments in stocks?

1) Less than $5,000

2) Between $5,000 and $9,999
3) Between $10,000 and $19,999
4) Between $20,000 and $29,999
5) $30,000 and more

9. Do you have a proper cyber account in this broker to doing electronic trading
for stock transaction?

Yes , No

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10. If you check “yes” in question 9, did you have an active account and make
transactions through this broker before you was involved in electronic trading?

Yes , No

11. Is electronic trading your primary way of making stock transactions?

Yes , No

12. If you answer “no" in question 11, what is your primary way of making stock
transactions?

 

1) By phone
2) By face-to-face at branch office
3) Others

 

13. How long have you been using electronic trading?

 

1) Less than 6 months

2) More than 6 months and less than 1 year
3) More than I year and less than 2 years
4) More than 2 years and less than 3 years
5) More than 3 years

14. How often do you use the electronic trading?

 

1) At least several times in a day
2) At least daily

3) At least weekly

4) Less frequently than weekly
5) At least monthly or less

15-16. Please indicate what percent of your stock transactions was made
through electronic trading during last six months.

15. What percent of your total number of orders was made through electronic
trading during last six months?

111

 

0) None

1) Between 1% and 20%
2) Between 21 % and 40%
3) Between 41 % and 60%
4) Between 61 % and 80%
5) Between 81 % and 100%

16. What percent of your total $value of orders was made through electronic
trading during last six months?

0) None

1) Between 1% and 20%
2) Between 21 % and 40%
3) Between 41 % and 60%
4) Between 61 % and 80%
5) Between 81% and 100%

17. Please describe your usage of each method for electronic trading.

1) Through the Internet (Web trading) %
2) Through the Internet (HTS - home trading system) %
3) Through wireless PDA %
4) Through ARS %
5) Other %

18-19. Please indicate what percent of your stock transactions was made
through this broker during last six months.

18. What percent of your total number of orders was made through this
broker during last six months?

0) None

1) Between 1% and 20%
2) Between 21 % and 40%
3) Between 41 % and 60%
4) Between 61 % and 80%
5) Between 81% and 100%

19. What percent of your total $value of orders was made through this broker
during last six months?

112

0) None

1) Between 1% and 20%
2) Between 21 % and 40%
3) Between 41 % and 60%
4) Between 61 % and 80%
5) Between 81% and 100%

Instruction: Using a five point scale where “5” is very important and “1” is not at
all important, please tell how important the use of electronic trading for each of
three separate functions in stock trading.

20. For communication with your primary broker (i.e., information gathering,
feedback, complaints and so on)

(Not at all important) 1 2 '3 4 5 (Very important)

21. For transaction with your primary broker (i.e., account set-up, negotiation,
order and so on)

(Not at all important) 1 2 3 4 5 (Very important)

22. For distribution of service from your primary broker (is. money transfer,
payment and so on)

(Not at all important) 1 2 3 4 5 (Very important)

(SECTION TWO: Customer Relationship Outcomes)
Instruction: The following section is to ask your current evaluation on your broker.
There are no right or wrong answers. Please circle a number that represents
your answer best for each statement. Circling 5 means that you strongly agree
with the statement, and circling 1 means that you strongly disagree.

23. This broker provides consistent and dependable performance.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

113

 

24. This broker provides its services at the time it promises to do so.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

25. This broker insists on error-free records and accurate bills.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

26. This broker’s service is easily and conveniently accessible.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

27. This broker responds promptly to customers’ requests.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

28. This broker gives customers personalized and individualized attention.
(Strongly disagree) 1 2 3 4 5 (Strongly agree)
29. This broker educates customers along the way so they can make more
informed decisions.
(Strongly disagree) 1 2 3 4 5 (Strongly agree)
30. This broker provides enough opportunity to interact with this broker and/or
other customers.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

31. This broker charges competitive commission rates.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

32. I feel that this broker manages my account in a good way.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

114

33. I am delighted with my overall relationship with this broker.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

34. I wish more of my suppliers were like this broker.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

35. I am very committed to continue the relationship with this broker.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

36. Maintaining a long-term relationship with this broker is very important to me.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

37. I would recommend this broker to someone who seeks my advice.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

38. I say positive things about this broker to other people.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

39. I gladly encourage friends and relatives to do business with this broker.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

40. I will consider this broker as the first choice if I buy new financial service or
product.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

41. I am willing to do buy other financial services through this broker.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

115

(SECTION THREE: Personal and Situational Factors)
Instruction: Please circle a number that represents your answer best for each

question. Circling 5 means that you strongly agree with the statement, and
circling 1 means that you strongly disagree.

42. If I heard a new information technology, I would look for ways to experiment
with it.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)
43. Among my peers, I am usually the first to try out new information
technologies.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

44. To make stock transactions, I need to use every available minute effectively.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

45. It is difficult to allocate some time during day for visiting this broker to make
financial transactions.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

46. I use electronic trading because I am motivated from my peers’ use.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

47. I believe that electronic trading is associated with high social status.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

48. I feel that electronic trading is the current social trend.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

116

49. I use electronic trading to take advantage of low prices.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)
50. I am very concerned about low prices.

(Strongly disagree) 1 2 3 4 5 (Strongly agree)

Instruction: Please circle a number that represents your answer best for each
question. Circling 5 means that you know a lot, and circling 1 means that you
don ’t know well.

51. To what extent do you know about stock trading?

(Do not know) 1 2 3 4 5 (Know a lot)

52. To what extent do you know about electronic channels including the Internet?

(Do not know) 1 2 3 4 5 (Know a lot)

Instruction: Please provide the following information.

53. What speed or access capacity in your computer used for electronic trading
or other things?

 

1) 9600 bps or less

2) 14400 bps

3) 28800 — 33600 bps

4) 56000 bps

5) High speed (Cable modem, ADSL or LAN)

117

(SECTION FOUR: General Information)

Instruction: Please provide the following information.

54. How old are you?

 

1) Younger than 25
2) Between 25 and 29
3) Between 30 and 39
4) Between 40 and 49
5) 50 or older
55. What is your marital status?

Single , Married

56. What is your gender?

Female , Male

 

57. What is your employment status?

 

1) Unemployed

2) Homemaker

3) Student

4) Part-time employee (less than 30 hours per week)
5) Full-time employee (30 or more hours per week)

58. If you are employed, what is your job title?

 

1) Plain worker

2) Assistant manager
3) Manager

4) General manager

5) Executive or higher

118

59. What is the level of education you have completed to date?

1) High school graduate or less
2) Attended college

3) Graduate 2-year college

4) Graduate 4-year college

5) Postgraduate work degree

60. What is your approximate total income from all sources last year (1999)?

1) Under $15,000

2) Between $15,000 and $29,999
3) Between $30,000 and $44,999
4) Between $45,000 and $59,999

5) $60,000 or more

Thank you very much for participating in our study. Now please return your
completed questionnaire and take a gift for you.

119

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120

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