電子商務研究智慧結構之演變－作者共引文分析的應用ijcs.topco-global.com/website/articles/2011vol3_no1_001_024.pdftechnology...

商略學報 2011 年，3 卷，1 期，001-024。

1

電子商務研究智慧結構之演變－作者共引文分析的應用

通訊作者李元墩為長榮大學經營管理研究所教授，地址 : 71101 台南市歸仁區大潭里長榮路一段 396 號，電話：+886-6-2785123 轉 2153，E-mail：[email protected]。作者余國訓為遠東科技大學行銷與流通管理系副教授，地址：74448台南市新市區中華路 49 號，電話 : +886-6-5979566 轉 5249，E-mail：[email protected]。作者李元德為長榮大學

經營管理研究所教授，地址：71101 台南市歸仁區大潭里長榮路一段 396 號，電話 :+886-6-2785123 轉 2036，E-mail：[email protected]。

本研究之目的為呈現內含在電子商務研究文獻智慧結構之演變。本研究以作者共引文分析與社會網絡分

析技術為研究方法；以 1996 年至 2008 年發表在 SSCI 及 SCI 期刊有關電子商務議題的研究文獻為對象，共

計分析了 1,794 篇論文及其 37,101 筆參考文獻資料。研究結果顯示當代電子商務研究呈現五個主要研究趨勢：

電子商務之消費者行為、資訊科技與電子市集、電子商務推薦系統、電子資料交換實行及電子市場之自我服

務與顧客滿意度。儘管有大量研究出現在電子商務領域，相關研究之文獻卻未受到應有的重視；本研究提出

一個可以讓學術界及實務界更了解電子商務研究發展趨勢之方法；此方法可描繪出當代電子商務研究領域的

重要主題及闡明其研究領域的發展及變化。

關鍵詞：電子商務、智慧結構、知識網絡、作者共引文分析、社會網絡分析。

論文編號：IJCS2011004

收稿 2011 年 1 月 12 日→第一次修正 2011 年 2 月 18 日→第二次修正 2011 年 3 月 2 日→正式接受 2011 年 3 月 15 日

余國訓遠東科技大學

李元墩長榮大學

李元德長榮大學

International Journal of Commerce and Strategy2011, Vol. 3, No. 1, 001-024

2

Evolution of the Intellectual Structure of Electronic Commerce Research: An Author Co-citation Analysis

The corresponding Author Yuan-Duen Lee, is a Professor in the Graduate School of Business and Operations Management, Chang Jung Christian University, Address: No.396, Sec. 1, Changrong Road, Gueiren District, Tainan City 71101, Taiwan, Tel: +886-6-278-5123 ext. 2153, E-mail: [email protected] Kuo-Hsun Yu, is an Associate Professor in the Department of Marketing and Logistics Management, Far East University, Address: No.49, Zhonghua Rd., Xinshi Dist., Tainan City 74448, Taiwan, Tel: +886-6-5979566 ext. 5249, E-mail：[email protected] Yender McLee, is a Professor in the Graduate School of Business and Operations Management, Chang Jung Christian University., Address: No.396, Sec. 1, Changrong Road, Gueiren District, Tainan City 71101, Taiwan, Tel: +886-6-278-5123 ext. 2036, E-mail: [email protected]

Kuo-Hsun YuFar East University

Yuan-Duen LeeChang Jung Christian University

Yender McLeeChang Jung Christian University

The purpose of this study is to explore and discuss the evolution of intellectual structure of electronic commerce research. Author co-citation analysis and social network analysis are used to trace the developmental path of electronic commerce research. The study analyzed 37,101 citations from 1,794 articles published in SSCI and SCI journals in the area of electronic commerce between 1996 and 2008, this study maps the knowledge network of electronic commerce studies. The results of this study suggest that contemporary electronic commerce research is organized along different concentrations of interest: consumer behaviors in electronic commerce, information technology and marketplaces, recommender systems in electronic commerce, implementation of electronic data interchange, e-satisfaction and self-service technology. The intellectual structure of electronic commerce literature has received little attention in spite of the fact that a significant number of studies have been done on electronic commerce. This study exposes researchers to a new way of profiling knowledge networks and their relationships the area of electronic commerce, thereby helping academia and practitioners better understand contemporary electronic commerce studies.

Key Words: Electronic Commerce, Intellectual Structure, Knowledge Network, Author Co-citation Analysis, Social Network Analysis.

Paper No.：IJCS2011004

Received January 12, 2011→First Revised February 18, 2011→Second Revised March 2, 2011→Accepted March 15, 2011

2011 K.-H.Yu, Y.-D.Lee and Y. M. Lee 3

Introduction

Globalization and information technologies have

changed the faces of business and organizations. There is

a growing interest in the use of electronic commerce as a

mean to perform business transactions. With the interest,

concurrent has been a heavy emphasis in academic

research on the electronic commerce phenomenon.

Electronic commerce is generally defined as the use of

information and communication technologies for sharing

information, maintaining relationships, and conducting

transaction among consumers or with suppliers ,

customers, or competitors (Zwass, 1996; 2003). To

capture fully the multifaceted phenomena of electronic

commerce, it is necessary to support a variety of

theoretical concepts and research methods from different

academic disciplines.

To overcome these problems, many researchers have

attempted to review and reflect on the development of

electronic commerce (Ngai and Wat, 2002; Shaw et al.,

1997; Wareham et al., 2005), but a quantitative analysis

of articles published in the electronic commerce literature

is still lacking. Filling this gap, this study uses author

co-citation analysis and social network analysis to gain

an impression of electronic commerce research and its

evolution from 1996 to 2008.

The objective of the study is to explore the evolution of

the intellectual structure of electronic commerce research

by analyzing citations in the Social Science Citation Index

(SSCI) and the Science Citation Index (SCI) from 1996

to 2008. More specifically, the goals of this study are to

discuss changes in the discipline's subfields and identify

emerging topics in order to propose a comprehensive

methodology that can be used to investigate the literature

of one specific academic discipline.

The research methods used for this study are author

co-citation and social network analysis. Using citation

analysis, interlinked nodes are discovered from which the

most influential publications and scholars in the electronic

commerce field are identified. Further, author co-citation

analysis is conducted by utilizing the social network

analysis to identify the intellectual structure of electronic

commerce studies.

The paper is divided into four main sections. The

first one is a review of literature related to electronic

commerce, author co-citation and social network

analysis; the second section contains a description of

the methodology employed; the third section presents

and discusses the results of the empirical study; and the

fourth section presents a summary and discussion of the

conclusions drawn from this investigation, indicates its

limitations, and offers suggestions for future research.

Literature Review

Meta-research of Electronic Commerce

In the past decade, we have seen active research in

electronic commerce which produced an impressive array

of literature relating to the study of electronic commerce.

Some reviews about electronic commerce research were

completed in recent years (Ngai and Wat, 2002; Shaw et

al., 1997; Wareham et al., 2005). Shaw et al. (1997) laid

out the dimensions of electronic commerce and developed

a framework for identifying the important research

issues associated with electronic commerce. Taking an

interdisciplinary view that integrates technology and

business modeling, they proposed the scope of electronic

commerce, which includes five major topics: (1)

enterprise management; (2) global EC infrastructure; (3)

interface with consumers; (4) linking with distributors/

retailers; and (5) linking with suppliers. The contribution

of Shaw et al. (1997) was an exploration of five potential

electronic commerce research areas that combine business

modeling and technology development and to provide a

number of research opportunities in this area.

As the research on this topic advanced, Ngai and Wat

(2002) presented a literature review and classification

scheme for electronic commerce research. Based on the

literature review, the sample articles in their study were

classified into four broad categories: application areas,

technological issues, support and implication, and others.

Ngai and Wat (2002) showed that an increasing volume

of electronic commerce research has been conducted for

a diverse range of areas. They indicated that electronic

commerce research is difficult to confine to specific

4 MarchInternational Journal of Commerce and Strategy

disciplines and suggested that the relevant material is

scattered across various journals. Recently, Wareham

et al. (2005) applied meta-analysis to profile available

electronic commerce research and also grouped the

sample articles into four major domains: information

technology and infrastructure, applications and industry

themes, business issues, and other issues.

At the same time, a few researchers have reviewed

certain subfields of electronic commerce. Kauffman

and Walden (2001) provided an overview of economic-

motivated electronic commerce studies from a variety

of disciplines and encouraged researchers in different

disciplines to collaborate and bring their expertise to bear

on issues in electronic commerce for which no single

perspective is sufficient. Recognizing the rapid increase in

the volume of research on electronic commerce customer-

relationship management, Romano and Fjermestad (2001)

found that electronic commerce customer-relationship

management researchers have applied a broad range of

methods to study a wide spectrum of subject matter.

It is worth noting that electronic marketplace literature

has attracted interest from many researchers in recent

years. These researchers have reviewed certain aspects

of electronic marketplaces, such as electronic market

design (Anandalingam et al., 2005) and corporate portals

(Dias, 2001). However, these reviews did not present a

full picture of the current electronic marketplace research

themes (Wang et al., 2008). In addition, studies also rate

researchers’ perceptions of journal quality and relative

prestige (Bharati and Tarasewich, 2002; Lee et al., 2007).

Another steam of meta-research is research commentaries

that discuss the major trends in academic discourse as

well as the most significant research questions (Straub

and Watson, 2001). Often, such commentaries have

argued that the phenomenon of interest is fundamentally

unique and requires great holistic or cross-disciplinary

focus (Geoffrion and Krishnan, 2003; Shaw et al., 1997).

In a related strain of research, we have found that

the interdisciplinary nature of electronic commerce

research attracts most agreement, but research directions

for electronic commerce are a controversial issue. To

overcome this problem, this study uses author co-citation

analysis and social network analysis to gain an impression

of electronic commerce research and its evolution from

1996 to 2008.

Author Co-citation Analysis

Author co-citation analysis (ACA) is a bibliometric

technique that uses a matrix of co-citation frequencies

between authors as its input (McCain, 1990) and that has

found widespread applicability. ACA, which uses seminal

authors in a discipline as the units of analysis, predicates

that the conceptual similarity in the works of such authors

will increase the likelihood of their being cited together

regularly (McCain, 1990). The frequency of co-citation

is therefore a measure of the proximity between authors

(White and Griffith, 1981).

General ly speaking, the formal and informal

communications that authors engage in are systematically

chronicled in journals that publish their works. Authors

working in a stream of research often cite one another's

publications as authors draw on common sources of

knowledge. Further, their works are likely to be frequently

co-cited by other authors working on intellectually

similar themes. The upshot of this process is an intricate

web of relationships between authors established through

the creation and dissemination of knowledge. Thus, co-

citations of seminal authors provide a basis for unraveling

the complex patterns of associations that exist among

them as well as trace the changes in intellectual currents

taking place over time.

ACA's unit of analysis is an individual author rather

than a specific paper or journal. It must be noted that

the name of an author is merely a label for the central

conceptual theme or idea that he or she represents

(Culman, 1986, 1987). This intellectual map is thus a

representation of ideational interactions among authors

established through the frequency of co-citation and the

overall distribution of co-citations that they share with

one another (McCain, 1990; White and McCain, 1998).

This makes ACA eminently suitable for explicating the

subfields that fall within the overall disciplinary domain

of electronic commerce. More specifically, ACA's ability

to reveal patterns of association between authors based

on their co-citation frequency makes it a prospective

methodology for understanding the evolution of an


academic discipline (White and McCain, 1998). The

versatility of the technique and its acceptance by diverse

disciplines make it appropriate for this study.

ACA has proven useful to describe, from an empirical

standpoint, the intellectual structure of one academic

discipline using an objective method. It has therefore been

applied in many academic areas such as management

(Acedo et al., 2001), marketing (Heisshmidt and Gorden,

1993), organizational behavior (Culnan et al., 1990),

conflict management (Ma et al., 2008), small enterprise

(Ratnatunga and Romano, 1997), management information

systems (White, 2003), strategic management (Acedo et

al., 2006; Nerur et al., 2008), international management

(Acedo and Casillas, 2005), knowledge management (Ma

and Yu, 2010), family business (Casillas and Acdeo, 2007),

RandD management (McMillan, 2008), and business

ethics (Ma, 2009). In these disciplines, this methodology

contributes to the identification of research gaps and to an

orientation toward future lines of study. This study also

aims to be a quick reference by which new researchers can

become familiar with electronic commerce as a field of

study.

Social Network Analysis

Network analysis was first used by sociologists to

explore the relationships between groups and individuals

(Scott, 1991). Over years, a series of tools and techniques,

which are useful in producing graphs representing

the relative distances between individuals, have been

developed. Further techniques have also been figured out

to explore structural patterns and the differing roles of

certain positions within the network. Network analysis is

becoming increasingly popular as a general methodology

for understanding complex patterns of interaction.

Advanced social network analysis (SNA) is the mapping

and measuring of relationships and flows between people,

groups, firms, animals, computers or other information/

knowledge processing entities. SNA is an analytical tool

that reveals the number of interactions and the closeness

of relationships between nodes within a network. In other

words, the nodes in the network are the people and groups

while the links show relationships or flows between the

nodes. SNA is a powerful diagnostic method used to

analyze the nature and pattern of relationships among

members of a particular domain. It is a collection of graph

analysis methods developed to analyze networks in social

science, computer networks, and communication studies,

among others. SNA provides mathematical definitions of

certain characteristics of the actors and the network itself:

the power of the actors, the range of influence, cohesion,

equivalence, and brokerage (Bonacich, 1987; Burt,

1992). These characteristics are expressed in terms of

corresponding network-structure parameters derived from

the relations among the actors.

According to Burt (1992), a social network is a group

of collaborating entities that are related to one another.

Mathematically, this is a graph wherein each participant in

the network is called an actor and depicted as a node in the

network. Actors can be persons, organizations, or groups,

or other sets of related entities. Relations between actors

are depicted as links between the corresponding nodes.

A co-citation matrix is inherently very similar to social

networks, a network of linked authors (Pilkington and

Teichert, 2006; Pilkington and Chai, 2008). There has been

an increased interest in the use of this method to analyze

the nature and pattern of informational relationships

among individual scholars or articles in one discipline

(Casey and McMillan, 2008; Ma et al., 2008; Pilkington

and Fitzgerald, 2006; Pilkington and Meredith, 2009). As

discussed above, this study proposes that SNA techniques

make it possible to study networks of knowledge such as

those characteristics of academic disciplines.

Methodology

Based on the objective of this study, we investigated

the evolution of the intellectual structure of electronic

commerce research between 1996 and 2008. Two reasons

justified selecting 1996 as the starting point. First, as

demonstrated by Ngai and Wat's (2002) study of nine

research journals, the number of articles on electronic

commerce has substantially increased since 1996.

Second, there were also only five articles collected by our

sampling method before 1996. Author co-citation analysis

and social network analysis are the main methods for

this study. Using citation and author co-citation (ACA),


four stages were proposed for this study, and each stage

required different approaches to examine the evolution of

electronic commerce studies.

First, the databases as the sources of electronic

commerce publications were identified. Then, data

collection and analysis techniques were designed to

collect the desired information about the topics, authors,

and journals related to electronic commerce research.

For the data presented herein, the Social Science

Citation Index (SSCI) and Science Citation Index (SCI)

were used as the databases. The SSCI and SCI are

widely used databases and include citations published in

about 8,000 referred journals. This database is an index

of citations managed by the U.S. Institute for Scientific

Figure 1 Design of the Study


Information (ISI). Using SSCI and SCI provided the most

comprehensive and widely accepted databases related to

electronic commerce publications.

As discussed in the literature review, electronic

commerce discourse has been vigorous in many

managerial disciplines, including information systems,

marketing, finance, and logistics, among the more

obvious, so data used in this study were not drawn from

journals chosen by peer researchers (Acedo and Casillas,

2005; Pilkington and Meredith, 2009). Instead, the entire

databases of SSCI and SCI from 1996 to 2008 served

as the context for conducting the analysis. In order to

choose sample articles, this study used a “key words”

method which utilizes the SSCI and SCI databases key

word search from an article's title. Using “e-commerce”

and “electronic commerce” as key words, this study

included 1,794 journal articles which cited 37,101 other

publications as references. The cited publications in these

papers include both published books and other journal

articles.

In the second stage, citation analysis was tabulated

for each of the source documents using the bibliometric

analysis package Bibexcel from Persson (2003).

Moreover, since our aim was to explore the evolution of

intellectual structure in electronic commerce area, it was

necessary to divide the study period into two sub-periods

for which the first time period was from 1996 to 2002,

and the second period was from 2003 to 2008, bearing in

mind that our intention was not to identify real periods

but simply to register changes over the course of time.

Using the Bibexcel, key nodes that were the most cited

authors in knowledge networks in electronic commerce

studies were identified and the structures were developed.

The third stage was to perform an author co-citation

analysis (ACA) based on the most cited authors from each

sub-period in order to trace relationships between them

and identify schools of thought and prevailing topics of

research. ACA was also tabulated for each of the source

documents using the Bibexcel package. ACA is based on

the distribution frequencies obtained from the citations

count obtained by forming the pairs possible from the

60 most frequently cited authors and counting all the

articles that cited both authors (See Figure 2). Based on

the total number of citations in the selected articles, the

top scholars were identified, and then a co-citation matrix

was built before a pictorial map was drawn to describe

the correlations among different scholars (See Table 1).

In doing so, we followed the procedures recommended

by McCain (1990) that explored the intellectual structure

of information science research by applying factorial

analysis techniques in an author co-citation analysis.

Once the co-citation matrices were obtained, this study

used Pearson's correlation as a measure of similarity

between author pairs because it registers the likeness in

shape of their co-citation count profiles over all other

authors in the set (McCain, 1990; White and Griffith,

1981; White and McCain, 1998). In Figure 3, the co-

citation counts across a partial author set and the overall

correlation coefficients are illustrated for two pairs

of electronic commerce authors (data from Yu et al.,

2008). It can be seen that Jarvenpaa and Gefen have

a high positive correlation of 0.892. Each is co-cited

frequently or infrequently with the same authors. By

contrast, authors from two extremely different electronic

commerce schools of thought, Gefen and Bakos, have

marked different co-citation patterns and a correlation of

-0.129 across the entire set of authors.

In Figure 3, the correlation matrix serves as a matrix

of inter-authors of similarity, and the higher the positive

correlation, the more similar two authors are in the

perceptions of citers. There are two ways to treat the

main diagonal when calculating correlation coefficients.

The first of these involves taking the sum of the three

highest scores and dividing them by two (White and

Griffith, 1981); the other way (McCain, 1990) is simply

to consider it missing data and to apply the criterion of

omitting the two cases. For the sake of simplicity, this

study decided to ignore the scores on the main diagonal

when calculating the correlation coefficients for the pairs

of authors.

In the final stage, using UCINET 6.0, author co-

citation analysis was conducted by utilizing the social

network analysis and factor analysis. The results of author

co-citation analysis identify the intellectual structure of

electronic commerce studies and the knowledge nodes

that have contributed most to the studies on electronic


Figure 2 Author Co-citation Count

Author 1 Author 2 Author 3 Author 4 Author 5

Author 1 1 1 3 1

Author 2 1 3 4 1

Author 3 1 3 18 10

Author 4 3 4 18 12

Author 5 1 1 10 12

Table 1 Author Co-citation Matrix (Extract)

commerce and their possible evolution patterns.

Results

The Findings from Citation Analysis

To identify the key scholars who have laid down the

ground work for electronic commerce, citation data were

tabulated for each of the 1,794 source documents using

the Bibexcel package. From a combination of journal

articles and books, the most cited scholar between 1996

and 2002 (the first seven years) was Kalakota, followed

by Bakos and Malone (See Table 2). For the second six

years, the statuses of important scholars have changed.

The most cited scholar was Gefen, followed by Davis and

Jarvenpaa (See Table 3).

These scholars have had an important influence on the

development of the electronic commerce area and thus

collectively define this field. It represents the focus of the

main authors in the field and this gives us an indication of


the popularity of certain electronic commerce topic.

The Intellectual Structure of Electronic Commerce Research: 1996-2002

In the next stage, data mapping was conducted, and

the intellectual structure of electronic commerce studies

was revealed using author co-citation analysis. Co-

citation analysis of authors involves recording the number

of papers that have cited a particular pair of authors.

The number of co-citations is interpreted as a measure

of similarity in the content of the two cited authors’

publications. By analyzing the references of published

articles, we can determine if any two references are

commonly referenced together, or “co-cited”. If a set

of such references tend to be frequently co-cited, then

this constitutes what we term a “structural knowledge

network” (Pilkington and Meredith, 2009). It is the set

of these networks, and the relationships between and

among them, that constitute the intellectual structure of a

field (Leydesdorff and Vaughan, 2006).

Co-citations were tabulated for each 1,794 source

documents using the Bibexcel package. Many of the

authors had very few co-citations that were either unlikely

to have had a significant impact the development of the

field and/or were too new to have had time to impact the

literature. To allow a focus purely on the critical themes,

Jarvenpaa Gefen Jarvenpaa Bakos

(r = +0.892*) (r = +0.013*)

Porter 7 2 7 25

Kalakota 8 5 8 12

Hoffman 22 15 22 8

Davis 15 15 15 12

Malone 8 3 8 37

Williamson 6 5 6 13

Maes 2 0 2 8

Zwass 5 2 5 13

Brynjolfsson 11 7 11 27

Shapiro 2 1 2 14

Partial Co-citation Count for Two Author Pairs

Bakos Jarvenpaa Porter Kalakota Hoffman Gefen

Bakos 1.000 0.013 0.863 0.631 -0.131 -0.129

Jarvenpaa 0.013 1.000 0.027 0.131 0.774 0.892

Porter 0.863 0.027 1.000 0.596 -0.009 -0.102

Kalakota 0.631 0.131 0.596 1.000 0.090 -0.047

Hoffman -0.131 0.774 -0.009 0.090 1.000 0.784

Gefen -0.129 0.892 -0.102 -0.047 0.784 1.000

Partial Co-citation Count for Two Author Pairs

Conversion to Correlation Matrix

Figure 3 Creation of Correlation Matrix

* Correlation over 32 Authors


Rank Author Frequency Rank Author Frequency

1 Kalakota, R. 48 31 Grover, V. 8

2 Bakos, J. Y. 46 32 Iacovou, C. L. 8

3 Malone, T. W. 40 33 Ives, B. 8

4 Hoffman, D. L. 27 34 Milgrom, P. 8

5 Porter, M. E. 23 35 Tapscott, D. 8

6 Williamson, O. E. 23 36 Bhimani, A. 7

7 Maes, P. 21 37 Glushko, R. J. 7

8 Riggins, F. J. 20 38 Goolsbee, A. 7

9 Benjamin, R. 17 39 Mayer, R. C. 7

10 Shapiro, C. 17 40 Sarkar, M. B. 7

11 Zwass, V. 17 41 Teece, D. J. 7

12 Barua, A. 16 42 Anderson, R. E. 6

13 Clemons, E. K. 16 43 Ba, S. 6

14 Davis, F. D. 16 44 Brynjolfsson, E. 6

15 Rayport, J. F. 15 45 Cheng, E. 6

16 Mclure, C. E. 13 46 Choudhury, V. 6

17 Wigand, R. T. 12 47 Fox, W. F. 6

18 Alba, J. 11 48 Hellerstein, W. 6

19 Chavez, A. 11 49 Hsu, M. 6

20 Hagel, J. 11 50 Kauffman, R. J. 6

21 Timmers, P. 11 51 Lee, H. G. 6

22 Applegate, L. M. 10 52 Liang, T. P. 6

23 Bailey, J. P. 10 53 Liu, C. 6

24 Elsenhardt, K. M. 10 54 Okeefe, R. M. 6

25 Jarvenpaa, S. L. 10 55 Poon, S. 6

26 Strader, T. J. 10 56 Raiffa, H. 6

27 Delone, W. H. 9 57 Rogers, E. M. 6

28 Guttman, R. H. 9 58 Shaw M. J. 6

29 Turban, E. 9 59 Watson, R. T. 6

30 Bentler, P. M. 8

Table 2 Author Citation Frequency (frequency ≧ 6): 1996-2002


Rank Author Frequency Rank Author Frequency

1 Gefen, D. 83 31 Delone, W. H. 16

2 Davis, F. D. 53 32 Taylor, S. 16

3 Jarvenpaa, S. L. 50 33 Churchill, G. A. 15

4 Porter M. E. 47 34 Malone, T. W. 15

5 Bakos, J. Y. 40 35 Ngai, E. W. T. 15

6 Brynjolfsson, E. 40 36 Shapiro, C. 15

7 Hoffman, D. L. 40 37 Shardanand, U. 15

8 McKnight, D. H. 39 38 Turban, E. 15

9 Kalakota, R. 36 39 Amit, R. 14

10 Pavlou, P. A. 26 40 Guttman, R. H. 14

11 Venkatesh, V. 26 41 Sarwar, B. 14

12 Resnick, P. 25 42 Clemons, E. K. 13

13 Zwass, V. 24 43 Culnan, M. J. 13

14 Mayer, R. C. 23 44 Fishbein, M. 13

15 Maes, P. 22 45 Gulatir, R. 13

16 Rogers, E. M. 22 46 Liang, T. P. 13

17 Straub, D. W. 21 47 Schafer, J. B. 13

18 Zhu, K. 21 48 Szymanski, D. M. 13

19 Doney, P. M. 20 49 Fornell, C. 12

20 Hair, J. 20 50 Ganesan, S. 12

21 Ajzen, I. 19 51 Morgan, R. M. 12

22 Iacovou, C. L. 19 52 Pazzani, M. 12

23 Lohse, G. L. 19 53 Reichheld, F. F. 12

24 Parasuraman, A. 19 54 Timmers, P. 12

25 Chircu, A. M. 18 55 Bagozzi, R. P. 11

26 Poon, S. 18 56 Benjamin, R. 11

27 Tan, Y. H. 18 57 Bharadwaj, A. S. 11

28 Anderson, J. C. 17 58 Goolsbee, A. 11

29 Nunnally, J. C. 17 59 Grover, V. 11

30 Alba, J. 16 60 Premkumar, G. 11

Table 3 Author Citation Frequency (frequency ≧ 11): 2003-2008


only authors with at least six citations in the first seven

years and eleven citations in the second six years were

carried forward to the analysis (See Tables 2 and 3).

This result in co-citation matrices included 59 authors

in the first seven years and 60 authors in the second six

years. To analyze the co-citation matrix of authors and

to identify the intellectual structure within the electronic

commerce field, social network analysis (SNA) was

adopted. SNA is an analytical tool that reveals the number

of interactions and, consequently, the closeness of the

relationships between nodes within a given network. It

produces diagrammatical representations of the relative

distances between nodes and illustrates structural patterns

and differing positions within the network (Wasserman

and Faust, 1997). An SNA of co-citation visualizes the

degree of relationship between core authors, which can

be used to represent research themes. The graphing

programmer NETDRAW (Version 1.0 which comes with

the social network software suite UCINET6.0, Borgatti

et al., 2002) was used to examine the co-citation matrices

for authors. Figure 4 shows the core of the co-citation

of authors in this sample of articles with links of greater

than or equal to three co-citations in order to keep the

diagrams relatively uncluttered and easier to interpret.

The number of each node presents the rank of citations

received by the chosen authors from our sample articles

as detailed in Table 2.

The diagram in Figures 4 is very telling and provides

a clear picture. It focuses only on the very core area and

a limited amount of the data available. Taking the co-

citation matrices and grouping the authors using a factor

analysis of the correlation between the entries determines

which authors are grouped together and there share a

common element. The co-citation correlation matrices

were factor analyzed using varimax rotation, a commonly

used procedure, which attempts to fit (or load) the

maximum number of authors on the minimum number of

factors. The diagonals were considered missing data and

apply the criterion of omitting the two cases (pairwise

delete) (McCain, 1990).

Six factors were extracted from the data and together

they explain over 70.7% of the variance in the correlation

matrix. Table 4 lists the six most important factors along

with the authors that had a factor loading of at least

0.7. The authors were tentatively assigned names to

the factors on the basis of our own interpretation of the

publications of the authors with high associated loadings.

Implicitly, our interpretation of the analysis results was

that the electronic commerce field comprises six basic but

different sub-fields: information technology and strategy,

essential and structure of electronic commerce, taxes in

electronic commerce, agent-based electronic commerce,

intermediaries in electronic markets, and diffusion of

electronic commerce. We made no attempt to interpret

the remaining factors on account of their relative small

eigenvalues (less than 1). They likewise have been

excluded from Table 4.

Figure 4 and Table 4 clearly indicate that the most

influential scholars in electronic commerce studies

between 1996 and 2002 clustered together. The main

research focused on information technology and strategy.

Porter's (1980) business-level generic strategy related to

cost, differentiation, and focus have attracted attention

from electronic commerce researchers. He submitted

the Five Forces Model of competitive strategy and

proposed that competitive priorities composed of cost,

quality, delivery, flexibility, and innovation. Porter’s

notion was crucial to electronic commerce research.

Later, Porter (2001) applied the Five Forces Model to

B2B e-marketplaces. According to Porter (2001), the

most important determinant of a marketplace’s profit

potential is the intrinsic power of the buyers and suppliers

in the product area (Porter, 2001). While the strategic

implications of the Internet have been addressed (Porter,

2001; Shapiro and Varian, 1999), the nature of products

and services has not received the same treatment. Shapiro

and Varian (1999) submitted the Open Innovation Model

which is characterized by the exploitation of intellectual

property in order to create value. Using improved

computing technologies and digital networks, knowledge-

based activities can be performed in cooperation with

other components in social systems in almost an infinite

number of ways when and how they are needed. From

an asset perspective, Brynjolfsson (1994) extended the

property-rights framework (Hart and Moore, 1990) by

including productive knowledge and information as


Figure 4 Intellectual Structure of Electronic Commerce Research (1996-2002)

Factor 1: information technology and strategy

30.7% variance Factor 2: essentials and structure of electronic commerce

12.1% variance Factor 3: taxes in electronic commerce

8.6% variance

Lee, H.G. 0.882 Liu, C. 0.890 Fox, W.F. 0.962

Brynjolfsson, E. 0.863 Ives, B. 0.825 Hellerstein, W. 0.962

Clemons, E.K. 0.842 Zwass, V. 0.821 McLure, C.E. 0.961

Kauffman, R.J. 0.831 Applegate, L.M. 0.812 Goolsbee, A. 0.930

Teece, D.J. 0.791 Strader, T.J. 0.802

Porter, M.E. 0.785 Delone, W.H. 0.799

Shapiro, C. 0.754 Jarvenpaa, S.L. 0.785

Grover, V. 0.738 Kalakota, R. 0.784

Bakos, J.Y. 0.704 Riggins, F.J. 0.749

Hoffman, D. 0.735

Wigand RT 0.710

Benjamin R 0.701

Factor 4: agent-based electronic commerce

7.8% varianceFactor 5: intermediaries in electronic markets

7.0% varianceFactor 6: diffusion of electronic commerce

4.5% variance

Glushko, R.J. 0.958 Sarkar, M.B. 0.728 Watson, R.T. 0.765

Chavez, A. 0.903 Ba, S. 0.700 Rogers, E.M. 0.758

Guttman, R.H. 0.725

Maes, P. 0.717

Table 4 Author Factor Loadings at 0.7 or Higher: 1996-2002


intangible assets that affect agents’ marginal products

when they have access to them.

Meanwhile, Clemons et al. (1993) emphasized that

information technology (IT) systems have the capability

to lower external coordination costs without increasing

the contractual risks associated with market transactions

and proposed that an IT system reduces transaction

risks such as the contractual hazards of shirking and

opportunism through improved monitoring and reduced

specificity in coordination. Furthermore, the potential

of the Internet and its associated technologies to enable

global electronic commerce has been widely documented

(Lee and Clark, 1997).

Based on Figure 4 and Table 4, the essentials and

structure of electronic commerce permeate the authors

in the second group. Zwass (1996) defined electronic

commerce as“ ……the sharing of business information,

maintaining business relationships, and conducting

business transaction by means of telecommunications

networks.” He pointed out that electronic commerce

includes not only buying and selling goods, but also

various processes within individual organizations that

support that goal. Similarly, Applegate et al. (1996)

also viewed electronic commerce as more than simply

buying and selling goods electronically. They pointed

out that electronic commerce involves using network

communications technology to engage in a wide range

of activities up and down the value-added chain both

within and outside the organization. In addition, Kalakota

and Whinston (1996) stated that four different types

of information technology are converging to create the

merging of this discipline as mostly customer-to-business

applications. Meanwhile, some research has studied

how to approach customers through home pages and to

measure user information satisfaction (Ives et al., 1983;

Liu et al., 1997).

The topic of taxes in electronic commerce permeates

the authors in the third group. There are some tax studies

on the Internet in the literature. Some of them provide

either conceptual or legal analysis for taxation of the

Internet (e.g. Fox and Murray, 1997; Hellerstein, 1997a,

1997b; McLure, 1997) or individual level analysis for the

impact of taxation on online purchases (e.g. Goolabee,

2000). Fox and Murray (1997) concluded that the sales

tax should be designed to be largely consistent with

the current electronic commerce structure. Advanced,

Goolsbee (2000) suggested that people living in high

sales taxes locations are significantly more likely to buy

online.

Agent-based electronic commerce permeates the

authors in the fourth group. Software agents help

automate a variety of tasks including those involved in

buying and selling products over the Internet. Guttman

et al. (1998) surveyed several of these agent-mediated

electronic systems by describing their roles in the context

of a Customer Buying Behavior (CBB) model and

proposed that traditional marketing models with concepts

from software agents research accommodate electronic

markets. Interaction among agents, an important aspect

on research of a multi-agent system, is set up on lower-

level data communication as well as control information

containing semantics and knowledge. Meanwhile, there

is some research focusing on the use of XML (Extensible

Markup Language) in agent communication (Glushko et

al., 1999).

In related research, Maes et al. (1999) proposed

that consumers experience two steps of alternative

evaluation before they make a purchase decision: product

brokering and merchant brokering. Product brokering

involves evaluation of product alternatives, and merchant

brokering is to determine who to buy from. Their research

on automated negotiation, which was initiated to satisfy

people's need for convenience, sought to solve problems

by using automated agent systems which conduct

negotiation tasks on behalf of people.

Intermediaries in electronic markets permeate the

authors in the fifth group. Research has generally focused

on the benefits of electronic markets from a transaction

cost economic perspective. For example, economic-based

research has examined the role of intermediaries (Sarkar

et al., 1995). Sarkar et al. (1995) have referred to how

intermediaries in electronic markets serve a variety of

roles and function from search and directory services, to

aggregation, to authentication and assurance. A variety of

electronic intermediary services available on the Internet

enables consumers to navigate extensive databases on


Figure 5 Intellectual Structure of Electronic Commerce Research (2003-2008)

websites and to locate their most preferred vendors,

thus significantly reducing search costs pertaining to

individual consumers or buying firms (Sarkar et al.,

1996). Consumers may be motivated by the ability to

implicitly derive a certain set of utilities by patronizing a

given type of shopping setting (Sarkar et al., 1996).

Diffusion of electronic commerce permeates the

authors in the last group. In examining the diffusion

of electronic commerce, Rogers's (1995) work on the

attributes of innovations that influence diffusion is well

known and has formed the basis of electronic commerce

research. The diffusion of innovations model describes

the “process by which an innovation is disseminated

over time among members of a social system” and

suggests that the impact of innovations will be maximized

when researchers and clinic staff work collaboratively

to redesign core processes around innovation and

allocate resources to creative incentives and monitor its

use (Rogers, 1995). Additionally, electronic commerce

researchers have come to realize that the acceptance of a

system is significantly dependent on the ease with which

people can operate it (Davis, 1989).

Watson et al. (1998) additionally proposed that there

are several reasons for companies to create their own

websites, including reducing the costs of matching buyers

and sellers, promoting company’ image, increasing

visibility, improving customer service, enabling market

expansion, and lowering stakeholder communication

costs. They also concluded that the Internet provides a

global infrastructure that enables compression of time and

space, integrated supply chains, mass customization and

navigational ability.

The Intellectual Structure of Electronic Commerce Research: 2003-2008

Similarly, using UCINET 6.0, Figure 5 shows the core

of the co-citation of authors in this sample of articles with

links of greater than or equal to three co-citations in order

to keep the diagrams relatively uncluttered and easier to

interpret. The number of each node presents the rank of

citations received by the chosen authors from the sample

articles as detailed in Table 3.

In the same way, five factors were extracted from

the data and together they explain over 74.2 % of the

variance in the correlation matrix of the second six years.

Table 5 lists the five most important factors along with the

authors that had a factor loading of at least 0.7. We also

tentatively assigned names to the factors on the basis of


our own interpretation of the publications of authors with

high associated loadings. Implicitly, our interpretation

of the analysis results was that the electronic commerce

field comprises five basic but different sub-fields:

consumer behavior in electronic commerce, information

technology and electronic marketplaces, recommender

systems in electronic commerce, implementation of

electronic data interchange, and e-satisfaction and self-

service technology. We also made no attempt to interpret

the remaining factors on account of their relative small

eigenvalues (<1). They have similarly been excluded

from Table 5.

Figure 5 and Table 5 also clearly indicate that the

most influential scholars in electronic commerce studies

between 2003 and 2008 clustered together. The main

research focused on consumer behavior in electronic

commerce. In examining consumer behavior in electronic

commerce, first, Davis (1989) proposed the technology

acceptance model (TAM) which is based on the theory

of reasoned action has two major constructs: perceived

usefulness and perceived ease of use. Further, Jarvenpaa

and Todd (1996) suggested that the most important

perceived benefit of Internet shopping is convenience,

while they indicated that poor customer service, poorly

designed websites, and perceived risk were cited by

online shoppers as negative factors and observed that the

Factor 1: consumer behavior in electronic commerce

34.7% variance Factor 2: information technology and electronic marketplaces

21.2% variance Factor 3: recommender systems in electronic commerce

8.2% variance

Doney, P.M. 0.967 Chircu, A.M. 0.920 Pazzani, M. 0.941

Mayer, R.C. 0.966 Clemons, E.K. 0.913 Schafer, J.B. 0.926

Tan, Y.H. 0.964 Bakos, J.Y. 0.871 Sarwar, B. 0.922

McKnight, D.H. 0.960 Malone, T.W. 0.853 Shardanand, U. 0.922

Pavlou, P.A. 0.952 Zhu, K. 0.849 Resnick, P. 0.912

Gefen, D. 0.945 Grover, V. 0.843 Guttman, R.H. 0.723

Ganesan, S. 0.928 Brynjolfsson, E. 0.833

Culnan, M.J. 0.923 Shapiro, C. 0.825

Jarvenpaa, S.L. 0.907 Bharadwaj, A.S. 0.810

Venkatesh, V. 0.851 Benjamin, R. 0.742

Davis, F.D. 0.821

Ajzen, I. 0.811

Taylor, S. 0.809

Bagozzi, R.P. 0.760

Hoffman, D.L. 0.757

Fishbein, M. 0.735

Reichheld, F.F. 0.711

Factor 4: implementation of electronic data interchange

6.1% variance

Factor 5: e-satisfaction and self-service technology

3.4% variance

Premkumar, G. 0.793 Szymanski, D.M. 0.800

Rogers, E.M. 0.733 Parasuraman, A. 0.763

Poon, S. 0.723

Table 5 Author Factor Loadings at 0.7 or Higher: 2003-2008


most influential factors that lure customers to the Internet

is discount price; therefore, creative pricing is the core

business model of many Internet companies.

Trust is vital to consumer behavior related to electronic

commerce. According to Mayer et al. (1995), trust and

risk perception are very strongly interrelated. They

regarded risk as an essential component of trust; one

must take a risk in order to engage in a trusting action.

Research in other arenas suggested that trust develops, at

least in part, as a function of successful/satisfying prior

experience with an individual or group (e.g. Doney and

Cannon, 1997; Ganesan, 1994).

Many researchers have focused on the topic of trust in

electronic commerce (Gefen, 2000; Gefen et al., 2003;

Pavlou, 2003; Tan and Theon, 2000). Tan and Theon

(2000) presented a generic model of trust for electronic

commerce consisting of two basic components, party

trust and control trust, based on the concept that trust in a

transaction with another party combines trust in the other

party and trust in the control mechanisms that ensure the

successful performance of the transaction.

In a re la ted s t ra in of research, Gefen (2000)

empirically revealed that individual's general trusting

propensity, which is the product of a lifelong socialization

process, is positively related to individual trust. Further,

Gefen et al., (2003) proposed a variable“trust”for

studying electronic commerce acceptance. Extending the

trust factor into the Technology Acceptance Model (TAM)

enables a better explanation of electronic commerce usage

behavior. The importance of trust in electronic commerce

can hardly be overestimated. McKnight and Chervany

(2001) distinguished trusting beliefs from trusting

intentions in the concept of trust toward Web vendors. In

electronic commerce, trusting beliefs include competence,

benevolence, integrity, and predictability exhibited by

Web vendors when they interact with consumers. Also,

trusting intentions include the consumer's willingness to

depend on and the subjective profitability of depending

on the Web vendors when making a transaction (McKnight

and Chervany, 2001).

Based on Figure 5 and Table 5, information technology

and electronic marketplaces permeate the authors in the

second group. Electronic marketplaces are defined as a

virtual gathering of buyers and sellers in an electronic

environment that coordinates transactions between

different individuals and firms (Malone et al., 1987). Early

work by Malone et al. (1987) argued for efficiency gains

in electronic marketplaces. It was expected that electronic

communication would rapidly reduce transaction costs in

electronic marketplaces and make them disintermediated

and frictionless (Chiru and Kauffman, 2000).

In a related strain of research, Bakos (1991) presented

a more circumscribed view of the electronic marketplace

as a facilitator of information about prices and products

and argued that the electronic marketplace reduce the

costs incurred to acquire information. Further, Bakos

(1997) concluded that lowering buyer's search costs

enables buyers to find low-cost sellers and that the

electronic marketplace will therefore promote price

competition among sellers.

Recommender systems in electronic commerce

permeate the authors in the third group. In examining the

recommender systems, Schafer et al., (2001) proposed

that recommender systems are an effective way to

achieve mass customization in electronic commerce.

Recommender systems enrich online store sales and are

an effective way to increase customer satisfaction and

consequently, customer loyalty. Recommender systems

also improve cross-selling by suggesting additional

products for the customer to purchase and help customers

identify products according to their interests. This calls

for personalized product recommendations.

In particular, for the field of recommender systems,

the Bayesian classifier, decision trees or instance based

classifiers have been commonly employed for improving

user models designed for the task of filtering useful items.

For instance, Bayesian classifiers and decision trees can

be found in significative recommender systems (Pazzani

and Billsus, 1997). Instance based classifiers are common

in collaborative filtering (CF) approaches (Resink et

al., 1994). One of the most successful recommendation

techniques to date is the CF method (Sarwar et al., 2000;

Shardanand and Maes, 1995), whose performance has

been proved in various electronic commerce applications

(Sarwar et al., 2000). The CF method automates the

“word of mouth” that we usually receive from our


family, friends, and co-workers (Shardanand and Maes,

1995). Therefore, CF-based recommender systems should

form a group of recommenders that plays the information

source for a target customer.

Implementation of electronic data interchange (EDI)

permeates the authors in the fourth group. Based on

an innovation diffusion perspective, Premkumar et al.

(1994) studied the demand uncertainty of a product and

developed a model to describe the tangible benefits of an

EDI project based on the theory of Brownian motion and

found that the size of the initial investment is a significant

barrier to EDI adoption.

The l i te ra ture has revealed tha t many of the

determinants associated with successful implementation

projects and organizational change, including top

management support, presence of a champion, and

adequate resource support, communication, and training,

are significantly associated with successful EDI adoption

and implementation (Premkumar and Ramamurthy, 1995).

Many of these factors have appeared in the technology

management l i terature and include the technical

compatibility of systems, availability of technical and

professional staff, managerial competencies, the size

of organizations, financial support, proactiveness, and

organizational readiness for EDI adoption (Iacovou et al.,

1995; Premkumar and Ramamurthy, 1995).

E-satisfaction and self-service technology permeate the

authors in the last group. Consumers’ perception about

Internet retailers is mainly built upon their interactions

with the websites. The gap between customer perception

and e-business website representations is the majority

cause of electronic commerce shortages. As researchers

(Szymanski and Hise, 2000) have suggested, information

quality is a strong determinant of consumer satisfaction

with Internet shopping. Szymanski and Hise (2000) also

found product information and site design to be critical in

creating a satisfying customer experience.

Further, in examining self-service technologies,

Parasuraman and Grewal (2000) proposed that companies

are drawn to self-service technologies by their promise

of greater cost efficiencies, enhanced service quality,

and attraction of new customers resulting from in-

person services. On the other hand, software engineers

must understand customer needs to better design

technology solutions. In short, modern service designs

require multidisciplinary approaches, methods, and tools

(Parasuraman and Zinkhan, 2002).

Evolution of the Intellectual Structure of Electronic Commerce Research

Table 6 points to a decline in the significance of

electronic commerce strategy and is accompanied by a

simultaneous change in treatment. Instead of exploring

particular information technology and marketing methods

associated with the best practice and the effective pursuit

of strategic objectives, firstly, there is a need to examine

strategy more generally and, secondly, there is a need

to integrate other specific information technology and

marketing practices with electronic commerce strategy.

By comparing the details shown in Tables 4 and 5,

we can see that discussions in the earlier data set on the

need for information technology to be at the heart of

electronic commerce are missing in the later set. The

later set has replaced these discussions with interests in

the link between information technology and marketing.

This gives greater prominence to the context of consumer

Electronic commerce sub-fields, 1996-2002 Electronic commerce sub-fields, 2003-2008

1. information technology and strategy 1. consumer behavior in electronic commerce

2. essentials and structure of electronic commerce 2. information technology and electronic marketplaces

3. taxes in electronic commerce 3. recommender systems in electronic commerce

4. agent-based electronic commerce 4. implementation of electronic data interchange

5. intermediaries in electronic markets 5. e-satisfaction and self-service technology

6. diffusion of electronic commerce

Table 6 Electronic Commerce Sub-fields


behavior, electronic marketplaces, and electronic

shopping.

In addition, it is easy to see from the comparison

that the key nodes in research focused on the current

electronic commerce knowledge network, as shown in

Figure 5, are more concentrated than their counterparts

in Figure 4, and closer links have been seen built among

scholars who are doing research on electronic commerce.

The density of the correlations between different scholars

has greatly increased. With more scholars and more

resource added to electronic commerce studies, a better

academic environment conducive for cross-fertilization

of research ideas in the electronic commerce field is

formed, and electronic commerce research will gain more

momentum for further development.

Conclusions

Summary and Conclusions

The intellectual structure of electronic commerce

literature has received little attention in spite of the

fact that a large number of studies have been done

on electronic commerce. To identify the intellectual

structure of electronic commerce studies, this study

exposes researchers to a new way of profiling key

themes, concepts and their relationships in the area of

electronic commerce, which will help academia and

practitioners better understand electronic commerce

studies. Using author co-citation and social network

analysis to analyze the citation and co-citation data

published in SSCI and SCI databases between 1996

and 2008, this study investigates electronic commerce

research and describes the intellectual structure of the

area of electronic commerce. The results of this study

suggest that contemporary electronic commerce research

is organized along different concentration of interests:

consumer behavior in electronic commerce, information

technology and electronic marketplaces, recommender

systems in electronic commerce, implementation of

electronic data interchange, and e-satisfaction and self-

service technology.

In addition, as shown in Figure 5, closer ties and

more associations among electronic commerce research

have been established in recent years. The inter-linkage

between different key authors will attract more resources

that help to strengthen the critical research themes in

electronic commerce when newly attracted scholars start

to use them to build even stronger theory and theoretical

framework, which in turn helps the electronic commerce

field to deal with more complex issues and concerns. The

consequence of more thinkers and scholars perusing their

interests advances the state of previous research ideas. In

turn, this process helps to further the depth and breadth

of the field, which is bound to attract even more scholars

and practitioners to continuously enrich theoretical cores

and may simultaneously lead to a stronger conceptual

framework or theoretical paradigm. As a result, the

intellectual structure of the electronic commerce field is

expanded, and its knowledge network's foundations are

consolidated. With this increasing dynamism and the

emergence of a continuously consolidating paradigm to

lead both the academic inquiry and industrial practice,

and through such an evolving process, electronic

commerce research is growing, and its knowledge

network is developing.

The contribution of this study is to provide a

favorable research direction in the area of electronic

commerce and propose a comprehensive methodology of

identifying the relative importance of different authors

in the development of the electronic commerce field.

The results of this study complement and improve the

findings of other studies that have approached the subject

from the qualitative perspective. More specifically,

the methodology of this study provides a quantitative

analysis of the state of the art as a complement to, but

never a substitute for, traditional qualitative methods

of reviewing the literature. It can be used as a tool to

identify the authors, articles, and journals most widely

read among the researchers in a given field and also to

detect relational links between them.

Limitations and Future Research

As discussed at the beginning of this study, there is a

need to be cautious and a need to understand that citation


data as a measure of scientific performance is not without

limitations. First, the sample articles were chosen from

1996 to 2008, which might affect the generalization of

this study. Choosing the time period is a topic of debate.

Different researchers can always argue for a different

time period for this line of research. Future research may

use the data from other periods of interest. Similarly, this

study divided the analysis into two periods, bearing in

mind that our intention was not to identify real periods

but simply to register changes over the course of time,

which cannot handle the changes of the dot.com market

collapse of 2000.

Second, it is difficult to avoid some degree of

subjectivity in deciding the number of authors to be

included in an analysis. At the same time, the choice of

authors is critical in determining the configuration of the

field (McCain, 1990), and hence it is important to be as

objective as possible. The researcher invariably has to

make some judgment calls balancing these contrasting

considerations. This study believed that the 60 authors

identified through objective criteria in our study are fairly

representative of the field. In the event that an important

author has been omitted, it would be fair to assume

that his or her core concepts would be embodied in the

publications of the authors included in this study.

Third, the identification of the authors included in

this study was based on the most cited lead authors. This

may understate the contributions of co-authors who are

influential scholars in the field, but who did not figure in

our list because they were not lead authors. Fourth, our

search criteria may be incomplete, and many good papers

that may not have been included.

Some of the limitations mentioned above have no

solution; these, however, are not exclusive to author

co-citation analysis but are rather present in any non-

experimental discipline and in management in particular.

Other, however, can be addressed and should provide an

incentive toward the methodology used in this study. In

this respect, future research is encouraged to combine

author co-citation analysis with content analysis. Content

analysis is a research tool used to determine the presence

of certain words or concepts within texts or sets of texts.

Researchers quantify and analyze the presence, meanings,

and relationships of such words and concepts, then make

inferences about the messages within the texts. With

content analysis, the time lag, often associated with

citation analysis, ceases to be a problem since the current

texts can always be used for analysis (while author co-

citation analysis requires the current texts be cited before

they can be useful data points) to determine whether the

studied texts are electronic commerce related or not and

further to determine their importance and potential impact

on the field of electronic commerce.

This study demonstrates the use of author co-citation

methodology, which has great potential for use in

management research. This type of analysis is possible in

a wide range of topics, especially newly developed areas

such as consumer behavior in electronic commerce and

electronic marketplaces, where bibliometric studies have

not yet been conducted. Additionally, this methodology

can be applied to other forms of scientific and technical

writing, such as congress proceedings, doctoral theses

and patents.

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余國訓為遠東科技大學行銷與流通管理系副教授，長榮大學經營管理博士。主要研究領域為電子商務、組織行

為。學術論文曾發表於電子商務學報 (TSSCI)、International Journal of Conflict Management(SSCI), Journal of Knowledge Management(SSCI) 等期刊。

Kuo-Hsun Yu is an Associate Professor at the Graduate School Marketing and Logistics Management, Far East University. He received his doctoral degree in Business and Operations Management from Chang Jung Christian University, Taiwan. His research areas include Electronic Commerce and Organizational Behavior. His research papers have been published at Journal of E-Business (TSSCI), International Journal of Conflict Management (SSCI), and Journal of Knowledge Management (SSCI) and others.

李元墩為長榮大學經營管理研究所教授，美國愛荷華州 Drake University 教育管理與人力資源博士。主要研究領

域為國際人力資源管理、組織行為與管理、領導學、知識管理。學術論文曾發表於中山管理評論 (TSSCI)，科技管

理學刊，Asia Pacific Management Review (TSSCI), Journal of Chinese Institute of Industrial Engineers (TSSCI), Journal of E-Business (TSSCI), Group Decision and Negotiation (SSCI), International Journal of Human Resource Management (SSCI), European Journal of Operational Research (SCI) 等期刊。

Yuan-Duen Lee is a Professor at the Graduate School of Business and Operations Management, Chang Jung Christian University. He received his doctoral degree in Educational Management and Human Resource from Drake University, USA. His research areas include IHRM, OB and Management, Leadership, and Knowledge Management. His research papers have been published at Sun Yat-Sen Management Review (TSSCI), Journal of Technology Management, Asia Pacific Management Review (TSSCI), Journal of Chinese Institute of Industrial Engineers (TSSCI), Journal of E-Business (TSSCI), Group Decision and Negotiation (SSCI), International Journal of Human Resource Management (SSCI), and European Journal of Operational Research (SCI) and others.

李元德為長榮大學經營管理所教授，加拿大 McGill University 管理哲學博士。主要研究領域為科學計量學為知識

網路泛研究方法論、科技創新創業策略管理、資訊科學與管理、國際企業管理。學術論文曾發表於 Small Business Economics(SSCI), International Journal of Technology Management(SSCI), International Journal of Conflict Management, (SSCI) and Technovation(SSCI).

Yender McLee is a Professor at the Graduate School of Business and Operations Management, Chang Jung Christian University. He received his doctoral degree in Management from McGill University, Canada. His research areas include TIM, MIS, Strategic and Particularly Meta-methodology via Paradigm-mining. His reach paper has been published at international journal articles, including recently in Small Business Economics (SSCI), International Journal of Technology Management (SSCI), International Journal of Conflict Management (SSCI), and Technovation (SSCI).

電子商務研究智慧結構之演變－作者共引文分析的應用ijcs.topco-global.com/website/articles/2011vol3_no1_001_024.pdftechnology...

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