factors for web mining adoption of b2c firms: taiwan experience

Electronic Commerce Research and Applications 3 (2004) 266–279

www.elsevier.com/locate/ecra

Factors for web mining adoption of B2C firms:Taiwan experience

Echo Huang *, Tzu-Chuan Chou 1

Department of Information Management, National Kaohsiung First University of Science and Technology,

2 Juoyue Road, Nantz District, Kaohsiung 811, Taiwan, ROC

Received 18 July 2003; received in revised form 9 February 2004; accepted 6 April 2004

Available online 15 June 2004

Abstract

This paper focuses on the web mining adoption of small and medium size enterprises (SMEs) in terms of organi-

zational innovation theories. It first examines the current literature of information systems studies and suggests that the

context of web mining adoption needs to be taken into account. This paper proposes an analytical model employing a

number of internal, external factors, and the stages of web mining adoption. The model explores the relationships

influencing the stages of web mining adoption. Empirical testing is based on a sample of 68 B2C firms from Taiwanese

SMEs. The results show that firm’s Internet strategy, internationalized strategy, and business complexity along with

competitive pressure influence on the stage of web mining adoption. The implications of findings for the management of

web mining adoption and suggestions for the future study are discussed.

� 2004 Elsevier B.V. All rights reserved.

Keywords: B2C firms; Electronic commerce; Web mining

1. Introduction

Similar to conventional industries, B2C firms

not only need to be profitable to survive, but also

need to be adaptable to the turbulent business

environment. However, attracting new online

customers or retaining existing one are not easy

[10]. In this circumstance, a historical archive of

customer information and data mining techniques

* Corresponding author. Tel.: +886-7-6011000x4119.

E-mail address: [email protected] (E. Huang).1 Tel.: +886-7-6011000x4121.

1567-4223/$ - see front matter � 2004 Elsevier B.V. All rights reserv

doi:10.1016/j.elerap.2004.04.002

are valuable to overcome this problem by drawing

and analyzing information on customer behaviorand activities, and providing analysis of customer

preference. In particular, web mining in the anal-

ysis of user behavior on the Internet has been in-

creasing rapidly to understand users’ common

behavior [33]. Web mining provides greater

purchasing and customer service options by in-

corporating data warehouses and knowledge

management projects. It is one of the best strate-gies to differentiate from competitors and enables

B2C firms to discover resource, extract informa-

tion and uncover general patterns [32], resulting in

a quicker respond to their customers and, in turn,

ed.

mail to: [email protected]

E. Huang, T.-C. Chou / Electronic Commerce Research and Applications 3 (2004) 266–279 267

a more satisfied and loyal customer. Accordingly,

the management of adoption of web mining is now

of a critically important issue.

Although the strategic potential of web mining

is now well recognized, a number of phenomena

show more efforts are need. First, previous studiesmainly focused either on the technical perspective

or on the application development of web mining

(e.g. [6,20]), and have little efforts on the factors

that affect the process of web mining deployment.

Adoption and diffusion of innovative technologies

have remained critical concerns in IT research. IT

investment and adoption decisions are more diffi-

cult than many other investment decisions [37] andmanagement now faces a dilemma concerning the

strategic use of IT. Second, most previous research

has concentrated on large business context, ig-

noring small and medium size enterprises (SMEs).

This is understandable, since IT has been the

privilege of large businesses due to the huge in-

vestment required in the past. However, SMEs

potentially constitute the most dynamic firms in anemerging economy and are the life-blood of

modern economics [15]. The importance of SMEs

in economic growth has made them a central ele-

ment in much recent policy making to promote

and facilitate the operation of the innovation

process within SMEs [19].

Third, studies (e.g. [20]) have revealed many

reasons for data mining such as to improve cus-tomer service, to build a long-term client rela-

tionship, to reduce marketing cost, and to increase

sales. Too often, the business is surfing in pre-

vailing views enthusiastically such as the adoption

of web mining without asking: ‘‘why some orga-

nizations do not?’’ That is, they do not explain

why some organizations adopt it earlier than

others, and why some may never adopt thisinnovation.

Accordingly, the adoption behavior of web

mining has not yet been convincingly demon-

strated. This study attempts to address these con-

cerns by viewing web mining as a technological

innovation and examining factors that facilitate

their initiation, adoption, and implementation. A

research model is then proposed and tested. Bothinternal and external contextual factors of web

mining adoption are investigated. Using data from

a national wide survey, impact of internal and

external e-commerce characteristics on the initia-

tion and adoption of web mining was examined.

This empirical test of the proposed research model

is drawn on a sample of 68 Taiwanese B2C firms in

terms of the stages of adoption. The next sectiondiscusses the background and the state-of-the-art

of the development of web mining. This is fol-

lowed by a detailed description of the proposed

model and research methodology. Subsequently,

the paper presents the data analysis and hypothe-

ses testing. The final section concludes by dis-

cussing future research directions.

2. Research background

In terms of resource perspective, data captured

in different operational databases over time could

further be extracted, transported and integrated

together in data warehouses for building decision

support systems [8]. Data mining is a subset ofknowledge discovery in databases, data ware-

houses, and data marts [8]. Web mining inherits

the characteristics of data mining, but also has its

own characteristics. Web mining generally refers

to data mining on the Internet and is a technology

of the interaction between data mining and the

World-Wide Web [54]. That is, Web mining is the

application of data mining techniques to discovermeaningful patterns, profiles, and trends from

Web sites. Web mining is critical for EC due to the

large number of visitors to EC sites [34]. However,

the term Web mining is being used in two different

ways. First, web content mining is the process of

discovering information from millions of Web

documents. Second, web usage mining is the pro-

cess of analyzing Web access logs on one or moreWeb localities.

Web mining is an increasingly important and

very active research field, which adapts advanced

machine learning techniques for understanding the

complex information flow of the World-Wide Web

[28]. This is especially true since markets and

competitive structures have grown increasingly

complicated and volatile. The potential of webmining to help people navigate, search, and visu-

alize the contents of the web is enormous [32].

268 E. Huang, T.-C. Chou / Electronic Commerce Research and Applications 3 (2004) 266–279

Accordingly, web mining facilitates business to

learn more about their customers and provide

personalized offerings. A brief review of the fol-

lowing reported cases provides strong evidence for

this statement.

• Amazon has created a favor when it revealed ithas been keeping track of customer book buy-

ing behavior and has composed best-seller lists

by ZIP codes, workplace, and colleges [43].

• Real Networks has used its popular software to

monitor and report user listening behaviors

[22].

• Yahoo analyzes which advertisements and

products on its web have the most appeal so itmay alter its price and sales strategies accord-

ingly [24].

Based on Daft’s [9] dual core model, the

adoption web mining is one form of administrative

innovation in today digital economy. Fichman [14]

proposed two different scopes of IS innovations in

distinguishing the organization’s willingness/abili-

ty to adopt. The first type of innovation imposes alow knowledge burden and few new user interde-

pendencies. The organization’s willingness is the

primary determinant to adopt. In contrast, the

second type of innovation imposes a high knowl-

edge burden or high user interdependencies. In this

case, the organization’s ability is the primary de-

terminant to adopt. Swanson [46] classified IS in-

novation within an organization into three types:(1) innovations affecting mainly the organization’s

IS processes, (2) innovations affecting the business

and IS processes in the organization and (3) in-

novations additionally affecting the actual prod-

ucts made by the organization as well as

integration with other businesses. Web mining

adoption is similar to Fichman’s Type II model

and Swanson’s Type II and III models.Apparently, Internet B2C firms utilize the

massive data they collected online in a variety of

way, including tailoring products to specific cus-

tomers. For example, a lower price may be offered

in the form of a rebate coupon if we have not

purchased something recently; alternatively, a

popular item may have a higher price since the

high demand suggests this particular good is lessprice elastic. However, the innovation diffusion

process usually starts out slowly, but takes off

rapidly after an initial period Rogers [39]. The

development of web mining is still at an infant

stage. For example, different systems generate

different data, stored in separate, incompatible,

and difficult to access databases. Moreover, the

web is too unstructured for web mining [32], andcompanies may have problems to overcome these

challenges because of fragmented data. Also, it

becomes difficult enough to retrieve data for

analysis, much less to obtain a broader overview of

the processes by which a company interacts with

its agents customers, and vendors. This fragmen-

tation contributes to major business inefficiencies,

and cripples a company’s ability to detect andidentify problems, let alone resolving them. Simi-

larly, potential synergies between different product

lines were not exploited. In addition, different

market segments, each with its own regulatory

environment, require extensive information for

effectively policy management and planning. These

problems become more serious in a SMEs context

since SMEs always have problems with both for-mulating and acquiring new knowledge and skills

[50].

Several important trends redirect attention to a

more contextualized approach in the study of or-

ganizations [57]. Also, Burns and Stalker [4]

identified the importance of organizational context

to researchers have studied the many ways in

which different organizational features can facili-tate innovation. Accordingly, in order to study the

adoption behavior of web mining of SMEs, this

study employs the contextualized approach. In the

review of innovation adoption research by Fich-

man [14], two major categories, innovation pro-

cesses and innovation variance, were identified.

The first concerns with examining the process of

adoption of an innovation, and the second therelationship between innovativeness, external en-

vironment, and organizational performance. This

study investigates what factors are considered by

senior managers in terms of new technology,

market pressure, and whether there is a trend

across organizations (based on different size, age,

line of business, and strategies). Impact of two sets

of antecedent factors on the adoption of webmining is described. External characteristics con-

sidered are perceived competitive pressure and


technological benefits; internal organizational

characteristics are industry type, size, age, degree

of business complexity, Internet strategy, interna-

tion strategy, and web site content strategy. The

detail of research model and hypotheses are dis-

cussed in the following section.

3. Research model and hypotheses

In planning for an IT change, managers con-

sider both external and internal forces. There is a

growing consensus among researchers that IS in-

novations in an organization differs from otherinnovations [14,46]. For example, Kwon and

Zmud [27] proposed five factors that influence

adoption such as innovation, organizational, en-

vironmental, task, and individual characteristics.

Tornatzky and Klein [48] suggested three innova-

tion characteristics influence adoption, which are

relative advantage, compatibility, and complexity.

Yang [53] proposed four external variables thatinfluence CASE tools adoption in Taiwan: per-

sonal, organizational, IS department, and system

development variables. Based on these studies,

nine contextual factors are considered in our re-

search model shown in Fig. 1.

Internal Factors•Industry-type•Firm’s Size•Firm’s age•Business Complexity•Internet Strategy•International strategy•Website content strategy

Internal Factors•Industry-type•Firm’s Size•Firm’s age•Business Complexity•Internet Strategy•International strategy•Website content strategy

External Factors•Perceived competitive pressure

•Perceived technological benefits

External Factors•Perceived competitive pressure

•Perceived technological benefits

Innovation Context of B2C Firms

Fig. 1. A model of web

3.1. Internal factors

Research in IT adoption identifies many orga-

nizational factors that influence adoption such as

top management support, size, and IT expertise.Firm size has long been an issue of IT adoption

and has been found to predict technology adop-

tion [11,52]. For example, Kuan and Chau [25]

suggest small business face substantially greater

risks in IS implementation than large business do

because of inadequate resources an limited edu-

cation about IS. Thong and Yap [47] found that

use of IT in a small business was influenced by sizeof the business and its CEO characteristics. Hence,

size is a positive factor associated with adoption

[47,49] and large companies can more easily ab-

sorb risks and cost of implementing web mining

[6]. On the other hand, technology adoption is not

only a form of innovation, but also a process of

organizational learning [52]. That is, based on

learning to learn perspective, effective adoptionskills and strategies are the basis of effective

learning from previous adoption. Apparently, an

mature firm is likely to have more IT adoption

experience than a nascent firm.

Moreover, Tornatzky and Klein [48] suggest

complexity is one of the significant factors

Web mining Adoption Stage

Web mining Adoption Stage

mining adoption.


correlated with adoption across studies. Com-

plexity refers to the degree of difficulty associated

with management and maintenance of a B2C

business [39]. The complexity of business products/

locations creates greater dependence for a suc-

cessful implementation, and therefore, increasesthe preference for adoption. Hence, complexity is

positively associated with adoption. Also, Drew

[12] suggests that SMEs in different industry

sectors may adopt different strategies for

e-commence. The IT industry related firms have

more IT domain knowledge and a higher extent of

digital. Since these firms are more information

intensive, they will depend on new techniques incollecting customer information. In this circum-

stance, the study predicts IT industry related firms

are likely to adopt web mining techniques.

Hypothesis 1. (H1). The size of a company is

positively related to the adoption of web mining.

Hypothesis 2. (H2). The age of a company ispositively related to the adoption of web mining.

Hypothesis 3. (H3). The complexity of a company

is positively related to the adoption of web mining.

Hypothesis 4. (H4). The IT industry related firms

with a higher extent of digital are more likely to

adopt web mining.

The adoption of IT is really one of alignment,

and organizations that are aware of IT’s new role

have usually made efforts to incorporate IT in

their strategic thinking [13]. As Barua et al. [2]

indicate, a firm may have to invest in IT, re-

gardless of its underlying cost structure, in re-

sponse to a competitor’s investment. Thus,strategic considerations are critical to the evalua-

tion of webmining. Chou et al. [7] also suggest

that management may fail to link the strategic

purpose of IT with the organization’s strategy,

and this will lead to reduce the effectiveness of

decision-making. Three different but related

strategies, Internet strategy, international strategy

and website content strategy are particular im-portant for B2C firms to taken into account of

web mining adoption.

However, users now span the globe and repre-

sent a wide variety of economic, political, and

cultural perspectives. Firms’ innovative strategies

exploit features of the Internet and WWW, such as

real-time communication, cost-effectiveness,

ubiquity and global reach, information-richnessand multimedia capability, and a user-friendly

interface, to create new value propositions for

customers [22]. Apparently, an information-

orientated Internet strategy will concern online

customer information much more than transac-

tion-oriented Internet strategy. Thus, firms with

information-orientated Internet strategy are likely

to adopt web mining.The exploration of international marketing ac-

tivity on the Internet and the associated emergence

of the global information superhighway have a

profound effect on the conduct of international

business in the new millennium [18]. Global

strategy is the way by which a business competes in

the global market [56]. The Internet can provide

companies with a low cost gateway to globalmarkets by helping to overcome many of the

barriers to internationalization commonly experi-

enced by companies [30]. However, firms still need

more effort to understand their foreign customers

to have effective market segmentation and cus-

tomer targeting strategies. In these circumstances,

firms with the strategy toward internationalize and

provide multiple languages web content need tolearn more from their customers and therefore are

more likely to adopt web mining.

Hypothesis 5. (H5). An information-oriented In-

ternet strategy is more likely to adopt web mining.

Hypothesis 6. (H6). Firms with the strategy to-

ward internationalized are more likely to adoptweb mining.

Hypothesis 7. (H7). Firms with multiple languages

web content are more likely to adopt web mining.

3.2. External factors

In reviewing the literature, studies (e.g. [21])offer an analysis of organizational innovation in

terms of institutional theories. In terms of insti-


tutional theory, survival arises out the conformity

to external rules and norms. A company’s desire to

stay ahead of its competition is a major factor in

adoption of IT [26]. That is, adoptions of new

techniques such as web mining are a form of or-

ganizational response to the external environ-mental change. Indeed, an organization must

adapt to changing environmental conditions by

altering its organizational characteristics such as

structure or process. Competition characteristics

therefore affect the adoption initiation and pro-

cess. For example, companies that dominate a

particular market tend to be leaders; they are ei-

ther responsible for IT innovations or are quick toadopt them once introduced by competitors. Thus,

a higher extent of competition will positively in-

fluence the willingness to adopt of an innovation.

This effect is particularly evident if the innovation

leads to a competitive advantage. Previous re-

search in IT adoption has shown that it has be-

come a strategic necessity to use these technologies

to compete in the market place.

Hypothesis 8. (H8). Firms perceived a higher level

of competitive pressure are more likely to adopt

web mining.

Previous researches argued that a rational

adoption decision in an organization would involve

evaluating the advantage of the new technology.Although, an application’s technical complexity

may hinder implementation success [48], several

studies have shown benefits introduced by new

technology impact its adoption. In studying EDI

adoption of small business, Kuan et al. [26] also

suggest that adopter firms perceived greater bene-

fits than non-adopter firms. Rogers [39] defined

relative advantage of an innovation as the degree towhich the innovation is perceived as better than the

idea it supersedes. Apparently, perceived benefit is

a significant variable that positively relate to

adoption of innovation. Web mining technologies

provide many benefits to the adopters in terms of

better customer service; more timely information

available for decision making, greater online in-

teraction, and a more focused marketing cam-paign. In a competitive marketplace, these benefits

are significant motivations for adoption.

Hypothesis 9. (H9). Firms perceived a higher level

of technological benefits are more likely to adopt

web mining.

4. Methodology

Investigating the issues presented above in-

volved empirical work undertaken in Taiwan B2C

firms. The constructs were operationalized in the

form of a questionnaire. Senior managers of IT or

EC Department from Top 500 companies listed in

the 2000 issue of Digital Week are selected as re-spondents. This particular audience is chosen as

these firms are generally leaders in IT applications,

and the managers are knowledgeable about their

firms’ MIS applications. Respondents were asked

to evaluate propositions based on web mining

project developed and implemented which they

had experience. A four-page survey was sent to

these IT managers. Six weeks later, 64 usableforms were returned. Repeated callbacks to non-

respondents were carried out twice. After another

six more weeks, data collection was concluded.

Four usable questionnaires were received from the

second wave of mailing, giving a total of 68 re-

spondents, and a 13.6% response rate. Such a low

response rate may be indicative that subjects were

unwilling to respond to unsolicited surveys, or thatcompanies have set a policy for rejecting survey

questionnaires.

4.1. Measurement

The variable, their operationalization and the

sources of variables are presented in Appendix A.

Seven single items with a nominal scale were usedto collect the respondents’ characteristics. Web

mining adoption stages are classified into un-

known, no adoption, initiation, adoption, and

implementation. Industry types are distinguished

by either IT or non-IT firms. Firm size is measured

by the number of employee and by total gross

sales. Complexity is classified into high volume

transactions/processing (with more than two di-verse types of business transactions) or low volume

transactions/processing (with only one type of

business transactions). Internet strategy is either

Table 1

Sample characteristics

Number of firms Percentage

Industry

e-Tailing 5 7.4

Airline 2 2.9

Traveling 1 1.5

Insurance 2 2.9

Banking 5 7.4

Information 32 47.1

Entertainment 7 10.3

Others 14 20.6

History

1–3 years 34 50


transaction- or information-oriented. Similarly,

two groups are created based on variables of in-

ternational strategy and website content. Interna-

tional strategies are classified into yes/no two

groups. Website content are divided into two

group-multiple languages versions and single lan-guage version. Two external factors, perceived

competitive pressure and perceived technology

benefits, were measured using multi-item indica-

tors that aimed to capture the underlying theo-

retical domain of the constructs. Most items were

measured using a seven-point Likert-type scales

ranging from strongly disagree to strongly agree.
4–7 years 16 23.5
8–10 years 3 4.4

11–1 5 years 6 8.8

Over 15 years 9 13.2

Annual sales revenue

Under 1000 m 12 17.6

1001–3000 m 11 16.2

3001 m–1 billion 20 29.4

1–10 billion 14 20.6

Over 10 billion 11 16.2

Employee

Under 10 18 26.5

11–50 17 25.0

51–100 11 16.2

101–150 1 1.5

151–250 21 30.9

Table 2

B2C technology adoption

Stage Number of firms Percentages

Unknown 6 8.8

No adoption 5 7.4

Initiation 24 35.3

Adoption 5 7.4

Implementation 28 41.2

5. Data analysis and hypotheses testing

This section presents the analysis results. First,

the nature of the data is explored. Second, factor

analysis is employed to confirm the dimensionality

of two external actors. Third, the regression

analysis and ANOVA are employed in testingproposed hypotheses.

Table 1 shows more than seven industries were

collected and information industry is the largest

sector, followed by entertainment industry. The

sample also includes firms of various size and hi-

story. The number of employees of the collected

samples is all less than 250. This indicates that the

collected data is suitable for the study of SMEs[12].

Table 2 shows the current status of web mining

in our sample. The leading category is implemen-

tation (41.2%), followed by initiation (35.3%),

while the smallest category is no adoption (7.4%).

When considering the major purposes of web

mining, web mining can be used for various pur-

poses: to increase sales, product management anddevelopment, sales support arrangement, channel

relationship management, support promotion

program, support marketing research, price man-

agement, customer relationship management. The

‘‘perceived benefits’’ factor presents whether the

sample firms realize these benefits in adoption web

mining. Table 3 shows the results of companies’

incentives toward web mining. All items showsover 70% of the firms do agree (from slightly to

strongly agree) with web mining can achieve these

benefits, but these is no significant difference be-tween each benefits. One possible explanation is

that the development of web mining is still at an

infant stage, and the adoption objectives for web

mining projects are likely to be blurred. There are

two possible reasons for this phenomenon. First,

the adopter firms fail to identify investment ob-

jectives or/and adoption objectives may attract less

attention. At this point, it is not clear which reasonis correct, but, without a clear identification of the

Table 4

The characteristics of web mining adopter

Characteristics Unknown No adoption Initiation Adoption Implementation

Industry

Non-IT 4 5 9 3 14

IT 2 0 15 1 14

History

1–3 years 3 3 14 2 12

4–7 years 0 1 4 0 11

8–10 years 0 0 0 3 0

11–15 years 0 0 4 0 2

Over 15 years 3 1 2 0 3

Annual sales revenue

Under 1000 m 2 1 7 0 2

1001–3000 m 0 2 0 2 7

3001 m–1 billion 1 0 7 0 12

1–10 billion 2 1 6 1 4

Over 10 billion 1 1 4 2 3

Business complexity

Direct marketing

Yes 1 0 10 1 15

No 5 5 14 4 13

Mail order

Yes 2 1 12 3 28

No 4 4 11 2 0

Logistics

Yes 1 0 6 1 13

No 5 5 18 4 15

Entry mode

Information 5 4 17 5 7

Transaction 1 1 7 0 21

Table 3

B2C companies’ incentives of web mining

Degree of perceived benefits Strongly

disagree

Disagree Slightly

disagree

No opinion Slightly

agree

Agree Strongly

agree

Increase Sales 0 3 (4.4%) 2 (2.9%) 13 (19.1%) 15 (22.1%) 18 (26.5%) 17 (25.0%)

Product management and

development

0 2 (2.9%) 1 (1.5%) 14 (20.6%) 15 (22.1%) 19 (27.9%) 17 (25.0%)

Support salesperson

arrangement

0 2 (2.9%) 1 (1.5%) 15 (22.1%) 14 (20.6%) 20 (29.4%) 16 (23.5%)

Channel relationship

management

0 2 (2.9%) 2 (2.9%) 14 (20.6%) 14 (20.6%) 20 (29.4%) 16 (23.5%)

Support promotion program 0 2 (2.9%) 2 (2.9%) 14 (20.6%) 14 (20.6%) 20 (29.4%) 16 (23.5%)

Support marketing research 0 2 (2.9%) 2 (2.9%) 14 (20.6%) 14 (20.6%) 20 (29.4%) 16 (23.5%)

Price management 0 2 (2.9%) 2 (2.9%) 14 (20.6%) 1 (1.5%) 20 (29.4%) 16 (23.5%)

Customer relationship

management

0 2 (2.9%) 4 (5.9%) 11 (16.2%) 17 (25.0%) 22 (32.4%) 12 (17.6%)



values of objectives, the resulting decisions are

likely to be sub-optimal.

Characteristics of adopters in our survey are

shown in Table 4. Our research indicated that IT

and non-IT firms do not show significant differ-

ence in web mining adoption. Similarly, the size ofa company is not a significant factor. However, the

complexity of a company’s business influences the

adoption preference. Transaction-oriented com-

panies are more likely to implement data mining

for their web sites. Most information-oriented

companies tend to initiate new technology. The

industry-specific factor shows that information

and computer companies tend to be adopters incomparison with other industries. Young compa-

nies, established for less than 5 years, are more

likely to adopt new technology, which may be

explainable in terms of fewer system conversion

required. Companies engaged in mail-order busi-

ness also show a higher adoption of web mining, as

do companies with global strategies.

Table 5

Results of regression analyses predicting adoption of web

mining

Independent variables T-value p-value b

Industry type 0.099 0.921 0.046

Firm age 0.531 0.597 0.114

Firm size 1.032 0.307 0.096

Complexity )3.355 0.001�� 0.262

Internet strategy 5.049 0.000�� 0.293

International strategy 2.224 0.030� 0.274

Content information )0.706 0.483 0.265

Technological benefit )0.147 0.883 0.196

Advertisement )0.653 0.516 0.114

Transaction )0.844 0.403 0.199

Planning )1.655 0.104 0.110

Information )0.536 0.594 0.101

Globalization 2.178 0.034� 0.151

R2 ¼ 0:666, adjusted R2 ¼ 0:641

F-value (p-value) ¼ 8.129 (<0.000)

df¼ 67

Notes: �p < 0:05; ��p < 0:01; ��p < 0:001.

6. Factor analysis

Since the two construct external factors, per-

ceived competitive pressure and perceived tech-

nological benefits, combined measures from a

number of different studies, it is necessary to

confirm their validity and reliability, to ensure themeasurement is accurate. Factor analysis of multi-

item indicators can be used to evaluate if the the-

orized items for a construct converge. The stan-

dard critieria of eigen-value greater 1.0, factor

loadings greater than 0.3, and a well-explained

factor structure were used in the analysis [55]. The

reliability of the constructs was assessed using

Cronbach’s a. Results indicated all the constructshave adequate alpha value (>0.65)and are well

within the thresholds suggested by Nunnally [31].

In terms of competitive pressure, Bartlett’s test

for sphericity was highly significant (Bar-

lett’s¼ 1187.64, p < 0:00), suggesting significant

correlations among at least some of the pressures.

The measure of sampling adequacy for each item

was acceptable, and the overall measure of sam-pling adequacy was 0.77, well within the accept-

able range [17]. We used the principal components

extraction method with oblimin rotation (d ¼ 0).

We determined the number of factors on the basis

of the minimum eigenvalue (greater than one) and

the screen plot. The factor analysis revealed five

underlying dimensions, which collectively explain

67.5% of the total variance in adoption.Five factors are extracted from this factor

analysis. The first factor is advertisement, which

represents how a company perceives the value of

Internet advertising; the second is transaction,

which describes a company’s technical limitation

of order processing, inquiry response, and security

mechanism; the third, integrated planning, repre-

sents a desire to achieve a more flexible and dy-namic web site; the fourth, comprehensive

information, describes customer requirements of

accessing online order, process, shipping, return-

ing, and maintenance information; the last, global

marketing, represents a desire to develop a global

marketing strategy. In term of perceived technol-

ogy benefits, only one underlying dimension is

extracted, collectively explaining 78% of the totalvariance in initiation and adoption.

Table 6

Compare mean of external factors

Stage Benefits Advertisement Transaction Planning Information Globalization

Unknown Mean 4.5833 5.0000 5.7500 5.6667 5.2783 3.8889

SD 0.84 0.54 0.75 0.76 1.21 1.66

No adoption Mean 2.9180 5.1000 4.7500 4.4000 5.4000 2.0833

SD 1.05 2.27 1.10 1.55 1.94 1.31

Initiation Mean 5.7371 5.5208 5.8854 5.0972 5.1675 4.8611

SD 1.01 1.45 0.98 1.37 1.39 0.91

Adoption Mean 4.5180 5.4000 5.5000 5.0667 4.2000 4.2667

SD 0.99 0.74 0.77 1.1 1.04 1.81

Implementation Mean 5.3061 5.8036 5.7679 5.5833 4.8911 5.2262

SD 1.04 0.72 0.68 1.20 1.31 1.36

F-value 9.117 0.859 1.979 1.291 0.725 0.0196

p-value 0.000�� 0.494 0.108 0.253 0.578 0.000��

Note: ��p < 0:001.


7. Hypothesis testing

After establishing items loaded appropriately

on their expected constructs, the hypotheses were

correlated using linear regressions. Results in

Table 5 support three hypotheses (H3, H5, and

H6), i.e., complexity, Internet strategy, and inter-

national strategy affect web mining adoption.Furthermore, Table 6, which compared the mean

on benefits and competitive pressure, supports the

last twohypotheses (H8,H9).Adopterfirmsperceive

significantlyhigherdegreeof technologybenefits and

competitive pressure than non-adopter firms.

8. Discussions and conclusions

From a sample of 68 IT managers, we examined

differences in adoption, measured by seven factors,

Table 7

Summary of the results of hypotheses testing

No. Hypothesis

HI The size of a company is positively related to the adoption

H2 The age of a company is positively related to the adoption

H3 The complexity of a company is positively related to the a

H4 The IT industry related firms with a higher extent of digita

H5 An information-oriented Internet strategy is more likely to

H6 Firms with the strategy toward internationalized are more

H7 Firms with multiple languages web content are more likely

H8 Firms perceived a higher level of competitive pressure are

H9 Firms perceived a higher level of technological benefits are

to understand the underlying internal and externalcharacteristics. For instance, we found a signifi-

cant relationship between adoption and Internet

entry strategy, but do firms with online transac-

tions result in preference for new technology?

Table 7 summarizes our findings.

Complexity was a critical variable even in B2C

firms. This is consistent with prior studies that have

demonstrated complexity is a critical factor in ITadoption and use. Concerning entry strategy,

transaction-oriented firms are more likely adopt

new technology than information-oriented firms.

Companies with online transaction applications are

more sensitive to technological benefits, and com-

petitive/operational/efficient pressure. TheANOVA

test for external factors, shown in Table 6, indicates

firms do not show significant difference in degree ofpressure for creating leading products/services.

Similarly, neither the pressure of a company to

Empirical evidence

of web mining No

of web mining No

doption of web mining Yes

l are more likely to adopt web mining No

adopt web mining Yes

likely to adopt web mining Yes

to adopt web mining No

more likely to adopt web mining Yes

more likely to adopt web mining Partially supported

Table 8

Summary of organizational innovativeness studies (1995–2001)

Study Subjects Use measure Did internal factors affect

adopt?

Did external factors

affect adopt?

Thong and Yap [47] Small businesses

CEOs

Information technology CEO innovativeness

CEO attitude

CEO IT technology

Size

Brudney et al. [3] Smaller local

governments –

Georgia cities

Computer technology Population

City services

Professionalism

Use of computer

experience

Subramanian and

Nilakanta [44]

Banking industry Banking technology Centralization

Formalization

Size

Slack

Specialization

Chenggalur-Smith and Executives Client–server technology Scope of application Market position

Duchessi [6] Mission-critical application Number of

clients

Budget

Premkumar and

Roberts [38]

Rural small

business

Video

conferencing

Top management

support

Relative

advantage

Satellite

communication

Size Compatibility

Complexity

Internet Competitive

EDI pressure

E-mail External pressure

Fax Vertical linkage

Chau and Tam [5] Senior executives Open systems Satisfaction level with

existing computing

systems

Migration cost

IT human-resource

availability

Tung et al. [49] Singapore 9

companies

Lotus notes Organization type

Kuan and Chau [25] Senior EDI Perceived direct benefits

Perceived financial cost

Perceived

technical

competence

Perceived industry

pressure

Perceived government

pressure


pursue a market-oriented strategy nor the degree ofa company’s globalization affects the adoption

preference. The pressure for creating comprehensive

website information shows a significant difference in

web mining adoption. Perceived technological ben-

efits are also a significant influence on adoption.

The overall validity of the research model de-

veloped using Organizational Innovation concepts

was supported in this study. Although not all hy-

potheses were proven, the statistics measuring themodel do fit, showing the research model was sta-

tistically valid. The results showed that organiza-

tional factor has a greater influence on the decision

model than external factors in terms of partial

contribution to the total variance. This indicates

when an organization decides whether to adopt

web mining, business operation needs may be the

most important consideration. Of the three factors

Table 9

The operational definitions and sources of constructs

Dimensions Constructs Operational definitions Sources

The stage of web mining adoption 1. Unknown

2. No adoption, initiation

3. Adoption

4. Implementation

[6,16]

Internal factors Industry types 1. IT firms

2. Non-IT firms

Self-defined

Size 1. The number of employee

2. Total gross sales

[11]

Business complexity 1. High volume transactions

2. Low volume transactions

[18]

Internet strategy 1. Transaction-oriented

2. Information-oriented

[18]

International strategies 1. With international strategies

2. Without international strategies

[23]

Website content 1. Multiple languages

2. Single language

[51]

External factors Perceived competitive pressure 1. How a company perceives the value of Internet

advertising

2. A company’s technical limitation of order processing,

inquiry response, and security mechanism;

3. A desire to achieve a more flexible and dynamic web site

4. Customer requirements of accessing online order, process,

shipping, returning, and maintenance information;

5. Desire to develop a global marketing strategy

[1,18,29,35,36,41,42]

Perceived technological benefits 1. Increase sales

2. Product management and development

3. Support salesperson arrangement

4. Channel relationship management

5. Support promotion program

6. Support marketing research

7. Price management

8. Customer relationship management

[19,40,45,53]

E.Huang,T.-C

.Chou/Electro

nic

Commerce

Resea

rchandApplica

tions3(2004)266–279

277


found to be significant in affecting adoption, in-

ternational strategy is most similar to organiza-

tional slack and/or size in Roger’s framework. The

Internet entry strategy identifies with complexity in

Roger’s framework. The competitive pressure

in Roger’s framework is still valid in this study.Table 8 gives an overview of the Organizational

Innovations studies. Our findings correspond with

Chengalur-Smith and Duchessi [6], Premkumar

and Roberts [38], and Kuan and Chau [25].

This study was conducted to explore factors

influencing the intention of B2C firms to adopt

web mining technology. As such, there is room for

further investigation. The following are some ad-vices for future studies. First, future studies should

investigate adopter performance and effectiveness.

Second, as web mining technology and customer

relationship management concepts are still rela-

tively new in Taiwan, this study has been unable to

measure the adopting behavior of such technolo-

gies, which has been suggested by Organizational

Innovativeness theory. Future studies should in-corporate this measure once the diffusion has

reached a reasonable base. This way, more com-

prehensive knowledge of web mining and customer

relationship management adoption behavior can

be understood. Third, the study of web mining

adoption can be extended to e-commerce firms.

Comparison can then be made between B2C and

B2B firms in terms of underlying factors that affecttheir adoption decisions.

Appendix A

See Table 9.

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factors for web mining adoption of b2c firms: taiwan experience

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