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International Journal of Information Management 28 (2008) 102113

Customer Knowledge Management and E-commerce:

The role of customer perceived risk

Carolina Lopez-Nicolasa,, Francisco Jose Molina-Castillob

aDepartamento de Organizacion de Empresas y Finanzas, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, SpainbDepartamento de Comercializacion e Investigacion de Mercados, Universidad de Murcia, Campus de Espinardo, 30100 Murcia, Spain

Abstract

The present research is designed to gain a deeper understanding of Customer Knowledge Management (CKM) tools inside the

e-commerce context. The relationship between the CKM literature and the e-commerce literature is evaluated through several user

characteristics such as risk preference, Internet preference and Internet knowledge and their impact on customers online perceived risk

and purchase intentions depending on the presence of certain CKM tools on the web site. The empirical study is based on a survey of 276

customers with previous online experience. By using multidimensional analysis, this study shows that the customers perceived risk

associated with different CKM tools plays an important role in explaining certain customer online behaviour. Therefore, the implications

of CKM tools for e-commerce activity are demonstrated and the managerial implications are highlighted.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: CKM tools; E-commerce; Perceived risk; Purchase intention; Customer perceptions

1. Introduction

In modern organizations, knowledge is the fundamental

basis of competition (Zack, 1999), and information

technology (IT) is a necessity (Bose, 2000) critical for

managing knowledge (Ofek & Sarvary, 2001). In the new

context, two major factors determine the future survival or

success of organisations: electronic commerce (Gupta,

Su, & Walter, 2004) and the knowledge from customers

(Tsai & Shih, 2004), encouraging the adoption of

e-commerce and the use of the Internet as a platform to

access and collect important knowledge from customers. Inother words, the success of e-commerce increasingly

depends on knowledge management (Borges, Almeida,

Gomes, & Cabral, 2007; Saeed, Grover, & Hwang, 2005).

Customer Knowledge Management (CKM) is the applica-

tion of knowledge management (KM) instruments and

techniques to support the exchange of knowledge between

an enterprise and its customers (Kolbe & Geib, 2005;

Rollins & Halinen, 2005; Rowley, 2002), enabling the

company to make appropriate strategic business decisions

(Rowley, 2002; Su, Chen, & Sha, 2006). However, there is

still a need to further elaborate on the concepts of customer

knowledge and CKM (Rollins & Halinen, 2005), since the

critical role of KM in gaining competitive advantage in the

market (Ofek & Sarvary, 2001) and within the e-commerce

context (Du Plessis & Boon, 2004; Tsai & Shih, 2004) is far

from fully understood.

Knowledge, defined as information combined with

experience, context, interpretation and reflection (Daven-port, De Long, & Beers, 1998), can be divided into explicit

knowledge and tacit knowledge (Nonaka, 1994). Specifi-

cally, customer knowledge can also be classified as knowl-

edge for, about or from the customer (Maswera,

Dawson, & Edwards, 2006; Salomann, Dous, Kolbe, &

Brenner, 2005; Su et al., 2006). KM is the explicit and

systematic management of vital knowledge and its asso-

ciated processes of creation, organisation, diffusion, use

and exploitation (Skyrme, 2001) and CKM is the external

perspective of KM (Rollins & Halinen, 2005). In order to

ARTICLE IN PRESS

www.elsevier.com/locate/ijinfomgt

0268-4012/$- see front matter r 2007 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ijinfomgt.2007.09.001

Corresponding author. Tel.: +34 968363762; fax: +34 968367537.

E-mail addresses: [email protected] (C. Lopez-Nicolas),

[email protected] (F.J. Molina-Castillo).
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put KM and CKM into practice, some organisations

may implement initiatives related to more humanistic

practices, while others are based on IT that may be hosted

in the corporate Intranet and/or web site (Wang, 2001).

Our research focuses on the latter, where knowledge

flows into and out of the company through certain CKM

tools hosted on the firms web site (Shared databases,Document repositories, Workflow applications and

Discussion forums) but whose implications for customer

perceptions need greater clarification for managerial

purposes.

Executives should use KM and e-commerce principles to

complement each other, as a way of electronic CKM,

making it possible to obtain priceless information and

knowledge from customers about their needs and purchase

intentions. Embedding KM programs that customers may

access within a companys web site may actually be an

obstacle to the increase of e-commerce (Bose, 2000), and

this suggests the need for more research in this area.

Therefore, by adopting an external KM perspective

(CKM), the aim of our investigation is to assist organisa-

tions in their Web initiatives for managing customer

knowledge. Online shopping is developing rapidly today

and e-commerce initiatives have been found to increase

the value of the firm. Researchers, however, agree that in

fact the amount of money involved remains very low

(Cases, 2002; Gupta et al., 2004). The perceived risk of

conducting transactions online has recently been consid-

ered to be the most important factor in explaining

consumers reluctance to complete simple online pur-

chase transactions (Forsythe & Shi, 2003). In this sense,

we are concerned with the fact that perceived risk indifferent CKM Web tools may influence the success

of e-commerce projects in terms of the purchase intentions

of consumers. Thus, our thesis is that hosting certain

CKM tools on the corporate web site, such as Shared

databases, Document repositories, Workflow applications

and Discussion forums, could cause an increase in

perceived Web risk and, in turn, a backward step in

customers purchase intentions through that site. We also

aim at analysing the role of other variables, such as a

customers risk preference, Internet knowledge and Inter-

net preference, on the model.

The paper is organized as follows. First, the most

common Web tools used in CKM are reviewed, consider-

ing the potential differences between CKM tools in terms

of perceived risk (Section 2). Next, relationships between

perceived risk associated to each CKM tool, purchase

intention linked to each CKM application and users

characteristics, namely, their risk preference, Internet

knowledge and Internet preference, are discussed, propos-

ing a theoretical model to be empirically tested (Section 3).

Then, the methodology and the measures used in

the survey are explained (Section 4) and research findings

are shown (Section 5). Finally, conclusions and limitations

are summarised and future research lines presented

(Section 6).

2. Differences between CKM tools in e-commerce

2.1. Online CKM tools

KM is especially being adopted by companies who have

invested in the Internet (Borges et al., 2007), in order to

manage customer knowledge. Through CKM Web applica-tions, organisations may obtain vital knowledge, adding an

extra dimension to marketing research activity (Cheung &

Huang, 2002) and improving customer service. Managing

the collection, storage and distribution of relevant knowl-

edge requires the integration of KM and CRM resulting in

CKM (Kolbe & Geib, 2005). Web-based customer data

become an important source for KM (Chou & Lin, 2002)

and the challenge is to convert customer data and

information to knowledge (Martin, 2001; Maswera et al.,

2006; Rowley, 2002), in order to segment the market

(Davenport, Harris, & Kohli, 2001; Su et al., 2006), to

customize products and marketing (Martin, 2001; Maswera

et al., 2006), to provide exceptional customer service

(Martin, 2001; Shah & Murtaza, 2005), to shorten product

development cycles and reduce the risk of the DNP process

(Su et al., 2006), to impact the customers perception of

service quality (Saloman et al., 2005) and achieve greater

customer loyalty and retention (Martin, 2001).

There are many solutions to managing both explicit and

tacit customer knowledge (Davenport et al., 2001). Recent

literature (Maswera et al., 2006; Romano & Fjermestad,

2003; Shah & Murtaza, 2005) suggests that the most

common Web tools in companies CKM efforts are Shared


and Discussion forums.

Shared databases: Businesses want its partners and

customers to be able to view and update databases

(Shah & Murtaza, 2005). For example, Cisco Systems

provides its customers access to the same internal

database that is used by its employees (Saeed et al.,

2005). Shared databases are considered to be impor-

tant tools of the trade for anyone in the supply chain

(Saeed et al., 2005).

Document repositories: Also called knowledge reposi-

tories, they typically store documents with knowledge

embedded in them (Kwan & Balasubramanian, 2003)

and may also be accessed via firms web sites so that

external agents can gain access to important catalo-

gues, manuals and documents to make buying

decisions. The objective is to externalise knowledge,

store it in repositories and make it explicit and

accessible, for later and broader access, across the

organisation via the corporate intranet (Kwan &

Balasubramanian, 2003), as an example of a codifica-

tion strategy for managing knowledge (Hansen,

Nohria, & Tierney, 1999). For instance, Benetton

provides web users (through www.benetton.com) with

important documents, such as their product catalogue,

ARTICLE IN PRESS

C. Lopez-Nicolas, F.J. Molina-Castillo / International Journal of Information Management 28 (2008) 102113 103
http://www.benetton.com/http://www.benetton.com/


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photo gallery, videos showing its infrastructure, or its

corporate social responsibility policy.

Workflow applications: These may defined as the

automation of a business process, in whole or part,

during which documents, information or tasks are passed

from one participant to another for action, according toa set of procedural rules (Workflow Management

Coalition, 1999). Some companies are beginning to

notify customers, by email or SMS, when the product or

service provided gets to the next step in the production

and delivery processes. For example, UPS offers parcel

tracking services through www.ups.com and Dell com-

puters sends emails to customers about new product

development phases in order to let them know the

situation of the product before it is received.

Discussion forums: Web discussion forums permit the

participation of a larger and more diverse set of people

and information resources (DeSanctis, Fayard, Roach,

& Jiang, 2003), thus allowing them to express their

needs, doubts and purchase intentions (Maswera et al.,

2006) and helping specialised knowledge workers to

make sense of other community perspectives (Hayes &

Walsham, 2001) and to develop new products and

services. Customers provide information and tacit

knowledge about themselves during engagement in an

online community (Rowley, 2002) and companies can

monitor online chat to make the site more relevant for

their customers (Ofek & Sarvary, 2001; Rowley, 2002).

For instance, Transcend, offer the opportunity to

customer to include questions on the Discussion forumsand propose new alternative to data storage not only on

product functionality but also on product design.

In conclusion, the volume of qualitative data available

via corporate web sites is growing and firms are looking

forward to extracting and understanding users thought

processes, wants, needs, and purchase intentions (Romano

& Fjermestad, 2003) contained in those CKM Web

applications. Nonetheless, including certain KM tools in

corporate web sites could be affecting other variables such

as customers perceived risk or customers purchase

intentions and, in the long run, the firms sales.

2.2. Perceived risk in CKM tools

One of the main concerns expressed in the academic

literature is related to the risk perceived by customers when

buying a specific good, both in traditional shopping and in

online environments. Consumer behaviour involves risk

since any action of a consumer will produce consequences

that he or she views with some amount of uncertainty

(Bauer, 1960). In this sense, perceived risk involves the

amount that would be lost if consequences of an act were

not favourable, combined with individuals subjective

feeling of the likelihood that the consequences will actually

be unfavourable (Mitchell, 2001). There is a consensus in

the literature that there are different dimensions compris-

ing the perceived risk construct (Table 1). Basically, risk

can be associated with the product and risk associated with

the place where the product is offered, and in e-commerce

the retail channel is the Internet.

Comparing perceived risk in traditional shopping to new

online environments, the risk level associated with certain

dimensions might be increased, while other risk forms mayappear only in the online context (Forsythe & Shi, 2003).

Specially significant in Internet shopping is the risk

associated with the product and security (Doolin, Dillon,

Thompson, & Corner, 2005; Pavlou, 2003), due to three

elements which characterise this context: a remote source

(namely the site on which the transaction takes place), an

interactive medium for sending the message and an online

command mode (Cases, 2002). However, online applica-

tions, such as the CKM tools examined here, may be good

(or bad) risk-relievers and Doolin et al. (2005) recommend

Internet retailing web sites to include certain features that

reduce the perceived risk. For instance, a Discussion forum

hosted on the corporate web site allows users to exchange

comments, recommendations and word of mouth about the

product, the company and the site, and are thus an

important mechanism to reduce consumers perceived risk

(Garbarino & Strahilevitz, 2004). Also, the presence of

electronic repositories containing product information and

demos on a web site may reduce the product risk perceived

by the user (Cases, 2002), while access to online documents

where security and privacy policies are clearly disclosed

might mitigate consumers perceived privacy risk (Doolin

et al., 2005), the most significant perceived risk dimension in

online shopping. In contrast, hosting other CKM tools on a

web site may augment the complexity of the site (Chen &

ARTICLE IN PRESS

Table 1

Dimensions of perceived risk

Dimension Definition

Associated with the product

Technical risk The probability that a purchased product

results in failure to function as expected

Service risk The probability that the firm will not offer a

good service in the future

Social risk The probability that a product purchased

results in the disapproval of family or

friends

Psychological risk The probability that a product results in

inconsistency with self-image

Associated with the place

Performance risk The probability that the buying process

does not perform as expected

Financial risk The probability that a purchase results in

loss of money or other resources

Time risk The probability that a purchase results in

loss of time to buy or retain the product

Delivery risk The probability that a purchase results inproblems when delivering the product to the

customer

C. Lopez-Nicolas, F.J. Molina-Castillo / International Journal of Information Management 28 (2008) 102113104
http://www.ups.com/http://www.ups.com/


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Macredie, 2005) and increase the risk perceptions of users.

This may be the case with Shared databases, where

authorised users and, unfortunately, hackers may have

access to important information and knowledge about

customers, thus making it possible to offer confidential data

to all internauts without their knowing. In this situation,

users will consider the web site to be less safe (Conchar,Zinkhan, Peters, & Olavarrieta, 2004) and perceive a higher

risk on the web site that hosts Shared databases. Finally,

Workflow applications on a web site automate specific

processes, some containing consumers private information

which may be accessed by unauthorised people. In this

situation, users may perceive a higher level of risk when

using that web site with Workflow tools.

For this reason, due to the specific characteristics of

every CKM tool described before, we posit that there could

be a distinct risk level associated with each one when

hosted on a corporate web site.

H1. The customers perceived risk associated to eachCKM tool hosted on a web site will be different.

3. Implications of CKM tools in e-commerce

The Internet is profoundly changing KM, promoting it

from a trend to an e-business reality (Borges et al., 2007).

The recent literature considers the Internet to be a new retail

channel (Gupta et al., 2004), with great potential for

commercial usage (Cheung & Huang, 2002). However, most

online consumers use information gathered online to make

purchases off-line (Forsythe & Shi, 2003; Shim, Eastlick,

Lotz, & Warrington, 2001), which means that the amount ofmoney involved in e-commerce remains very low (Saeed et

al., 2005). Many factors may explain why Internet browsers

do not become online shoppers, but the present article

focuses on perceived risk and users characteristics in order

to shed light on the variables affecting consumers purchase

intentions in the online context (Fig. 1).

3.1. Perceived risk

Among the reasons commonly cited for consumers

aborting purchase attempts are a reluctance to supply

personal and credit card information, technical problems

with web sites, and problems in locating products (Shim

et al., 2001). Consumers perceptions of risk are consi-

dered to be central to different steps in the buying

process: their evaluations, choices, and behaviours

(Garbarino & Strahilevitz, 2004), since consumers are

more often motivated to avoid mistakes than to maxi-mise utility in purchasing (Conchar et al., 2004). Thus, in

online contexts, an increase in the risk perceived by

customers could reduce their intention to buy through

that web site.

Perceived risk toward a product category has been

shown to be negatively associated with purchase inten-

tions toward that product category (Westland, 2002).

Similar logic should hold true for perceived risk

toward a particular shopping channel. Indeed, several

studies have suggested that risk perceptions toward

remote purchasing methods can affect related shopping

behaviour (Mitchell, 2001). Thus, consumers who

perceive fewer risks or concerns toward online shopp-

ing are expected to make more online purchases than

more risk-laden consumers (Miyazaki & Fernandez,

2001).

The perceived risk associated with online transac-

tions may reduce perceptions of behavioural and environ-

mental control, affecting negatively transaction intentions

(Forsythe & Shi, 2003). Perceived risk has been found to

have a negative influence on consumers attitudes or

intentions to purchase online (Novak, Hoffman, & Yung,

2000). Given the uncertain context of e-commerce, it is

expected that perceived risk would lower consumers

intentions to use Internet sites for transactions (Pavlou,2003). The thesis of the present research is that hosting

CKM tools such as Shared databases, Document reposi-

tories, Workflow applications and Discussion forums in a

web site could cause an increase in perceived Web risk and,

in turn, reduce costumers purchase intentions on that site.

These statements give us the chance to formulate the

following hypothesis:

H2. The higher the customers perceived risk associated

with a CKM tool hosted in a web site, the lower the

purchase intention from that customer.

ARTICLE IN PRESS

H2

Risk

preference

Internet

knowledge

Purchase

intention

Internet

preference

H3

H4

H6H5

Perceived Risk

associated to

each CKM tool

H1

Fig. 1. CKM tools in e-commerce.



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3.2. User characteristics

3.2.1. Risk preference

As Conchar et al. (2004) explain in their exhaustive

review of perceived risk, risk preference has also been

studied, for instance, as risk tolerance (Sitkin & Pablo,

1992) or risk propensity (Forsythe & Shi, 2003). Riskpreference is a psychological feature of a users personality

and may be defined as a decision-makers tendency to take

(or avoid) risks (Conchar et al., 2004).

Regarding the online environment, Chen and He (2003)

empirically found a similar link between risk preference

and risk perceptions. Basing their study on structural

equation modelling, they concluded that the higher a

persons risk preference, the lower his/her perceived risk.

Nevertheless, decision-makers who enjoy the challenge

that risks entail will be more likely to undertake risky

actions (Sitkin & Pablo, 1992), meaning that risk preferr-

ing individuals will be willing to incur high risk and will

complete transactions on the most risky orders (Westland,

2002). In line with this, Conchar et al. (2004) state

that a person with high-risk affinity will prefer an

alternative perceived as more risky. In those situations,

users who are risk-seekers will perceive higher levels of

risk than risk-averse individuals. Thus, we may hypothe-

size a positive relationship between risk preference and

perceived risk.

H3. The higher the users risk preference, the higher

the perceived risk associated with a CKM tool hosted in

a web site.

3.2.2. Internet knowledge

Often called Internet experience, this is defined as

the consumers skill or ability obtained by visiting several

web sites and using various value-added services offered

on a broad range of web sites, and not as experience

with one particular web site (Nysveen & Pedersen, 2004).

Consumers knowledge about the Internet is important

in understanding customers perceptions, attitudes, and

behaviour in online environments (Shim et al., 2001).

Specifically, Internet experience contributes to more

effective use of web site applications in a way that

experienced Internet users have more positive attitudes to

using a web site (Chen & Macredie, 2005). Many marketers

believe that experience gained through simple usage

of the Internet for non-purchase purposes such as

information gathering and non-commercial communica-

tion will lead consumers to discover that privacy and

security risks are often exaggerated (Miyazaki & Fernan-

dez, 2001). It has been found that the more frequently

a consumer uses the Internet, the more knowledgable

he/she has in using the Internet and the consumer feels less

risk associated with the Internet (Chen & He, 2003). Based

on previous research, we posit that Internet knowledge

may be a factor in reducing users risk perceptions in the

online context.

H4. The higher the users Internet knowledge, the lower

the perceived risk associated with a CKM tool hosted in a

web site.

3.2.3. Internet preference

Humancomputer experiences are usually playful and

exploratory (Bierly & Daly, 2002), expanding the time andeffort devoted to exploring new options and experimenting

with new possibilities. In this sense, the Web may be

characterized as pleasurable, fun, enjoyable and as some-

thing that enables the Web user to escape from reality

(Chung, Chen, & Nunamaker, 2005). Internet preference

relates to the users personality feature associated with

enjoying with Internet exploration and surfing. This

exploratory behaviour positively influences the users

attitudes toward the web site (Das, Echambadi, McCardle,

& Luckett, 2003) and, in turn, may be a significant factor in

e-commerce acceptance and online purchase intentions

(Richard & Chandra, 2005).

H5. The higher the users Internet preference, the higher

the purchase intention from the user.

On the other hand, Internet preference may be a

consequence of the users Internet knowledge and experi-

ence. As consumer knows more about this channel, he/she

enjoys more when navigating on the Internet (Cheung &

Huang, 2002). It has been found recently that people

skilled at using the Internet really enjoy exploring web sites

they hear about, thus showing a higher Internet preference

and, indirectly, improving attitudes towards the site

(Chen & He, 2003). That is, Internet skills have a positive

influence on exploratory behaviour (Richard & Chandra,2005). Also, Das et al. (2003) found empirically that

users considered as experts or experienced in navigating the

Web did use the Web for fun and excitement, as a

recreational way to relax and to spend their time. Thus,

based on the literature, we may hypothesize that Internet

knowledge and experience may positively influence Internet

preference.

H6. The higher the users Internet knowledge, the higher

the users Internet preference.

All the links hypothesized basing on the literature review

are shown in the model graphically presented in Fig. 1. The

theoretical framework we propose integrates KM and

e-commerce areas by considering the impact CKM tools

may have on different key variables of e-commerce.

4. Methodology

4.1. Sample and data collection

In order to contrast our hypothesis we conducted an

experiment among Internet customers. A sample of 276

undergraduate students from different courses at a large

university was chosen. The sample was selected with an

attempt to concentrate on future business leaders who are

ARTICLE IN PRESS



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familiar with the kind of instruments used in this research

and who, nowadays, play an important role as Internet

customers. A self-administered questionnaire was prepared

for use in the survey, and this was pre-tested on 10 IT and

Business experts. A number of suggestions were obtained

on how to improve the questionnaire substantially. Once

the modifications were included, the questionnaire wasgiven to the students. The survey instrument started with

several questions concerning previous e-commerce experi-

ence, and these were followed by sections where each

student was asked to value the perceived risk and purchase

intentions associated with each CKM tool. Students were

provided with a description of each CKM tool (Shared


and Discussion forums), the place where they usually

appear on a web site and their main utility for firms and

customers. Finally, customers were rewarded with entry

into a contest for a DVD player in order to increase their

involvement with the research project.

4.2. Measure development and scale properties

The variables for this research were measured using

multi-item scales tested in previous studies. The response

categories for each scale were ranked between 0 (strongly

disagree) and 10 (strongly agree) because pre-testing

showed that items were better understood when valuing

each of the concepts from 0 to 10, since Spanish students

are normally marked in their courses using a similar range.

This procedure has also been consistently applied in the

literature. For measuring the perceived risk component we

drew upon the work of the first author that proposed thisconstruct, Bauer (1960), together with other articles which,

in recent years, have also paid attention to it, namely,

Mitchell (2001). Finally, for perceived risk measurement,

we considered four of the components most frequently

cited in the literature and related to the risk associated with

the place that offers the product (performance risk,

financial risk, time risk, delivery risk). Risk preference

was assessed through the work of Chen and He (2003).

Internet knowledge was measured through the work of

Novak et al. (2000) and Internet preference based on the

work of McKnight, Choudhury, and Kacmar (2002).

Purchase intention was measured through two items based

on the study ofChen and He (2003). A detailed descriptionof the scales can be found in the Appendix.

Before testing the hypotheses, we discuss the scale

reliability of all the measures in this study (Table 2). We

conducted a confirmatory factor analysis (CFA) including

the independent and dependent constructs with Lisrel 8.5 for

the Shared database model (w2(94) 182.49, CFI 0.96,

IFI 0.96, NFI 0.92, NNFI 0.95, GFI 0.92,

RMSEA 0.059, RMR 0.052), the Document reposi-

tories model (w2(94) 141.04, CFI 0.98, IFI 0.98,

NFI 0.94, NNFI 0.97, GFI 0.94, RMSEA 0.043,

RMR 0.045), the Workflow model (w2(94) 181.91,

CFI 0.96, IFI 0.96, NFI 0.92, NNFI 0.94,

GFI 0.92, RMSEA 0.058, RMR 0.044), and the

Discussion forum model (w2(94) 173.39, CFI 0.96,

IFI 0.96, NFI 0.92, NNFI 0.95, GFI 0.93,

RMSEA 0.055, RMR 0.050). The principal adjustment

indices (absolute, incremental and parsimony) of the five-

factor model for each CKM tool suggest a good fit of the

specification for our measures of the independent and

dependent variables. All of the loadings for the items on

their respective constructs were large and significant (smallest

t-value 3.62), which provides evidence of convergent

validity (Bagozzi & Yi, 1988). Regarding the nature of the

individual parameters and the internal structure of the

model, all factor loadings were significant and all of themexceeded the 0.7 level required as a basis for research. The

reliability of the multi-item scales was assured by calcula-

ting the composite reliability index suggested by Bagozzi

and Yi (1988) and with the average variance extracted

index proposed by Fornell and Larcker (1981). As shown in

Table 2, both indexes are inside the recommendations of the

ARTICLE IN PRESS

Table 2

Descriptive statistics and reliability

Mean S.D. No. of items

remain

Cronbachs

alpha

Eigenvalue Lowest

t-value

SCRa AVEb

Internet knowledge 6.11 2.01 4 0.90 3.10 14.56 0.91 0.72

Risk preference 4.78 2.19 4 0.80 2.15 9.98 0.81 0.60

Internet preference 5.63 2.51 3 0.87 2.45 14.11 0.89 0.73

Perceived risk of Shared databases 4.11 2.21 4 0.78 2.41 8.80 0.80 0.50

Perceived risk of Document repositories 3.24 1.95 4 0.81 2.59 11.38 0.82 0.53

Perceived risk of Workflow 3.63 2.19 4 0.81 2.53 9.16 0.82 0.53

Perceived risk of Discussion forums 3.08 1.98 4 0.80 2.49 10.36 0.80 0.50

Purchase intention of Shared databases 5.01 2.37 2 0.83 1.72 5.81 0.84 0.63

Purchase intention of Document repositories 5.57 2.25 2 0.76 1.61 6.67 0.78 0.64

Purchase intention of Workflow 5.87 2.34 2 0.78 1.63 7.04 0.79 0.65

Purchase intention of Discussion forums 5.79 2.44 2 0.80 1.67 3.62 0.80 0.63

aScale composite reliability.bAverage variance extracted.



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literature, which provides evidence of a good adjustment of

each construct. In addition, Evidence of discriminant validity

among the dimensions of each construct was provided by

three different procedures recommended in the literature as

follows: (1) when a 95% confidence interval constructed

around the correlation estimate between two latent variables

never includes the value 1 (Anderson & Gerbing, 1988);(2) when the hypothesised four-factor model has a signifi-

cantly better fit to the data than an alternative model in

which the correlation estimate between latent constructs is

constrained to the value 1 (Anderson & Gerbing, 1988);

(3) when the individual average variance extracted for each

latent variable exceeds the squared correlation between both

latent variables (Fornell & Larcker, 1981).

5. Research findings

5.1. Differences between CKM tools in e-commerce

To test whether or not differences exist between the

variables for each CKM Web tool, a statistical analysis

based on the mean differences among the constructs

was conducted. Results revealed that difference exists in

perceived risk among all the CKM tools considered

except between Document repositories and Workflow. This

supports hypothesis H1 about the distinct perceived risk

associated to each CKM tool. Moreover, as can be seen in

Fig. 2, the higher customer perceived risk appears when

Shared database tools are hosted in a web site. On the

contrary, Discussion forum tools produce a lower per-

ceived risk on the part of customers. This means that

the presence of Discussion forums, where word of mouthcan be shared, may relieve the risk perceived online.

This finding is similar to that described in Cases (2002),

who proposed that sharing word of mouth online is a

risk-reliever. In addition, the empirical data support the

idea, previously discussed in the literature review, that the

fact that web sites use Shared databases and/or Workflow

applications means that they are perceived by users as

riskier than other web sites where Document repositories

and/or Discussion forums are hosted.

5.2. Implications of hosting CKM tools in e-commerce

The proposed structural model for each CKM tool is

specified from the hypothesized relationships in Fig. 1,

discussed in the text as H2H6. Conventional maximum

likelihood estimation techniques were used to test the

model. However, it is generally agreed that researchers

should compare rival models and not just test the

performance of a proposed model (Bagozzi & Yi, 1988).

Our proposed five-factor model was compared with

another model that also estimates the relation of Internet

knowledge with purchase intention. The underlying

assumption built into this alternative model is based on

the proposal of several authors (e.g. Miyazaki & Fernan-

dez, 2001) that the higher the Internet knowledge, the

higher the probability of shopping online. Therefore, we

test our theoretical model (TM) against an alternative

model specification (AM) that considers this extra relation-

ship. Anderson and Gerbing (1988) recommend this

procedure and suggest the use of a Chi-square difference

test (CDT) to test the null hypothesis: TMAM 0.

Compared with a less parsimonious model (AM) that also

considers the direct relationship between Internet knowl-

edge and purchase intentions, a non-significant CDT

would lead to the acceptance of the more parsimoniousTM. The non-significant change in Chi-square between our

model (TM) and the alternative one (AM) for every CKM

tool, leads us to consider TM as a better specification.

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4,11

3,23

3,61

3,08

0

1

2

3

4

Shared

databases

Documents

Repositories

Workflow Discussion

Forums

CKM tool

Mean values are expressed for each CKM tool

Mean differences T-student

Shared databases and documents repositories 0.86*** 7.24

Shared databases and workflow 0.49*** 3.73

Shared databases and discussion forums 0.99*** 7.14

Document repositories and workflow 0.38*** 3.64

Document repositories and discussion forums 0.14 1.53

Workflow and discussion forums 0.52*** 4.77

Significance levels: ***p


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Results indicate that the fit of our proposed model was

much better than the fit of the respecified model for every

CKM tool.

According to this, the fit of the model for Shared

databases is satisfactory (w2(98) 185.23, CFI 0.96,

IFI 0.96, NFI 0.92, NNFI 0.95, GFI 0.92,

RMSEA 0.05, RMR 0.05) and all the hypothesis wereconfirmed, thus revealing the mediating role of perceived

risk and Internet preference in our model. Moreover, an

indirect effect was found between Internet knowledge and

purchase intention (0.10; po0.05), thus demonstrating the

positive effect of this variable on the probability of the

customer of buying online.

The overall adjustment for the Document repositories

also offer a good fit (w2(98) 153.48, CFI 0.97,

IFI 0.97, NFI 0.93, NNFI 0.97, GFI 0.93,

RMSEA 0.04, RMR 0.05) and, similarly to what

happened with the previous model, all the hypotheses were

confirmed. Similarly, an indirect effect between Internet

knowledge and purchase intention was also found (0.09;

po0.05).

In contrast to the previous model, the Workflow model

offered different findings. The overall fit of the model was

acceptable (w2(98) 194.63, CFI 0.95, IFI 0.95,


RMR 0.05), but in terms of the hypotheses, our results

confirm that the relationship between Internet knowledge

and perceived risk (g12 0.08, p40.10) and between

Internet preference and purchase intention (b32 0.02,

p40.10) were not supported. This means that an adequate

Internet knowledge does not necessarily lead to a reduction

in the perceived risk associated to Workflow tools. Anotherimportant finding is that Internet knowledge does not

relate to customer purchase either directly or indirectly.

Finally, the model for Discussion forums also offers

unexpected results. Even though the overall fit of the

structural model is inside the recommendations of

the literature (w2(98) 178.56, CFI 0.96, IFI 0.96,


RMR 0.05) one of the hypotheses was not confirmed;

specifically, the one that relates the perceived risk

associated to Discussion forums tools and purchase

intention. This means that managers should not be

discouraged from including this type of CKM tool, because

they do not only lead to lower purchase intention based onhigher levels of perceived risk, but there is also a second

effect where purchase intention increases when customers

have a preference for the Internet.

On the other hand, when comparing the four models

(one model for each CKM tool) shown in Figs. 36, some

interesting results are found. First, the impact of Internet

knowledge on Internet preference is similar in every CKM

tool, with the estimated coefficient for this link being

around 0.5 in all of the models. So, we may state that

Internet knowledge is a good predictor for Internet

preference for any CKM tool. Second, the strength of the

impact that risk preference has on perceived risk is different

depending on the CKM tool considered. Specifically,

estimated coefficients are higher in the case of Discussion

forums and Document repositories rather than in the case

of Shared databases and Workflow applications. So, the

link between risk preference and perceived risk is stronger

when the web site offers Discussion forums and Document

repositories and weaker when the company provides online

access to Shared databases and Workflow applications.

Finally, the results show that the inclusion of Discussion

forums is unique among CKM tools in not having an

impact negatively on customers purchase intentions. This

finding, together with the fact that this CKM tool has been

proven to be a risk-reliever, makes Discussion forums themost advisable CKM Web application.

6. Conclusions and managerial implications

Many organisations consider KM to be the fundamental

basis of competition (Zack, 1999) and a critical enabler of

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CFI=0.95 IFI=0.95 NFI=0.91 NNFI=0.94 GFI=0.92 RMSEA=0.06 RMR=0.05



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good customer support and service (Shah & Murtaza,

2005). Besides, KM capabilities rely strongly on IT

infrastructure in order to improve customer response and

provide faster decision-making (Chung et al., 2005). Smart

companies seek knowledge about and from their

customers (Chen & Macredie, 2005) and web sites can be

the first point of contact between a company and itscustomers (Chou & Lin, 2002) and the means to obtain

knowledge about and from customers (Maswera et al.,

2006). By integrating KM into their e-commerce activities,

as a way of online CKM, firms can automate existing

processes and dramatically reduce cycle times throughout

the supply chain; they can enhance communication,

collaboration, and corporation between knowledge teams

(including virtual teams) using intranet technologies and

between the organisation and members of its external

constituent organisations using extranet technologies.

From present research, it may be drawn the conclusion

that incorporating certain web site features has a positive

impact on customer perceptions, as suggested by Heinze

and Hu (2006), and that the implementation of Internet

based CKM will positively impact on e-business perfor-

mance, as Borges et al. (2007) have recently found, in terms

of online purchase intentions. Nevertheless, we have

demonstrated that certain CKM tools may be harmful

for the organisation, as examined more fully below.

The results of this research are essential for academic

and managerial purposes because they try to fill, to some

extent, the gap that exists between KM and e-commerce

activity, by analysing the antecedents and consequences of

CKM Web tools hosted in corporate web sites. Moreover,

this research extends the literature that identifies a negativerelation between perceived risk associated with certain

CKM tools and purchase intentions in the online context.

On the other hand, managers should take into account the

implications of hosting some CKM applications on their

web sites, because there could be an important effect on

customer perception of a web site or on the final sales

volume. The empirical findings show that there is an

important link between KM and e-commerce, especially

regarding the differences between CKM tools hosted on a

web site, in terms of customers perceived risk. Moreover,

results reveal that hosting Document repositories or

Discussion forum tools in the corporate web site constitu-

tes a significant risk-reliever, in comparison to Shared

databases and Workflow tools. We have also found that

the impact of customers perceived risk on purchase

intention is not the same for every CKM tool considered.

Specifically, results suggest that hosting Discussion forums

enhances the probability of customer purchases in contrast

with the situation of Shared databases, Document reposi-

tories or Workflow applications. Consequently, Discussion

forums have been found as the CKM tools that are most

commendable in e-commerce initiatives.

Despite its important contributions for academics and

practitioners, this study also has some limitations. We

conducted our study with 276 students, so there are some

problems with the external validity of the results. For that

reason, it could be interesting to test this research with

other customers in order to generalize our findings.

Moreover, it will be useful to replicate this study using

an online survey and, if possible, with data from real firms

selling products on the Internet. Also, examining differ-

ences between sectors or types of products may be helpfulfor managerial implications. Finally, further research is

needed about how some other variables, such as demon-

strations and guarantees (Gupta et al., 2004) or user gender

(Garbarino & Strahilevitz, 2004), and other newer CKM

tools, such as weblogs or wikis (Wagner & Bolloju, 2005),

could modify risk perceptions and results.

Acknowledgements

Financial support from Fundacio n CajaMurcia is grate-

fully acknowledged.

Appendix

Internet knowledge (based on Novak et al., 2000)

I know tools for searching products on the Internet.

I know how to find in the Internet what I look for.

Compared to other things I do with the computers, I

consider myself as high skilled in using the Internet.

Compared to other sports or hobbies, I consider myself

as high skilled in using the Internet.

Internet preference (based on McKnight et al., 2002)

I like to explore new web sites.

Among my colleagues, I am usually the first to try out

new web sites.

When I have some free time, I often explore new web

sites.

Risk preference (based on Chen & He, 2003)

I like to test myself every now and then by doing

something a little risky.

Sometimes I will take a risk just for the fun of it.

I sometimes find it exciting to do things for which I

might get into trouble.

Excitement and adventure are more important to me

than security.

Online product perceived risk (based on different studies;

Bauer, 1960; Mitchell, 1999, 2001) Which are your

perceptions if you find each of the following tools on a

web site (Shared databases, Document repositories, Work-

flow applications and Discussion forums)? Please provide a

separate response to each of the tools.

The web site might not process correctly my purchase

order.

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My personal data might be lost or use incorrectly.

Time required to buy and obtain the product will be

longer.

Product delivery may last long or be incomplete.

Online purchase intention (based on Chen & He, 2003;

Pavlou, 2003)

If this online retailer has the product I need to buy, I

intend to buy it from the retailer.

I would consider purchasing from this web site in the

future.

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Carolina Lopez-Nicolas (Ph.D., University of Murcia) is an assistant

professor at the Department of Management and Finance at the

University of Murcia (Spain). She holds a BA in Business Administration

from University of Murcia and a BA Honours in Accounting and Finance

in Europe from Manchester Metropolitan University. She has been a

Visiting Professor at the Delft University of Technology in 2005 and 2007.

Her current research relates to knowledge management, electronic business,

electronic commerce and strategy. She has published on these topics in such

journals as Journal of Knowledge Management, Journal of Enterprise

Information Management, International Journal of E-Collaboration, and

International Journal of Internet Marketing and Advertising.

Francisco Jose Molina-Castillo (Ph.D., University of Murcia) is an

Assistant Professor of Marketing at the University of Murcia (Spain).

He has a Masters Degree in Business and Foreign Trade, including a

period of training at the Spanish Chamber of Commerce in Vienna,

Austria. He received his BA in Business Administration from the

University of Murcia and a BA Honours in Accounting and Finance in

Europe from Manchester Metropolitan University. He has been a Visiting

Professor at the Delft University of Technology in 2005 and 2007. His

research interests focus on new product launch and electronic business. He

has published on these topics in such journals as Telematics and

Informatics and in the International Journal of Internet Marketing and

Advertising.

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