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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
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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
databases, Document repositories, Workflow applications
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,
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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 &
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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
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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.
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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
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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
databases, Document repositories, Workflow applications
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
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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,
NFI 0.91, NNFI 0.94, GFI 0.92, RMSEA 0.06,
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,
NFI 0.92, NNFI 0.95, GFI 0.92, RMSEA 0.05,
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=0.96 IFI=0.96 NFI=0.92 NNFI=0.95 GFI=0.92 RMSEA=0.05 RMR=0.05
Significance levels: ***p
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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
Significance levels: ***p
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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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