Description
Most firms have started to realize the importance of KM in streamlining their operations and processes to
improve organizational performance. So in this paper, we try to survey and present a model for
measuring success of KM in small- and medium-sized enterprises (SMEs). This study is the first empirical
test of an adaption of the Jennex and Olfman (J&O) KM success model considered a better description of
KM success due to its strong theoretical grounding to analysis the influences of KM and inter-actions on
workers' productivity in Taiwanese SMEs settings. Structural equation modeling techniques are applied
to data collected through questionnaires from 277 knowledge workers. All the hypothesized relationships
between the variables are significantly supported by the data. The findings served as useful
reference points for researchers interested in investigating issues related to the successful implementation
of KM, and for practitioners aiming to achieve the benefits of KM in SMEs.
Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises
Mei-Hsiang Wang
*
, Tarng-Yao Yang
Department of Information Management, Southern Taiwan University of Science and Technology, Taiwan
a r t i c l e i n f o
Article history:
Received 25 April 2013
Accepted 2 December 2015
Available online xxx
Keywords:
IS success model
KM success model
Small- and medium-sized enterprises
(SMEs)
Knowledge management
a b s t r a c t
Most ?rms have started to realize the importance of KM in streamlining their operations and processes to
improve organizational performance. So in this paper, we try to survey and present a model for
measuring success of KM in small- and medium-sized enterprises (SMEs). This study is the ?rst empirical
test of an adaption of the Jennex and Olfman (J&O) KM success model considered a better description of
KM success due to its strong theoretical grounding to analysis the in?uences of KM and inter-actions on
workers' productivity in Taiwanese SMEs settings. Structural equation modeling techniques are applied
to data collected through questionnaires from 277 knowledge workers. All the hypothesized relation-
ships between the variables are signi?cantly supported by the data. The ?ndings served as useful
reference points for researchers interested in investigating issues related to the successful imple-
mentation of KM, and for practitioners aiming to achieve the bene?ts of KM in SMEs.
© 2015 College of Management, National Cheng Kung University. Production and hosting by Elsevier
Taiwan LLC. All rights reserved.
1. Introduction
In Taiwan, small- and medium-sized enterprises (SMEs) exert a
strong in?uence and constitute approximately 97.63% of all enter-
prises and make up 77.12% of the Island's overall employment. In the
face of the volatility and rate of change in business environments,
SMEs are facing the unprecedented challenges brought about by the
knowledge economy and to continue to retain ?exibility and inno-
vation is actually a vital topic. KM has become a critical component
for maintaining competitive advantages and many organizations are
exploring the ?eld of KM in order to improve and sustain their
competitiveness. Faced with competitive dilemmas may be solved
by the implementation of KM to enhance competitiveness. That is,
KM has the potential to make SMEs more competitive and innova-
tive and the ability of KMto lead to sustainable performance is even
more critical. Such as Friedman and Prusak (2008) noted that KM
can be used to improve both individual and organizational perfor-
mance, and has become a critical issue in industrial practices.
Okunoye and Karsten (2002) stated that KM has indeed become the
underlying sources for successful organizations regardless of their
size and geographical locations. KM has now become a widely
spread business discipline, it is no longer the concern of just large
organizations. As asserted by Frey (2001), although major corpora-
tions have led the way in introducing and implementing KM, it is
increasingly important for SMEs to manage their collective intellect.
Information systems success is one of the most widely used
dependent variables in information systems research. Measuring
the success of systems is critical to understand the value, effect of
management operations and investment on them (DeLone &
McLean, 2003). Therefore, since 1992, several studies have been
examined the success of different information systems and
measured it experimentally (Lee & Lee, 2009; Lin & Shao, 2000;
Muylle, Moenaert, & Despontin, 2004; Wang, Wang, & Shee,
2007). However, few studies have concentrated on measuring KM
success. As Kulkarni, Ravindran, and Freeze (2006e2007) note,
there has been a lack of adequate theoretical modeling and
empirical examination of factors leading to KM success. Markus
(2001) has also indicated that getting employees to use KMSs
effectively to improve organization performance is still a central
issue for both researchers and practitioners. In proposing a success
model of KM and empirically investigating multidimensional re-
lationships among success measures, this study is based on the
Jennex and Olfman (J&O) model (2005) and Kulkarni et al.'s
(2006e2007) KM success model. The J&O model is based on
several case studies and quantitative research studies and is
* Corresponding author.
E-mail address: [email protected] (M.-H. Wang).
Peer review under responsibility of College of Management, National Cheng
Kung University.
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Asia Paci?c Management Review xxx (2016) 1e13
Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
theoretically grounded on the DeLone and McLean IS Success
Model, which has been accepted for several years and has been
validated by several studies, applied within the KM context.
Although KM have been widely implemented in organizations,
their availability does not guarantee that employees will be willing
to spend time and effort to use them. Measuring KM success is
therefore crucial for providing a basis on which companies can
evaluate KM, stimulating management to focus on critical aspects
of the business, and justifying investments in KM activities. The
measurement of KM success is also valuable for building and
implementing ef?cient KM initiatives and systems from the per-
spectives of KM practitioners (Jennex & Olfman, 2005).
SMEs need to respond rapidly to these emerging changes to
ful?ll their customer needs more rapidly. In order to further utilize
KM for seamless business operations and decision-making, adop-
tion of KMin SMEs has become the emerging agenda in developing
business strategies. To manage knowledge resources is considered
the main objective of pursuing KM in business operations in
Taiwanese SMEs. However, most studies of KM implementation
have been heavily focused on large companies. As such, existing
research ?ndings are mainly large companies oriented, thereby
re?ecting their situations. SMEs face unique KMchallenges that are
distinct from those of their larger business. Directly applying these
results into the SMEs environment may not be suf?cient without an
understanding of their very own and speci?c conditions. Previous
studies fall short of studying and identifying the adoption of KM
from the SMEs perspective. They have not considered the differ-
ences of company size as well as the speci?c features of SMEs that
could affect KM. Even, in recent years, many researchers have been
focusing on the development of practical implementation of KM in
SMEs (Chan & Chao, 2008; Denning, 2006; Handzic, 2004; Tseng,
2007). There are issues existing where SMEs fail to realize and
recognize the potential bene?ts of KM. A better understanding of
the adoption for implementing KM in SMEs is needed in order to
ensure the success of their efforts. Such as Jennex, Smolnik, and
Croasdell (2008) noted, to assess the bene?ts of implementing
KM and its status of KM readiness within an organization's
practices is an important issue that requires further exploration. In
spite of KM importance for sustainable competitiveness, in most
SMEs there is an absence of systematic KM (Wong & Aspinwall,
2005).
In spite of the vast literature on KM, there has been little or no
empirical evidence for Taiwanese SMEs. Due to SMEs have some
unique features (limited ?nancial and human resources, ?at
structure, informal managerial styles, centralized decision-making,
focus on the day-to-day business operations) that deeply in?uence
the way they can approach KM. In the context of SMEs, a ?eld
where research on KMis still fragmented and quite limited (Durst &
Edvardsson, 2012). Hence, this research attempts to propose a
success model for KM and to empirically investigate the multi-
dimensional relationships among the success measures based on
KM success model for Taiwanese SMEs. In order to understand KM
practices in SMEs, do we need a new concept of KM and new
interpretive frameworks that are different from those normally
adopted in the case of large ?rms? We examine the following
research questions: (1) What are the in?uences of system quality,
knowledge quality, and service quality on KM use in SMEs setting?
(2) What are the individual and combined in?uences of system
quality, knowledge quality, and service quality on user satisfaction
in SMEs setting? (3) What is the effect of KM use on user satis-
faction in SMEs setting? (4) What is the individual and combined
in?uences of KM use and user satisfaction on net bene?ts in SMEs
setting? A potential contribution of this study focuses on the less
explored SMEs in Taiwan context and provides some insight for
companies that are not sure how to implement KM into their
business operations, further take the necessary action based on
these assessments.
2. Theoretical background
2.1. Knowledge management in SMEs
KMis becoming a growing concern in management research and
practice because of its role in determining ?rminnovation capability
and in enhancing working life quality of knowledge workers. KM
may be particularly relevant for SMEs. SMEs tend to be relatively
more dynamic and agile than larger organizations, and more ready
to learn. How to effectively establish and sustain good KM practices
in SMEs in order to ensure their competitiveness is important. KM
refers to managing the corporation's knowledge by means of a
systematic and organizational speci?ed process for acquiring,
organizing, sustaining, applying, sharing and renewing both tacit
and explicit knowledge by employees to enhance organization
performance and create value (Davenport & Prusak, 1998). Tiwana
(2001) claims that ‘KM can be extended to management of organi-
zational knowledge for creating business value and generating a
competitive advantage’, ‘KM enables the creation, communication,
and application of knowledge of all kinds to achieve business goals’,
‘KM is the ability to create and retain greater value from core
business competencies’. KMS supports the use of information
through knowledge acquisition, knowledge sharing and knowledge
application for improvement. This captured knowledge is then
stored in knowledge repositories to be shared between individuals
and departments. Subsequently, the knowledge is applied in busi-
ness situations, and introduces other ideas and frames of reference
to ultimately create newknowledge. As newknowledge is created, it
needs to be captured and stored, shared and applied, and the cycle
continues KM practices are applied to help the organization
strengthen its competitive advantage, and assist knowledge workers
to leverage their skills and their ability to offer business value.
Therefore, KMis the process through which an organization uses its
collective intelligence to accomplish its strategic objectives. KM
process should start by recognizing and identifying the knowledge
to be captured, shared and applied, to enable the organization and
its workforce to achieve a sustainable and competitive advantage.
In fact, KM can provide several bene?ts to SMEs, such as better
communication, improved customer service, faster response times,
enhanced innovativeness, greater ef?ciency in processes and pro-
cedures, and reduced risk of loss of critical capabilities (Edvardsson
& Durst, 2013). In this regard, Dotsika and Patrick (2013) underline
that the implementation of KM initiatives in SMEs may be even
more crucial, as knowledge can be their single key resource. For
Taiwanese SMEs, they have to rely on their own ability to improve
products and processes, providing customers with value-adding
innovations and learning capabilities. Due to resource constraints,
SMEs are particularly required to absorb knowledge from external
sources (Durst &Edvardsson, 2012). KMcan provide quick and easy
access to external sources of knowledge and newand more intense
communication channels with partner organizations. Furthermore,
it can erase traditional constraints on SMEs innovation ability,
while leveraging their ?exibility and responsiveness.
2.2. KM success models
A stream of research has been conducted to identify IS success
measures. DeLone and McLean (2003) introduced a comprehensive
taxonomy in order to organize this diverse research. The DeLone
and McLean (D&M) IS success model is based on the review and
integration of 180 research studies that used some form of system
success as a dependent variable. The model identi?es six
M.-H. Wang, T.-Y. Yang / Asia Paci?c Management Review xxx (2016) 1e13 2
Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
interrelated dimensions of success, each of which has its own
measures for determining impact on success and other dimensions.
The key focus of the model is the relationships, and it demonstrates
that the system and information quality aspects of a system lead to
increased system use and user satisfaction. Information quality is
based on the use of accurate data, whereas system quality is based
on the technical infrastructure and interface involved. User satis-
faction tends to increase use, whereas use tends to lead to some
level of user satisfaction, making these dimensions dif?cult to
separate. System use leads to system success. DeLone and McLean
(2003) subsequently revisited the D&M IS success model by
incorporating subsequent IS success research and addressing crit-
icisms of the original model. One hundred and forty-four articles
from refereed journals and ?fteen papers from the International
Conference on Information Systems (ICIS), citing the D&M IS suc-
cess model, were reviewed, with fourteen of these articles report-
ing on studies that empirically investigated the model. The result of
this revision was the modi?ed D&M IS success model. Major
changes include the addition of a service quality dimension, to
address services provided by the IS groups, the modi?cation of the
use dimension into a use/intent to use dimension, and the combi-
nation of the individual and organizational impact dimensions into
an overall net bene?ts dimension. The modi?cation of the use
variable to include intent to use is important for this paper.
Jennex and Olfman (2003) devised a KMS success model that is
based on the D&MIS success model. KMSs involve IT-based systems
that have been developed to support and enhance the processes of
knowledge creation, storage/retrieval, transfer, and application.
KMS success can be de?ned as making KMS components more
effective by improving their search speed and accuracy, among
other qualities. KMSs that enhance search and retrieval functions
enhance decision-making effectiveness by improving the ability of
the decision maker to ?nd and retrieve appropriate knowledge in a
more timely manner. In other words, enhancing KMS effectiveness
makes KMSs more successful, in addition to being a re?ection of
KMsuccess. This implies that by increasing KMS effectiveness, KMS
success and decision-making capability are enhanced, thereby
positively in?uencing organizations. KM success is crucial for un-
derstanding how initiatives and systems should be designed and
implemented. Previous literature offers a number of perspectives
on KM success. The J&O (2006) KM success model, based on the
D&M (2003) IS success model, combines KM and KMS success.
Therefore, in this study, we consider KM and KMS success to be
interchangeable and use the termKM to refer to both KM and KMS,
and the term success to refer to both success and effectiveness.
KM is complex and multi-faceted concepts. As suggested by
Kulkarni et al. (2006e2007), a KMsuccess model needs to cover the
effects of all the different types of KM activities that may be
involved. A more comprehensive view of KM must include the
speci?c processes required to acquire, store, retrieve, and apply
knowledge (Gold, Malhotra, & Segars, 2001). As such, KM success
can be de?ned as capturing the right knowledge, getting the right
knowledge to the right user, and using this knowledge to improve
individual performance. Considering the many view of KM, in this
paper the de?nition of “KM success” means that the organization's
employees manage and use the knowledge lead to the organiza-
tion's bene?ts (such as better decision-making, faster response
time to key issues, increasing productivity and job effectiveness,
sharing best practice etc.) or KM could provide the appropriate
knowledge to those that need it when it is needed.
2.3. Related work
Although KM is becoming a growing concern in management
research and practice, we lack an understanding of how ?rms
create knowledge and how this is translated into competitive ad-
vantages or enhanced customer relations (Edvardsson &Oskarsson,
2011). Numerous works on KM are reported in literature. The main
focus of KM research to date has been on processes and structures
within large organizations in order to improve their performance
and competitive standing. In this section, we discuss the related
work in KM. Organizations have long acknowledged that KM is an
important mechanism for gaining competitive advantages and
improving performance. KM issues attracted signi?cant number of
research to examine whether KM really works in the organizations
and the success factors. Most of the literature on KM and its
application has, until recently, been centered on large organiza-
tions. Pertinent issues in small businesses have to a large extent
been neglected. However, small businesses do not necessarily share
the same characteristics and ideals as large ones. There are certain
unique features of SMEs that need to be understood before KM is
implemented in their environment. To date KM in SMEs have been
discussed in many empirical studies, but KM is rarely studied in
Taiwanese SMEs setting. KM, especially in SMEs setting, has not yet
been fully explored. Table 1 shows the brief looks at the current
body of studies related to KM in SMEs setting. More papers were
published after 2011, indicating a growing interest in the subject.
However, the small number of papers clearly indicates serious lack
of knowledge in this ?eld of study.
To sum up, most of the literature on KM and its application has,
until recently, been centered on large organizations. The literature
that examines KMin the context of SMEs is still scarce and provides
fragmented insights (Durst & Edvardsson, 2012; Dwivedi,
Venkitachalam, Sharif, Al-Karaghouli, & Weerakkody, 2011;
Ribi ere & Christian, 2013). Relevant issues in SMEs have to a large
extent been neglected. Such as Durst and Edvardsson (2012)
stressed that the body of research about KM in SMEs is rather
limited compared to the large number of studies concerning
big companies. In light of this, the paper aims to redress some of
this imbalance in the literature by putting KM into the context of
SMEs.
3. Research methodology
3.1. Research model
In order to present a model for measuring KM success, a
comprehensive model is presented for measuring the success of
KM. Because KM systems are kinds of information systems and
workers use themfor working. But the revised D&Mmodel, in spite
of all its strengths, still has defects. In this paper, we formulate our
theoretical framework basing on J&O (2005) conceptualization of
KMsuccess model, adapted to socio-technical view. Fromthe socio-
technical perspective, measures of success should combine both
technological and human elements (Garrity & Sanders, 1998; Skok
& Kalmanovitch, 2005). Within the KM context, J&O (2005) KM
success model is a multi-dimensional model, whose interrelated
dimensions are based on the work of DeLone and McLean (2003).
The revised KMsuccess model is shown in Fig. 1. According to Fig. 1,
all components of the widely used model D&M, are included in this
conceptual model to measure the success of KM. Also, a new rela-
tionship between intention to use and system use components is
added to the previous D&M model.
3.2. Research hypotheses
Initially, KMSs are implemented and subsequently, various de-
grees of system, information, and service quality are examined.
Knowledge workers experience these quality dimensions by using
KMSs in making decisions and conducting work. The three quality
M.-H. Wang, T.-Y. Yang / Asia Paci?c Management Review xxx (2016) 1e13 3
Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
Table 1
Recent studies on KM in SMEs context.
Source Key points Subject Findings
Wong and Aspinwall (2004) To Look at their characteristics, their
advantages and disadvantages, their
strengths and weaknesses, and their
key problems and issues, all associated
with KM.
To redress some of this imbalance in
the literature by putting KM into the
context of small businesses.
Recognition of all these elements is
crucial in order to provide a well-
suited KM approach for small
businesses.
Wong and Aspinwall (2005) The perceptions of companies and a
group of academics, consultants and
practitioners.
To investigate the CSFs for adopting
KM in SMEs.
UK SMEs. A total of 11 factors, comprising 66
elements were considered in the
survey instrument.
Saloj€ arvi, Furu, and Sveiby (2005) Examining the relationship between
sustainable sales growth and
knowledge management activities.
108 Finnish and thematic interviews
with 10 companies.
Higher levels of KM-Maturity were
found to correlate positively with long-
term sustainable growth.
Gray (2006) To explore SME capacity to absorb and
manage knowledge as a prior
condition to the successful adoption of
innovations and entrepreneurial
growth.
1500 SME owners across regular
quarterly SERTeam surveys and from
other large scale studies.
There were signi?cant age, educational
and size effects that in?uence SME
acquisition and assimilation of
knowledge.
Edvardsson (2006) To expand our knowledge on KM in
SME (focusing on Icelandic SMEs).
Questionnaire sent to the Chief
Executive of Icelandic SMEs.
Icelandic ?rms rely on an unsystematic
manner of sharing and utilizing
knowledge, few have a KM strategy
and they mainly use unsophisticated
ICT technologies.
Those who had KM reported many
bene?ts, such as improved decision
making, better customer handling,
improved staff retention and increased
competitive advantage.
Valkokari and Helander (2007) Integrating business network and KM
to bridge the KMand strategic business
network.
Literature review and analysis. Provides a typology of the strategic
SME network types and their key KM
challenges based on a synthesis of
existing literature.
SME network typology presented can
be used by managers of SMEs in
evaluating their current KM practices
level.
Supyuenyong, Islam,
and Kulkarni (2009)
To understand how the special
characteristics of SMEs in?uence their
KM processes.
KMprocess fromcapture of knowledge
to its eventual reuse.
Four SMEs in Thailand. Ownership and management structure
as well as culture and behavior
characteristics of SMEs seem to have a
more positive effect than other SMEs
characteristics on KM processes.
Edvardsson (2009) To examine whether the popularity of
KMin SMEs in Iceland has decreased or
declined since 2004.
Questionnaire sent to the Chief
Executive of Icelandic SMEs (2007),
repetition of a previous survey (2004).
KM is not losing ground among SMEs
in Iceland in 2004e2007.
Many more ?rms have no KM strategy
than in 2004.
Those who had KM reported many
bene?ts, such as improved decision
making, better customer handling,
improved staff retention and increased
competitive advantage.
Migdadi (2009) To develop a conceptual research
model which comprises both CSFs and
outcomes.
Empirically assesses the relationships
between CSFs and performance
outcomes in SMEs.
25 SMEs in Saudi Arabia. Study underlined the positive
relationship between CSFS and KM
outcomes (i.e., systematic knowledge
activities, employee development,
customer satisfaction, good external
relationships and organizational
success).
Steenkamp and Kashyap (2010) To provide empirical evidence of SME
managers' perceptions about the
importance and contribution of
intangible assets to their business.
Postal questionnaire sent to New
Zealand SMEs.
Findings indicated that intangibles are
important and are perceived as value
drivers of business success.
Customer satisfaction was ranked as
the most important, followed by
customer loyalty, corporate reputation,
and product reputation.
Lee and Lan (2011) To examine the infrastructure and
process capabilities of Taiwanese SMEs
To conducts a comparative analysis of
KM in SMEs in Hong Kong.
SMEs in Taiwan in six sections. A successful KM implementation
depends on a harmonious
amalgamation of infrastructure and
process capabilities, including
technology, culture and organizational
structure.
Soon and Zainol (2011) To examine the importance of the
knowledge creation process, by
looking at knowledge management
enablers such as learning and T-shaped
skills.
Questionnaire, 110 replies, Malaysia. Learning and T-shaped skills are
positively related to the knowledge
creation process, enhancing
organizational creativity and
performance.
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
dimensions and the use of KMSs in?uence the individual value of
using KMSs. The hypothesized relationships among the three
quality dimensions, two use dimensions, and single bene?t
dimension is based on the theoretical and empirical work reported
on by DeLone and McLean (2003), Jennex and Olfman (2005), and
Kulkarni et al. (2006e2007). Based on the literature review and
theoretical analysis, the hypothesized relationships can be
described as follows:
(1) Quality dimensions and use dimensions
There is quite strong support in the literature both theoretical
and empirical; the results indicated that system quality and infor-
mation quality positively affected both system use and user satis-
faction (DeLone & McLean, 1992, 2003; Rai, Lang, & Welker, 2002;
Seddon &Kiew, 1996). KMsystemquality in our model is a measure
of how well the KM systems support and enhance KM-related ac-
tivities. In contrast to some prior studies that have operationalized
IS quality by a simpli?ed measure called ease of use, our measure of
KM system quality captures multiple dimensions of the quality of a
KMsystem. Knowledge workers may ?nd value in using knowledge
if the system quality is adequate and the KM system reduces the
extra effort required to ?nd or contribute, hence the belief that
system quality leads to a high level of KM use and user satisfaction.
Systems characterized by their ease of use are those that are clear
and understandable, and which require little mental effort to use.
Higher system quality has been found to be a signi?cant determi-
nant of user satisfaction in IS literature (Wixom & Todd, 2005).
Iivari (2005) has found that a positive relationship exists between
system quality and use. Since a KMS is also a type of information
system, it is reasonable to expect that higher levels of system
quality will enable knowledge workers to accomplish their tasks
more quickly, thereby increasing users' overall satisfaction. KMSs
that are easier to use will thus involve lower thresholds of use,
resulting in increased use.
On the other hands, previous research has established that in-
formation quality is positively related to use. In the context of KM
success, knowledge quality can be substituted for information
quality, as it involves the type of content contained with the system.
Knowledge quality is de?ned as the degree to which the knowledge
contained in a KMS is useful in assisting the user to accomplish
tasks. Rai et al. (2002) and Halawi, Mccarthy, and Aronson (2007)
found that information (or knowledge) quality is signi?cantly
related to use. The relationship between knowledge quality and KM
use is thus expected to be positive, re?ecting the increased bene?ts
that are perceived to be derived from using a system that contains
high-quality knowledge. Higher-quality knowledge better ful?lls
users' information needs, thereby increasing use. In other words, if
the quality of knowledge content is high, then a knowledge worker
Table 1 (continued)
Source Key points Subject Findings
Wei, Choy, and Chew (2011) To study the implementation of KM
processes in Malaysian SMEs.
Questionnaire, 70 replies from SMEs
owners/managers, Malaysia.
Some of the highest bene?ts of KM are
related to innovation, improved
decision-making processes,
competitive advantage, ef?ciency and
product/service quality.
Liao (2011) To study the performance effects of
interaction of KM with HRM control.
Survey among managers in computer
and peripheral equipment
manufacturing industries in Taiwan.
The ?ndings show that ?rms
emphasizing personalization strategy
and HRM behavioral control have a
better performance (growth rate,
market share, pro?tability etc.).
When codi?cation strategy is used by
?rms, the combination with output
based HRM will improve their
performance.
No single HRM system is related to
?rms combining strategies.
Cap o-Vicedo, Mula,
and Cap o (2011)
To provide a social network model for
improving KM in multi-level supply
chains formed by SMEs.
Case studies among 10 construction
?rms in Spain.
The ?ndings show how establishing
these inter-organizational
relationships between construction
?rms improves con?dence,
communication and team spirit.
The result is a higher degree of
innovation, fewer losses and
improvement in ef?ciency and
production.
Durst and Edvardsson (2012) To review research on KM in SMEs to
identify gaps in the current body of
knowledge, which justify future
research directions.
Literature review of 36-refereed
empirical articles on KM.
The areas of KM are relatively well
researched topics; whereas those of
knowledge identi?cation, knowledge
storage/retention and knowledge
utilization are poorly understood in the
SME context.
Edvardsson and Durst (2013) To identify what we know about the
bene?ts of KM for SMEs.
To propose an approach comprising a
literature review in order to
understand knowledge bene?ts for
SMEs.
Literature review. Highlight the bene?ts of knowledge
management in the areas of employee
development, innovation, customer
satisfaction and organizational success.
To identify nine empirical studies
which ful?lled the selection criteria.
1. Quality Dimensions
System Quality
2. Use Dimensions
KM Use
User Satisfaction
3. Benefit Dimension
Net Benefit Knowledge Quality
Service Quality
Fig. 1. Revised KM success model.
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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is more likely to perceive that KM initiatives contribute to
enhanced job performance, hence the belief that knowledge quality
leads to a high level of KM use and user satisfaction.
There is little existing literature that examines the relationship
between service quality and use at the individual level. Many KM
projects are specially aimed at developing a knowledge-intensive
culture by encouraging behavior such as knowledge sharing
(Davenport, Thomas, &Cantrell, 2002). Gold et al. (2001) noted that
the most signi?cant hurdle to effective KM is organizational cul-
ture. This means that KM success requires complete solutions that
go beyond providing users with an IT-based KM system (Kulkarni
et al., 2006e2007). A suitable organizational climate, which man-
ifests itself in the behavior of the workers in a ?rm, must be
established in order to facilitate KM use among members. Knowl-
edge workers' behaviors that are relevant to KM activities may be
in?uenced by the environments of their ?rms, and thus the in-
?uences of setting are expected to strongly determine KM use in
KM settings, including those with regard to knowledge accumula-
tion, knowledge sharing, knowledge utilization, knowledge inter-
nalization, and knowledge creation.
Additionally, in line with the KM success model, we propose
that a combination of system quality, knowledge quality, and
service quality determines the level of KM use and overall user
satisfaction. Based on the literature review and theoretical
analysis, this paper intends to validate the following empirical
hypotheses:
H1. System quality is positively associated with KM use.
H2. Knowledge quality is positively associated with KM use.
H3. Service quality is positively associated with KM use.
H4. System quality is positively associated with user satisfaction.
H5. Knowledge quality is positively associated with user
satisfaction.
H6. Service quality is positively associated with user satisfaction.
(2) KM use and user satisfaction
KM is a social process, whereby the key point is on encour-
aging the use of knowledge within organizations (Tzortzaki &
Mihiotis, 2014). Relevant literature has found that use is one of
the most frequently assessed categories in measuring IS success
(Straub & Limayem, 1995). As Seddon (1997) has indicated, use is
a good proxy for IS success when it is not mandatory. In com-
parison with the J&O KM Success Model, which combines use and
user satisfaction, we think use is an appropriate measure of
success and a key variable in understanding KM success. There-
fore, the individual dimension of use has been emphasized in this
study to re?ect the nature, extent, and appropriateness of use in
knowledge management. In our model, KM use is applied as an
overall measure of KM-relevant activities, and is not tied to a
single system. Although research examining the relationship be-
tween use and user satisfaction is scarce, a few studies have
examined the reverse relationship, that is, the relationship be-
tween user satisfaction and use. Such as Rai et al. (2002) found
that there was strong support for the positive relationship be-
tween user satisfaction and system use. Additional research is
required to evaluate this relationship. In the KM context, Halawi
et al. (2007) identi?ed a signi?cant relationship between inten-
tion to use and user satisfaction. Chiu, Chiu, and Chang (2007)
found a signi?cant relationship between use and user satisfac-
tion in an e-learning context. In a study on medical information
systems in which use was mandatory, Iivari (2005) found that use,
measured by the amount of daily use and frequency of use, was
signi?cantly related to user satisfaction. Given this, and based on
the D&M model, we argue that a relationship between use and
user satisfaction is entirely possible in the KM context. If a user
?nds it easy to implement KM-related activities, he or she is more
likely to get the correct knowledge for a task through KM, leading
to higher user satisfaction. As such, we propose the following
hypothesis:
H7. KM use is positively associated with user satisfaction.
(3) KM use dimensions and the bene?t dimension
Nowadays, knowledge is widely recognized as the most crucial
competitive factor that can substantially support and foster an
enterprise's adaptation, survival and outstanding performance
(Bohn, 1994; Boisot, 1998; O'Dell & Grayson, 1998; Palacios and
Garrigos, 2006). Organizations have long acknowledged that KM
is an important tool for gaining competitive advantages and
improving performance (Denning, 2006; Grif?th, Malhotra, & Neal,
2003). KM is considered to facilitate the achievement of higher
performance and ef?cient responses to customers' needs and re-
quirements (Feng, Sun, & Zhang, 2010). Some observations show a
positive relationship between KM and organizational performance
(Andreeva & Kianto, 2012; Edler, 2003; Edvardsson, 2006, 2009;
Kluge, Stein, & Licht, 2001; KPMG Consulting, 2000; Lim &
Ahmed, 2000). Such as Tzortzaki and Mihiotis (2014) suggested
that by managing knowledge, organizations can ?rst and most
importantly enhance their pro?tability. Moreover, they can
improve on ef?ciency, which has a positive impact on their market
position as they operate more intelligently on the market.
Guimaraes and Igbaria (1997) reported a positive effect of system
usage and user satisfaction on the impact of end-user jobs for
client/server systems success. Igbaria and Tan (1997) found that
user satisfaction and system usage are important factors affecting
individual impact. The study of Torkzadeh and Doll (1999) indi-
cated that user satisfaction has signi?cant correlation with the four
dimensions of impact scale: task productivity, task innovation,
customer satisfaction, and management control. On the other
hands, Omerzel and Antoncic (2008) pointed out that effective KM
improves the organization's capability to survive, grow and main-
tain competitive advantage.
Different stakeholders might have different opinions regarding
what constitutes a bene?t (Seddon, Staples, Patnayakuni, &
Bowtell, 1999). Edvardsson and Durst (2013) found that SMEs can
bene?t from KM activities. Our model evaluates success as an
improvement in net bene?t, based on the use and impact of KM.
Since the focus of this study is on the measurement of KM success
from the perspective of knowledge workers, net bene?t in this
study refers to knowledge workers' perceived net bene?t evalua-
tion of relevant KM activities. Many organizations are spending a
great deal of resources in launching KMto support their knowledge
work and cultivate learning behavior within organization. If
knowledge workers perceive KM as having potential value in terms
of increasing work effectiveness, decision-making quality or
fostering creativity and innovativeness, it will reinforce the success
of a KM effort. Furthermore, in accordance with the D&M model,
this study proposes that the two dimensions of KM use and user
satisfaction both lead to greater net bene?t. Therefore, the extent to
which KM use is deemed essential for a knowledge worker's job
performance may re?ect its KM quality. If so, a knowledge worker
will participate in KM activities to enhance his or her job perfor-
mance. This suggests adding a causal path from KM use to work
performance. Based on this, we have formulated the following
hypotheses:
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
H8. KM use is positively associated with net bene?t.
H9. User satisfaction is positively associated with net bene?t.
3.3. Targets for questionnaire survey
A survey-based approach is appropriate for this investigation
because our goal is to test the theoretical model, which is devel-
oped based on insights in earlier software development research
(Pinsonneault & Kraemer, 1993). Furthermore, knowledge now
plays and will continue an important role in the future in deter-
mining a ?rm's capability to innovate and hence, its long-run
effectiveness and survival. A growing percentage of the total
workforce is composed of knowledge workers. Thus, this study
performed an in-depth analysis of the in?uence of KM imple-
mentation on the task performance of knowledge workers for
various Taiwanese industries. A ?eld where research on KM is still
fragmented and quite limited, we focus our study in the context of
SMEs using convenience sampling technique.
For this study, we aimed to collect data from 323 managers and
practitioners working in production, marketing, sales, ?nance and
administration departments, employed in 21 SMEs. Respondents
are from various industry sectors but are categorized under three
main areas including high-tech, manufacturing, and knowledge
services industry such as software development, innovation, and
cultural. And they already used various forms of KMSs, and who
had implemented KM-relevant activities. The survey was con-
ducted from July 2010 to October 2010. Of the 323 participants
solicited, 46 respondents in the sample did not participate in or
complete the study, yielding a response rate of 85.8%.
3.4. Measures of KM/KMS success model
(1) Measures of three quality dimensions
All the constructs and measures in this study were based on
existing instruments and KM/KMS literature. The items in the
questionnaire that was employed were measured using a seven-
point Likert scale, ranging from (1) strongly disagree to (7)
strongly agree.
3.4.1. System quality
System quality is de?ned by how well a KMS performs the
functions of knowledge creation, storage/retrieval, transfer, and
application. System quality includes sub-dimensions such as ease
of search, ease of navigation, response speed, and ease of
communication with other users (Wixom & Todd, 2005). It repre-
sents the quality of the information system processing involved,
which includes software and data components, and is a measure of
the extent to which the system is technically sound. In the J&O
model, system quality jointly covers the aspects of a KMS that are
found to be most critical, based on empirical observation, in un-
derstanding what system quality is in KM settings. The dimensions
of system quality indicate the capability of an organization to
develop, operate, and maintain a KMS. This construct captures
ideas about the networks, databases, and other hardware involved
in a KMS, as well as the experience and expertise behind the KMS
initiative and the usage competence of typical KMS users.
3.4.2. Knowledge quality
Knowledge quality refers to the quality of the outputs that a
KMS produces, whether in the form of reports or online screens.
Knowledge quality typically includes sub-dimensions such as
knowledge accuracy, completeness, timeliness, and relevance
(Wixom & Todd, 2005). It is determined by whether the right
knowledge, with suf?cient contextual information, is captured and
made available to the right users at the right time. In the J&O
model, knowledge quality involves richness and linkages. Richness
means that a suf?cient amount of knowledge is available to make
the knowledge useful. Linkages are the knowledge and topic maps
or listings of expertise available to an organization.
3.4.3. Service quality
According to the J&O model, service quality involves those as-
pects of a KMS that ensure it adequately supports users in using the
KMS effectively. This dimension comprises two sub-dimensions.
The ?rst sub-dimension, encourage, has to do with the allocation
of adequate resources, encouragement and direction, and control.
The second sub-dimension, resource service quality, involves sup-
port from the organization, with regard to how to use the KMS in
general, howto capture knowledge as part of work, and how to use
the KMS in business processes.
(2) Measures of two use dimensions
3.4.4. KM use
To measure KM use, we have applied the knowledge circulation
process of Lee, Lee, and Kang (2005), including knowledge accu-
mulation, knowledge sharing, knowledge utilization, knowledge
internalization, and knowledge creation, in order to understand the
conditions of use for knowledge management. The dimension of
use refers to the degree to which a knowledge worker believes he
or she has incorporated procedures for the capture and use of
knowledge of various types of decision-making activities and in the
utilization of the outputs of the system.
3.4.5. User satisfaction
The user satisfaction dimension is a construct that measures
users' satisfaction with KM. User satisfaction is based on subjective
evaluations of various outcomes of the knowledge management
systems existing within an organization.
(3) Measures of the bene?t dimension
3.4.6. Net bene?t
The bene?t dimension involves the overall bene?ts of KM,
which means that KM success is essentially de?ned as improved
performance. An individual's use of KM in?uences his or her per-
formance in the workplace. The bene?t dimension construct
combines impacts on both user change and performance, and
recognizes that the use of KM may increase the effectiveness of
knowledge workers. KM gives users a better understanding of
decision-making contexts, improves their decision-making, alters
their activities, and changes their perceptions of importance.
4. Data analysis and results
4.1. Data collection
The questionnaire was distributed and collected from the
practitioners/managers of businesses that are categorized as SMEs
based in Taiwan to test our research model. Table 2 lists the de-
mographic information collected from respondents with regard to
gender, age, educational level, work position, industry type, and
total number of employees at their companies.
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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4.2. Data analysis
In the study, we adopt partial least square (PLS) method to
analyze the data. PLS is a structural equation modeling technique
which uses a component-based approach to evaluate the rela-
tionship within, and variance explained by a structural equation
model. The PLS technique is increasingly being used in IS research
because it requires minimal sample size and places negligible de-
mands on residual distributions (Chin, 1998). Benaroch,
Lichtenstein, and Robinson (2006) pointed out that PLS has the
ability to handle relatively small sample sizes, making it appro-
priate for our data set. Besides, it is suitable for our study because it
can give more accurate estimates of mediating effect by accounting
for the measurement error that attenuates the estimated relation-
ships and improves the validation of theories (Henseler & Fassott,
2010). Also, PLS works better when the objective is ‘prediction’,
the model is relatively complex, and the phenomenon under study
is new or changing (Chin & Newsted, 1999). Overall, it ensures
robust solutions in estimating complex relationships among vari-
ables (Chin, 2010).
In accordance with Anderson and Gerbing (1988), the data
analysis process of SEM was divided into two steps: (1) measure-
ment model analysis, which involved following the initial analysis
with a con?rmatory factor analysis (CFA) to measure the reliability
and validity of the latent variables, and (2) structural model anal-
ysis, in which hypotheses were tested by examining path co-
ef?cients and their signi?cance.
(1) Measurement model analysis
We pre-tested our survey questionnaires by asking pro-
fessionals handling management information systems to assess
their logical consistency, ease of understanding, sequencing of
items, contextual relevance, and suggestions on item contents and
instrument structure. Our study was found to have both face and
content validity.
According to Anderson and Gerbing (1988), the measurement
model provides a con?rmatory assessment of reliability, conver-
gent validity, and discriminant validity. Cronbach's alpha, individ-
ual item reliability, and composite reliability (CR) tests were
performed in order to verify reliability. First, each construct in this
study was measured in terms of each factor, according to Cron-
bach's alpha values. As shown in Table 3, all the Cronbach's alpha
values range from 0.91 (for the US) to 0.95 (for SQ, KQ, KU). Ac-
cording to Nunnally (1978), the lowest limit for Cronbach's alpha
values should be 0.7. The Cronbach's alpha value of each construct
was above 0.7, which indicated high internal consistency. In addi-
tion, individual item reliability was assessed by examining the
factor loadings of the measures with their respective constructs.
The reliabilities of individual items are considered adequate when
loadings exceed 0.5 (Rivard & Huff, 1988). The results of our factor
loading analysis showed that four items, KU 1, 2, 20, and 21, had
values of less than 0.5 and thus these items were eliminated from
further analysis. Finally, we assessed reliability by examining
composite reliability (CR), and found that our CR was over 0.7,
indicating that the scales involved were of satisfactory reliability
(Chin, 1998; Fornell & Larcker, 1981). We thus found that the reli-
ability of our scales was acceptable.
Convergent validity is the degree to which multiple attempts to
measure the same concept are in agreement. In this study, we
assessed convergent validity by examining average variance
extracted (AVE). As seen in Table 3, the AVE for all constructs was
above 0.5, which indicates that the scales had good convergent
validity (Fornell & Larcker, 1981).
To assess discriminant validity, we evaluated the measures
when the square root of each factor's AVE was larger than its cor-
relation with other factors (Chin, 1998). Table 4 provides the results
of the analysis and the discriminate validity assessed by using the
correlation of latent variables, wherein the square roots of the
average variances were calculated for each of the constructs along
the diagonal. We found that all square roots of AVE were larger than
their corresponding coef?cients of correlation with other factors.
Overall, our analyses demonstrated that the study scales possessed
convergent and discriminant validity.
(2) Structural model analysis
In this study, a PLS structural model analysis using Smart PLS
was conducted for each hypothesis path coef?cient and the per-
centage of the variance explained (R
2
) values. Path coef?cients
represent the strength of the relationships between dependent and
independent variables. R
2
was used as an indicator of the overall
predictive strength of the model. The greater an R
2
value, the better
a model's predictive quality (Fornell & Bookstein, 1982; Wixom &
Watson, 2001). The results for H1 through H9 were determined
by using PLS, as presented in Fig. 2. As can be seen, the positive
correlations between the constructs suggest that there were
grounds for expecting their signi?cant effects on each other. First,
we tested the relationship between quality construct and KU. H1
tested the relationship between SQ and KU. A strong positive
relationship was observed (b ¼ 0.31, p < .001). H3 tested the rela-
tionship between KQ and KU, and a positive and signi?cant rela-
tionship was found (b ¼ 0.17, p < .05). SEQ was found to have a
positive effect on KU, thereby supporting H5 (b ¼ 0.18, p < .05).
In order to illustrate the in?uences of quality on user satisfac-
tion, we examined the relationship between SQ, KQ, SEQ, and US.
H2 showed a positive relationship between SQ and US (b ¼ 0.01,
p < .05). With regard to H4, the link between KQ and US was found
to be signi?cant (b ¼ 0.37, p < .001). For H6, the relationship of SEQ
and US were tested, and strong positive relationships were
observed (b ¼ 0.41, p < .001). The results are expressed in the same
Table 2
Demographic characteristics of the sample.
Variables Categories Frequency Percent
Gender Male 137 49.5%
Female 140 50.5%
Age 50 20 7.2%
Education High school 71 25.6%
Junior college 69 24.9%
University 108 39.0%
Master 25 9.0%
Ph. D. 4 1.5%
Positions Management staff 96 34.7%
Non-management staff 181 65.3%
Industry types Traditional manufacturing 116 41.9%
Service 34 12.3%
Cultural and educational industry 34 12.3%
Transport industry 5 1.8%
Wholesale and retail 3 1.1%
IT services/software industry 23 8.3%
Finance and insurance 32 11.6%
Communications electronics 4 1.4%
Hospital 6 2.2%
Tourism and catering industry 5 1.8%
Nonpro?t groups 15 5.4%
# of Employee 1e50 98 35.4%
51e100 16 5.8%
101e500 86 31.0%
501e1000 43 15.5%
>1000 34 12.3%
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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Table 3
Summary of reliability and validity for measures.
Construct Measure item Factor loading AVE CR Cronbach's a
System
quality (SQ)
sq1. Your KS allows you to do both information and people searches. 0.71 0.67 0.96 0.95
sq2. Whenever you search the KS knowledge base and/or yellow pages, the retrieved knowledge is
always what you need.
0.81
sq3. Whenever you search the KS knowledge base and/or yellow pages, the returned linkage always
directs you to the right person.
0.84
sq4. Whenever you search the KS knowledge base and/or yellow pages, the retrieved results
normally display quickly.
0.87
sq5. Your KS search function is easy to use. 0.80
sq6. Your KS is not subject to frequent problems and crashes. 0.77
sq7. Your KS allows you to ?nd most of the organizational information/knowledge online. 0.79
sq8. Whenever you search the KS, you don't need to try different ways to locate the needed
information.
0.85
sq9. Whenever you search the KS, you don't need to try different ways to locate the right person. 0.86
sq10. Whenever you search the KS, you don't need to access more than one system to locate the
needed information.
0.86
sq11. Whenever you search the KS, you don't need to access more than one systemto locate the right
person.
0.83
Knowledge
quality (KQ)
kq1. Your KS provides information/knowledge that is exactly what you need. 0.78 0.62 0.96 0.95
kq2. Your KS provides information/knowledge that uses recognized vocabulary rather than highly
specialized terminology.
0.80
kq3. Your KS provides information/knowledge that is adequate for you to complete tasks. 0.83
kq4. Your KS provides contextual information/knowledge so that you can truly understand what is
being accessed.
0.81
kq5. Your KS provides contextual information/knowledge so that you can easily apply it to your
work
0.80
kq6. Your KS provides up-to-date information/knowledge. 0.80
kq7. The knowledge portal of your KS links you to a complete collection of documents and data. 0.80
kq8. The yellow pages of your KS guides you to connect to the people with the know-how for which
you are seeking.
0.85
kq9. Your organization keeps updating its knowledge portal so that you have access to current
documents and data.
0.80
kq10. Your organization keeps updating its yellow pages so that you can locate newly hired or
acquired expertise without a problem.
0.79
kq11. The knowledge management system enables me to control the settings of knowledge
documents.
0.77
kq12. The knowledge management system enables me to control the presentation of knowledge
documents.
0.79
kq13. The knowledge management system enables me to de?ne my favorite knowledge. 0.70
kq14. The knowledge management system can record my retrieval and reading history. 0.68
Service
quality (SEQ)
seq1. Whenever you have dif?culties with your KS, there is a speci?c person (or group) exist to help
you.
0.79
seq2. You have suf?cient time to engage in dialogue online with your coworkers about important
problems and solutions.
0.85 0.71 0.94 0.92
seq3. You are encouraged to engage in online exploration and experimentation by your peers. 0.89
seq4. You are encouraged to engage in online exploration and experimentation by your supervisor 0.85
seq5. Your organization actively endorses knowledge sharing. 0.84
seq6. Your organization encourages online discussion of new ideas and working methods. 0.83
KM use (KU) ku3. I fully understand the core knowledge necessary for my tasks. 0.67 0.51 0.95 0.95
ku4. We refer to corporate database before processing tasks. 0.72
ku5. We extensively search through customer and task-related databases to obtain knowledge
necessary for the tasks.
0.68
ku6. We try to store expertise on new tasks design and development. 0.71
ku7. We try to store legal guidelines and policies related to tasks. 0.76
ku8. We are able to systematically administer knowledge necessary for the tasks and store it for
further usage.
0.70
ku9. We document such knowledge needed for the tasks. 0.74
ku10. We summarize education results and store them. 0.75
ku11. We share information and knowledge necessary for the tasks. 0.75
ku12. We improve task ef?ciency by sharing information and knowledge. 0.77
ku13. We promote sharing of information and knowledge with other teams. 0.77
ku14. We developed information systems, like intranet and electronic bulletin boards, to share
information and knowledge.
0.70
ku15. EDI is extensively used to facilitate processing tasks. 0.66
ka16. Work ?ow diagrams are required and used in performing tasks. 0.72
ku17. There exists a culture encouraging knowledge sharing. 0.73
ka18. There exist incentive and bene?t policies for new idea suggestions in utilizing existing
knowledge.
0.68
ku19. There are research and educational programs. 0.70
ku22. I can use the Internet to obtain knowledge for the tasks. 0.64
ku23. I can refer to best practices and apply them to my tasks. 0.68
User
satisfaction (US)
us1. As a whole, I am satis?ed with the knowledge management system. 0.96 0.91 0.95 0.91
us2. As a whole, the knowledge management system is successful. 0.95
(continued on next page)
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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manner as explained by Wang et al. (2007) and Wixom and Todd
(2005).
A strong positive relationship was also found between KUand US,
thereby supporting H7 (b ¼ 0.08, p < .05). Approximately 36% of the
variance inKUandmore than61%of the variance inUSwas explained
by SQ, KQ, and SEQ. These results were entirely consistent with those
of previous studies (DeLone&McLean, 2003; Doll &Torkzadeh, 1988;
Kulkarni et al., 2006e2007; Rai et al., 2002; Seddon, 1997; Wu &
Wang, 2006). Finally, in determining the bene?t dimension, we
noted that IB was affected by KU(b ¼0.57, p < .001), as well as by US
(b ¼ 0.23, p < .001), which supported H8 and H9. In addition, 51% of
the variance in IB was explained by KU and US.
As can be seen from Table 5, overall, our hypothesized research
model was supported. The total effects (considering both direct and
indirect effects) on net bene?t are 0.18 for system quality, 0.19 for
knowledge quality, 0.20 for service quality, 0.59 for KM use, and
0.23 for user satisfaction. Especially, KM use was found to have the
strongest direct and total effect on net bene?t, indicating the
importance of the use of KM in increasing net bene?t. This means
that the implement of KM activities that are more relevant by
knowledge workers enables them to obtain greater bene?ts. In
addition, Table 5 shows that the total effect of systemquality on KM
use and of service quality on user satisfaction and net bene?t, are
greater than others. Furthermore, based on Fig. 2, a total of 51%
variance of net bene?t is explained by system quality, knowledge
quality, service quality, KM use, and user satisfaction together; 61%
of the variance of user satisfaction is explained by system quality,
knowledge quality, service quality, and KM use; 36% of the variance
of KM use is explained by system quality, knowledge quality, and
service quality.
5. Conclusion
5.1. Discussion
KM has received particular attention over the past two decades,
as it offers a means for organizations to gain competitive
Table 4
Correlations among study variables.
Construct KU NB KQ SEQ US SQ
1 KM use (KU) 0.71
2 Net bene?t (NB) 0.68 0.83
3 Knowledge quality (KQ) 0.53 0.55 0.79
4 Service quality (SEQ) 0.52 0.46 0.73 0.84
5 User satisfaction (US) 0.50 0.51 0.72 0.73 0.95
6 System quality (SQ) 0.56 0.54 0.74 0.70 0.61 0.82
Note. Square root of AVE is on the diagonal.
Fig. 2. Hypothesis testing results of PLS analysis.
Table 5
The direct, indirect, and total effect of dominants on net bene?t.
Direct effect Indirect effect Total effect
KU US NB KU US NB KU US NB
SQ 0.31 0.01 0.02 0.18 0.31 0.03 0.18
KQ 0.17 0.37 0.01 0.19 0.17 0.38 0.19
SEQ 0.18 0.41 0.01 0.20 0.18 0.41 0.20
KU 0.08 0.57 0.02 0.08 0.59
US 0.23 0.23
Table 3 (continued)
Construct Measure item Factor loading AVE CR Cronbach's a
Net bene?t (NB) nb1. Your KMS helps you to detect work-related problems. 0.82 0.69 0.95 0.94
nb2. Your KMS enlightens you to new ways of thinking. 0.84
nb3. Your KMS changes the way you do things in a way bene?cial to the organization's overall
interest.
0.87
nb4. Your KMS improves the decisions you make. 0.84
nb5. Your KMS helps you to make fewer mistakes. 0.82
nb6. Your KMS allows better experience transfer and knowledge reuse. 0.85
nb7. Your KMS reduces duplicate work. 0.76
nb8. Your KMS allows you faster cycle time to problem resolution. 0.85
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
advantages. It is true that in many SMEs there is an absence of
systematic KM, this does not imply that KM is less important than
for large companies; indeed, it can be argued that this is a
distinctive factor for SMEs' survival (Durst & Edvardsson, 2012). An
important implication is that managers should create an environ-
ment to support KMrelevant activities. Prior research has indicated
that the measurement of KM effectiveness or success is crucial to
understanding how KM should be built and implemented. This
study presents and validates a model of KM success from a
knowledge-based perspective, based on the J&OKMsuccess model,
which captures the multidimensional and interdependent nature
of KM success. Through theoretical discussions and literature re-
view, a questionnaire survey and statistical analysis on SMEs in
Taiwan, the results indicate that a correlation exists between KM
quality, KM use and KM successful implementation. Overall, the
results of empirical investigation are positive and supportive were
consistent with most prior IS research. That is, the better the KM
qualities of system, knowledge, and services, the more KM use and
user satisfaction will be, which can lead to better net bene?t. The
results were consistent with previous IS success model research
(DeLone & McLean, 2004; Molla & Licker, 2001; Rai et al., 2002;
Seddon, 1997; Seddon & Kiew, 1996).
Among them, systemquality is the most signi?cant determinant
of KM use than others. As suggested by Markus (2001), knowledge
use may depend on how remote and dissimilar knowledge users
are from knowledge generators. Users from different functional
areas or with differences in terms of breadth and depth of knowl-
edge may face dif?culty in de?ning search terms (when using a
KMS). Users who do not know the right jargon, terminology,
questions to ask, or symptoms to report will drown in unnecessary
or unhelpful knowledge. It is therefore important to develop and
provide users a system with a feature-rich interface that will
retrieve and present different types of knowledge in an ef?cient
manner. Alternatively, the system may put them in touch with
experts who can provide the needed knowledge and help them
interpret and apply the available knowledge (Kulkarni et al.,
2006e2007). In addition, compared with system quality and
knowledge quality, service quality has a greater in?uence on user
satisfaction. Users may start to consider KM to be a part of their
working life. Thus, system operation is no longer an important
issue. Its effect may be important during the initial implementation
but subsides over time.
With regard to the implementation of KM, measuring multiple
KM success variables continues to be important. This model pro-
vides a rich pro?le of the dynamics surrounding quality measures,
KM use, satisfaction evaluation, and net bene?t. Our research also
con?rms that KM use, user satisfaction and net bene?t are com-
plementary yet distinct constructs, and that KM use is mediated
through user satisfaction in its in?uence on the net bene?t of KM.
To develop KM successfully, it is essential to ensure KM access at
the workplace, provide the relevant knowledge for users, and
maintain service levels, all of which are helpful in increasing KM
use and the perception of user satisfaction, which, in turn, are
helpful in increasing the net bene?t of KM.
The primary contribution of this research is in furthering our
understanding of how to assess and promote KM success in SMEs
context. It makes several contributions to this area. First, our results
are validated within a Taiwan context, whereas most previous
studies have been based on other countries' companies. We use a
comprehensive instrument to measure an individual impact
construct, considering both change variables and performance
variables, which is an approach that has been lacking in most
existing studies on KMsuccess models. Second, the study examines
the direct and indirect linkages among quality dimensions, use
dimensions, and bene?t dimensions, which, to the best of our
knowledge, has not been previously explored in the KM context.
Our results provide evidence of the direct and indirect effects of
quality on individual impact, and indicate that of all the constructs,
KM use has the greatest effect on individual impact. Third, in
contrast to previous research on KM success models, we used a
multifaceted instrument for KM use that included knowledge
accumulation, knowledge sharing, knowledge utilization, knowl-
edge internalization, and knowledge creation. Thus, our measure-
ment instrument for KM use is more comprehensive than those
used in most previous studies as it includes constructs knowledge
circulation process. We also used more comprehensive instruments
for system quality (eleven items), knowledge quality (fourteen
items), and service quality (six items) compared to previous studies
on knowledge workers.
5.2. Theoretical implications
To date, the implementation of KM in SMEs has not been sys-
tematically investigated. Existing studies have explored KM activ-
ities and strategies from large companies' perspectives and have
not considered the needs of smaller businesses. This paper is aimed
to bridge this gap. Such as Durst and Edvardsson (2012) stressed,
the adoption of KMS is generally considered to impact on ?rm
performance, but we lack empirical evidence supporting this idea
on SMEs' performance. Burgess, Sellitto, and Karanasios (2009)
pointed out that there is a need to further develop a proper un-
derstanding of KM in SMEs context as they are different from large
organizations. However, factors in?uencing the success of KM have
seldom been empirically examined in prior research and whether
traditional IS success models can be extended to investigating KM
success has not been examined thus far in Taiwanese settings. Until
now, the D&M IS Success Model is a generally accepted model for
assessing success of an IS. This research was conducted in response
to a call for studies on the continuous challenges and tests involved
in applying IS success models in different contexts. Based on the
D&M and J&O models, we proposed and validated a comprehen-
sive, multidimensional model of KM success, which considers six
success measures: system quality, knowledge quality, service
quality, KM use, user satisfaction, and net bene?t. The model pre-
sented in this paper is a viable approach to assessing KM success
and meets the spirit and intent of DeLone and McLean (1992, 2002).
5.3. Managerial implications
Intense competition, ?ckle consumers, shorter product life cy-
cles, and globalization are some of the driving forces that have led
to the increased inspection of the usage, application, and leveraging
of knowledge in organizations (Anantatmula & Kanungo, 2006).
The study concentrated on KM quality and use that practitioners
considers important when implementing KM in SMEs. The KM
success model has three basic dimensions as antecedents to KM
success: system quality, which deals with the technical infra-
structure; knowledge quality, which deals with KM strategy for
identifying critical knowledge and how that knowledge is stored;
and service quality, which deals with management support and
allocation of resources. These dimensions deal with ensuring that
the KM implementation meets the needs of the users and the or-
ganization. Furthermore, the model could provide some guidelines
at placing the CSFs into a theoretical framework that explained how
they led to KMsuccess. Probably, this is the ?rst study to present an
integrative viewpoint for implementing KM successfully in SMEs. It
is hoped that this research ?nding can serve as a reference for SMEs
in the implementation of KMand provide a great help in enhancing
management performance.
M.-H. Wang, T.-Y. Yang / Asia Paci?c Management Review xxx (2016) 1e13 11
Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
In the era of knowledge economy, organizations are increasingly
tend to management knowledge. Research in the area of Taiwanese
SMEs practices has an added signi?cance because of the increased
importance of Asia. For suitable implementing and more enjoying
the bene?ts of KM, a model for measuring success of KM is
essential. Taken together, the results of this study are important to
KM practitioners: (1) provide a basic for SMEs valuation; (2)
stimulate management to focus on what is important; (3) justify
investments in KM activities; and (4) build and implement ef?cient
KM initiatives and systems. Given the prevalence of SMEs on the
one hand and their resource limitations on the other, there is a
strong need on this topic to provide actual proof of the imple-
mentation of KM activities which would help SME owners to make
better decisions regarding resource allocation.
5.4. Limitations and directions for future research
The validity of the KMsuccess model cannot truly be established
on the basis of a single study, and so caution should be exercised in
generalizing our ?ndings. Certain limitations must be considered
while utilizing the results of this study. The most important limi-
tation in this research was sample. The sample in this study
comprised an instrument which was self-administered. Secondly,
the empirical results are derived from a sample of Taiwanese SMEs
and hence the ?ndings might be country-speci?c. Further, the data
for this study are based the respondents' perceptions, which may
vary widely across industries, ownership and function and work
experience of respondents within the SMEs. Future research can
select SMEs from other countries to examine and enlarge the
generalization of the ?ndings. On the other hand, applying the KM
success model in different stages could provide a more compre-
hensive picture that would increase our understanding of KM
development. In addition, it should also be considered that many
important exogenous variables might in?uence KM success. This
study provides a foundation for further research that could
contribute to the existing knowledge in this area. Additional
research is required to explore the applicability of the success
model in more diverse settings.
Acknowledgments
This manuscript is supported by the research fund NSC 97-2410-
H-218-005, MOST 104-2410-H-218-018 granted by the Ministry of
Science and Technology, R.O.C.
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doc_476691261.pdf
Most firms have started to realize the importance of KM in streamlining their operations and processes to
improve organizational performance. So in this paper, we try to survey and present a model for
measuring success of KM in small- and medium-sized enterprises (SMEs). This study is the first empirical
test of an adaption of the Jennex and Olfman (J&O) KM success model considered a better description of
KM success due to its strong theoretical grounding to analysis the influences of KM and inter-actions on
workers' productivity in Taiwanese SMEs settings. Structural equation modeling techniques are applied
to data collected through questionnaires from 277 knowledge workers. All the hypothesized relationships
between the variables are significantly supported by the data. The findings served as useful
reference points for researchers interested in investigating issues related to the successful implementation
of KM, and for practitioners aiming to achieve the benefits of KM in SMEs.
Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises
Mei-Hsiang Wang
*
, Tarng-Yao Yang
Department of Information Management, Southern Taiwan University of Science and Technology, Taiwan
a r t i c l e i n f o
Article history:
Received 25 April 2013
Accepted 2 December 2015
Available online xxx
Keywords:
IS success model
KM success model
Small- and medium-sized enterprises
(SMEs)
Knowledge management
a b s t r a c t
Most ?rms have started to realize the importance of KM in streamlining their operations and processes to
improve organizational performance. So in this paper, we try to survey and present a model for
measuring success of KM in small- and medium-sized enterprises (SMEs). This study is the ?rst empirical
test of an adaption of the Jennex and Olfman (J&O) KM success model considered a better description of
KM success due to its strong theoretical grounding to analysis the in?uences of KM and inter-actions on
workers' productivity in Taiwanese SMEs settings. Structural equation modeling techniques are applied
to data collected through questionnaires from 277 knowledge workers. All the hypothesized relation-
ships between the variables are signi?cantly supported by the data. The ?ndings served as useful
reference points for researchers interested in investigating issues related to the successful imple-
mentation of KM, and for practitioners aiming to achieve the bene?ts of KM in SMEs.
© 2015 College of Management, National Cheng Kung University. Production and hosting by Elsevier
Taiwan LLC. All rights reserved.
1. Introduction
In Taiwan, small- and medium-sized enterprises (SMEs) exert a
strong in?uence and constitute approximately 97.63% of all enter-
prises and make up 77.12% of the Island's overall employment. In the
face of the volatility and rate of change in business environments,
SMEs are facing the unprecedented challenges brought about by the
knowledge economy and to continue to retain ?exibility and inno-
vation is actually a vital topic. KM has become a critical component
for maintaining competitive advantages and many organizations are
exploring the ?eld of KM in order to improve and sustain their
competitiveness. Faced with competitive dilemmas may be solved
by the implementation of KM to enhance competitiveness. That is,
KM has the potential to make SMEs more competitive and innova-
tive and the ability of KMto lead to sustainable performance is even
more critical. Such as Friedman and Prusak (2008) noted that KM
can be used to improve both individual and organizational perfor-
mance, and has become a critical issue in industrial practices.
Okunoye and Karsten (2002) stated that KM has indeed become the
underlying sources for successful organizations regardless of their
size and geographical locations. KM has now become a widely
spread business discipline, it is no longer the concern of just large
organizations. As asserted by Frey (2001), although major corpora-
tions have led the way in introducing and implementing KM, it is
increasingly important for SMEs to manage their collective intellect.
Information systems success is one of the most widely used
dependent variables in information systems research. Measuring
the success of systems is critical to understand the value, effect of
management operations and investment on them (DeLone &
McLean, 2003). Therefore, since 1992, several studies have been
examined the success of different information systems and
measured it experimentally (Lee & Lee, 2009; Lin & Shao, 2000;
Muylle, Moenaert, & Despontin, 2004; Wang, Wang, & Shee,
2007). However, few studies have concentrated on measuring KM
success. As Kulkarni, Ravindran, and Freeze (2006e2007) note,
there has been a lack of adequate theoretical modeling and
empirical examination of factors leading to KM success. Markus
(2001) has also indicated that getting employees to use KMSs
effectively to improve organization performance is still a central
issue for both researchers and practitioners. In proposing a success
model of KM and empirically investigating multidimensional re-
lationships among success measures, this study is based on the
Jennex and Olfman (J&O) model (2005) and Kulkarni et al.'s
(2006e2007) KM success model. The J&O model is based on
several case studies and quantitative research studies and is
* Corresponding author.
E-mail address: [email protected] (M.-H. Wang).
Peer review under responsibility of College of Management, National Cheng
Kung University.
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Asia Paci?c Management Review xxx (2016) 1e13
Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
theoretically grounded on the DeLone and McLean IS Success
Model, which has been accepted for several years and has been
validated by several studies, applied within the KM context.
Although KM have been widely implemented in organizations,
their availability does not guarantee that employees will be willing
to spend time and effort to use them. Measuring KM success is
therefore crucial for providing a basis on which companies can
evaluate KM, stimulating management to focus on critical aspects
of the business, and justifying investments in KM activities. The
measurement of KM success is also valuable for building and
implementing ef?cient KM initiatives and systems from the per-
spectives of KM practitioners (Jennex & Olfman, 2005).
SMEs need to respond rapidly to these emerging changes to
ful?ll their customer needs more rapidly. In order to further utilize
KM for seamless business operations and decision-making, adop-
tion of KMin SMEs has become the emerging agenda in developing
business strategies. To manage knowledge resources is considered
the main objective of pursuing KM in business operations in
Taiwanese SMEs. However, most studies of KM implementation
have been heavily focused on large companies. As such, existing
research ?ndings are mainly large companies oriented, thereby
re?ecting their situations. SMEs face unique KMchallenges that are
distinct from those of their larger business. Directly applying these
results into the SMEs environment may not be suf?cient without an
understanding of their very own and speci?c conditions. Previous
studies fall short of studying and identifying the adoption of KM
from the SMEs perspective. They have not considered the differ-
ences of company size as well as the speci?c features of SMEs that
could affect KM. Even, in recent years, many researchers have been
focusing on the development of practical implementation of KM in
SMEs (Chan & Chao, 2008; Denning, 2006; Handzic, 2004; Tseng,
2007). There are issues existing where SMEs fail to realize and
recognize the potential bene?ts of KM. A better understanding of
the adoption for implementing KM in SMEs is needed in order to
ensure the success of their efforts. Such as Jennex, Smolnik, and
Croasdell (2008) noted, to assess the bene?ts of implementing
KM and its status of KM readiness within an organization's
practices is an important issue that requires further exploration. In
spite of KM importance for sustainable competitiveness, in most
SMEs there is an absence of systematic KM (Wong & Aspinwall,
2005).
In spite of the vast literature on KM, there has been little or no
empirical evidence for Taiwanese SMEs. Due to SMEs have some
unique features (limited ?nancial and human resources, ?at
structure, informal managerial styles, centralized decision-making,
focus on the day-to-day business operations) that deeply in?uence
the way they can approach KM. In the context of SMEs, a ?eld
where research on KMis still fragmented and quite limited (Durst &
Edvardsson, 2012). Hence, this research attempts to propose a
success model for KM and to empirically investigate the multi-
dimensional relationships among the success measures based on
KM success model for Taiwanese SMEs. In order to understand KM
practices in SMEs, do we need a new concept of KM and new
interpretive frameworks that are different from those normally
adopted in the case of large ?rms? We examine the following
research questions: (1) What are the in?uences of system quality,
knowledge quality, and service quality on KM use in SMEs setting?
(2) What are the individual and combined in?uences of system
quality, knowledge quality, and service quality on user satisfaction
in SMEs setting? (3) What is the effect of KM use on user satis-
faction in SMEs setting? (4) What is the individual and combined
in?uences of KM use and user satisfaction on net bene?ts in SMEs
setting? A potential contribution of this study focuses on the less
explored SMEs in Taiwan context and provides some insight for
companies that are not sure how to implement KM into their
business operations, further take the necessary action based on
these assessments.
2. Theoretical background
2.1. Knowledge management in SMEs
KMis becoming a growing concern in management research and
practice because of its role in determining ?rminnovation capability
and in enhancing working life quality of knowledge workers. KM
may be particularly relevant for SMEs. SMEs tend to be relatively
more dynamic and agile than larger organizations, and more ready
to learn. How to effectively establish and sustain good KM practices
in SMEs in order to ensure their competitiveness is important. KM
refers to managing the corporation's knowledge by means of a
systematic and organizational speci?ed process for acquiring,
organizing, sustaining, applying, sharing and renewing both tacit
and explicit knowledge by employees to enhance organization
performance and create value (Davenport & Prusak, 1998). Tiwana
(2001) claims that ‘KM can be extended to management of organi-
zational knowledge for creating business value and generating a
competitive advantage’, ‘KM enables the creation, communication,
and application of knowledge of all kinds to achieve business goals’,
‘KM is the ability to create and retain greater value from core
business competencies’. KMS supports the use of information
through knowledge acquisition, knowledge sharing and knowledge
application for improvement. This captured knowledge is then
stored in knowledge repositories to be shared between individuals
and departments. Subsequently, the knowledge is applied in busi-
ness situations, and introduces other ideas and frames of reference
to ultimately create newknowledge. As newknowledge is created, it
needs to be captured and stored, shared and applied, and the cycle
continues KM practices are applied to help the organization
strengthen its competitive advantage, and assist knowledge workers
to leverage their skills and their ability to offer business value.
Therefore, KMis the process through which an organization uses its
collective intelligence to accomplish its strategic objectives. KM
process should start by recognizing and identifying the knowledge
to be captured, shared and applied, to enable the organization and
its workforce to achieve a sustainable and competitive advantage.
In fact, KM can provide several bene?ts to SMEs, such as better
communication, improved customer service, faster response times,
enhanced innovativeness, greater ef?ciency in processes and pro-
cedures, and reduced risk of loss of critical capabilities (Edvardsson
& Durst, 2013). In this regard, Dotsika and Patrick (2013) underline
that the implementation of KM initiatives in SMEs may be even
more crucial, as knowledge can be their single key resource. For
Taiwanese SMEs, they have to rely on their own ability to improve
products and processes, providing customers with value-adding
innovations and learning capabilities. Due to resource constraints,
SMEs are particularly required to absorb knowledge from external
sources (Durst &Edvardsson, 2012). KMcan provide quick and easy
access to external sources of knowledge and newand more intense
communication channels with partner organizations. Furthermore,
it can erase traditional constraints on SMEs innovation ability,
while leveraging their ?exibility and responsiveness.
2.2. KM success models
A stream of research has been conducted to identify IS success
measures. DeLone and McLean (2003) introduced a comprehensive
taxonomy in order to organize this diverse research. The DeLone
and McLean (D&M) IS success model is based on the review and
integration of 180 research studies that used some form of system
success as a dependent variable. The model identi?es six
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
interrelated dimensions of success, each of which has its own
measures for determining impact on success and other dimensions.
The key focus of the model is the relationships, and it demonstrates
that the system and information quality aspects of a system lead to
increased system use and user satisfaction. Information quality is
based on the use of accurate data, whereas system quality is based
on the technical infrastructure and interface involved. User satis-
faction tends to increase use, whereas use tends to lead to some
level of user satisfaction, making these dimensions dif?cult to
separate. System use leads to system success. DeLone and McLean
(2003) subsequently revisited the D&M IS success model by
incorporating subsequent IS success research and addressing crit-
icisms of the original model. One hundred and forty-four articles
from refereed journals and ?fteen papers from the International
Conference on Information Systems (ICIS), citing the D&M IS suc-
cess model, were reviewed, with fourteen of these articles report-
ing on studies that empirically investigated the model. The result of
this revision was the modi?ed D&M IS success model. Major
changes include the addition of a service quality dimension, to
address services provided by the IS groups, the modi?cation of the
use dimension into a use/intent to use dimension, and the combi-
nation of the individual and organizational impact dimensions into
an overall net bene?ts dimension. The modi?cation of the use
variable to include intent to use is important for this paper.
Jennex and Olfman (2003) devised a KMS success model that is
based on the D&MIS success model. KMSs involve IT-based systems
that have been developed to support and enhance the processes of
knowledge creation, storage/retrieval, transfer, and application.
KMS success can be de?ned as making KMS components more
effective by improving their search speed and accuracy, among
other qualities. KMSs that enhance search and retrieval functions
enhance decision-making effectiveness by improving the ability of
the decision maker to ?nd and retrieve appropriate knowledge in a
more timely manner. In other words, enhancing KMS effectiveness
makes KMSs more successful, in addition to being a re?ection of
KMsuccess. This implies that by increasing KMS effectiveness, KMS
success and decision-making capability are enhanced, thereby
positively in?uencing organizations. KM success is crucial for un-
derstanding how initiatives and systems should be designed and
implemented. Previous literature offers a number of perspectives
on KM success. The J&O (2006) KM success model, based on the
D&M (2003) IS success model, combines KM and KMS success.
Therefore, in this study, we consider KM and KMS success to be
interchangeable and use the termKM to refer to both KM and KMS,
and the term success to refer to both success and effectiveness.
KM is complex and multi-faceted concepts. As suggested by
Kulkarni et al. (2006e2007), a KMsuccess model needs to cover the
effects of all the different types of KM activities that may be
involved. A more comprehensive view of KM must include the
speci?c processes required to acquire, store, retrieve, and apply
knowledge (Gold, Malhotra, & Segars, 2001). As such, KM success
can be de?ned as capturing the right knowledge, getting the right
knowledge to the right user, and using this knowledge to improve
individual performance. Considering the many view of KM, in this
paper the de?nition of “KM success” means that the organization's
employees manage and use the knowledge lead to the organiza-
tion's bene?ts (such as better decision-making, faster response
time to key issues, increasing productivity and job effectiveness,
sharing best practice etc.) or KM could provide the appropriate
knowledge to those that need it when it is needed.
2.3. Related work
Although KM is becoming a growing concern in management
research and practice, we lack an understanding of how ?rms
create knowledge and how this is translated into competitive ad-
vantages or enhanced customer relations (Edvardsson &Oskarsson,
2011). Numerous works on KM are reported in literature. The main
focus of KM research to date has been on processes and structures
within large organizations in order to improve their performance
and competitive standing. In this section, we discuss the related
work in KM. Organizations have long acknowledged that KM is an
important mechanism for gaining competitive advantages and
improving performance. KM issues attracted signi?cant number of
research to examine whether KM really works in the organizations
and the success factors. Most of the literature on KM and its
application has, until recently, been centered on large organiza-
tions. Pertinent issues in small businesses have to a large extent
been neglected. However, small businesses do not necessarily share
the same characteristics and ideals as large ones. There are certain
unique features of SMEs that need to be understood before KM is
implemented in their environment. To date KM in SMEs have been
discussed in many empirical studies, but KM is rarely studied in
Taiwanese SMEs setting. KM, especially in SMEs setting, has not yet
been fully explored. Table 1 shows the brief looks at the current
body of studies related to KM in SMEs setting. More papers were
published after 2011, indicating a growing interest in the subject.
However, the small number of papers clearly indicates serious lack
of knowledge in this ?eld of study.
To sum up, most of the literature on KM and its application has,
until recently, been centered on large organizations. The literature
that examines KMin the context of SMEs is still scarce and provides
fragmented insights (Durst & Edvardsson, 2012; Dwivedi,
Venkitachalam, Sharif, Al-Karaghouli, & Weerakkody, 2011;
Ribi ere & Christian, 2013). Relevant issues in SMEs have to a large
extent been neglected. Such as Durst and Edvardsson (2012)
stressed that the body of research about KM in SMEs is rather
limited compared to the large number of studies concerning
big companies. In light of this, the paper aims to redress some of
this imbalance in the literature by putting KM into the context of
SMEs.
3. Research methodology
3.1. Research model
In order to present a model for measuring KM success, a
comprehensive model is presented for measuring the success of
KM. Because KM systems are kinds of information systems and
workers use themfor working. But the revised D&Mmodel, in spite
of all its strengths, still has defects. In this paper, we formulate our
theoretical framework basing on J&O (2005) conceptualization of
KMsuccess model, adapted to socio-technical view. Fromthe socio-
technical perspective, measures of success should combine both
technological and human elements (Garrity & Sanders, 1998; Skok
& Kalmanovitch, 2005). Within the KM context, J&O (2005) KM
success model is a multi-dimensional model, whose interrelated
dimensions are based on the work of DeLone and McLean (2003).
The revised KMsuccess model is shown in Fig. 1. According to Fig. 1,
all components of the widely used model D&M, are included in this
conceptual model to measure the success of KM. Also, a new rela-
tionship between intention to use and system use components is
added to the previous D&M model.
3.2. Research hypotheses
Initially, KMSs are implemented and subsequently, various de-
grees of system, information, and service quality are examined.
Knowledge workers experience these quality dimensions by using
KMSs in making decisions and conducting work. The three quality
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
Table 1
Recent studies on KM in SMEs context.
Source Key points Subject Findings
Wong and Aspinwall (2004) To Look at their characteristics, their
advantages and disadvantages, their
strengths and weaknesses, and their
key problems and issues, all associated
with KM.
To redress some of this imbalance in
the literature by putting KM into the
context of small businesses.
Recognition of all these elements is
crucial in order to provide a well-
suited KM approach for small
businesses.
Wong and Aspinwall (2005) The perceptions of companies and a
group of academics, consultants and
practitioners.
To investigate the CSFs for adopting
KM in SMEs.
UK SMEs. A total of 11 factors, comprising 66
elements were considered in the
survey instrument.
Saloj€ arvi, Furu, and Sveiby (2005) Examining the relationship between
sustainable sales growth and
knowledge management activities.
108 Finnish and thematic interviews
with 10 companies.
Higher levels of KM-Maturity were
found to correlate positively with long-
term sustainable growth.
Gray (2006) To explore SME capacity to absorb and
manage knowledge as a prior
condition to the successful adoption of
innovations and entrepreneurial
growth.
1500 SME owners across regular
quarterly SERTeam surveys and from
other large scale studies.
There were signi?cant age, educational
and size effects that in?uence SME
acquisition and assimilation of
knowledge.
Edvardsson (2006) To expand our knowledge on KM in
SME (focusing on Icelandic SMEs).
Questionnaire sent to the Chief
Executive of Icelandic SMEs.
Icelandic ?rms rely on an unsystematic
manner of sharing and utilizing
knowledge, few have a KM strategy
and they mainly use unsophisticated
ICT technologies.
Those who had KM reported many
bene?ts, such as improved decision
making, better customer handling,
improved staff retention and increased
competitive advantage.
Valkokari and Helander (2007) Integrating business network and KM
to bridge the KMand strategic business
network.
Literature review and analysis. Provides a typology of the strategic
SME network types and their key KM
challenges based on a synthesis of
existing literature.
SME network typology presented can
be used by managers of SMEs in
evaluating their current KM practices
level.
Supyuenyong, Islam,
and Kulkarni (2009)
To understand how the special
characteristics of SMEs in?uence their
KM processes.
KMprocess fromcapture of knowledge
to its eventual reuse.
Four SMEs in Thailand. Ownership and management structure
as well as culture and behavior
characteristics of SMEs seem to have a
more positive effect than other SMEs
characteristics on KM processes.
Edvardsson (2009) To examine whether the popularity of
KMin SMEs in Iceland has decreased or
declined since 2004.
Questionnaire sent to the Chief
Executive of Icelandic SMEs (2007),
repetition of a previous survey (2004).
KM is not losing ground among SMEs
in Iceland in 2004e2007.
Many more ?rms have no KM strategy
than in 2004.
Those who had KM reported many
bene?ts, such as improved decision
making, better customer handling,
improved staff retention and increased
competitive advantage.
Migdadi (2009) To develop a conceptual research
model which comprises both CSFs and
outcomes.
Empirically assesses the relationships
between CSFs and performance
outcomes in SMEs.
25 SMEs in Saudi Arabia. Study underlined the positive
relationship between CSFS and KM
outcomes (i.e., systematic knowledge
activities, employee development,
customer satisfaction, good external
relationships and organizational
success).
Steenkamp and Kashyap (2010) To provide empirical evidence of SME
managers' perceptions about the
importance and contribution of
intangible assets to their business.
Postal questionnaire sent to New
Zealand SMEs.
Findings indicated that intangibles are
important and are perceived as value
drivers of business success.
Customer satisfaction was ranked as
the most important, followed by
customer loyalty, corporate reputation,
and product reputation.
Lee and Lan (2011) To examine the infrastructure and
process capabilities of Taiwanese SMEs
To conducts a comparative analysis of
KM in SMEs in Hong Kong.
SMEs in Taiwan in six sections. A successful KM implementation
depends on a harmonious
amalgamation of infrastructure and
process capabilities, including
technology, culture and organizational
structure.
Soon and Zainol (2011) To examine the importance of the
knowledge creation process, by
looking at knowledge management
enablers such as learning and T-shaped
skills.
Questionnaire, 110 replies, Malaysia. Learning and T-shaped skills are
positively related to the knowledge
creation process, enhancing
organizational creativity and
performance.
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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dimensions and the use of KMSs in?uence the individual value of
using KMSs. The hypothesized relationships among the three
quality dimensions, two use dimensions, and single bene?t
dimension is based on the theoretical and empirical work reported
on by DeLone and McLean (2003), Jennex and Olfman (2005), and
Kulkarni et al. (2006e2007). Based on the literature review and
theoretical analysis, the hypothesized relationships can be
described as follows:
(1) Quality dimensions and use dimensions
There is quite strong support in the literature both theoretical
and empirical; the results indicated that system quality and infor-
mation quality positively affected both system use and user satis-
faction (DeLone & McLean, 1992, 2003; Rai, Lang, & Welker, 2002;
Seddon &Kiew, 1996). KMsystemquality in our model is a measure
of how well the KM systems support and enhance KM-related ac-
tivities. In contrast to some prior studies that have operationalized
IS quality by a simpli?ed measure called ease of use, our measure of
KM system quality captures multiple dimensions of the quality of a
KMsystem. Knowledge workers may ?nd value in using knowledge
if the system quality is adequate and the KM system reduces the
extra effort required to ?nd or contribute, hence the belief that
system quality leads to a high level of KM use and user satisfaction.
Systems characterized by their ease of use are those that are clear
and understandable, and which require little mental effort to use.
Higher system quality has been found to be a signi?cant determi-
nant of user satisfaction in IS literature (Wixom & Todd, 2005).
Iivari (2005) has found that a positive relationship exists between
system quality and use. Since a KMS is also a type of information
system, it is reasonable to expect that higher levels of system
quality will enable knowledge workers to accomplish their tasks
more quickly, thereby increasing users' overall satisfaction. KMSs
that are easier to use will thus involve lower thresholds of use,
resulting in increased use.
On the other hands, previous research has established that in-
formation quality is positively related to use. In the context of KM
success, knowledge quality can be substituted for information
quality, as it involves the type of content contained with the system.
Knowledge quality is de?ned as the degree to which the knowledge
contained in a KMS is useful in assisting the user to accomplish
tasks. Rai et al. (2002) and Halawi, Mccarthy, and Aronson (2007)
found that information (or knowledge) quality is signi?cantly
related to use. The relationship between knowledge quality and KM
use is thus expected to be positive, re?ecting the increased bene?ts
that are perceived to be derived from using a system that contains
high-quality knowledge. Higher-quality knowledge better ful?lls
users' information needs, thereby increasing use. In other words, if
the quality of knowledge content is high, then a knowledge worker
Table 1 (continued)
Source Key points Subject Findings
Wei, Choy, and Chew (2011) To study the implementation of KM
processes in Malaysian SMEs.
Questionnaire, 70 replies from SMEs
owners/managers, Malaysia.
Some of the highest bene?ts of KM are
related to innovation, improved
decision-making processes,
competitive advantage, ef?ciency and
product/service quality.
Liao (2011) To study the performance effects of
interaction of KM with HRM control.
Survey among managers in computer
and peripheral equipment
manufacturing industries in Taiwan.
The ?ndings show that ?rms
emphasizing personalization strategy
and HRM behavioral control have a
better performance (growth rate,
market share, pro?tability etc.).
When codi?cation strategy is used by
?rms, the combination with output
based HRM will improve their
performance.
No single HRM system is related to
?rms combining strategies.
Cap o-Vicedo, Mula,
and Cap o (2011)
To provide a social network model for
improving KM in multi-level supply
chains formed by SMEs.
Case studies among 10 construction
?rms in Spain.
The ?ndings show how establishing
these inter-organizational
relationships between construction
?rms improves con?dence,
communication and team spirit.
The result is a higher degree of
innovation, fewer losses and
improvement in ef?ciency and
production.
Durst and Edvardsson (2012) To review research on KM in SMEs to
identify gaps in the current body of
knowledge, which justify future
research directions.
Literature review of 36-refereed
empirical articles on KM.
The areas of KM are relatively well
researched topics; whereas those of
knowledge identi?cation, knowledge
storage/retention and knowledge
utilization are poorly understood in the
SME context.
Edvardsson and Durst (2013) To identify what we know about the
bene?ts of KM for SMEs.
To propose an approach comprising a
literature review in order to
understand knowledge bene?ts for
SMEs.
Literature review. Highlight the bene?ts of knowledge
management in the areas of employee
development, innovation, customer
satisfaction and organizational success.
To identify nine empirical studies
which ful?lled the selection criteria.
1. Quality Dimensions
System Quality
2. Use Dimensions
KM Use
User Satisfaction
3. Benefit Dimension
Net Benefit Knowledge Quality
Service Quality
Fig. 1. Revised KM success model.
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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is more likely to perceive that KM initiatives contribute to
enhanced job performance, hence the belief that knowledge quality
leads to a high level of KM use and user satisfaction.
There is little existing literature that examines the relationship
between service quality and use at the individual level. Many KM
projects are specially aimed at developing a knowledge-intensive
culture by encouraging behavior such as knowledge sharing
(Davenport, Thomas, &Cantrell, 2002). Gold et al. (2001) noted that
the most signi?cant hurdle to effective KM is organizational cul-
ture. This means that KM success requires complete solutions that
go beyond providing users with an IT-based KM system (Kulkarni
et al., 2006e2007). A suitable organizational climate, which man-
ifests itself in the behavior of the workers in a ?rm, must be
established in order to facilitate KM use among members. Knowl-
edge workers' behaviors that are relevant to KM activities may be
in?uenced by the environments of their ?rms, and thus the in-
?uences of setting are expected to strongly determine KM use in
KM settings, including those with regard to knowledge accumula-
tion, knowledge sharing, knowledge utilization, knowledge inter-
nalization, and knowledge creation.
Additionally, in line with the KM success model, we propose
that a combination of system quality, knowledge quality, and
service quality determines the level of KM use and overall user
satisfaction. Based on the literature review and theoretical
analysis, this paper intends to validate the following empirical
hypotheses:
H1. System quality is positively associated with KM use.
H2. Knowledge quality is positively associated with KM use.
H3. Service quality is positively associated with KM use.
H4. System quality is positively associated with user satisfaction.
H5. Knowledge quality is positively associated with user
satisfaction.
H6. Service quality is positively associated with user satisfaction.
(2) KM use and user satisfaction
KM is a social process, whereby the key point is on encour-
aging the use of knowledge within organizations (Tzortzaki &
Mihiotis, 2014). Relevant literature has found that use is one of
the most frequently assessed categories in measuring IS success
(Straub & Limayem, 1995). As Seddon (1997) has indicated, use is
a good proxy for IS success when it is not mandatory. In com-
parison with the J&O KM Success Model, which combines use and
user satisfaction, we think use is an appropriate measure of
success and a key variable in understanding KM success. There-
fore, the individual dimension of use has been emphasized in this
study to re?ect the nature, extent, and appropriateness of use in
knowledge management. In our model, KM use is applied as an
overall measure of KM-relevant activities, and is not tied to a
single system. Although research examining the relationship be-
tween use and user satisfaction is scarce, a few studies have
examined the reverse relationship, that is, the relationship be-
tween user satisfaction and use. Such as Rai et al. (2002) found
that there was strong support for the positive relationship be-
tween user satisfaction and system use. Additional research is
required to evaluate this relationship. In the KM context, Halawi
et al. (2007) identi?ed a signi?cant relationship between inten-
tion to use and user satisfaction. Chiu, Chiu, and Chang (2007)
found a signi?cant relationship between use and user satisfac-
tion in an e-learning context. In a study on medical information
systems in which use was mandatory, Iivari (2005) found that use,
measured by the amount of daily use and frequency of use, was
signi?cantly related to user satisfaction. Given this, and based on
the D&M model, we argue that a relationship between use and
user satisfaction is entirely possible in the KM context. If a user
?nds it easy to implement KM-related activities, he or she is more
likely to get the correct knowledge for a task through KM, leading
to higher user satisfaction. As such, we propose the following
hypothesis:
H7. KM use is positively associated with user satisfaction.
(3) KM use dimensions and the bene?t dimension
Nowadays, knowledge is widely recognized as the most crucial
competitive factor that can substantially support and foster an
enterprise's adaptation, survival and outstanding performance
(Bohn, 1994; Boisot, 1998; O'Dell & Grayson, 1998; Palacios and
Garrigos, 2006). Organizations have long acknowledged that KM
is an important tool for gaining competitive advantages and
improving performance (Denning, 2006; Grif?th, Malhotra, & Neal,
2003). KM is considered to facilitate the achievement of higher
performance and ef?cient responses to customers' needs and re-
quirements (Feng, Sun, & Zhang, 2010). Some observations show a
positive relationship between KM and organizational performance
(Andreeva & Kianto, 2012; Edler, 2003; Edvardsson, 2006, 2009;
Kluge, Stein, & Licht, 2001; KPMG Consulting, 2000; Lim &
Ahmed, 2000). Such as Tzortzaki and Mihiotis (2014) suggested
that by managing knowledge, organizations can ?rst and most
importantly enhance their pro?tability. Moreover, they can
improve on ef?ciency, which has a positive impact on their market
position as they operate more intelligently on the market.
Guimaraes and Igbaria (1997) reported a positive effect of system
usage and user satisfaction on the impact of end-user jobs for
client/server systems success. Igbaria and Tan (1997) found that
user satisfaction and system usage are important factors affecting
individual impact. The study of Torkzadeh and Doll (1999) indi-
cated that user satisfaction has signi?cant correlation with the four
dimensions of impact scale: task productivity, task innovation,
customer satisfaction, and management control. On the other
hands, Omerzel and Antoncic (2008) pointed out that effective KM
improves the organization's capability to survive, grow and main-
tain competitive advantage.
Different stakeholders might have different opinions regarding
what constitutes a bene?t (Seddon, Staples, Patnayakuni, &
Bowtell, 1999). Edvardsson and Durst (2013) found that SMEs can
bene?t from KM activities. Our model evaluates success as an
improvement in net bene?t, based on the use and impact of KM.
Since the focus of this study is on the measurement of KM success
from the perspective of knowledge workers, net bene?t in this
study refers to knowledge workers' perceived net bene?t evalua-
tion of relevant KM activities. Many organizations are spending a
great deal of resources in launching KMto support their knowledge
work and cultivate learning behavior within organization. If
knowledge workers perceive KM as having potential value in terms
of increasing work effectiveness, decision-making quality or
fostering creativity and innovativeness, it will reinforce the success
of a KM effort. Furthermore, in accordance with the D&M model,
this study proposes that the two dimensions of KM use and user
satisfaction both lead to greater net bene?t. Therefore, the extent to
which KM use is deemed essential for a knowledge worker's job
performance may re?ect its KM quality. If so, a knowledge worker
will participate in KM activities to enhance his or her job perfor-
mance. This suggests adding a causal path from KM use to work
performance. Based on this, we have formulated the following
hypotheses:
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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H8. KM use is positively associated with net bene?t.
H9. User satisfaction is positively associated with net bene?t.
3.3. Targets for questionnaire survey
A survey-based approach is appropriate for this investigation
because our goal is to test the theoretical model, which is devel-
oped based on insights in earlier software development research
(Pinsonneault & Kraemer, 1993). Furthermore, knowledge now
plays and will continue an important role in the future in deter-
mining a ?rm's capability to innovate and hence, its long-run
effectiveness and survival. A growing percentage of the total
workforce is composed of knowledge workers. Thus, this study
performed an in-depth analysis of the in?uence of KM imple-
mentation on the task performance of knowledge workers for
various Taiwanese industries. A ?eld where research on KM is still
fragmented and quite limited, we focus our study in the context of
SMEs using convenience sampling technique.
For this study, we aimed to collect data from 323 managers and
practitioners working in production, marketing, sales, ?nance and
administration departments, employed in 21 SMEs. Respondents
are from various industry sectors but are categorized under three
main areas including high-tech, manufacturing, and knowledge
services industry such as software development, innovation, and
cultural. And they already used various forms of KMSs, and who
had implemented KM-relevant activities. The survey was con-
ducted from July 2010 to October 2010. Of the 323 participants
solicited, 46 respondents in the sample did not participate in or
complete the study, yielding a response rate of 85.8%.
3.4. Measures of KM/KMS success model
(1) Measures of three quality dimensions
All the constructs and measures in this study were based on
existing instruments and KM/KMS literature. The items in the
questionnaire that was employed were measured using a seven-
point Likert scale, ranging from (1) strongly disagree to (7)
strongly agree.
3.4.1. System quality
System quality is de?ned by how well a KMS performs the
functions of knowledge creation, storage/retrieval, transfer, and
application. System quality includes sub-dimensions such as ease
of search, ease of navigation, response speed, and ease of
communication with other users (Wixom & Todd, 2005). It repre-
sents the quality of the information system processing involved,
which includes software and data components, and is a measure of
the extent to which the system is technically sound. In the J&O
model, system quality jointly covers the aspects of a KMS that are
found to be most critical, based on empirical observation, in un-
derstanding what system quality is in KM settings. The dimensions
of system quality indicate the capability of an organization to
develop, operate, and maintain a KMS. This construct captures
ideas about the networks, databases, and other hardware involved
in a KMS, as well as the experience and expertise behind the KMS
initiative and the usage competence of typical KMS users.
3.4.2. Knowledge quality
Knowledge quality refers to the quality of the outputs that a
KMS produces, whether in the form of reports or online screens.
Knowledge quality typically includes sub-dimensions such as
knowledge accuracy, completeness, timeliness, and relevance
(Wixom & Todd, 2005). It is determined by whether the right
knowledge, with suf?cient contextual information, is captured and
made available to the right users at the right time. In the J&O
model, knowledge quality involves richness and linkages. Richness
means that a suf?cient amount of knowledge is available to make
the knowledge useful. Linkages are the knowledge and topic maps
or listings of expertise available to an organization.
3.4.3. Service quality
According to the J&O model, service quality involves those as-
pects of a KMS that ensure it adequately supports users in using the
KMS effectively. This dimension comprises two sub-dimensions.
The ?rst sub-dimension, encourage, has to do with the allocation
of adequate resources, encouragement and direction, and control.
The second sub-dimension, resource service quality, involves sup-
port from the organization, with regard to how to use the KMS in
general, howto capture knowledge as part of work, and how to use
the KMS in business processes.
(2) Measures of two use dimensions
3.4.4. KM use
To measure KM use, we have applied the knowledge circulation
process of Lee, Lee, and Kang (2005), including knowledge accu-
mulation, knowledge sharing, knowledge utilization, knowledge
internalization, and knowledge creation, in order to understand the
conditions of use for knowledge management. The dimension of
use refers to the degree to which a knowledge worker believes he
or she has incorporated procedures for the capture and use of
knowledge of various types of decision-making activities and in the
utilization of the outputs of the system.
3.4.5. User satisfaction
The user satisfaction dimension is a construct that measures
users' satisfaction with KM. User satisfaction is based on subjective
evaluations of various outcomes of the knowledge management
systems existing within an organization.
(3) Measures of the bene?t dimension
3.4.6. Net bene?t
The bene?t dimension involves the overall bene?ts of KM,
which means that KM success is essentially de?ned as improved
performance. An individual's use of KM in?uences his or her per-
formance in the workplace. The bene?t dimension construct
combines impacts on both user change and performance, and
recognizes that the use of KM may increase the effectiveness of
knowledge workers. KM gives users a better understanding of
decision-making contexts, improves their decision-making, alters
their activities, and changes their perceptions of importance.
4. Data analysis and results
4.1. Data collection
The questionnaire was distributed and collected from the
practitioners/managers of businesses that are categorized as SMEs
based in Taiwan to test our research model. Table 2 lists the de-
mographic information collected from respondents with regard to
gender, age, educational level, work position, industry type, and
total number of employees at their companies.
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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4.2. Data analysis
In the study, we adopt partial least square (PLS) method to
analyze the data. PLS is a structural equation modeling technique
which uses a component-based approach to evaluate the rela-
tionship within, and variance explained by a structural equation
model. The PLS technique is increasingly being used in IS research
because it requires minimal sample size and places negligible de-
mands on residual distributions (Chin, 1998). Benaroch,
Lichtenstein, and Robinson (2006) pointed out that PLS has the
ability to handle relatively small sample sizes, making it appro-
priate for our data set. Besides, it is suitable for our study because it
can give more accurate estimates of mediating effect by accounting
for the measurement error that attenuates the estimated relation-
ships and improves the validation of theories (Henseler & Fassott,
2010). Also, PLS works better when the objective is ‘prediction’,
the model is relatively complex, and the phenomenon under study
is new or changing (Chin & Newsted, 1999). Overall, it ensures
robust solutions in estimating complex relationships among vari-
ables (Chin, 2010).
In accordance with Anderson and Gerbing (1988), the data
analysis process of SEM was divided into two steps: (1) measure-
ment model analysis, which involved following the initial analysis
with a con?rmatory factor analysis (CFA) to measure the reliability
and validity of the latent variables, and (2) structural model anal-
ysis, in which hypotheses were tested by examining path co-
ef?cients and their signi?cance.
(1) Measurement model analysis
We pre-tested our survey questionnaires by asking pro-
fessionals handling management information systems to assess
their logical consistency, ease of understanding, sequencing of
items, contextual relevance, and suggestions on item contents and
instrument structure. Our study was found to have both face and
content validity.
According to Anderson and Gerbing (1988), the measurement
model provides a con?rmatory assessment of reliability, conver-
gent validity, and discriminant validity. Cronbach's alpha, individ-
ual item reliability, and composite reliability (CR) tests were
performed in order to verify reliability. First, each construct in this
study was measured in terms of each factor, according to Cron-
bach's alpha values. As shown in Table 3, all the Cronbach's alpha
values range from 0.91 (for the US) to 0.95 (for SQ, KQ, KU). Ac-
cording to Nunnally (1978), the lowest limit for Cronbach's alpha
values should be 0.7. The Cronbach's alpha value of each construct
was above 0.7, which indicated high internal consistency. In addi-
tion, individual item reliability was assessed by examining the
factor loadings of the measures with their respective constructs.
The reliabilities of individual items are considered adequate when
loadings exceed 0.5 (Rivard & Huff, 1988). The results of our factor
loading analysis showed that four items, KU 1, 2, 20, and 21, had
values of less than 0.5 and thus these items were eliminated from
further analysis. Finally, we assessed reliability by examining
composite reliability (CR), and found that our CR was over 0.7,
indicating that the scales involved were of satisfactory reliability
(Chin, 1998; Fornell & Larcker, 1981). We thus found that the reli-
ability of our scales was acceptable.
Convergent validity is the degree to which multiple attempts to
measure the same concept are in agreement. In this study, we
assessed convergent validity by examining average variance
extracted (AVE). As seen in Table 3, the AVE for all constructs was
above 0.5, which indicates that the scales had good convergent
validity (Fornell & Larcker, 1981).
To assess discriminant validity, we evaluated the measures
when the square root of each factor's AVE was larger than its cor-
relation with other factors (Chin, 1998). Table 4 provides the results
of the analysis and the discriminate validity assessed by using the
correlation of latent variables, wherein the square roots of the
average variances were calculated for each of the constructs along
the diagonal. We found that all square roots of AVE were larger than
their corresponding coef?cients of correlation with other factors.
Overall, our analyses demonstrated that the study scales possessed
convergent and discriminant validity.
(2) Structural model analysis
In this study, a PLS structural model analysis using Smart PLS
was conducted for each hypothesis path coef?cient and the per-
centage of the variance explained (R
2
) values. Path coef?cients
represent the strength of the relationships between dependent and
independent variables. R
2
was used as an indicator of the overall
predictive strength of the model. The greater an R
2
value, the better
a model's predictive quality (Fornell & Bookstein, 1982; Wixom &
Watson, 2001). The results for H1 through H9 were determined
by using PLS, as presented in Fig. 2. As can be seen, the positive
correlations between the constructs suggest that there were
grounds for expecting their signi?cant effects on each other. First,
we tested the relationship between quality construct and KU. H1
tested the relationship between SQ and KU. A strong positive
relationship was observed (b ¼ 0.31, p < .001). H3 tested the rela-
tionship between KQ and KU, and a positive and signi?cant rela-
tionship was found (b ¼ 0.17, p < .05). SEQ was found to have a
positive effect on KU, thereby supporting H5 (b ¼ 0.18, p < .05).
In order to illustrate the in?uences of quality on user satisfac-
tion, we examined the relationship between SQ, KQ, SEQ, and US.
H2 showed a positive relationship between SQ and US (b ¼ 0.01,
p < .05). With regard to H4, the link between KQ and US was found
to be signi?cant (b ¼ 0.37, p < .001). For H6, the relationship of SEQ
and US were tested, and strong positive relationships were
observed (b ¼ 0.41, p < .001). The results are expressed in the same
Table 2
Demographic characteristics of the sample.
Variables Categories Frequency Percent
Gender Male 137 49.5%
Female 140 50.5%
Age 50 20 7.2%
Education High school 71 25.6%
Junior college 69 24.9%
University 108 39.0%
Master 25 9.0%
Ph. D. 4 1.5%
Positions Management staff 96 34.7%
Non-management staff 181 65.3%
Industry types Traditional manufacturing 116 41.9%
Service 34 12.3%
Cultural and educational industry 34 12.3%
Transport industry 5 1.8%
Wholesale and retail 3 1.1%
IT services/software industry 23 8.3%
Finance and insurance 32 11.6%
Communications electronics 4 1.4%
Hospital 6 2.2%
Tourism and catering industry 5 1.8%
Nonpro?t groups 15 5.4%
# of Employee 1e50 98 35.4%
51e100 16 5.8%
101e500 86 31.0%
501e1000 43 15.5%
>1000 34 12.3%
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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Table 3
Summary of reliability and validity for measures.
Construct Measure item Factor loading AVE CR Cronbach's a
System
quality (SQ)
sq1. Your KS allows you to do both information and people searches. 0.71 0.67 0.96 0.95
sq2. Whenever you search the KS knowledge base and/or yellow pages, the retrieved knowledge is
always what you need.
0.81
sq3. Whenever you search the KS knowledge base and/or yellow pages, the returned linkage always
directs you to the right person.
0.84
sq4. Whenever you search the KS knowledge base and/or yellow pages, the retrieved results
normally display quickly.
0.87
sq5. Your KS search function is easy to use. 0.80
sq6. Your KS is not subject to frequent problems and crashes. 0.77
sq7. Your KS allows you to ?nd most of the organizational information/knowledge online. 0.79
sq8. Whenever you search the KS, you don't need to try different ways to locate the needed
information.
0.85
sq9. Whenever you search the KS, you don't need to try different ways to locate the right person. 0.86
sq10. Whenever you search the KS, you don't need to access more than one system to locate the
needed information.
0.86
sq11. Whenever you search the KS, you don't need to access more than one systemto locate the right
person.
0.83
Knowledge
quality (KQ)
kq1. Your KS provides information/knowledge that is exactly what you need. 0.78 0.62 0.96 0.95
kq2. Your KS provides information/knowledge that uses recognized vocabulary rather than highly
specialized terminology.
0.80
kq3. Your KS provides information/knowledge that is adequate for you to complete tasks. 0.83
kq4. Your KS provides contextual information/knowledge so that you can truly understand what is
being accessed.
0.81
kq5. Your KS provides contextual information/knowledge so that you can easily apply it to your
work
0.80
kq6. Your KS provides up-to-date information/knowledge. 0.80
kq7. The knowledge portal of your KS links you to a complete collection of documents and data. 0.80
kq8. The yellow pages of your KS guides you to connect to the people with the know-how for which
you are seeking.
0.85
kq9. Your organization keeps updating its knowledge portal so that you have access to current
documents and data.
0.80
kq10. Your organization keeps updating its yellow pages so that you can locate newly hired or
acquired expertise without a problem.
0.79
kq11. The knowledge management system enables me to control the settings of knowledge
documents.
0.77
kq12. The knowledge management system enables me to control the presentation of knowledge
documents.
0.79
kq13. The knowledge management system enables me to de?ne my favorite knowledge. 0.70
kq14. The knowledge management system can record my retrieval and reading history. 0.68
Service
quality (SEQ)
seq1. Whenever you have dif?culties with your KS, there is a speci?c person (or group) exist to help
you.
0.79
seq2. You have suf?cient time to engage in dialogue online with your coworkers about important
problems and solutions.
0.85 0.71 0.94 0.92
seq3. You are encouraged to engage in online exploration and experimentation by your peers. 0.89
seq4. You are encouraged to engage in online exploration and experimentation by your supervisor 0.85
seq5. Your organization actively endorses knowledge sharing. 0.84
seq6. Your organization encourages online discussion of new ideas and working methods. 0.83
KM use (KU) ku3. I fully understand the core knowledge necessary for my tasks. 0.67 0.51 0.95 0.95
ku4. We refer to corporate database before processing tasks. 0.72
ku5. We extensively search through customer and task-related databases to obtain knowledge
necessary for the tasks.
0.68
ku6. We try to store expertise on new tasks design and development. 0.71
ku7. We try to store legal guidelines and policies related to tasks. 0.76
ku8. We are able to systematically administer knowledge necessary for the tasks and store it for
further usage.
0.70
ku9. We document such knowledge needed for the tasks. 0.74
ku10. We summarize education results and store them. 0.75
ku11. We share information and knowledge necessary for the tasks. 0.75
ku12. We improve task ef?ciency by sharing information and knowledge. 0.77
ku13. We promote sharing of information and knowledge with other teams. 0.77
ku14. We developed information systems, like intranet and electronic bulletin boards, to share
information and knowledge.
0.70
ku15. EDI is extensively used to facilitate processing tasks. 0.66
ka16. Work ?ow diagrams are required and used in performing tasks. 0.72
ku17. There exists a culture encouraging knowledge sharing. 0.73
ka18. There exist incentive and bene?t policies for new idea suggestions in utilizing existing
knowledge.
0.68
ku19. There are research and educational programs. 0.70
ku22. I can use the Internet to obtain knowledge for the tasks. 0.64
ku23. I can refer to best practices and apply them to my tasks. 0.68
User
satisfaction (US)
us1. As a whole, I am satis?ed with the knowledge management system. 0.96 0.91 0.95 0.91
us2. As a whole, the knowledge management system is successful. 0.95
(continued on next page)
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
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manner as explained by Wang et al. (2007) and Wixom and Todd
(2005).
A strong positive relationship was also found between KUand US,
thereby supporting H7 (b ¼ 0.08, p < .05). Approximately 36% of the
variance inKUandmore than61%of the variance inUSwas explained
by SQ, KQ, and SEQ. These results were entirely consistent with those
of previous studies (DeLone&McLean, 2003; Doll &Torkzadeh, 1988;
Kulkarni et al., 2006e2007; Rai et al., 2002; Seddon, 1997; Wu &
Wang, 2006). Finally, in determining the bene?t dimension, we
noted that IB was affected by KU(b ¼0.57, p < .001), as well as by US
(b ¼ 0.23, p < .001), which supported H8 and H9. In addition, 51% of
the variance in IB was explained by KU and US.
As can be seen from Table 5, overall, our hypothesized research
model was supported. The total effects (considering both direct and
indirect effects) on net bene?t are 0.18 for system quality, 0.19 for
knowledge quality, 0.20 for service quality, 0.59 for KM use, and
0.23 for user satisfaction. Especially, KM use was found to have the
strongest direct and total effect on net bene?t, indicating the
importance of the use of KM in increasing net bene?t. This means
that the implement of KM activities that are more relevant by
knowledge workers enables them to obtain greater bene?ts. In
addition, Table 5 shows that the total effect of systemquality on KM
use and of service quality on user satisfaction and net bene?t, are
greater than others. Furthermore, based on Fig. 2, a total of 51%
variance of net bene?t is explained by system quality, knowledge
quality, service quality, KM use, and user satisfaction together; 61%
of the variance of user satisfaction is explained by system quality,
knowledge quality, service quality, and KM use; 36% of the variance
of KM use is explained by system quality, knowledge quality, and
service quality.
5. Conclusion
5.1. Discussion
KM has received particular attention over the past two decades,
as it offers a means for organizations to gain competitive
Table 4
Correlations among study variables.
Construct KU NB KQ SEQ US SQ
1 KM use (KU) 0.71
2 Net bene?t (NB) 0.68 0.83
3 Knowledge quality (KQ) 0.53 0.55 0.79
4 Service quality (SEQ) 0.52 0.46 0.73 0.84
5 User satisfaction (US) 0.50 0.51 0.72 0.73 0.95
6 System quality (SQ) 0.56 0.54 0.74 0.70 0.61 0.82
Note. Square root of AVE is on the diagonal.
Fig. 2. Hypothesis testing results of PLS analysis.
Table 5
The direct, indirect, and total effect of dominants on net bene?t.
Direct effect Indirect effect Total effect
KU US NB KU US NB KU US NB
SQ 0.31 0.01 0.02 0.18 0.31 0.03 0.18
KQ 0.17 0.37 0.01 0.19 0.17 0.38 0.19
SEQ 0.18 0.41 0.01 0.20 0.18 0.41 0.20
KU 0.08 0.57 0.02 0.08 0.59
US 0.23 0.23
Table 3 (continued)
Construct Measure item Factor loading AVE CR Cronbach's a
Net bene?t (NB) nb1. Your KMS helps you to detect work-related problems. 0.82 0.69 0.95 0.94
nb2. Your KMS enlightens you to new ways of thinking. 0.84
nb3. Your KMS changes the way you do things in a way bene?cial to the organization's overall
interest.
0.87
nb4. Your KMS improves the decisions you make. 0.84
nb5. Your KMS helps you to make fewer mistakes. 0.82
nb6. Your KMS allows better experience transfer and knowledge reuse. 0.85
nb7. Your KMS reduces duplicate work. 0.76
nb8. Your KMS allows you faster cycle time to problem resolution. 0.85
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Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
advantages. It is true that in many SMEs there is an absence of
systematic KM, this does not imply that KM is less important than
for large companies; indeed, it can be argued that this is a
distinctive factor for SMEs' survival (Durst & Edvardsson, 2012). An
important implication is that managers should create an environ-
ment to support KMrelevant activities. Prior research has indicated
that the measurement of KM effectiveness or success is crucial to
understanding how KM should be built and implemented. This
study presents and validates a model of KM success from a
knowledge-based perspective, based on the J&OKMsuccess model,
which captures the multidimensional and interdependent nature
of KM success. Through theoretical discussions and literature re-
view, a questionnaire survey and statistical analysis on SMEs in
Taiwan, the results indicate that a correlation exists between KM
quality, KM use and KM successful implementation. Overall, the
results of empirical investigation are positive and supportive were
consistent with most prior IS research. That is, the better the KM
qualities of system, knowledge, and services, the more KM use and
user satisfaction will be, which can lead to better net bene?t. The
results were consistent with previous IS success model research
(DeLone & McLean, 2004; Molla & Licker, 2001; Rai et al., 2002;
Seddon, 1997; Seddon & Kiew, 1996).
Among them, systemquality is the most signi?cant determinant
of KM use than others. As suggested by Markus (2001), knowledge
use may depend on how remote and dissimilar knowledge users
are from knowledge generators. Users from different functional
areas or with differences in terms of breadth and depth of knowl-
edge may face dif?culty in de?ning search terms (when using a
KMS). Users who do not know the right jargon, terminology,
questions to ask, or symptoms to report will drown in unnecessary
or unhelpful knowledge. It is therefore important to develop and
provide users a system with a feature-rich interface that will
retrieve and present different types of knowledge in an ef?cient
manner. Alternatively, the system may put them in touch with
experts who can provide the needed knowledge and help them
interpret and apply the available knowledge (Kulkarni et al.,
2006e2007). In addition, compared with system quality and
knowledge quality, service quality has a greater in?uence on user
satisfaction. Users may start to consider KM to be a part of their
working life. Thus, system operation is no longer an important
issue. Its effect may be important during the initial implementation
but subsides over time.
With regard to the implementation of KM, measuring multiple
KM success variables continues to be important. This model pro-
vides a rich pro?le of the dynamics surrounding quality measures,
KM use, satisfaction evaluation, and net bene?t. Our research also
con?rms that KM use, user satisfaction and net bene?t are com-
plementary yet distinct constructs, and that KM use is mediated
through user satisfaction in its in?uence on the net bene?t of KM.
To develop KM successfully, it is essential to ensure KM access at
the workplace, provide the relevant knowledge for users, and
maintain service levels, all of which are helpful in increasing KM
use and the perception of user satisfaction, which, in turn, are
helpful in increasing the net bene?t of KM.
The primary contribution of this research is in furthering our
understanding of how to assess and promote KM success in SMEs
context. It makes several contributions to this area. First, our results
are validated within a Taiwan context, whereas most previous
studies have been based on other countries' companies. We use a
comprehensive instrument to measure an individual impact
construct, considering both change variables and performance
variables, which is an approach that has been lacking in most
existing studies on KMsuccess models. Second, the study examines
the direct and indirect linkages among quality dimensions, use
dimensions, and bene?t dimensions, which, to the best of our
knowledge, has not been previously explored in the KM context.
Our results provide evidence of the direct and indirect effects of
quality on individual impact, and indicate that of all the constructs,
KM use has the greatest effect on individual impact. Third, in
contrast to previous research on KM success models, we used a
multifaceted instrument for KM use that included knowledge
accumulation, knowledge sharing, knowledge utilization, knowl-
edge internalization, and knowledge creation. Thus, our measure-
ment instrument for KM use is more comprehensive than those
used in most previous studies as it includes constructs knowledge
circulation process. We also used more comprehensive instruments
for system quality (eleven items), knowledge quality (fourteen
items), and service quality (six items) compared to previous studies
on knowledge workers.
5.2. Theoretical implications
To date, the implementation of KM in SMEs has not been sys-
tematically investigated. Existing studies have explored KM activ-
ities and strategies from large companies' perspectives and have
not considered the needs of smaller businesses. This paper is aimed
to bridge this gap. Such as Durst and Edvardsson (2012) stressed,
the adoption of KMS is generally considered to impact on ?rm
performance, but we lack empirical evidence supporting this idea
on SMEs' performance. Burgess, Sellitto, and Karanasios (2009)
pointed out that there is a need to further develop a proper un-
derstanding of KM in SMEs context as they are different from large
organizations. However, factors in?uencing the success of KM have
seldom been empirically examined in prior research and whether
traditional IS success models can be extended to investigating KM
success has not been examined thus far in Taiwanese settings. Until
now, the D&M IS Success Model is a generally accepted model for
assessing success of an IS. This research was conducted in response
to a call for studies on the continuous challenges and tests involved
in applying IS success models in different contexts. Based on the
D&M and J&O models, we proposed and validated a comprehen-
sive, multidimensional model of KM success, which considers six
success measures: system quality, knowledge quality, service
quality, KM use, user satisfaction, and net bene?t. The model pre-
sented in this paper is a viable approach to assessing KM success
and meets the spirit and intent of DeLone and McLean (1992, 2002).
5.3. Managerial implications
Intense competition, ?ckle consumers, shorter product life cy-
cles, and globalization are some of the driving forces that have led
to the increased inspection of the usage, application, and leveraging
of knowledge in organizations (Anantatmula & Kanungo, 2006).
The study concentrated on KM quality and use that practitioners
considers important when implementing KM in SMEs. The KM
success model has three basic dimensions as antecedents to KM
success: system quality, which deals with the technical infra-
structure; knowledge quality, which deals with KM strategy for
identifying critical knowledge and how that knowledge is stored;
and service quality, which deals with management support and
allocation of resources. These dimensions deal with ensuring that
the KM implementation meets the needs of the users and the or-
ganization. Furthermore, the model could provide some guidelines
at placing the CSFs into a theoretical framework that explained how
they led to KMsuccess. Probably, this is the ?rst study to present an
integrative viewpoint for implementing KM successfully in SMEs. It
is hoped that this research ?nding can serve as a reference for SMEs
in the implementation of KMand provide a great help in enhancing
management performance.
M.-H. Wang, T.-Y. Yang / Asia Paci?c Management Review xxx (2016) 1e13 11
Please cite this article in press as: Wang, M.-H., & Yang, T.-Y., Investigating the success of knowledge management: An empirical study of small-
and medium-sized enterprises, Asia Paci?c Management Review (2016),http://dx.doi.org/10.1016/j.apmrv.2015.12.003
In the era of knowledge economy, organizations are increasingly
tend to management knowledge. Research in the area of Taiwanese
SMEs practices has an added signi?cance because of the increased
importance of Asia. For suitable implementing and more enjoying
the bene?ts of KM, a model for measuring success of KM is
essential. Taken together, the results of this study are important to
KM practitioners: (1) provide a basic for SMEs valuation; (2)
stimulate management to focus on what is important; (3) justify
investments in KM activities; and (4) build and implement ef?cient
KM initiatives and systems. Given the prevalence of SMEs on the
one hand and their resource limitations on the other, there is a
strong need on this topic to provide actual proof of the imple-
mentation of KM activities which would help SME owners to make
better decisions regarding resource allocation.
5.4. Limitations and directions for future research
The validity of the KMsuccess model cannot truly be established
on the basis of a single study, and so caution should be exercised in
generalizing our ?ndings. Certain limitations must be considered
while utilizing the results of this study. The most important limi-
tation in this research was sample. The sample in this study
comprised an instrument which was self-administered. Secondly,
the empirical results are derived from a sample of Taiwanese SMEs
and hence the ?ndings might be country-speci?c. Further, the data
for this study are based the respondents' perceptions, which may
vary widely across industries, ownership and function and work
experience of respondents within the SMEs. Future research can
select SMEs from other countries to examine and enlarge the
generalization of the ?ndings. On the other hand, applying the KM
success model in different stages could provide a more compre-
hensive picture that would increase our understanding of KM
development. In addition, it should also be considered that many
important exogenous variables might in?uence KM success. This
study provides a foundation for further research that could
contribute to the existing knowledge in this area. Additional
research is required to explore the applicability of the success
model in more diverse settings.
Acknowledgments
This manuscript is supported by the research fund NSC 97-2410-
H-218-005, MOST 104-2410-H-218-018 granted by the Ministry of
Science and Technology, R.O.C.
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