a framework for quality dimensions of knowledge management systems
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A framework for quality dimensions ofknowledge management systemsMohammad Saleh Owlia aa Industrial Engineering Department , Yazd University , Yazd, IranPublished online: 20 Nov 2010.
To cite this article: Mohammad Saleh Owlia (2010) A framework for quality dimensions ofknowledge management systems, Total Quality Management & Business Excellence, 21:11,1215-1228, DOI: 10.1080/14783363.2010.529351
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A framework for quality dimensions of knowledge managementsystems
Mohammad Saleh Owlia
Industrial Engineering Department, Yazd University, Yazd, Iran
As knowledge management (KM) philosophy and systems are being recognised bymany organisations worldwide, the quality of such systems has become moreimportant in satisfying the needs of knowledge users. Defining the characteristics ofquality is the first step in a measurement process that, in turn, is itself a prerequisitefor any improvement program. This paper proposes a conceptual framework for thequality dimensions of knowledge management systems (KMS); it is based on theinvestigation of quality attributes in other environments as well as factors affectingquality in this specific context. The resulting framework consists of eight dimensionscalled Functionality, Completeness, Reliability, Usability, Access, Serviceability,Flexibility, and Security.
Keywords: quality; knowledge management systems; dimensions; customer-orientation
Introduction
In the current competitive environment, knowledge management (KM) plays a vital role
for business success. Success is characterised by the ability of companies to consistently
create new knowledge, quickly disseminate it, and embody it in new products and services
(Tiwana, 2002). This is because, as Peter Drucker mentioned, the most valuable assets of
companies in the twenty-first century are knowledge and knowledge workers (Drucker,
1999). This is especially true for knowledge-intensive companies and institutions as
they mostly rely on their soft resources (Paulzen & Perc, 2002).
Realising the importance and benefits of this dominating factor, KM is now accepted
and is widely practiced in many world-class organisations (Eldridge, Balubaid, & Barker,
2006). According to Alavi (2000), three forces: the volatility of business and competitive
environment; globalisation; and knowledge intensive products and services are fueling the
need for strategies and systems for managing organisational knowledge. The growth of
interest in KM has been parallel to the advances in computers, networks, and data manage-
ment systems by which knowledge can be shared and transferred among people around the
world (Bose, 2004).
Despite the implementation of KM projects, many organisations have failed to realise
the expected benefits of KM (Kim, Yu, & Lee, 2003). According to Alavi and Leidner
(1999) the existing body of work on knowledge management systems (KMS) consists
primarily of the general and conceptual principles of KMS, few case studies and little
research have been undertaken on the benefits and costs of its implementation.
ISSN 1478-3363 print/ISSN 1478-3371 online
# 2010 Taylor & Francis
DOI: 10.1080/14783363.2010.529351
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∗Email: [email protected], [email protected]
Total Quality Management
Vol. 21, No. 11, November 2010, 1215–1228
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The difficulty in measuring knowledge has led its management without measurement
(Ahmed, Lim, & Mohamed Zairi, 1999). Developing and using standardised metrics can
show benefits and convince management and stakeholders as to the value of KM initiatives
(Bose, 2004).
The ‘quality of knowledge’ is an issue that should be addressed if KM is to be effec-
tive. This is true for KMS as it provides processes and technologies essential for achieving
organisational goals and objectives. The quality of knowledge that enterprises apply to
their key business processes is a key element for success in the worlds’ competitive
environment (Ndlela & du Toit, 2001). Drucker earlier emphasised the need for systematic
work on the quality and productivity of knowledge for the performance capacity, if not
survival, of any organisation in the knowledge society as they will increasingly come to
depend on those two factors (Shariq, 1997).
KM metrics have been developed in the last decade but little work has been done on
the quality aspects of KMS (Chen & Chen, 2005). To enhance the quality of KMS, the
dimensions of quality should be first defined and then measured. More research is
needed to better define these measures and to make them applicable for enterprises.
This research aims to define a conceptual framework for the quality dimensions of
KMS. The findings should assist organisations in measuring the perceptions of customers
of KMS according to which the quality can be improved. It also helps organisations to
prioritise KM strategies based on feedback from the market.
In this paper, literature on different measures related to KM and other information
systems will be reviewed in order to identify a comprehensive set of KMS quality dimen-
sions. From another point of view, the quality dimensions proposed for similar or relevant
environments will be studied for possible compatibility. Combining these two viewpoints,
a new conceptual framework for quality dimensions of KMS will be proposed.
A review of basic concepts
Knowledge has been defined as ‘a fluid mix of framed experience, values, contextual infor-
mation, and expert insight that provides a framework for evaluating and incorporating new
experiences and information’ (Davenport & Prusak, 1998). As the concepts related to ‘knowl-
edge’ itself have been widely discussed in the literature, this paper focuses on KM aspects.
According to Alavi and Leidner (2001), KM is the process of creating, storing/retriev-
ing, transferring and applying knowledge; this includes creating internal knowledge,
acquiring external knowledge, storing knowledge in documents and routines, updating
knowledge, and sharing knowledge internally and externally. Jennex (2005) defines KM
as ‘the practice of selectively applying knowledge from previous experiences of
decision-making to current and future decision making activities with the express
purpose of improving the organisation’s effectiveness’. Wong and Aspinwall (2006)
provide a different definition – ‘a formalized and active approach to manage and optimize
knowledge resources in an organisation’.
Most KM projects have one of three aims: (1) to make knowledge visible and show the
role of knowledge in an organisation; (2) to develop a knowledge-intensive culture; (3) to
build a knowledge infrastructure (Davenport & Prusake, 1998). A common KM program
involves the development of a knowledge repository and the forming and nurturing of the
communities of practice (Bose, 2004). Focusing on knowledge transfer (as the main
process in KM, four modes proposed by Nonaka (1994): (1) Socialisation (tacit to
tacit); (2) Internalisation (explicit to tacit); (3) Externalisation (tacit to explicit); and (4)
Combination (explicit to explicit) are to be considered.
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KMS support KM by facilitating knowledge flow among people (those who know to
those who need to know) throughout the organisation, while knowledge evolves and
grows during the process (Boss, 2004). According to Jennex (2005), KMS are ‘a system
that is created to support the capture, storage, search, retrieval, and application of
knowledge’. Support includes management support, processes, and IT applications and
components, the system uses a variety of repositories including computer, paper, and
self-memory based repositories.
Three main KMS functions are: (1) the coding and sharing of best practices; (2) the
creation of corporate knowledge directories; and (3) the creation of knowledge networks
(Alavi & Leidner, 2001). From the technology-based view, KMS involve six categories:
knowledge-based systems (KBS); data mining (DM); information and communication
technology (ICT); artificial intelligence (AI)/expert systems (ES); database technology
(DT); and modeling (Liao, 2003). From a different viewpoint, some organisations focus
more on fostering a knowledge sharing culture, promoting organisational learning,
encouraging teamwork, and managing human resources towards achieving KM (Wong
& Aspinwall, 2006). These two approaches can be classified as codification and persona-
lisation strategies (Hansen, Nohria, & Tierney, 1999).
The technological side of KMS has been widely discussed and practiced; it has been a
logical development of Information Systems into more comprehensive areas. The
complexity of KMS, however, comes from the human side ‘because it originates and is
applied in the minds of human beings. People who are knowledgeable not only have
information, but also have the ability to integrate and frame the information within the
context of their experience, expertise, and judgment’ (Grover & Davenport, 2001).
Quality dimensions and measurement
Measurement, in general, has been seen as a prerequisite for management (you cannot
manage what you cannot measure) as it provides the basis through which it is possible
to control and improve processes. Concerning KM, different reasons have been raised
for the necessity of measurement (Ahmed et al., 1999; Ahn & Chang, 2004; Boss,
2004; Liebowitz & Suen, 2000):
. To determine the results of knowledge practices on business results and objectives.
. To provide a scoreboard to monitor performance levels.
. To determine what to pay attention to and improve.
. To give an indication of bottlenecks and the cost of poor implementation.
. To give a standard for making comparisons.
. To provide enthusiasm and support for KM by measurable success.
However, the inherently intangible characteristic of knowledge makes its measurement
difficult. Although many researchers and practitioners have developed metrics and models
to measure ‘knowledge’, in response to this difficulty, some have tried not to directly
measure knowledge and instead, to assess how much each entity of knowledge contributes
to the actual business performance (Ahn & Chang, 2004; Liebowitz & Wright, 1999).
Taking software as a much simple knowledge-based product, metrics are typically
‘direct’ or ‘indirect’ measures. Direct measures are normally size-oriented metrics like a
thousand lines of code per person per month, and indirect measures are function-oriented
metrics such as the number of user inputs and outputs (Liebowitz & Suen, 2000).
In terms of defining quality itself, it is a concept that is understood and defined
differently by different people. As Garvin (1988) said, the four disciplines philosophy,
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economics, marketing, and operations management (engineering) have each interpreted
the term differently. He identified five principal approaches to defining quality: transcen-
dent, product-based, user-based, manufacturing-based, and value-based. While the first
group translates quality as excellence, the product-based approach views it as a set of mea-
surable attributes. Juran’s (1988) definition of ‘fitness for use’ and the ‘conformance to
requirements’ definition by Crosby (1979) can be introduced as representatives of user-
based and manufacturing-based approaches respectively. In the final approach, quality
is taken as a combination of excellence and price.
However, a prevailing, and still powerful definition from the end of the 1980s, is a cus-
tomer-driven definition of quality that defines quality as ‘meeting or exceeding customer
expectations’ (Evans & Lindsay, 2008). A more precise definition has been provided by
ISO 8402 in which quality has been defined as ‘the totality of features and characteristics
of a product or service that bears on its ability to satisfy stated or implied needs’
(ISO 8402, 1986). The words ‘characteristics’ and ‘satisfy needs’ in the definition
imply two important points that are: (1) quality is what satisfies customer’s needs; and
(2) quality is a set of characteristics that can be measured qualitatively or quantitatively.
Hence, defining the dimensions of each ‘entity’ (the word that is used recently instead of
product or service for generalisation purpose) according to the needs or expectations of
customers is one of the most important steps in quality achievement and improvement.
Quality dimensions according to Gronroos (1990) can be classified into three groups:
technical quality, functional quality, and corporate image. The dimensions associated with
technical quality are those that can objectively be measured regardless of customer’s
opinions, whilst those concerned with functional quality are related to the interaction
between the provider and recipient of the service and are often perceived in a subjective
manner. The corporate image dimension relates to the overall picture of an organisation
perceived by the customers; it is the result of a combination of technical and functional
quality dimensions as well as factors like the price of the product (or service) and the repu-
tation of the company.
In the following sections, the quality dimensions for products and services will be
examined for consistency with KM environment. Research on quality dimensions of
KM-related products and services (e.g. software, data, information, e-services) is then
reviewed to catch relevant dimensions. On this basis, a specific framework for KMS
will be proposed. The content validity of the framework will be investigated through
checking quality factors amassed from the literature.
Quality dimensions for products and services
The quality of services, in general, differs from the quality of manufactured products
due to its special characteristics including intangibility, simultaneity, and heterogeneity
(Parasuraman, Zeithaml, & Berry, 1985). This is certainly true for KMS since most
quality attributes cannot be seen, felt, or touched in advance; production and consump-
tion of the service are inseparable because human interactions play an important role;
and quality varies markedly in different organisations. Although the quality dimensions
of a product may seem remote from those of systems like KMS, they are still
appropriate in providing ideas for generalisation; in fact, KMS can also be seen as a
virtual product.
Garvin (1988) proposed the following eight dimensions for quality that, as he stated,
can define both product and service quality, although they appear to be more products
oriented.
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(1) Performance. Performance is concerned with the primary operating characteristics of
a product. For KMS, performance can be defined as the main functions expected
from the system including knowledge creation, knowledge storage and retrieval,
knowledge distribution, and knowledge application (Alavi, 2000).
(2) Features. Those characteristics that supplement the basic performance functions are
called features. For KMS, features may mean more advanced technologies
embedded in the system. For example, using artificial intelligence or expert
systems may not be necessarily a main component of KMS but it could strengthen
the system effectiveness. As Garvin stated, drawing a line to separate performance
characteristics from features is often difficult.
(3) Reliability. Reliability, defined as the probability of a product working fault-free
within a specified time period, appears to be more relevant to goods than services.
However, for KMS it could be the degree to which the knowledge, information, and
data transferred are correct, accurate, and up to date.
(4) Conformance. Conformance refers to the extent to which a product meets established
standards/specifications. When applied to KMS, the definition is similar i.e. the
degree to which the KMS meets relevant standards. The standards could be about
software or hardware, or communication technologies used in the system. For
example, it is suggested that using an intranet-based design is a better choice than
using technology such as Lotus Notes (Tiwana, 2002).
(5) Durability. Although durability, as a measure of a product life, looks less meaningful
in this instance, an indirect interpretation can refer to the life cycle of knowledge. In
this view, durability is not a positive attribute for knowledge.
(6) Serviceability. Serviceability, concerned with repairs and field services, might seem
to be synonymous with durability i.e. more consistent with products. However, an
aspect of this dimension that is appropriate for KMS relates to customers’ enquiries.
The way in which a system handles enquiries from users (for example searching
knowledge items) can be considered as another measure of quality.
(7) Aesthetics, and (8) Perceived quality. These two dimensions, as Garvin stated,
include those features that are subjective to customers’ opinions. They can be com-
pared with the functional and corporate categories of dimensions discussed earlier.
Aesthetics can be distinguished from performance, as it is a matter of personal
judgment. For KMS, aesthetics are features related to the general appearance of
the system mainly considered in the ‘interface layer’ of the system (Tiwana, 2002).
Perceived quality refers to the reputation factors influencing the customers’ image of the
corporation. For KMS, reputation comes back to the developers of the system whether
external consultants/vendors or internal project teams. Table 1 summarises the way in
which the products’ quality dimensions can be interpreted for KMS.
Viewing KMS as a service can also help to generalise service quality dimensions in
this sector. However, in addition to the common features present in service industries
there are also specific characteristics that result in unique dimensions having to be con-
sidered. This is certainly the case for KM where the characteristics tend to be complex.
Like products, the degree to which a service is fault (mistake)-free is attributed to
reliability. Other factors concerned are accuracy, keeping promises and consistency.
Consistency in receiving the same service each time (Sasser, Olsen, & Wyckoff, 1978),
is comparable with variability reduction in manufacturing which in turn defines the
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conformance of the product to specifications. For KMS, while keeping promises relates to
the system developers, accuracy and consistency are of great concern.
Timeliness is defined as a quick response to customers is one of the quality determinants
in general services; it is important for KMS to provide knowledge ‘when’ needed. This
interacts with another dimension ‘access’ which can be considered as the degree to which
information and knowledge are available to share and use. Combining these two, KMS
should provide ‘anytime and anywhere access’ (Tiwana, 2002). Understanding users
and their needs, is a prerequisite for knowledge transfer. Although ‘courtesy’ and ‘respect’
as expected from ordinary services’ staff appear less appropriate for KMS, it can be
interpreted as user-friendliness of the system in terms of ease of use and ease of retrieval.
‘Competence’ can be seen as the richness of the knowledge present in KMS. In this
respect, the degree of ‘communication’ between different knowledge users is of paramount
importance. This is especially true for tacit knowledge that needs open, supportive, critical,
and reflective conversation between participants (Tiwana, 2002). ‘Credibility’ is generally
related to the reputation and trustworthiness of an organisation as perceived by the
customers; it can be grouped into the ‘image’ category of dimensions. The same
meaning can be applied to KMS as the degree to which users trust the system. How a
system responds to customers’ feedbacks and how well it solves associated problems is
always of importance to clients. Entitled ‘recovery’ (Gronroos, 1990), ‘redress’ (Stewart
& Walsh, 1989), and ‘handling complaints, solving problems’ (Haywood-Farmer, 1988),
this dimension can be generalised for KMS when taking users of the system into account.
‘Performance’ and ‘completeness’ of service are equivalent to the performance and
features dimensions of products. A categorisation of knowledge as core knowledge,
advanced knowledge, and innovative knowledge (Zack, 1999) seem similar to the dimen-
sions in which performance is equivalent to core knowledge while the two others fell into
completeness dimension. As far as technology concerned, these two dimensions can be
viewed as ‘must-have tools’ and ‘should have tools’ (Tiwana, 2002).
The dimension ‘security’ can be attributed to the confidentiality of information and
knowledge; this is critical for allowing people to express their ideas without the fear of
being known. ‘Flexibility’ can be defined as the degree to which acquiring knowledge in
different situations/ conditions is possible. The ‘tangibles’ dimension of service quality
can be interpreted as the physical infrastructure needed to run effective KMS. Table 2
shows a list of service quality dimensions together with proposed interpretations for KMS.
Table 1. Garvin’s dimensions of quality and KMS.
Dimensions Definition for KMS
1. Performance Primary functions of KMS e.g. knowledge creation, storage and retrieval,distribution, and application
2. Features Secondary/supplementary/more advanced functions and technologies ofKMS e.g. artificial intelligence or expert systems
3. Reliability The extent to which knowledge, information, data used/ transferred arecorrect, accurate, and up to date
4. Conformance The degree to which KMS meet established software, hardware, andcommunication standards
5. Durability Lifecycle of knowledge6. Serviceability How well KMS handle users’ enquiries7. Aesthetics Attractiveness of the interface layer of KMS for users8. Perceived quality Reputation of system past performance
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Quality dimensions for knowledge-based products and services
Although few studies have focused on the quality dimensions of KMS, a relatively wide
range of publications were found elaborating quality dimensions for other relevant
products and services. In this section, these dimensions are reviewed in an attempt to
generalise them to the whole system.
Software quality metrics have been one of the earliest works on knowledge-related
products. Using the research by McCall, Richards, and Walters (1977) and Boehm, Brown,
Kaspar, Lipow, and MacCleod (1978), 11 software quality factors were identified and used
in software engineering (Watts, 1987). Quality factors are those attributes of the software
that define its fitness for its intended purpose and use (Clarke & Soltan, 1995). According
to Usrey and Dooley (1996), the 11 factors are still inclusive and form the basis for the
ISO/IEC 9126 standard for software quality evaluation. The definitions of each factor
together with a proposed interpretation for KMS are used as a starting point for the
development of a new model.
(1) Correctness. The extent to which a piece of software complies with its specifications
is referred to as correctness. This is similar to the definition of conformance for
products and so the same meaning can be applied to KMS.
(2) Reliability. The definition of reliability corresponds to the degree to which a piece
of software is fault-free, the focus here being on accuracy. The same meaning is
applicable for KMS.
(3) Efficiency. Software efficiency is defined as the amount of computing resources and
code required by a program to perform a function and includes both execution
and storage efficiency. Although efficiency is a productivity rather than quality
dimension, an equivalent concept for KMS can be the extent to which users
retrieve knowledge stored in the system; this shows how effective the system is
to responding to the needs of users.
(4) Integrity (security). Integrity has been defined as the extent to which access to soft-
ware or data by unauthorised persons can be controlled. Although according to the
Table 2. Service quality dimensions and KMS.
Dimension Definition for KMS
Reliability The degree to which knowledge is correct, accurate, and up to date.The degree of consistency in knowledge processes
Responsiveness How well KMS respond to demands by usersUnderstanding customers Understanding the needs of system usersAccess The extent to which knowledge is available for user.Competence The richness of knowledgeCourtesy The degree to which system is user-friendly, easy to useCommunication How well system participants take part in knowledge conversationsCredibility The degree of trustworthiness of the systemSecurity Confidentiality of information/ knowledge shared when necessaryTangibles Effectiveness of physical infrastructure of KMSPerformance Primary (core) knowledge provided in the systemCompleteness Supplementary (advanced, innovative) knowledgeFlexibility The degree to which acquiring knowledge in different situations/
conditions is possibleRecovery, Redress How well system handles customers’ enquiries and solve the problems
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core philosophy of KM, an open and easy access to knowledge is favored, confiden-
tiality of information is still an important factor.
(5) Usability. Usability corresponds to the effort required for learning and using a piece
of software. The same meaning is true for KMS.
(6) Maintainability. Software maintainability is defined as the effort required for error
detection and correction. Considering the similarity between this and the service-
ability dimension of products, the same interpretation regarding the handling of
users’ enquiries can be made for KMS.
(7) Testability. The effort required to test the structure and correctness of a program is
termed testability. The concept can be generalised for KMS addressing the extent
to which the system is testable.
(8) Expandability, (9) Portability, (10) Reusability, (11) Interoperability. All of these
dimensions are common in reflecting the degree of software flexibility. Expandabil-
ity is concerned with the effort required for modifying a program; portability relates
to how easily a piece of software can be transferred from one environment to
another; reusability corresponds to the extent to which a program can be used in
other applications; and interoperability relates to the effort required to couple one
program with another. All the concepts seem meaningful for KMS.
Having reviewed a selection of literature on the quality dimensions of data (Jarke,
Jeusfeld, Quix, & Vassiliadis, 1999; Strong, Lee, & Wang, 1997; Pipino, Lee, & Wang,
2002), information (Bosser, 2005; Lee & Strong, 2003; Wang, 1998), information
systems (Bokhari, 2005; Chumpitaz & Paparoidamis, 2004; Lee, Hwang, & Wang,
2006; Myerscough, 2002; Tepandi, 1997; Whyte & Bytheway, 1996), and e-services
(Hernon & Calvert, 2005; Jun, Yang, & Kim, 2004; Kim & Stoel, 2004; Lee & Lin,
2005; Madu & Madu, 2002; Stockdale & Borovicka, 2001; Yang & Fang, 2004).
Table 3 summarises the findings according to the similarities found between the items.
Quality dimensions for KMS
The items found from researching the quality dimensions of products, services, and knowl-
edge-based entities provided a basis for developing a model for the quality dimensions of
KMS. The literature was also investigated in an attempt to test the content validity of the
measures and to discover possible additional quality factors. Although few references
addressed the quality dimension aspect directly, some useful elements were found.
Tiwana (2002) in describing the structure of KMS pointed out several features that could
be interpreted as quality dimensions of the system. They were: transparency, decentralised,
open and distributed, integrated, interactive, interoperability, scalability, performance,
functionality, consistency, visual clarity, navigation and control, relevancy, feedback,
mission focused, usability, standard, intuitive, flexibility, future-proofed, legacy integrative.
Ngai and Chan (2005) in research to evaluate KM tools using analytic hierarchy process
(AHP) used functionality as one of the three main criteria that influence KM practitioners’
choice of KM tools. The sub-criteria for the functionality were document management,
collaboration, communication, measurement, workflow management and scalability.
Another branch of relevant literature was on the success factors of KMS. DeLone and
McLean (2003) proposed a model of IS success which has been the basis for further
developments. In the latest revision of the model (2003), it comprised of six dimensions:
Information Quality, System Quality, Service Quality, Intention to Use/Use, User
Satisfaction, and Net Benefits. Jennex and Olfman (2004) adapted the model for the
KMS context and proposed six dimensions with 15 sub-dimensions; they were System
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Table 3. Quality dimensions for different knowledge products and services.
Software Data Information Information Systems E-Services
Correctness Correctness Validity SpecificationReliability Accuracy
CurrencyVolatilityFree of error
AccuracyPrecisionCurrency
AccuracyPrecisionReliabilityUpkeep
ReliabilityAccuracyCurrency
Efficiency MinimalityValue-Added
Value-Added EffectivenessSystem usage
Efficiency
Integrity Security Security SecurityPrivacy
Usability InterpretabilityEase ofunderstandingEase ofmanipulation
Current positionInterpretabilityEase ofunderstanding
FriendlinessTrainingLearnabilityMemorabilityReporting
FulfillmentEase of useConvenienceUsability
Responsiveness ResponsivenessUnderstandingAttitude
ResponsivenessCourtesyPersonalisationCustomisationEmpathy
MaintainabilityTestability
Traceability Disposition DocumentationControlFormatTechnical assistanceDelivery andInstallation
RecoverySupportFollow-upServicesServiceability
ExpandabilityPortabilityReusabilityInteroperability
Compatibility Flexibility Flexibility
UsefulnessRelevancy
Relevance Business alignmentNecessityRelevancyDirectionMeaningfulness
FunctionalityPerformance
Objectivity ObjectivityCompetence Competence
CompletenessAmount of data
CompletenessAmount of data
IntegrationComplexity
CompletenessFeatures
AccessibilityAvailabilityTimeliness
AccessTimeliness
AccessibilityAvailabilityResponse timeTimeliness
SystemavailabilityAccessResponse timeTimeliness
CredibilityReputationBelievability
ReputationBelievabilityAuthority
Loyalty CredibilityAssuranceTrust
ConciserepresentationConsistentrepresentation
PresentationFormatConsistentrepresentationCoherence
Front officeMarketing
AestheticsAppearanceWeb site designStructure
ParticipationCommunication
CollaborationCommunicationTransactioncapabilityStorage capability
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Quality (Form, Level, Technological Resources), Knowledge/ Information Quality
(Linkages, Richness, Knowledge Strategy/Process), Service Quality (Management
Support, User KM Service Quality, IS KM Service Quality), Intent to Use/ Perceived
Benefit (Capability, Usefulness), Use/User Satisfaction (Utilisation, Knowledge
Application), and Net Benefits (Change, Performance). Although an empirical test
(Liu, Olfman, & Ryan, 2005) showed limited support for the theoretical model, but it
still provides useful ideas for comparison.
A basic argument to this branch of research is whether they really reflect the success
factors of the system. While success factors imply factors affecting the success of an
organisation/a system, the models are in fact combinations of system quality dimensions
and effectiveness measures. Jennex and Olfman (2005) analyzed the literature on KM/KMS critical success factors (CSF) and grouped them into twelve factors: knowledge
strategy, motivation and commitment of users, integrated technical infrastructure, organ-
isational culture and structure, enterprise wide knowledge structure, senior management
support, learning organisation, clear goal and purpose, measures, easy knowledge use,
business processes, and security/protection of knowledge. Jennex, Smolnik, and
Croasdell, (2007), however, rejected the definitions and defined KM success factors as
mixtures of success and effectiveness measures. Another issue is whether to consider
‘behind the scene’ factors; the examples are knowledge strategy/process and management
support. The models have taken a view in which success factors are combinations of
process and outcome measures (Jennex et al., 2007). Taking a customer-oriented approach
to quality necessitates defining quality dimensions as those factors that are important for
the ‘customers’ in their interactions with the product or service.
Despite above arguments, the items from the model by Jennex and Olfman (2004) were
analyzed for relevancy to quality dimensions of KMS. The relevant sub-dimensions together
with their equivalents from previous findings were: Technological Resources: (Transaction
capability, Storage capability), Form (Access, Performance, Integrity), Level (Currency),
Richness (Accuracy, Timeliness, Completeness), Linkages (Competence), User KM
Service Quality (usability), IS KM Service Quality (Support, Maintainability, Reliability,
Access), Capability (Capability), Usefulness (Usefulness), Utilisation (System Usage,
Efficiency), Knowledge Application (Value-Added), and Performance (Performance).
In the only literature directly on the quality dimensions of KMS, Rao and Osei-Bryson
(2007) proposed a series of quality dimensions for the main components of KMS i.e.
individual knowledge items, the knowledge sources (or retainers), and ontology. The
knowledge sources themselves were divided into codified knowledge retainers and person-
alised knowledge retainers. While the paper presents a relatively wide coverage of quality
Table 4. Quality dimensions for KMS.
Dimension Definition
Functionality The degree to which the system meets organisational objectives, operationalstandards and users’ knowledge needs
Completeness How sufficient and comprehensive is the systemReliability The degree to which knowledge is correct, accurate, consistent, and up to dateUsability The effort required for using and involving in the systemAccess The extent to which knowledge is available for usersServiceability How well a KMS handles customers’ enquiriesFlexibility The degree to which acquiring knowledge in different situations/conditions is
possibleSecurity Confidentiality of information/knowledge shared when necessary
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dimensions for KMS, some shortcomings were found on the results. The main criticism is
that KMS has been divided into its components and so there is no unified model for the
whole ‘system’; this has also made overlaps between the groups. The second point is
that the research has taken an intrinsic and technical viewpoint to quality dimensions
and lacks a functional approach. Hence, subjective aspects of quality have not been
taken into account. This is in spite of the fact that well known models of quality
dimensions rely on subjective measures according to the users viewpoint for product
and services (Lee et al., 2006). Because of the intuitive and humanistic nature of knowl-
edge (Davenport & Prusak, 1998), special attention should be given to the judgment of
people when assessing the quality of KMS.
Table 5. Quality dimensions and their corresponding characteristics for KMS.
Dimensions Characteristics
Functionality Meeting organisational objectivesSatisfying users’ needsSystem usageProviding primary (core) knowledgeProviding primary functions including knowledge creation, storage and retrieval,
distribution, and application
Completeness Providing supplementary (advanced, innovative) knowledgeProviding supplementary/more advanced functions and technologies e.g. artificial
intelligence or expert systemsMeeting established software, hardware, and communication standards
Reliability AccuracyFault freeConsistencyCurrencyCredibility, trustworthinessLegacy
Usability Easy to useFriendlinessTraining, learnabilityAppearanceCommunication, knowledge conversation and sharing
Serviceability PersonalisationCustomizationHandling users’ enquiriesSolving system problemsResponsiveness, how well a KMS responds to demand by users.
Access AccessibilityAvailabilityResponse timeTimeliness
Flexibility FlexibilityCompatibilityInteroperabilityScalabilityFuture-proofed
Security SecurityPrivacyControl
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Adding new items to the previous findings, combining synonym items, and removing
those that do not seem relevant to KMS gave 36 attributes or ‘quality characteristics’. Based
on similarities, they were grouped into eight dimensions named Functionality, Completeness,
Reliability, Usability, Access, Serviceability, Flexibility, and Security. Analysing empirical
results will show how valid the groupings are and whether there is overlap across the
dimensions (construct validity). Table 4 shows the quality dimensions and their definitions
and Table 5 represents the characteristics corresponding to each dimension.
Conclusions
Customer-orientation is the cornerstone of any quality initiative in today’s competitive
world. This approach highlights the need for further identification and clarification of
the role that ‘customers’ play in KM. A first step in satisfying customer needs is the deter-
mination of how quality dimensions/factors are perceived by them. This information,
together with the prioritised objectives of a particular organisation, will form the platform
from which a quality program can be developed.
The aim of this paper was to develop a basis upon which the quality of KMS could be
measured and improved. A conceptual framework was proposed for the quality dimen-
sions of KMS; this was an integration of traditional product/service quality dimensions
and those attributes specific to the knowledge environment. The result was a 36-item fra-
mework grouped into eight dimensions called Functionality, Completeness, Reliability,
Usability, Access, Serviceability, Flexibility, and Security. An empirical study is
needed to examine the validity of the framework.
Acknowledgements
The author would like to thank Professor Frada Burstein from Monash University and Pro-
fessor Peter Woods from Multimedia University for their constructive comments. This
research has been carried out during sabbatical leave at Multimedia University, Faculty
of Information Technology.
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