Linking
Knowledge Management and Information Technology to Business Performance:
A
Literature Review and a Proposed Model
Haris Papoutsakis,
Technological Education Institute of Crete, Ramon Salvador Vallès, Universidad Politécnica
de Catalunya
ABSTRACT:
Knowledge has long ago been
recognized as an important asset for sustaining competitive advantage.
Recently, the use of information technologies within an organization has been
identified, by many companies, as an important tool for managing or sharing
organizational knowledge in order to improve business performance. However,
most current empirical studies have explored the relationships among these
three factors either in isolation or in pairs of two at a time.
The
paper classifies studies
published by various researchers from 1996 to 2003 into five categories based
mainly on the theoretical viewpoint and the measurement approach that each one
is putting forward. Furthermore, we highlight certain important parameters of
every measurement system that do not fall under any of these five knowledge
management measurement perspectives. In conclusion, and as a result of critical
synthesis, we situate our own proposition and we present briefly the results of
our own study together with some guidelines for management.
Keywords: Knowledge Management, Information Technology, Business Performance, Literature Review
1. Introduction
A significant number of
relatively recent contributions (Drucker 1985, 1990,
1991; Grant 1996a, 1997, 2000; Nonaka & Takeuchi
1995; Sveiby 1992, 1997; von Krogh
et al, 1998, 2000a, 2000b) have supported that evolution,
that is to a great extent based on the administration of knowledge that
leads towards innovation, learning, creativity and novelty. However, neither
all of the above scientists nor every manager in the industry would give to
this reflection the same significance. As a result, companies attempting to
deploy Knowledge Management (KM) are very often confused by the variety of
actions emerging under the KM umbrella.
A second group of
scientists (McFarlan et al 1983; Davenport &
Short, 1990; Henderson & Venkatraman 1993; Venkatraman 1994; Applegate et al 1999; McNurlin
& Sprague 2004) are emphasizing the prospect that the emerging Information
Technology (IT) may become the driving force behind the required business
transformation. In order to take full advantage of the opportunities
facilitated by IT, in particular when applied to KM, senior managers should manage
IT in order to successfully combine it with the strategic objectives of their
company.
The above two KM- and
IT-centred mainstreams, in the literature reviewed for this paper, appear to be
equally valid in the manufacturing and service sectors. Many companies have
tried, with varied achievement rates, to leverage knowledge assets by
centralizing KM functions or by investing heavily in IT. In parallel, an
increasing number of articles and research have proposed and tested models for
the management of knowledge, with or without the support of information
technologies. A considerably smaller number of such studies, though, have
investigated into how companies can leverage knowledge in order to improve
Business Performance (BP).
The rest of the paper is
organized in three sections. In the following section the theoretical framework
is defined, built upon the ‘knowledge-based theory of the
firm’ endorsed primarily by Robert Grant and Karl-Erik Sveiby. In section three we present the literature we
reviewed, primarily on issues linking knowledge management and information technology to business
performance. In section four, after a synthesis of the reviewed methods and
models we situate our own model within the above framework.
2. Theoretical
Background
Our attempt to
study, through a literature review, the links among KM and IT that lead to
improved BP, is done within the context of the knowledge-based theory of the
firm. In a series of recently published management books (Quinn 1992; Drucker 1993; Nonaka & Takeuchi
1995; Prusak 1997; Davenport & Prusak 2000) the implications of knowledge-based work and
knowledge-based competitive advantages are outlined and the role of knowledge
within the firm is highlighted. What is interesting about these books is the
fact that they all integrate theory with practice, in the so called
‘knowledge-based view of the firm’, and therefore surpass the
division between academic research and management practice (Grant, 1997).
On the other
hand, amongst academics, the ‘knowledge-based view of the firm’ has
received influences from various research lines. Based upon Polanyi’s
‘epistemology’, the ‘resource-based theory’ (Barney
1991, 1996; von Krogh & Roos
1995; Wernerfelt 1984, 1995) is acknowledged as the
most dominant among them. Other research lines, like ‘organizational
capabilities and competences’ (Prahaland &
Hamel 1990), ‘innovation and new product development’ (Teece 1998, 2000; Wheelwright & Clark 1992) and
‘organizational learning’ (Argyris 1986, 1991) have
also contributed significantly.
Grant (Grant & Fuller, 1995; Grant 1996a, 1996b, 1997) in a series of articles, and Sveiby (2001) presented in a very clear way the fundamentals of a knowledge-based theory of the firm. According to Grant (1997), recapitulating on his previous work, the knowledge-based view is founded on a set of basic assumptions. First, knowledge is a vital source for value to be added to business products and services and a key to gaining strategic competitive advantage. Second, explicit and tacit knowledge vary on their transferability, which also depends upon the capacity of the recipient to accumulate knowledge. Third, tacit knowledge rests inside individuals who have a certain learning capacity. The depth of knowledge required for knowledge creation sometimes needs to be sacrificed to the width of knowledge that production applications require. Forth, most knowledge, and especially explicit knowledge, when developed for a certain application ought to be made available to additional applications, for reasons of economy of scale.
Sveiby (2001) believes that people can use their competence to create value in two directions: by transferring and converting knowledge externally or internally to the organization they belong to. When the managers of a firm direct the efforts of their employees internally, they create tangible goods and intangible structures such as better processes and new designs for products. When they direct their attention outwards, in addition to delivery of goods and money they also create intangible structures, such as customer relationships, brand awareness, reputation and new experiences for the customers.
It is under this theoretical perspective that we are reviewing the literature, relevant to our investigation, in the following section.
3. Previous
Empirical Studies
Linking knowledge management and
information technologies with business performance has never been an easy task.
Comparing KM projects to their two prevailing predecessors [total quality
management and business process re-engineering], Armistead (1999; pp. 143)
notices that authors on KM “… do not use the same hard measures of
success consistently”. He believes that for a knowledge-based view to be
useful, it must help improve some key performance indicators (like quality,
flexibility and cost). Referring to manufacturing companies he notes that
operational processes, which depend more on knowledge, are expected to perform
well against measurements of quality in consistence, while at the same time
they improve productivity.
Various researchers have approached
the issue from different perspectives that we have classified into five
categories: (a) accounts and/or audit type of studies; (b) studies based on the
balanced scorecard; (c) studies that evaluate and measure the impact; (d)
quantitative measures studies; and (e) studies of the causal relations between
KM and BP, with or without the involvement of IT. Finally, we have included
under a separate section certain important parameters of every measurement
system that do not fall under any of the above KM measurement perspectives. In
the following paragraphs we present the most dominant of these perspectives and
we situate our own proposition within this framework.
3.1. Accounts and Audits
Larsen et al (1999) studied the
intellectual capital accounting statements of five Scandinavian firms,
utilizing specific metrics. Despite the debate against the creation of
intellectual accounts by means of specific metrics, we believe that the
proposed by Larsen et al (1999) model merits some credit. It provides a matrix
with definitions of human, structural and customer capital that demonstrates
how the firm’s intellectual capital can be made visible with the use of
the proposed metrics. They are based on Statistical Information (‘What
Is’), Internal Key Indicators (‘What Is Done’) and Effect
Measures (‘What Happens’) all three elements tightly coupled during
the creation of the firm’s intellectual accounts. Larsen et al (1999) in
a self critical way conclude that “… there is no set model for intellectual
capital statements, and they do not provide a bottom-line indicator of the
value of intellectual capital.” According to the authors “…
intellectual capital statements are situational … they are not concerned
merely with metrics (… and they) do not disclose the value of the
firm’s intellectual resources. Instead, they disclose aspects of the
firm’s knowledge management activities.” But they declare that
intellectual capital accounting statements “… do not just ‘measure’,
they also ‘report’ and ‘act’ “(pp. 18-19).
In a very comparable way other
researchers consider that knowledge audits play a key role in identifying both
knowledge assets and the appropriate KM strategy for the firm. For Liebowitz et al (2000) conducting a knowledge audit is one
of the first critical steps in the knowledge management area. In the same way
that a traditional manufacturing company will first inventory its physical
assets, an aspiring knowledge organization should inventory its intellectual
capital assets. The knowledge audit they propose (based on a case study of a
small size
The models proposed by Larsen et al (1999)
and Liebowitz et al (2000) are both based on
intellectual capital measures and the authors themselves are the first ones to
question their generability.
3.2. Balanced Scorecard
Criticizing financial measures like
the Return on Investment (ROI) and Economic Value Added (EVA), used by senior
management in several organizations, Knight (1999) proposes a Balanced
Performance Measurement System (BPMS) based on the Kaplan and Norton (1992)
Balanced Scorecard. According to the author, increasing ROI attracts new
investors and drives stock prices high, while EVA, which aims to improve
profits and market value by keeping low-cost dept, when used alone, disregards
intellectual assets and long term investments in training and information
technologies.
In concluding his criticism, Knight
(1999) argues that “Leveraging intellectual capital requires a company to
become a knowledge-based organization and to revise its performance measures
accordingly” (pp. 23). The model he proposes functions in three levels.
The first two provide all information needed for making a business case for the
company’s KM project. In the final level, BPMS is used to measure and
leverage the organization’s intellectual capital and its financial
performance that involves the level of profitability and growth achieved. Based
on the equation: Market Value = Book Value + Intellectual Capital, Knight
proposes generic performance indicators that can be used by almost any
organization, in order to evaluate measurable performance objectives. The model
provides for more indicators to be developed and used by middle level
management in order to face unique situations. Unfortunately, the model has
been applied on a hybrid organization –based on real-life experiences, as
the author claims– lacking, in this way, credential for serious
generalization.
3.3. Evaluate and Measure the
Impact
This is an approach that has gained
greater support and appreciation within the business world (CEOs and senior
executives) rather than the academia. Cohen (1998) reports Jan Torsilibri (of Booz Allen &
Hamilton) saying that “… the value of knowledge cannot be directly
measured, but it is possible to measure outcomes: changes in profitability,
efficiency, or rate of innovation that follow from knowledge efforts.”
(pp. 33). And he gives the example of Buckman
Laboratories that “… has used the increase in percentage of sales
from new products as a measure of innovation and attributes the improvement to
the firm’s development of a better knowledge culture and
infrastructure“(note 7, pp. 39).
Firestone (2001), under a similar
business perspective, proposes the Comprehensive Benefit Estimation (CBE)
framework that presents the basic concepts, methodology and tools for producing
improved KM benefit estimates. CBE is firmly coupled to corporate goals, and
distinguishes benefits according to their relative importance. Firestone claims
that various degrees of comprehensiveness are appropriate for different
corporate situations, while he recognizes that CBE might not be practical in many
situations. So, instead of a single methodology he is proposing an
‘abstract pattern’ of CBE that could easily be tailored, in
different ‘ideal type’ situations, to achieve a feasible estimation
procedure. Three such ideal situations are presented in Firestone’s
paper.
In the first case (where no prior
work on development of an Enterprise Performance Management (EPM), Balanced
Scorecard, Enterprise Resource Planning (ERP), or Data Warehousing system has
been done – a situation hard to imagine in any major corporation today)
Firestone admits that CBE is not the best solution. As an alternative he
proposes the use of the Analytical Hierarchy Process (AHP, Saaty
1990) a method that does not need prior measured data to work except data
generated by AHP itself. Firestone considers the second case as a better
developed business environment (where ERP and/or Data Warehousing systems
already exist) more favorable for implementing CBE, as much of the work will
have already been done. He proposes to once again apply the AHP, but this time
using the real data. Finally, case three (where a Balanced Scorecard or EPM
system is already available) is recognized as the most favorable situation for
implementing CBE, as most of the data gathering and measurement will have already
been completed.
The works of Cohen (1998) centering
on corporate profitability and efficiency, and Firestone (2001) centering on
corporate goals and benefits, are both in the direction of our investigation,
but they do not provide a direct link to business performance.
3.4. Quantitative Measures
Return on Investment (ROI) is
probably the most popular among the quantitative measures of KM project impact.
Compared to cost or book value it is considered a much better tool for the
assessment of the business performance.
Kingsley (2002) who studies law
firms’ profit models, the costs of KM systems and document reuse
statistics, develops a framework for measuring the Return on Investment (ROI)
and the Cost of Information (COI) and proposes tools to evaluate alternative
knowledge-sharing strategies. He sees ROI as the return (or incremental gain)
from a project minus its cost and proposes its use for measuring
‘hard’ returns. In addition, and for measuring ‘soft’
benefits, Kingsley utilizes COI, a figure of specific value to law offices that
calculates the expense of knowledge sharing by comparing the per-document cost
of the system to the average rate of reuse. Kingsley calculates COI figures in
three popular knowledge-sharing strategies and finds a rather high COI of $25
for Bibliographic Coding, while lower COI is calculated for Subject Matter and
Context Indexing ($5 to $7) and Advanced Search Tools ($3).
Despite the fact that under such
perspective, the final ‘I’ in the ROI abbreviation could be
interpreted as Investment, Ideas, or Information, according to our
understanding, ROI can only capture part of a KM project’s impact, mainly
because such projects always have accidental effects that can not be easily
captured as financial return.
3.5. Causal Relations
Nelson and Cooprider
(1996) are investigating the causal effects of knowledge shared between
Information System (IS) groups and their line customers, to the performance of
the IS group. They base their empirical study on data collected through
interviews and questionnaires addressed to managers of 86 IS groups and their
line customers, in the
The perspective of Chong et al (2000) is also very close to the one proposed
for our research. In their effort to provide a well-defined framework that
relates investment in expertise or internal competencies to corporate
performance, they first conducted key informant interviews with 20 managers of
four companies that belonged to financial services, energy and consultancy
sectors. The final survey sample consisted of 25 FTSE 500 organizations from
the financial services and technological sectors, in the
Lee and Choi
(2003) propose a method to measure organizational performance, built upon a
rather complicated research model that interconnects knowledge management
factors, such as enablers and processes, with performance. The model links
seven KM enablers (among them collaboration, trust and IT support which are
also used in our model) with Nonaka’s knowledge
creation model and organizational creativity, in order to measure their impact
on organizational performance. The questionnaire-based survey was conducted
among 58 major Korean companies covering manufacturing, service and financial
business sectors. The use of IT as an enabler, affecting knowledge creation,
has been adopted in the model proposed for our study, where we also investigate
the impact of IT on business performance. The Lee and Choi
method illustrates cause and effect links among the proposed model components
in a similar way to the one we do in our study.
3.6. Time and Other Issues
In the course of our previous
literature study we have encountered certain other issues that are related to
KM and the measurement of its effect on business performance that do not fall
under any of the above KM measurement perspectives. For example,
Time is also a measurement issue:
Not only ‘what’ we measure but ‘when’ we expect
measurable results must be part of the measurement system. The American
Productivity and Quality Center (APQC) during its 2000 consortium implemented a
multi-client benchmark among some of the most advanced early knowledge
management adopters from both the US and Europe. According to the report that
appears in APQC (2001), although they recognize five stages of KM project implementation
in the so-called KM Measurement Bell Curve, only during the more structured
ones measurement is considered of importance. During the early implementation
stage, measurement rarely takes place, but interviewing key stakeholders
–the methodology used in our study– is recommended. As companies
move into more advanced stages the need for measurement steadily increases and
during the latest stages, when KM becomes a way of doing business, the
importance of KM-specific measures diminishes.
APQC recognize that measuring
knowledge management is not simple, and is in fact analogous to measuring the
contribution of marketing, employee development or any other management or
organizational competency. But this does not stop APQC from proposing certain
types of measurements appropriate for each stage.
4. Synthesis
and the Proposed Model
The extended literature analysis
presented above, where business performance in general is the focus, yields
some observations that have guided the design of our research in the course of
the Doctoral Thesis. First, it points out that the link between knowledge
management, information technology, and business performance is not a simple
issue. It involves two basically different research areas: The measurement
–in terms of both qualitative and quantitative results– of a KM
project’s impacts and, at the same time, the identification of the
cause-effect relationship that exists between KM, IT, and the overall business
performance enhancement. In the literature reviewed, some studies captured KM
contribution by measuring outcomes such as knowledge satisfaction, whereas
others adopted conventional performance measures (such as ROI and EVA) or more
abstract and tailored to the company ones, like CBE. In our own research we
have chosen qualitative indicators in order to evaluate the cause and effect
relationships among our variables.
Second, the role of shared knowledge
among a company’s departments is not consistent, despite the fact that
the knowledge transfer process has been studied extensively. Trust and
influence have only been recognized as antecedents of shared knowledge by
Nelson and Cooprider (1996), while Lee and Choi (2003) consider trust and information technology as
knowledge creation enablers among seven others.
Third, an integrative model
combining shared knowledge and information technology with business performance
is still missing. Although some studies investigate the relationship between KM
and performance, or IT and performance, they fail to explore the relationships
among KM, IT and performance simultaneously. We strongly believe that if
managers become conscious of the fact that these relationships have interactive
features, they can stand a much better chance of improving their
departments’ or company’s performance.
As in our own research we are aiming to gain insight into the
essential factors influencing manufacturing performance, we chose to develop
and empirically test a conceptual model containing the minimum selected
theoretical constructs. Three have been our major concerns, upon building our
research model. First, we did not want to propose a model that delineates all
the variables or processes that affect manufacturing performance. Second, we
wanted to focus on shared knowledge as the leading expression of knowledge
management, among the manufacturing, quality and R&D groups of a firm. Third,
information technology, in our model, has been perceived to affect both
manufacturing performance and shared knowledge.
Therefore, we have opted for our model to highlight a few key factors that can explain a large proportion of the variation noted in manufacturing performance. We modified the sharing knowledge model validated and used by Nelson & Cooprider (1996) and we enhanced it with links allowing us to draw conclusions on the role and contribution of information technology as an enabler and facilitator towards both manufacturing performance and shared knowledge. The proposed evaluation model shows cause and effect links between sharing knowledge, its components, information technology and manufacturing performance. In this respect we consider the proposed model more consistent than the intellectual capital or the tangible and intangible approach used in other studies.
Figure
1. The Shared Knowledge and Information Technology - Evaluation
Model
Schematically, the empirical evaluation model which we used in order to test
the investigation hypotheses, illustrates the relationships among the five
variables as shown in Figure 1. To
a great extent, our hypotheses derive from theoretical statements found in the
literature related to knowledge management and information technology and
systems. Survey data collected from 51 medium to large size Spanish industrial companies with a total of 112
manufacturing groups, representing 5 industrial sectors (alimentation,
automotive, chemical and pharmaceutical, electro-mechanical, and textile) were
analyzed to test the model.
The proposed evaluation model has been tested empirically using path
analysis, a regression-based technique that permits
the testing of causal models using cross-sectional data and normalized path
coefficients (betas) in order to
determine the strength and direction of causal paths or relations. The
investigation hypotheses have been tested and fully or partially supported, by
the significance -or insignificance- of the relevant paths.
5. Conclusions
Three questions have guided our research in the course of the Doctoral Thesis:
1. What are the major components or antecedents of shared knowledge?
2. What is the nature of the relationships among shared knowledge, its components and the manufacturing group performance?
3. What is the role of information technology support towards (a) sharing knowledge and (b) the manufacturing performance?
During the course of our study we satisfactorily answered each of these questions. For the first one, we conceptualized –building upon relevant literature– the two antecedents of sharing knowledge: mutual trust and mutual influence. Perhaps more significantly, we demonstrated the ability to evaluate these constructs in a reliable and valid way.
Questions two and three were answered through the multiple regression analysis on our research data. For the second question the results of this analysis show that:
¨ There is a positive relationship between shared knowledge and manufacturing performance (i.e. increasing levels of shared knowledge among manufacturing, quality and R&D groups, leads to increased manufacturing group performance).
¨ Shared knowledge mediates the relationship between manufacturing performance and mutual influence, while mutual trust affects manufacturing performance mainly through shared knowledge but also in a direct way.
Finally, for the third question our empirical study has demonstrated that:
¨ Information technology significantly affects manufacturing performance, and has a less significant effect on shared knowledge, as it mainly influences explicit to explicit knowledge transactions.
In general, we can state that the above results adequately fulfill the aim of our study which was to investigate the contribution of shared knowledge and information technology to manufacturing performance. Upon concluding we have summed up some guidelines to management deriving from the extensive review of the relevant literature and the results of our investigation:
¨ Managers should become aware that the great challenge is settled on investment in knowledge processes and knowledge workers and should, thus, make sure that their subordinates (a) include in their objectives the task to share knowledge and available information with colleagues in collaborating groups; and (b) are entirely aware of the information technology resources available (special groupware software and equipment).
¨ Management should not underestimate the unique characteristic of knowledge being one of the few assets that grows almost exponentially when shared. As employees from one group share knowledge with colleagues in the collaborating group, the interactive potential of their knowledge grows.
¨ Managers should also be aware that sharing knowledge in a meaningful manner requires a well balanced merge of technology with the company’s culture, in a way that creates an environment supporting collaboration. Trust has been identified, through our study, as one of the company’s core values. Management, thus, has to create a climate of trust in the organization for knowledge sharing to become reality.
¨ Finally, senior management has also a very important role to play. Senior executives have the difficult task to manage the middle-level managers in an effort to minimize the negative effects due to (a) resistance to change and (b) the various barriers to communication (structural, as well as language and cultural barriers).
Factors that help eliminate such negative effects may include joint training on interdependent tasks, joint planning sessions and formation of cross-functional teams. In addition, strategic rotation (the temporary movement of managers from one group to another) can lead to mutual trust and influence, the true antidote to both resistance to change and barriers to communication.
Based on the literature and the results of our research, we have demonstrated that the significant contributions of the following are useful conclusions for researchers and the business community alike:
1. shared knowledge to manufacturing group performance, and
2. information technology mainly to the manufacturing group performance and only secondarily to sharing knowledge
Manufacturing, Quality and R&D groups have the opportunity to increase shared knowledge and, in this manner, to positively affect manufacturing performance by developing mutual trust and influence through repeated periods of positive face-to-face or IT-based communication, social interaction, and common goal accomplishment.
NOTE: Detailed results together with the complete conclusions of our
study are presented in the Doctoral Thesis under the title “The
Contribution of Shared Knowledge and Information Technology to Manufacturing
Performance: An Evaluation Model” which we defended at the UPC
(Universidad Politécnica de Catalunya)
in
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Meet the Authors:
Haris Papoutsakis, School of Applied Technology,
Technological Education Institute (TEI) of Crete, P.O. Box 1939, GR-71004 Heraklion, Crete, Greece; Tel. +30 2810 379857; E-mail:
harispap@career.teicrete.gr; Contact address: 9, G. Georgiadi St., GR-71305
Heraklion, Crete, Greece; Tel. +30 2810 229246, Fax
379281, mob. +30 6947 083878
Assistant Professor Haris Papoutsakis
is an academic faculty member in the
Ramon Salvador Vallès, Escuela Técnica Superior de Enginyeros Industriales de
Professor Titular Ramon Salvador Vallès, PhD has developed his professional career within
the Business Administration and Information Technology Management area for a
period of twenty years, sharing lectures; research; and tuition work at the
university (UPC). He has been working for private companies and public
institutions, ENHER (Spanish Power Company) and Generalitat
de Catalunya (Catalan Government) being among them.
He is the author and co-author of several books, book chapters and journal
articles on Information Systems Management, Business Administration and
e-Commerce.