Knowledge-based strategy selection: a hybrid model and its implementation

Date08 February 2016
Published date08 February 2016
AuthorRoozbeh Hesamamiri,Mohammad Mahdavi Mazdeh,Atieh Bourouni
Knowledge-based strategy
selection: a hybrid model and its
Roozbeh Hesamamiri, Mohammad Mahdavi Mazdeh and
Atieh Bourouni
Industrial Engineering Department,
Iran University of Science and Technology, Tehran, Iran
Purpose – The purpose of this paper is to develop a novel hybrid multi-criteria decision-making
(MCDM) model to help organizations select their knowledge-based strategy effectively. Knowledge
management (KM) initiatives are often started with the selection of a strategy, which is a critical
decision for a successful KM implementation.
Design/methodology/approach – KM initiatives are often started with the selection of a strategy,
which is a critical decision for a successful KM implementation. Thus, the aim of this paper is to develop
a novel hybrid MCDM model to help organizations select their knowledge-based strategy effectively.
Findings – Results illustrate that the proposed model is efcient to consider the complex interactions
among criteria and provides a consistent decision with less pair-wise comparisons. Furthermore, a case
study indicates that a “codication versus tacitness” strategy is preferred over other strategies
considering nine main domain criteria.
Originality/value – The contribution of this paper is threefold: it addresses the gaps in KM literature
on the effective and efcient assessment of KM strategy selection; it provides a comprehensive and
systematic framework that combines analytic network process (ANP) and consistent fuzzy preference
relations (CFPR) to assess KM implementation strategy; and it illustrates a real-world study to exhibit
the applicability of the proposed approach and the efcacy of the framework.
Keywords Strategy, Knowledge management, Banking sector, Analytical network process,
Consistent fuzzy preference relations
Paper type Research paper
1. Introduction
Recently, companies have been considering knowledge as a core strategic resource to
gain a competitive advantage and effectively respond to their customers’ needs
(Ordoñez de Pablos, 2014). The relevance and importance of knowledge is becoming
widely critical in business during the transition from the industrial era to the
information and knowledge age. In the new knowledge age, the signicance of effective
knowledge management (KM) has been addressed by several researchers and industry
analysts (Chen et al., 2011;Chang and Ahn, 2005). KM is dened as the collection of
processes that govern the creation, acquisition, collection, dissemination and utilization
of knowledge, which often starts with the selection of a strategy, a critical decision for a
successful implementation (Jennex, 2012). In this context, strategy refers to the
organizational plan and enabling condition for organizational KM (Nonaka and
Takeuchi, 1995). With the globalization of the world’s nancial markets, banks have to
The current issue and full text archive of this journal is available on Emerald Insight at:
based strategy
Received 14 March 2015
Revised 19 May 2015
Accepted 13 July 2015
VINEJournal of Information and
KnowledgeManagement Systems
Vol.46 No. 1, 2016
©Emerald Group Publishing Limited
DOI 10.1108/VJIKMS-03-2015-0020
consider KM to be more efcient in their operations (Taherparvar et al., 2014;Nattapol
et al., 2010;Kridan and Goulding, 2006). Dening a knowledge-based strategy that will
be approved by senior management and frontline staff is a complex but essential rst
step of KM initiatives.
However, despite the growing interest in knowledge-based strategy making and the
factors that inuence the success of KM, little that is specic has been said about the
analytical methods rms use to systematically evaluate and identify knowledge-based
strategy to gain competitive advantage out of their KM investments. Knowledge-based
strategy selection is a strategic concern, and involves subjective and qualitative conclusions
(Bierly and Chakrabarti, 1996). Specically, choosing a knowledge-based strategy is based
on an evaluation that considers multiple factors such as an organization’s resources, time
requirements, environment, market status, business strategy, culture and technology.
Therefore, the model of knowledge-based strategy selection needs to consider several
complex and interrelated factors in a more reasonable and rational manner.
Hence, knowledge-based strategy selection is a kind of mu1ti-criteria decision-making
(MCDM) problem, and requires MCDM methods to solve. MCDM methods involve multiple
and regularly conicting criteria that allow decision-makers (DMs) to handle complex
valuation and assessment problems to achieve a certain objective. The analytic hierarchy
process (AHP) has been declared a useful tool for solving complex, unstructured strategy
selection problems for many years (Saaty, 1980). AHP requires pair-wise comparisons for all
feasible strategies to evaluate different alternatives. AHP can deal with only hierarchical
relationships, and is unable to consider interdependencies among criteria. The analytic
network process (ANP) was proposed to solve such problems with criteria
interdependencies (Saaty, 1996). In contrast to AHP, ANP considers a network system in
which all criteria and alternatives involved are related. Because there are numerous
interdependencies among criteria when evaluating KM strategies, ANP as a relatively new
MCDM method is appropriate (Mahdavi Mazdeh et al., 2013).
In spite of their wide use in diverse elds (Vidal et al., 2010;Wu, 2008), both AHP and
ANP methods obligate an organization to perform a large number of comparisons,
which may cause incomplete decision-making consideration and inconsistency in
evaluators’ judgments. The number of comparisons in AHP and ANP results in DMs’
confusion, and reduces strategy selection efcacy and accuracy. Moreover, while the
number of criteria or comparison levels increases, the consistency of a decision matrix
decreases. In other words, inconsistency occurs given increasing hierarchies of criteria
or alternatives. To consider this dilemma, Herrera-Viedma et al. (2004) proposed
consistent fuzzy preference relations (CFPR) to avoid inconsistent solutions in the
decision-making processes. Using CFPR requires less time, and would greatly improve
decision-making efciency, has computational simplicity and guarantees the
consistency of decision matrices. Some examples of its wide applications include group
decision-making (Zhu and Xu, 2014), supply chain efciency (Wang and Chen, 2011),
distance e-learning effectiveness (Chao and Chen, 2009), measuring the probability of
successful KM (Wang and Chang, 2007a,2007b), partnership selection (Wang and Chen,
2007) and merge strategy selection in banking (Wang and Lin, 2009).
The proposed model structures the problem of multi-criteria knowledge-based strategy
selection in a network form. It also explicitly captures interdependencies among these
various factors, and enables a more methodical analysis. Moreover, this method is
implemented in a case study, and analytical results are illustrated for further managerial

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