Knowledge-sharing through social interaction in a policy-driven industrial cluster

Publication Date07 October 2013
Date07 October 2013
AuthorChristopher Richardson
SubjectStrategy,Entrepreneurship,Business climate/policy
through social interaction in
a policy-driven industrial cluster
Christopher Richardson
Graduate School of Business, Universiti Sains Malaysia, Penang, Malaysia
Purpose – The purpose of this paper is to investigate whether knowledge-flows through social
interaction occur within the context of a policy-driven industrial cluster.
Design/methodology/approach – The paper follows a single-case approach, adopting Malaysia’s
Multimedia Super Corridor cluster as the unit of analysis. Semi-structu red interviewswith firm- and
non-firm-respondents in the cluster constitute the prime source of data.
Findings – Spontaneous social interaction leading to knowledge diffusion within the cluster may be
lacking. However, policymakers may be able to remedy this somewhat by organising wo rkshops,
conferences and other events to help firms gain additional knowledge, although thesemeasu res should
ideally complement, rather than replace, sp ontaneous face-to-face meetings.
Practical implications – Although policymakers may implement certain measures to try to
compensate for the shortage of knowledge flows through social interaction (e.g. organising more
“formal” events such as workshops and exhibitions), it is argued that these may not be sufficient in
ensuring the long-term, self-sustaining success of the cluster.
Originality/value – The paper integrates extant literature on “organic” industrial clusters into
a pre-planned, purpose-built, policy-driven cluster context. Research on policy-driven clusters is
somewhat limited, with attention from scholars primarily focused on organic clusters. This paper
attempts to bridge the gap for future research in the area.
Keywords Knowledge-sharing, Policy-driven clusters, Social interaction
Paper type Case study
1. Introduction
The association between industrial clusters and regional economic development has
prompted policymakers in various parts of the world to adopt cluster-based approaches
to policy (Enright, 1999; Fromhold-Eisebith and Eisebith, 2005; Lundequist and Power,
2002). Clusters are often considered a potential source of foreign direct investment,
employment, R&D, and export earnings (Casper, 2007; Porter, 2000; Pouder and St. John,
1996), and are thus particularly appealing to authorities in developing and emerging
markets (Jussawalla, 2003). Moreover, studies have shown that clustering can boost
innovation (Baptista and Swann, 1998; Bell, 2005; Waters and Smith, 2008), productivity
(Henderson, 1986; Puig et al., 2009), and competitiveness (Porter, 2000).
In many cases, the key to the success of clusters lies in the creation and diffusion
of tacit knowledge that takes place within the region (Boschma, 2005; Saxenian, 1994).
Tacit knowledge is strongly embedded, often requiring regular, informal, face-to-face
Gertler, 2003). It is argued that operating in an industrial cluster can increase the
The current issue and full text archive of this journal is available at
Journal of Entrepreneurship and
Public Policy
Vol. 2 No. 2, 2013
pp. 160-177
rEmeraldGroup PublishingLimited
DOI 10.1108/JEPP-08-2011-0010
The author gratefully acknowledges the comments and suggestions of Mo Yamin, Rudolf
Sinkovics, and the two anonymous reviewers. This research was supported by a doctoral grant
from the Economic and Social Research Council (ESRC).
potential for firms to acquire such knowledge from one another because of the intense
social networking that characterises these regions (Swann and Prevezer, 1998).
However, whether industrial clusters that are develop ed from scratch by
policymakers – primarily to contribute to economic development – can generate flows
of knowledge through localised social interaction is not clear. One the one hand, simply
bringing firms and othereconomic actors in one industry into a particular geographical
area may not necessarily lead to intense social interaction of the kind seen in regions
such as Silicon Valley (Saxenian, 1994). On the other hand, given the heavy investments
made in developing thecluster, policymakers may be particularly ke enfor the cluster to
succeed, and thus may put in place-specific measures to promote knowledge-sharing,
irrespective (or as a result) of any limited spontaneous social networking.
With this in mind, the main objective of this paper is to shed some light on
whether policy-driven clusters offer a favourable setting for the acquisition of
knowledge by firms through social interaction. Malaysia’s Multimedia Super Corridor
(MSC) industrial cluster was chosen as the context for the study. The MSC is an
information and communications technologies (ICT) c luster stretching from Malaysia’s
capital, Kuala Lumpur, to the Kuala Lumpur International Airport, with its heart in
Cyberjaya, a purpose-built city around 50 kilometres south of the capital. The MSC was
designed to spearhead Malaysia’s transformation from a manufactu ring economy to
a knowledge-based economy.
2. Literature review
2.1 The importance and nature of knowledge dissemination
In today’s world, knowledge is the most important resource a firm can possess
(O’Hagan and Green, 2002). “Knowledge” comprises all cognitions and abilities
individuals use to solve problems, make decisions and understand incoming
information (Doring and Schnellenbach, 2006). With rapidly evolving technologies
and markets and an unpredictable and uncertain economic environment, today’s
firms must be adaptable and ready to engage in a process of sustained learning in
order to survive (Amin and Wilkinson, 1999). In other words, the ability of firms to
not only create but also to learn or acquire new knowledge is critical to their
competitive advantage (Boschma, 2005).
With ICTs allowing people to communi cate increasingly effectively at great
distances (Rallet and Torre, 1999), firms are now able to acquire new knowledge more
rapidly than ever before. In spite of these technological advances, however, face-to-face
interaction remains the key mechanism for the exchange of mo re complex, tacit forms
of knowledge between individuals (Dicken and Malmberg, 2001; Gaspar and Glaeser,
1998; Leamer and Storper, 2001). This is because tacit knowledge is often highly
subjective, personal, and difficult to put into words (O’Hagan and Green, 2002). The
circulation of this type of knowledge, therefore, tends to be geographica lly limited,
making it seem somewhat “sticky” in nature (Maskell and Malmberg, 1999).
2.2 Industrial clusters and knowledge-flows
Industrial clusters are often considered suitable environments for the diffusion of this
sticky type of knowledge (Acs et al., 2002; Malmberg et al., 1996; Sorenson, 2003), with
frequent face-to-face interaction among proximate entrepreneurs and other actors
being a common cluster characteristic (Dahl and Pedersen, 2004). These regular,
informal meetings in clusters often lead to personal relationships and “insider” status
(Porter, 1998), allowing firms to tap into one another’s pool of knowledge.
sharing through
social interaction

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