Mechanisms of Knowledge Sharing within the Industry Cluster

Literature Review

Introduction of the Two Levels Model of Knowledge Sharing

Geographical proximity only predisposes companies to the possibility of sharing information with others in a shared industry, whereby, on its own, geographical proximity is not a predictor of knowledge sharing among companies regardless of other industry and market factors they may be sharing. The factors that determine the extent of knowledge sharing in industry clusters are grossly understudied, which means that the mechanisms of the processes that encourage or discourage knowledge sharing are poorly understood(Ibrahim and Fallah 2005; Malmberg and Power 2005). In this regard, it is necessary to identify the other factors that promote knowledge sharing, which Mitchell, Burgess and Waterhouse (2010) identify in their research on knowledge sharing among clustered firms in a shared industry. However, spatial distance is not the only factor that determined the extent to which companies share the knowledge in their possession for purposes of mutual benefits, and Mitchell, Burgess and Waterhouse (2010) identify relational proximity as the other aspect of firm closeness that can determine knowledge sharing, especially in an emerging new industry cluster that is the focus of this study. Based on their findings, a model of study for this research has been formulated, the two level model, in which each aspect of proximity is treated as a knowledge sharing level on its own. One the level is the one determined by distance proximity, hereby referred to as the distance proximity level, and the other level is the one determined by relational proximity, hereby known as the relational proximity level. Secondary sources will be used to discuss the roles played by the two in determining the extent of knowledge sharing, discuss the mechanisms through which these levels operate, determine the relationship between the mechanisms of these levels, and identify the similarities and differences between the two levels.

Distance Proximity Level

The distance proximity level is primarily dictated by geography, whereby companies are said to have distance proximity, especially in the case where they operate in the same market, sell products to similar customer segments, and produce products that are closely related. Since operating in the same geographical location necessitates competition in order for companies to survive, close physical proximity predisposes companies to withhold the knowledge in their possession since doing so keeps them at a competitive advantage relative to other companies in their immediate environment. Although the distance proximity level does not correlate directly to increased collaboration among companies in the same industry, it does put them in a position where clusters based on trustful relations, short cognitive distance, common language, immediate comparison and easy observation (Malmberg and Maskell 2002). Due to these variables that correlate with the distance proximity level of knowledge sharing, geographical proximity cannot be studied in isolation, but should always be evaluated in relation to and association with other proximity dimensions that, together with distance proximity, affect interaction among companies in a common cluster (Boschma 2005). On the other hand, since locational proximity predisposes companies to other dimensions of proximity, the distance proximity level is necessary to any study of knowledge sharing in industry clusters since other dimensions and levels of knowledge sharing cannot exist without distance proximity.

In spite of the positive role of distance proximity to knowledge sharing in industry clusters, Boschma (2005) pointed out that close distance proximity, especially if it is too little or too much, may have negative effects on company innovation primarily due to lock-in. In order to account for all the perspectives of the factors that affect knowledge sharing, Boschma (2005) included cognitive, social, organizational and institutional perspectives of in addition to the geographical aspect. By so doing, the author managed to show that the various dimensions of the distance proximity level produce effects on knowledge sharing in combination or isolation to result in solutions for industry problems, both current and anticipated. The ultimate effects of these mechanisms include more effective control and coordination, and increase flexibility and openness, both of which are solutions to problems of too little proximity and too much proximity respectively.

Geographical proximity is still the primary factor in knowledge production and innovation activities despite increased ability to communicate without geographical limitations due to modern information communication technologies. Although experts suggest that the importance of distance proximity in knowledge creation and sharing may reduce and ultimately disappear with time due to telecommunications developments, the role played by the distance proximity level in knowledge sharing remains important to innovation in the current market environment. In fact, Sonn and Storper (2008) found out that, despite various predictions about the reducing role of geography, the role of geographical proximity on innovation and proximity is currently increasing. Furthermore, multiple combinations of the mechanisms and effects of geographical proximity results in proven increases in innovative activities regardless of the industry in which the companies of interest operate (Silvestre and Dalcol 2009). Geographical proximity, which is the primary component of the distance proximity level, interacts closely with organisational proximity as defined by Lagendijk and Lorentzen (2007). In their definition of the relationship between organisational and geographical proximity, the authors point out that some various factors that may lead to increases in the closeness of an organisation with its peers include the internal characteristics of the organisation itself. For instance, although two firms may be located in the same geographical region, their interaction in knowledge sharing is more likely to be more productive if those companies share characteristics like targeting a similar market segment or being start-ups in an emerging industry (Lagendijk and Lorentzen 2007). In addition, organisational culture factors that result in commitment of resources in communication and building global connections rather than local ones play an important role in improving knowledge sharing within industry clusters.

Management of knowledge through collection, accumulation, organisation and sharing highly determines the competitive ability of market players within a region, which underpins the central role played by geography on successful and sustainable business. According Schamp, Rentmeister, and Lo (2004), knowledge management among companies in industry networks is a demanding and cognitive process that requires other proximity dimensions other than the geographical dimensions. For instance, company players must be able to interact socially, which means that workers from competing companies have to associate for the mutual benefit of all the participating parties (Schamp, Rentmeister, and Lo 2004). By so doing, the knowledge sharing becomes a complex process that presents a governance challenge since the management of companies have to balance between withholding knowledge in order to build a competitive edge but still divulge enough to contribute to the growth and development of the industry.

Relational Proximity Level

Geographical proximity, which is the central factor in the distance proximity level discussed above, depends on the spatial distance between the separate units in an industry cluster and close proximity is an antecedent to other forms of proximity and interaction. On the other hand, relational proximity facilitates interaction of units in an industry cluster as a result of the level at which the units are embedded into the routines and relations of the systems in question (Boschma 2005). In addition, as opposed to the impersonal nature of the distance proximity level, the relational proximity level is more personal as it is responsible for the flow of knowledge across interpersonal networks. Furthermore, this level has been observed to play a central role in the transfer of information, increased learning and solving complex problem solving since they necessitate precise sharing of knowledge (Knoben and Oerlemans 2006). Some of the dimensions of relational proximity that affect the extent of knowledge sharing in industry clusters include personal, organisational institutional and cognitive dimensions, whereby their individual or combined effects affect knowledge sharing.

Boschma (2005) defined organisational proximity as the extent to which organisations share their relations, especially economic, whereby organisations with similar economic antecedents and consequences are said to have closer organizational proximity than companies without shared economic factors. For instance, companies in a highly competitive and developed free market are less likely to participate in knowledge sharing activities within an industry cluster than companies in an emerged and underdeveloped industry. This difference in knowledge sharing is as a result of the consequence that companies in an emerging industry are more concerned about the survival of the industry where companies in an established industry are more concerned about the sustainability of their own competitiveness and their survival as the industry evolves (Boschma 2005). Organisational proximity enables companies to formally and informally balance between sharing knowledge that is complementary to the one possessed by others in the cluster, and a combination of which results in mutual benefits and organisational synergy. Industry actors like employees, managers and other stakeholders play an important role in ensuring the success and sustainability of companies, whereby the interaction of one company with another in sharing knowledge is dictated by the personal proximity among them (Knoben and Oerlemans 2006). Personal proximity involves the bonds that result from kinship and friendships, relationship-based commitments and emotional bonds, and can results in increased knowledge sharing in industry clusters (Boschma 2005). In this case, the effects of the relational proximity level are also contributed by the concepts of both personal proximity that results from personal relationships based on emotional bonds and friendships, and the personal relationships that results from the personal relationships that result due to arrangements by organisations (Knoben and Oerlemans 2006).

Cognitive proximity arises from the extent to which companies share knowledge, their approach to processing the knowledge, and their choice and approach to sharing that knowledge with other players in the market (Postrel 2002). In addition, it includes the amount of the knowledge in their possession companies are willing to share with other companies, especially in the spirit of contributing to the knowledge pool without losing their competitive edge. Cognitive proximity has two primary components, namely, technical and professional proximity that denote sharing of the technical and professional knowledge in their possession. Professional networks are usually normalised by institutional proximity, whereby the extent to which a company’s workforce can share technical and professional knowledge with other firms is determined by institutional proximity. Institutional proximity, in turn, is affected by the extent to which organisations are regulated both informally and formally, and the extent to which companies share operational procedures in spite of differences in their organisational cultures (Inkpen and Tsang 2005). These similarities in operational procedures are usually brought about by common experiences in a shared market environment that forces them to adopt common perspectives and policies.

Although geographical proximity is usually confused with relational proximity, the former only acts as a precursor for the latter, whereby companies can only have a shared market environment, experience and forces to adopt similar policies only if they are in the same geographical region. In addition to the role of distance in determining relational proximity, other factors determine how companies share their knowledge to other market players, including linguistic, experiential, educational, occupational, institutional and industrial dimensions (Gertler 2008). These dimensions of the relational proximity level determine the extent to which the distance proximity level affects the relational proximity level, especially in regard to the minimum proximity level required for companies to successfully collaborate in sharing knowledge for the benefit of the whole industry cluster.

Functional proximity also closely defines the relational proximity level, especially in regard to how companies can share the knowledge that is useful to their own operations or projects with the aim of equipping other companies in the industry cluster with the skills required to complete their projects more successfully and in a sustainable manner. Although other dimensions of the relational proximity level enables companies to share their knowledge, the functional proximity dimension plays a facilitative role, where it allows companies to share knowledge that is directly applicable rather than the common form of knowledge that results to different results and applications by different companies (Moodysson and Jonsson 2007). In the spirit of securing this specialised knowledge for direct application, companies have found it necessary to build global industry clusters by collaborating with companies that are otherwise out of reach in the global proximity level. Global collaborations are even more useful than local ones due to increased variety in terms of experience and access to information and tools that would otherwise be inaccessible. Social networking through the internet is one of the ways through which modern companies share the knowledge in their possession with other companies in the market, especially through relational mechanisms, assortative mechanisms and proximity mechanisms (Rivera, Soderstrom, and Uzzi 2010). These mechanisms determine how ties are formed among companies in an industry cluster, how they persist due to the similarities and positive relations among market players, and how they dissolve due to market forces and differences among the players.

Relationship between the Two Levels

Although the relational and distance proximity levels are distinct, they are closely correlated, whereby some of the mechanisms through which these levels affect knowledge sharing are similar while others have a causative relationship (Knoben and Oerlemans 2006). For instance, the distance proximity determines the extent to which companies share common market environmental conditions, and the relational proximity determines if the reactions of companies to these market conditions are similar enough to allow for sharing of knowledge within an industry cluster without losing a competitive edge. While closer geographical distance results in higher effects of the distance proximity level, longer distance geographical distance reduces its effects; hence reducing the extent to which companies in an industry cluster can share knowledge for the benefit of all market players (Gertler 2008). Currently, this also affects the relational proximity level indirectly, although this may be set to change as companies adopt modern information technologies to achieve globalised industry clusters (Gertler 2008). In addition to the use of globalised technologies, the form and type of knowledge to be shared within an industry cluster determines the extent to which companies share the knowledge in their possession with their peers in spite of the effects of geographical distance between them. In this case, barring the effects of emerging underlying factors, collocation is a central determinant of successful knowledge sharing since it allows for the creation of both informal and formal connections based on labour mobility, reduced costs of transactions and ad-hoc interactions (Iammarino and McCann 2006). Studies have shown that increases in the physical distance between companies reduces the extent to which they interact with each other, which shows a direct causative relationship between the distance proximity level and the relational proximity level (Inkpen and Tsang 2005). In this case, the physical proximity between companies in an industry cluster acts as a precursor for other knowledge sharing mechanisms, especially in regard to those that arise from sharing a common market environment and similar organisational responses.

One primary similarity between the two levels of proximity is the fact that proximity is a source of value, whereby, whether distance or relational, the higher the proximity, the better the chances are that companies in a shared industry cluster will share the knowledge in their possession with other industry players. In addition, the benefits of both levels of proximity can only be reaped as a result of four primary antecedents including, trust between and among companies, their ability to identify useful knowledge, their ability to assimilate the knowledge into their operations and projects, and close proximity to sources of knowledge and expertise. Global communication channels have the potential to eliminate the necessity for geographical closeness for knowledge sharing to occur, but until they do, the distance proximity level plays a central role in determining the extent of knowledge sharing among industry players.


Boschma, Ron. 2005. “Proximity and Innovation: A Critical Assessment.” Regional Studies 39 (1). Taylor & Francis: 61–74.

Gertler, Meric. 2008. “Buzz without Being There? Communities of Practice in Context.” Community, Economic Creativity, and Organization 1 (9). Oxford University Press Oxford: 203–27.

Iammarino, Simona, and Philip McCann. 2006. “The Structure and Evolution of Industrial Clusters: Transactions, Technology and Knowledge Spillovers.” Research Policy 35: 1018–36. doi:10.1016/j.respol.2006.05.004.

Ibrahim, Sherwat, and M Hosein Fallah. 2005. “Drivers of Innovation and Influence of Technological Clusters.” Engineering Management Journal 17 (3).

Inkpen, AC, and EWK Tsang. 2005. “Social Capital, Networks, and Knowledge Transfer.” Academy of Management Review 30: 146–65. doi:10.5465/AMR.2005.15281445.

Knoben, Joris, and Leon A G Oerlemans. 2006. “Proximity and Inter-Organizational Collaboration: A Literature Review.” International Journal of Management Reviews 8 (2). Wiley Online Library: 71–89.

Lagendijk, Arnoud, and Anne Lorentzen. 2007. “Proximity, Knowledge and Innovation in Peripheral Regions: On the Intersection between Geographical and Organizational Proximity.” European Planning Studies 15 (4). Taylor & Francis: 457–66.

Malmberg, Anders, and Peter Maskell. 2002. “The Elusive Concept of Localization Economies: Towards a Knowledge-Based Theory of Spatial Clustering.” Environment and Planning A 34 (3). PION LTD 207 BRONDESBURY PARK, LONDON NW2 5JN, ENGLAND: 429–50.

Malmberg, Anders, and Dominic Power. 2005. “How Do Firms in Clusters Create Knowledge?” Industry and Innovation 12 (4). Taylor & Francis: 409–31.

Mitchell, Rebecca, John Burgess, and Jennifer Waterhouse. 2010. “Proximity and Knowledge Sharing in Clustered Firms.” International Journal of Globalisation and Small Business 4 (1). Inderscience: 5–24.

Moodysson, Jerker, and Ola Jonsson. 2007. “Knowledge Collaboration and Proximity the Spatial Organization of Biotech Innovation Projects.” European Urban and Regional Studies 14 (2). Sage Publications: 115–31.

Postrel, Steven. 2002. “Islands of Shared Knowledge: Specialization and Mutual Understanding in Problem-Solving Teams.” Organization Science 13 (3). INFORMS: 303–20.

Rivera, Mark T, Sara B Soderstrom, and Brian Uzzi. 2010. “Dynamics of Dyads in Social Networks: Assortative, Relational, and Proximity Mechanisms.” Annual Review of Sociology 36. Annual Reviews: 91–115.

Schamp, Eike W, Bernd Rentmeister, and Vivien Lo. 2004. “Dimensions of Proximity in Knowledge-Based Networks: The Cases of Investment Banking and Automobile Design.” European Planning Studies 12 (5). Taylor & Francis: 607–24.

Silvestre, Bruno dos Santos, and Paulo Roberto Tavares Dalcol. 2009. “Geographical Proximity and Innovation: Evidences from the Campos Basin Oil & Gas Industrial Agglomeration.” Technovation 29 (8). Elsevier: 546–61.

Sonn, Jung Won, and Michael Storper. 2008. “The Increasing Importance of Geographical Proximity in Knowledge Production: An Analysis of US Patent Citations, 1975-1997.” Environment and Planning A 40 (5): 1020.


Leave a Reply

Fill in your details below or click an icon to log in: Logo

You are commenting using your account. Log Out /  Change )

Google+ photo

You are commenting using your Google+ account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )


Connecting to %s