Sharing Scientific and Social Knowledge in a Performance Oriented Industry: An Evaluation Model

Sharing Scientific and Social Knowledge in a Performance Oriented Industry: An Evaluation Model

Haris Papoutsakis
Copyright: © 2011 |Pages: 29
DOI: 10.4018/978-1-60960-203-1.ch013
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The chapter evaluates the contribution of shared knowledge and information technology to manufacturing performance. For this purpose, a theoretical model was built and tested in praxis through a research study among manufacturing, quality and R&D groups. The social character of science is perceived as a matter of the aggregation of individuals, not their interactions, and social knowledge as simply the additive outcome of mostly scientists, members of the three groups, making sound scientific judgments. The study results verify the significant contribution of shared knowledge to the manufacturing group performance. They also demonstrate that information technology influences notably the manufacturing group performance and, in a less significant way, the sharing of knowledge. Study results are useful to researchers and the business community alike as they may be used as a springboard for further empirical studies and can help put together strategies involving knowledge management and information technology.
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Theoretical Background

In the relevant literature, most attempts to investigate the links among KM and IT that lead to improved business performance, are done within the environment of the knowledge-creating company (Nonaka 1991; Nonaka and Takeuchi 1995). Building upon this pioneer work, Grant, in a series of articles (1995 with Baden-Fuller, 1996a, 1996b, 1997) and Sveiby (1997, 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. Fourth, 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.

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