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Analysing clusters of skills in R&D — core competencies, metaphors, visualization, and the role of IT

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Abstract:

Though it is now universally accepted that companies should try to align their R&D activities with their business objectives, achieving this alignment is notoriously difficult in practice. The rise of the core competence framework has been very helpful in creating, and legitimizing, a language in which issues of technical competence and R&D strengths can be followed through to their consequences for competitive advantage. Companies are starting to express their R&D priorities explicitly in terms of core competencies.

Without effective IT support, core competence concepts are often applied arbitrarily. This has led to accusations that core competence theory can become yet another battlefield upon which companies play out their internal political battles. Computer-based techniques can help counteract this danger by enabling large volumes of relatively objective data to be collected, then making it possible to analyse and draw out patterns from this data, and finally enabling the data to be represented effectively.

It is in this last area of data representation that information technology is now of particular benefit. In order to make the core competence approach sufficiently robust as a basis for decision making, it is necessary to collect and process large volumes of data. However, this data is normally difficult to represent in such a way that managers can assimilate it. In our recent experience, we have come to realize the particular importance of effective representations and metaphors, and have started to shift our own emphasis towards these areas in addition to analysis per se.

The paper shows how core competence approaches can support R&D management decision making by exploring the roles of data collection, analysis and representation. Information technology is an integral part of these approaches, and we draw out some generalized lessons for the successful use of IT in decision support.

Document Type: Research Article

DOI: http://dx.doi.org/10.1111/1467-9310.00079

Affiliations: Scientific Generics, Harston Mill, Harston, Cambridge, CB2 5NH, UK jklein@scigen.co.uk

Publication date: January 1, 1998

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