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Knowledge intermediation strategies: a dynamic capability perspective
[Managing assets and skills: the key to a sustainable competitive advantage]

Author

Listed:
  • Namatié Traoré
  • Nabil Amara
  • Khalil Rhaiem

Abstract

This study investigates (i) differences in knowledge intermediation strategies among knowledge and technology transfer organizations (KTTOs) and (ii) the factors that explain such differences. It uses data from 212 Canadian KTTOs. When knowledge delivery capacity (KDC) and knowledge integration capacity (KIC) dimensions of knowledge intermediation are simultaneously accounted for, four categories of KTTOs emerge, namely, (1) knowledge stores; (2) knowledge match providers; (3) knowledge integrators; and (4) knowledge brokers. This heterogeneity results in a differentiation in KTTOs' service delivery strategies. Factors that are conducive to custom-made solutions include (i) increased innovativeness; (ii) higher absorptive capacity; (iii) stronger information search and storage capabilities; (iv) effective customer knowledge management (CKM); and (v) increased networking capabilities. Larger knowledge intermediaries suffer from internal organizational stickiness that prevents them from delivering custom-made services. KTTOs with a low degree of formalization and centralization in decision-making are likely to adopt intermediation strategies aimed at reaching the largest possible number of users. Some managerial and public policy implications are drawn.

Suggested Citation

  • Namatié Traoré & Nabil Amara & Khalil Rhaiem, 2021. "Knowledge intermediation strategies: a dynamic capability perspective [Managing assets and skills: the key to a sustainable competitive advantage]," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 30(1), pages 49-74.
  • Handle: RePEc:oup:indcch:v:30:y:2021:i:1:p:49-74.
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    File URL: http://hdl.handle.net/10.1093/icc/dtaa036
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    Cited by:

    1. Rhaiem, Khalil & Halilem, Norrin, 2023. "The worst is not to fail, but to fail to learn from failure: A multi-method empirical validation of learning from innovation failure," Technological Forecasting and Social Change, Elsevier, vol. 190(C).

    More about this item

    JEL classification:

    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • L24 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Contracting Out; Joint Ventures
    • I23 - Health, Education, and Welfare - - Education - - - Higher Education; Research Institutions
    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities

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