Title: The impact of knowledge attributes on technological learning routine within industrial clusters

Authors: Jingjing Guo; Bin Guo; Xiaoling Chen; Jian Du

Addresses: Institutes of Science and Development, Chinese Academy of Sciences, Beiyitiao 15, Haidian District, Beijing 100190, China ' School of Management, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China ' College of Business and Administration, Zhejiang University of Technology, Chaowang Road 18, Hangzhou 310024, China; School of Management, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China ' School of Management, Zhejiang University, Yuhangtang Road 866, Hangzhou 310058, China

Abstract: From a knowledge processing perspective, this paper defines the concept of technological learning routine based on four distinct processes in technological learning: knowledge acquisition, knowledge maintenance, knowledge reactivation and knowledge transformation. We propose that knowledge attributes (i.e., knowledge tacitness and knowledge heterogeneity) have significant impacts on the intensity and the variety of the technological learning routine within industrial clusters. Survey data from 231 industrial cluster firms reveals that knowledge tacitness has a positive and significant influence on both the intensity and the variety of the technological learning routine, while knowledge heterogeneity is negatively related to the variety of the technological learning routine within industrial clusters. This study contributes to the literature through clarifying the operationalisation of the technological learning routine construct and providing a comprehensive understanding of the relationship between the knowledge attributes and technological learning routine within industrial clusters.

Keywords: knowledge tacitness; knowledge heterogeneity; technological learning routine; industrial cluster.

DOI: 10.1504/IJTM.2018.095632

International Journal of Technology Management, 2018 Vol.78 No.3, pp.234 - 260

Accepted: 29 Mar 2017
Published online: 16 Oct 2018 *

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