Focusing on what you know will narrow your horizon? The role of knowledge base cohesion in learning from brokering

ABSTRACT

Structural holes are believed to be a source of good ideas, important inputs for exploratory innovation. Nevertheless, firms that span structural holes may not always be good learners, as their learning orientation also matters. Drawing upon organisational learning theory and the knowledge recombination perspective, we argue that cohesive knowledge base plays a contingent role in the association between spanning structural holes (SSH) and new knowledge exploration (NKE). Based on panel data of Chinese automakers, our results indicate that global cohesion of knowledge base positively moderates the relationship between SSH and NKE. In contrast, local cohesion of the knowledge base negatively moderates the positive effect of SSH on NKE. These findings advance our understanding of the nuanced role of cohesive knowledge base in learning from brokering.

KEYWORDS:

• Knowledge base
• local cohesion
• global cohesion
• structural holes
• new knowledge
• automobile sector

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article

Notes

1 As He et al. (2004, 485) noted, ‘Exploration versus exploitation should be used with reference to a firm itself and its existing capabilities, resources and processes, not to a competitor or at the industry level’.

2 A firm’s knowledge base refers to ‘a set of elements or individual pieces of knowledge representing the content of what the organization knows’ (Yayavaram and Ahuja 2008, 333).

3 The idea problem refers to the difficulties or obstacles to generating new ideas (Obstfeld 2005).

4 The action problem arises from the difficulties of coordination around structural holes with partners who have different areas of expertise, a unique perspective, and even use a different professional language (Tiwana 2008).

5 For details about the system, please go to http://www.gsxt.gov.cn/index.html.

6 We reach the same conclusions by using graphic analysis based on the hypothesis-specific models as well.

Additional information

Funding

This work was supported by National Natural Science Foundation of China: [Grant Number 71502056, 71673082]; Hunan Provincial Science and Technology Department: [Grant Number 2018JJ3083, 2020JJ3017, 2020JJ5114].

Notes on contributors

Bolin Wang

Bolin Wang is a PhD candidate at the Business School of Hunan University. His research interests focus on organisational behaviours and innovation in Chinese business sectors. His work has been published in several top-tier Chinese management-related journals.

Ling Yuan

Ling Yuan (PhD, China) is a Professor at the Business School of Hunan University. His research interests focus on organizational behaviour and knowledge management. His work has been published in the Journal of Knowledge Management, Systems Research and Behavioral Science, Technological Analysis & Strategic Management, Chinese Management Studies and top-tier Chinese management-related journals.

Jian Li

Jian Li (Ph.D., London UK) is an Associate Professor at the Business School of Hunan University. His research interests focus on technology innovation, R&D collaborations and FDI spillovers in China. He has previously published in Research Policy, Regional Studies, Journal of Technology Transfer, R&D Management, International Business Review, Journal of Knowledge Management, Scientometrics, Technological Analysis & Strategic Management, and Journal of Engineering and Technology Management. Dr. Jian Li is the corresponding author and can be contacted at [email protected]; [email protected].