206
Views
1
CrossRef citations to date
0
Altmetric
Articles

Innovate with whom? The bridging effect of organizational learning capability for knowledge-intensive SMEs

ORCID Icon &
Pages 657-683 | Published online: 28 Nov 2022
 

ABSTRACT

With the advent of the knowledge economy, inbound open innovation for knowledge-intensive SMEs has attracted more and more attention. However, the distinctiveness between them and traditional SMEs in this process remains undiscussed. This paper investigates how different partnerships influence knowledge-intensive SMEs' innovative performance by demonstrating the organisational learning capability mechanism. Based on the survey data from 248 Chinese knowledge-intensive SMEs, this paper finds that both market-based and science-based partnerships play nonnegligible roles in knowledge-intensive SMEs' innovative performance, and organisational learning capability partially mediates these relationships. In addition, when the environmental turbulence increases, knowledge-intensive SMEs prefer science-based partnerships. The research results indicate that knowledge-intensive SMEs have different partner selection strategies for open innovation compared to traditional SMEs, which could be affected by the turbulence of the external environment. This study sheds light on the relationship between open innovation and the performance of knowledge-intensive SMEs.

Disclosure statement

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

Notes

1 According to the National Bureau of Statistics of China (http://www.gov.cn/gongbao/content/2019/content_5419213.htm), knowledge-intensive manufacturing generally includes ICT manufacturing, new equipment manufacturing, new materials manufacturing, medicine and medical industry, environmental protection industry, etc. Knowledge-intensive service industries generally include ICT services, R&D and technical service, etc.

2 According to the standard of the Ministry of Science & Technology of China (http://www.innofund.gov.cn/).

3 The data on the number of SMEs in China is collected from the China Economic Census Yearbook 2018 (http://www.stats.gov.cn/tjsj/pcsj/jjpc/4jp/zk/indexce.htm), and the data on knowledge-intensive SMEs is collected on the official website of the Ministry of Science and Technology of the People's Republic of China (https://www.most.gov.cn/index.html)

4 The data is collected on the official website of the Ministry of Science and Technology of the People's Republic of China (https://www.most.gov.cn/index.html) as of May 2022 (the last update of the data in this paper).

5 From the news on the official website of the Ministry of Science and Technology of the People's Republic of China (https://www.most.gov.cn/index.html).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China under Grant Nos. 71672172.

Notes on contributors

Jun Jin

Jun Jin received the Ph.D. degree in economics from the University of St. Gallen, St. Gallen, Switzerland, in 2005. She is currently an Associate Professor with the Department of Innovation, Entrepreneurship, and Strategy, School of Management, Zhejiang University, Hangzhou, China. She is also the Visiting Professor with the Singapore University of Technology and Design, Singapore. Her research interests include the areas of global innovation, catching up strategies, open innovation, and eco-innovation.

Meng Li

Meng Li is currently working toward the Ph.D. degree in management science and engineering with Zhejiang University, Hangzhou, China. His current research interests include open innovation, optimal distinctiveness.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 444.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.