Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2024
Abstract
We examine the relationship between innovation and performance in the agricultural industry by studying how a firm's patent portfolio position in the knowledge landscape moderates the relationship between three firm search dimensions (scope, specialisation, and commitment) and the firm's financial performance. To represent dynamism in the knowledge landscape, we apply topic modelling to 67,120 patent texts of 571 firms and introduce two complementary perspectives on how knowledge can be dynamically categorised. This allows us to create two representations of the knowledge structure to identify contested (scarce) clusters of high (low) recent inventive activity and emergent clusters where knowledge ambiguity has reduced. Our findings suggest that search scope and commitment prove more valuable near scarce clusters, whereas search specialisation is most valuable in contested clusters. In addition, we find that all three search dimensions are positively moderated by proximity to emergent clusters, but vary in their effectiveness. Our results explain an additional 1% in within-firm financial performance (EBITDA). We discuss implications for innovation and strategy research as well as for practice.
Keywords
Innovation;search;patents;agriculture;topic modelling;Latent Dirichlet Allocation
Discipline
Strategic Management Policy | Technology and Innovation
Research Areas
Strategy and Organisation
Publication
Innovation: Management, Policy and Practice
Volume
26
Issue
1
First Page
85
Last Page
114
ISSN
1447-9338
Identifier
10.1080/14479338.2022.2062365
Publisher
Taylor & Francis
Citation
Simon J.D. SCHILLEBEECKX; LIN, Yimin; and GEORGE, Gerard.
Innovation in dynamic knowledge landscapes: using topic modelling to map inventive activity and its implications for financial performance. (2024). Innovation: Management, Policy and Practice. 26, (1), 85-114.
Available at: https://ink.library.smu.edu.sg/lkcsb_research/7009
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/14479338.2022.2062365