The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.
Market Orientation, Generative Learning, Innovation Strategy and Business Performance Inter‐Relationships in Bioscience Firms
Open accessAbstract
We propose conceptual arguments to establish relationships between market orientation and generative learning and their respective impact on exploitative innovation strategy and explorative innovation strategy. We then consider the ambidextrous association between both forms of innovation strategy and business performance. This model is subject to an empirical test using data generated from 160 bioscience firms. Using structural equation modelling, two mutually exclusive paths are specified where market orientation leads to exploitative innovation strategy, while generative learning leads to explorative innovation strategy. We then find that the ambidexterity exhibited by firms in the form of exploitative innovation strategy and explorative innovation strategy significantly explains improvements in firms’ business performance. Discussion is given to these findings and managerial implications are presented along with avenues for further research.
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Birkinshaw, J., & Gupta, K. (2013) · Academy of Management Perspectives · doi:10.5465/amp.2012.0167 · Cited by 734
Johnson, J. S., Friend, S. B., & Lee, H. S. (2017) · Journal of Product Innovation Management · doi:10.1111/jpim.12397 · Cited by 282
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Metadata
- Authors
- Morgan, R. E., & Berthon, P.
- Venue
- Journal of Management Studies
- Publisher
- Wiley
- Published
- 2008-11-18
- Volume
- 45
- Issue
- 8
- Pages
- 1329-1353
- ISSN
- 0022-2380 (ISSN-L)
- 1467-6486
- OpenAlex
- W2023927111
- Cited by
- 311
- References
- 108
- FWCI
- 25.46
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Morgan, R. E., & Berthon, P. (2008). Market Orientation, Generative Learning, Innovation Strategy and Business Performance Inter‐Relationships in Bioscience Firms. Journal of Management Studies. https://doi.org/10.1111/j.1467-6486.2008.00778.x
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
