The economics of tacit knowledge in patent based technology transfer agreements

Hardik Daga et al.

The Journal of World Intellectual Property2025https://doi.org/10.1111/jwip.70007article
ABDC C
Weight
0.37

What the paper says

Abstract This paper examines how the absence of tacit knowledge in technology transfer agreements based on patents leads to economic inefficiencies both at the firm level and across national innovation systems. Patent‐based technology transfers typically convey codified knowledge such as blueprints and technical documentation but often fail to incorporate tacit knowledge—experiential, non‐codifiable know‐how critical for effective implementation. This oversight is especially detrimental in industries where adaptation, trial‐and‐error learning, and process optimization are key to successful adoption. The paper highlights the economic costs of excluding tacit knowledge from licensing transactions and argues that technology recipients are frequently left dependent on licensors for post‐transfer support, prolonging inefficiencies. While it briefly discusses the potential of disclosure tools like the “best mode” requirement to address this gap, the paper primarily advocates contractual, policy, and institutional mechanisms to embed tacit knowledge transfer into technology agreements. The study proposes structured learning provisions, long‐term collaborative arrangements, and policy incentives as practical reforms to facilitate fuller and more effective knowledge diffusion.

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https://doi.org/https://doi.org/10.1111/jwip.70007

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@article{hardik2025,
  title        = {{The economics of tacit knowledge in patent based technology transfer agreements}},
  author       = {Hardik Daga et al.},
  journal      = {The Journal of World Intellectual Property},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1111/jwip.70007},
}

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.