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Journal Article

Categories, attention, and the impact of inventions

Strategic Management Journal 42 (5): 992–1023
Balázs Kovács, Gianluca Carnabuci, Filippo Carlo Wezel (2021)
Strategy and general management; Technology, R&D management
Category contrast, invention, patents, search, attention

Research Summary
Whereas prior innovation and strategy literature studied how attentional and search dynamics influence the creation of inventions, we examine how these same processes affect the impact of inventions after their creation. We theorize that inventions classified in “high‐contrast” technological categories garner more attention by potential users and, hence, accrue more citations than otherwise‐equivalent inventions classified in “low‐contrast” categories. We test this hypothesis via three studies. First, we estimate citation‐count models among all USPTO patents granted between 1975 and 2010. Second, we conduct a “twin patents” test comparing inventions patented both at the USPTO and at the EPO. Third, we examine minute‐by‐minute search logs from a sample of USPTO examiners. These studies support our hypothesis and extend current understandings of attentional and search dynamics in the innovation process.

Managerial Summary
Patents that receive more citations tend to have greater economic value and greater impact on future technological developments. We show that the number of citations a patent receives does not only depend on its inherent technological value, but also on seemingly neutral classification decisions affecting the likelihood that it will be noticed by potential future users. We test our arguments via three related studies. Our results demonstrate that inventions classified in “high‐contrast” technology classes garner considerably more attention – and hence citations – than twin‐inventions classified in “low‐contrast” classes. The key managerial implication is that, whenever feasible, nudging an invention towards higher‐contrast classes will increase its future worth. The key policy implication is that maximizing categorical contrast across technology classes will help users identify relevant prior patents.
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