Firms have increasingly been competing through design. We show how computer vision techniques can be leveraged to measure the visual similarity of design rights across large data sets of product design images. Thus we extract and standardize 611,810 unique design images embedded in US design rights (1976–2020), adapt the structural similarity index measure to quantify design similarities between images, and rigorously validate the resulting design rights similarity measure. We then use that measure to produce novel empirical evidence that the similarity density of a design space exhibits an inverted U-shape with respect to the likelihood of that space’s design rights being litigated—a relationship proposed previously but never tested. Our design rights similarity measure should facilitate the exploration of new research questions in the fields of design rights, innovation, and strategy. We grant open access to our code and data resources to encourage research in these areas.
(co-authored with Tian Cian and Cornelia Storz)
Ansprechpartner: David Heller
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