Abstract
Many studies have proven the relevance of patent characteristics to predict firms’ economic returns. The most studied ones concern the (technological, scientific or radically new) type of knowledge embedded into the patents; the technological impact on society, measured by the forward citations; the economic value attributed by the firms to the patents, measured by their renewal and, more recently, the closeness of the patent to the firm’s technological profile. We build on this literature, focusing on a less studied topic, the characteristics associated to the academic patents held by firms and the profit stream generated by these assets. We empirically examine these research issues using longitudinal data from a cross-industry study of 712 units of observation over a recent 10-year period (1996–2007). The paper focuses on the units’ idiosyncratic effects and the heterogeneous impact of the academic patents. We analyse the effect of academic patents characteristics with a one- and a three-year time lag structure, following the literature indication that academic patents can show a different impact at medium-long term. Contrary to previous findings, what matters for academic patents to improve firms’ economic performance both at short and at long term is not their radicalness or explorative nature, but the stock of technical and scientific knowledge on which inventions are based, measured through the backward citations to patent and non-patent literature and the closeness to firm’s core technologies, in which companies have good competences and invest more resources. These results open the way to more in-depth analyses.
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Notes
- 1.
These two good-quality datasets (i.e. Lotti & Marin, 2013 and APE-INV) provide a unique tool to investigate the research questions at hand on a European country. Furthermore, given that the two datasets have already been largely employed (separately) in academic research, they are scientifically validated, thus providing reliability to our results.
- 2.
The descriptive statistics of our sample are extremely close to the statistics of the entire academic patent population. Therefore, the representativeness of our sample, notwithstanding the presence of missing values, is not biased.
- 3.
Precisely a one standard deviation increases in academic patent technological closeness led to a 0.3 standard deviation decrease in the projected OPM, all other variables held constant.
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Cerulli, G., Marin, G., Pierucci, E., Potì, B. (2022). The Heterogeneous Impact of Academic Patent Characteristics on Firms’ Economic Performance. In: Azagra-Caro, J.M., D'Este, P., Barberá-Tomás, D. (eds) University-Industry Knowledge Interactions. International Studies in Entrepreneurship, vol 52. Springer, Cham. https://doi.org/10.1007/978-3-030-84669-5_3
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