From knowledge to impact. An investigation of the commercial outcomes of academic engagement with industry

ABSTRACT

The multifaceted issues regarding university-industry relations are an increasing focus of attention of both scholars and practitioners, as a means of enhancing current knowledge transfer practices and policies. One of the central questions is whether and how the mechanisms underlying different types of university-industry collaborations (collaborative research, contract research, consulting) influence universities’ research commercialisation outcomes (patenting, licensing, spin-off generation). Results of six negative binomial models in Generalized Estimating Equations based on longitudinal panel data on Italian universities, reveal that while collaborative research with industry leads to an increase in patenting activities and spin-off generation, contract research and consulting boost licensing opportunities. Results also reveal complementarities between different types of university-industry linkages, fostering all the types of research commercialisation outcomes. Managerial and policy implications are discussed at the end of this paper.

KEYWORDS:

• University-industry relations
• collaborative research
• contract research
• research commercialisation

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 The conditions for complementarity between two practices corresponds to the definition of strict supermodularity (Milgrom and Roberts 1990). In the conventional sense of scope economics, two activities are complementary if adding an activity while the other activity is already being performed has a higher incremental effect on performance than performing the activity in isolation. To test the hypothesis, we refer to the interaction of different types of engagement on the relevant outcome measures.

2 The Variance Inflation Factor (VIF) for the interaction term reported a significant reduction (model 1: from 11.23 to 1.21; model 2: from 11.21 to 1.26; model 3: from 10.62 to 1.21). Due to variables standardization, all average VIF values fell below 2.

3 Using continuous measures, complementarity implies that cross-partial derivatives of the outcome functions with respect to different engagement practices are positive.

4 The joint significance tests reported significant p-values (p < 0.05).

5 The strength of the interaction effect is reflected in the difference between the Wald chi-square statistic of models without vs. with interaction.

6 Simulated values of dependent variables were obtained by setting all the control variables at their average values, while allowing variation of dependent variables over a range of +/− 2 SDs, multiplied by coefficients derived from models 4–6.

Additional information

Notes on contributors

Cristina Marullo

Cristina Marullo, PhD, is Assistant Professor of Innovation Management at the Institute of Management, Sant’Anna School of Advanced Studies, Pisa, Italy. Her research interests deal with R&D and innovation management and university-industry collaborations, with a special emphasis on entrepreneurial processes and dynamics.

Andrea Piccaluga

Andrea Piccaluga, PhD, is Full Professor of Business Administration and Innovation Management at the Institute of Management, Sant’Anna School of Advanced Studies, Pisa (Italy). His research deals with R&D and innovation management, open innovation and university-industry knowledge and technology transfer, with a focus on the creation and growth processes of academic spin-off companies.

Fabrizio Cesaroni

Fabrizio Cesaroni, PhD, is Full Professor of Management at the Department of Economics, University of Messina, Italy. His main research interests are in the management of technological innovation and intellectual property rights, and in firms' technology strategies. He is also interested in the management of technology transfer processes from non-profit research organizations (universities and public research laboratories) to industry.