University-Industry Knowledge Interactions

Collaboration between universities and industry partners is thought to facilitate knowledge diffusion and provide resources and new ideas for academic researchers. However, recent evidence also suggests a possible trade-off or cost with regard to individual productivity. Given its focus on quantitative output, the literature is rather silent on possible qualitative shifts in researchers’ agendas when engaging with industry partners. We contribute to a discussion on potential negative effects of university-industry engagement by comparing the topic distributions of PhD theses based on collaborative and noncollaborative research. The results indicate little difference between the two kinds of dissertation projects. We conclude that fears of agenda setting in collaborative research are unwarranted.

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.

In this paper we seek to realise the potential that Spaapen and van Drooge’s productive interactions concept offers, but which we argue has been lost through its operationalisation as a process of ‘counting interactions’. Productive interactions arise through moments of contact between two very different systems (the societal and the scientific), and each system values societal impact in very different ways. Finding mutual value in that interaction is important, and we argue that value in both arises when network arrangements shift, as academic disciplines solve urgent scientific problems and as societies improve living conditions. Productive interactions approach assumes the value-frameworks of the wider networks within which particular knowledge sets become actionable. However, our constructive critique highlights the omission of the wider elements of science and social systems within which productive interactions takes place (and whose dynamics ultimately determine the final scientific and societal impact of that research). Indeed, research evaluation to date has not considered the consequences of the productive interactions in terms of these changing relationships. To contribute to this lacuna, we propose a model that conceptualises a meso-level system comprising interactions between actors within two subsystems, highlighting the importance of coupling between researchers and users, valuation signals given to particular productive interactions from researcher and societal communities and the way these signals in turn embed useful knowledge practices. We apply it to a set of examples of productive interactions in the field of social sciences and humanities (SSH) gathered in the framework of a European project.

Countries deploy a variety of policy instruments to promote university-industry knowledge transfer. While these instruments are often discussed in isolation, they are implemented collectively and may reinforce and complement but also weaken or even negatively affect each other. This chapter presents a conceptual framework to map policy instruments for knowledge transfer and assess the interactions between them. Positive interactions occur, for example, when a new grant scheme to support spin-offs is accompanied by the adoption of more flexible regulations regarding the participation of university professors in firms, leading to a stronger combined impact. In contrast, negative interactions are associated with potential contradictions between policy instruments or with the coexistence of various policies targeting simultaneously the same types of actors, which increases complexity, creates confusion and results in higher administrative costs. The conceptual framework developed in this chapter also aims to explain how the choice of policy instruments is influenced by national contexts and broader international trends. This framework is a useful tool for those involved in the design and evaluation of university-industry knowledge transfer policies, while offering a broad point of departure for future research.

Long-term university–industry contract research benefits both universities and the industry, as it can potentially reduce transaction costs and improve the quality of such collaborations. Nevertheless, trade-offs between the advantages and disadvantages of long-term contracts motivate firms to enter stage-gate contracts (i.e., a shorter contract period with an expectation of renewal or extension) to avoid uncertainty over collaboration’s performance. This study addresses two less understood questions in the contract renewal or extension decision: longitudinal changes in the strength of the commitment to the collaboration and the determinants of renewals. We empirically test these issues with 1562 research contracts from a leading Japanese university, and we match this database to a questionnaire survey results obtained from its industrial counterparts. Our empirical test identified an inverse-U-shaped effect on the degree of commitment in the time elapsed since the first research contract. We also found that firms are more likely to renew or extend a contract when they perceive technological knowledge learning or co-publish an academic paper. Our findings suggest that university–industry contract research focused on academic research-related activities (or academic researcher’s “going my way”) is likely to establish long-term collaborations.

There is a paucity of studies which have analysed the role of internal processes for academic spin-offs in a systematic way. We focus on a specific nuance of internal processes which relate to the management practices that universities can put in place to influence the growth of academic spin-offs. Building upon recent literature on the empirical economics of management, we investigate whether and how different forms of management practices contribute to the growth of academic spin-offs. We collect survey data on universities’ management practices by focusing on technology transfer offices, as well as drawing on a longitudinal sample of 790 Italian university spin-offs founded by 42 different Italian universities, which were observed over the period of 2006 to 2014. Our findings show that management practices help to explain the growth of academic spin-offs, although their effect varies across management practices. Supporting operations in the form of patenting and training as well as the existence of incentives are found to have a positive association with spin-off growth. A negative association is instead found for target setting (spin-off growth targets) and hiring of external management with private sector experience. We provide an explanation of these results by pointing to a combination of adverse selection, short-termism and weak enforceability by universities. Drawing on our results, the adoption of management practices by university managers and policy makers should be carefully considered, as they might have unintended consequences on the growth of academic spin-offs.

The aim of the present work is to understand whether the institutional dynamics of Public Research Organizations (in particular, the flexible structure of research units and their physical proximity to firms, as well as their research specialization) have a positive impact on Technology Transfer, helping to create and maintain collaborations with firms. The main subject of the research are large PROs, i.e., research bodies characterized by a wide coverage of scientific fields and by the presence of a geographically distributed research network. In order to address our research question, the paper exploits the results of a multiple case study analysis performed on the Institutes of the CNR, the National Research Council of Italy. The results show the positive impact on technology transfer of the internal organization and flexible structure of large PROs, as well as the positive externalities of research units localization across different proximity dimensions. The conclusions of our work offer policy suggestions and cues for future research.

Previous research shows that women are under-represented among academic entrepreneurs, indicating a gender gap in this field. Using a case-oriented approach combining interview analysis and fuzzy-set analysis, we explore potential barriers to women’s engagement in academic entrepreneurship as perceived by both the researchers and the heads of technology transfer offices (TTOs). The inclusion of the latter group foreshadows the relevance of different actors who can influence the gender gap in academic entrepreneurship settings. We thus contribute to the body of knowledge about female academic entrepreneurship. The potential barriers are modelled as internal and external. We reveal that internal barriers (e.g., work-family balance and ambition) are perceived as more crucial than external barriers by both groups of respondents. However, TTOs and researchers seem to partially disagree about those barriers, which may impact the effectiveness of mechanisms implemented to mitigate the gender gap in academic entrepreneurship. Moreover, although both TTOs and researchers recognise the gender gap, neither party identified TTOs as responsible for reducing the associated disparities. Our fuzzy-set analysis, performed to explore the causal relationships between different gender gap conditions and female academic entrepreneurial activity, reveals two combinations of barriers underlying women’s low engagement in academic entrepreneurship.

This paper investigates PhD entrepreneurship. We focus on the university factors most closely associated to: (1) students’ success in starting a business venture; (2) students’ startup intention; (3) students’ abandoning the entrepreneurial idea. The empirical analysis is based on data from a questionnaire survey, administered in 2016 in Italy. We focus on four factors related to the university entrepreneurial environment: (1) university entrepreneurship policy frameworks; (2) PhD orientation to business problems; (3) entrepreneurship training; (4) PhD lab reputation. We find that the academic environment can have a fundamental impact on students’ decisions to start new ventures and on their entrepreneurial attitude.

While studies find support that international academic mobility stimulates academic entrepreneurship, we lack understanding on the relation between these two increasingly relevant phenomena. We draw upon the resource-based view to disentangle this underexplored relation and theorize that international academic mobility augments the academics’ identification of entrepreneurial opportunities through the accumulation and processing of external and heterogenous knowledge and that an academic’s interpersonal social network acts as a mechanism through which such knowledge is transferred.