Explaining the gap between policy aspirations and implementation: the case of university knowledge transfer policy in the United Kingdom

The paper proposes a conceptual framework to explain why, particularly when policies deal with complex and ambiguous issues, an increasing gap may open up between government-set objectives and the instruments used for policy implementation and evaluation: the former are characterized by increasing breadth and ambiguity, while the latter become progressively narrower in scope. The case of policies in support of university-industry knowledge transfer in the United Kingdom is used as an illustration of how these processes can play out in practice

Assessing the impact of knowledge transfer policies: an international comparison of models and indicators of universities’ knowledge transfer performance

This chapter discusses how to appropriately measure the effectiveness of knowledge transfer from university to industry. It shows that the assessment systems implemented in several countries (UK, US, Canada, Australia and Europe) adopt rather narrow views of what constitute relevant knowledge transfer activities and their impacts, leading to the selection of partial indicators that might not allow all institutions to represent their knowledge transfer performance accurately. We derive some implications for the measurement of universities’ performance and for the assessment of policies in support of knowledge production and transfer more generally

The contribution of business services to the export performances of manufacturing industries. An empirical study on 5 European countries

This paper outlines some critical issues connected with the choice of appropriate indicators in the measurement of universities’ performance in knowledge transfer

Indicators of university–industry knowledge transfer performance and their implication for universities: evidence from the United Kingdom

The issue of what indicators are most appropriate in order to measure the performance of universities in knowledge transfer (KT) activities remains relatively under-investigated. The main aim of this paper is to identify and discuss the limitations to the current measurements of university-industry KT performance, and propose some directions for improvement. We argue that university-industry KT can unfold in many ways and impact many stakeholders, and that, especially in highly differentiated university systems, choosing indicators focused on a narrow range of activities and impacts might limit the ability of universities to accurately represent their KT performance. Therefore, KT indicators should include a variety of activities and reflect a variety of impacts, so as to allow comparability between different institutions and avoid the creation of undesirable behavioural incentives. To illustrate these issues empirically, we discuss the case of the United Kingdom’s Higher Education –Business and Community Interaction (HE-BCI) survey

Strategic alliances and firm performance in startups with a social mission

Innovation with a social purpose is strictly linked to entrepreneurship and economic development. However, those startups that pursue a social mission often operate in really novel markets and raise some scepticism in the eyes of investors. Startups can improve their business performance by leveraging on equity and non-equity based strategic alliances, so to pursue growth. However, sustainable growth requires to attract the right investments at the right stage of development of the startup. This study draws on international business theory and proposes a novel framework that explains the mechanisms regulating strategic alliances and firm performance in a startups context. We use a sample of 3,913 UK high-tech startups engaging in social innovation to test our hypotheses and we derive an explanation for some of the mechanisms behind strategic alliances effect on startups performance, startups scalability and the balance needed between performance and the pursuit of a social mission

Assessing the Impact of University-Industry Collaborations: a Multi-Dimensional Approach

The growing importance of university-industry knowledge transfer has prompted government bodies at all levels to devise ways to support and encourage collaborations between universities and industry (UICs). These collaborations have been shown to be effective knowledge transfer channels and are particularly likely to generate long-term benefits for firms and various stakeholders. Funds are made available to support collaborative research projects, for example by the European Commission Framework Programmes, the Advanced Technology Programme in the United States, the Research Councils in the United Kingdom, government programmes in Germany and the Netherlands, and many others. Despite these increases in funding, the literature shows that the assessment of the impact of interventions in support of UICs is usually based on a narrow range of metrics, mainly focused on capturing the income accrued from the collaboration and a few other quantitative output indicators. There is, therefore, a need for more in-depth investigations into the impact that UICs have on a broad range of stakeholders, over time, in order to support a transition towards more accurate and comprehensive approaches to impact assessment. In our study, we propose a theoretical framework to identify the multiple dimensions of such impact. By focusing on the case of the United Kingdom, and in particular on one type of government-supported university-industry collaboration scheme, Knowledge Transfer Partnerships (KTPs), we discuss the application of this framework to our empirical investigation of fourteen case studies of recent KTPs, and we explore ways to standardize the measurement of at least some of these impact dimensions, in order to contribute to the debate on how to build better indicators of UIC performance

Does affective evaluation matter for the success of university-industry collaborations? A sentiment analysis of university-industry collaborative project reports

University-industry collaborations (UICs) play a crucial role in the knowledge-based economy; however, past research has paid surprisingly little attention to the role played by the ‘subjective’ determinants of collaborations and their influence on ‘objective’ collaboration outcomes. By performing a sentiment analysis on a dataset of 415 final reports from completed UICs, we find that there is a negative relationship between the collaborators’ perceived challenges and benefits of UICs, mediated by negative affective evaluation. Instead, a positive affective evaluation of the UIC is positively correlated with its perceived benefits, which, in turn, are a predictor of an important objective outcome of UICs: the likelihood of future collaboration. A positive affective evaluation also negatively moderates the positive relationship between perceived challenges and negative affective evaluation. Therefore, a positive affective evaluation may increase the likelihood of future collaboration, even in a context in which a UIC is perceived to be challenging. Besides generating theoretical implications, our findings are of significant value for practitioners, as we highlight the need to regulate perception and affective evaluation to achieve successful UICs. We showcase sentiment analysis as a helpful foresight tool to identify those UICs that are more likely to continue over time

Does affective evaluation matter for the success of university-industry collaborations? A sentiment analysis of university-industry collaborative project reports

University-industry collaborations (UICs) play a crucial role in the knowledge-based economy; however, past research has paid surprisingly little attention to the role played by the ‘subjective’ determinants of collaborations and their influence on ‘objective’ collaboration outcomes. By performing a sentiment analysis on a dataset of 415 final reports from completed UICs, we find that there is a negative relationship between the collaborators’ perceived challenges and benefits of UICs, mediated by negative affective evaluation. Instead, a positive affective evaluation of the UIC is positively correlated with its perceived benefits, which, in turn, are a predictor of an important objective outcome of UICs: the likelihood of future collaboration. A positive affective evaluation also negatively moderates the positive relationship between perceived challenges and negative affective evaluation. Therefore, a positive affective evaluation may increase the likelihood of future collaboration, even in a context in which a UIC is perceived to be challenging. Besides generating theoretical implications, our findings are of significant value for practitioners, as we highlight the need to regulate perception and affective evaluation to achieve successful UICs. We showcase sentiment analysis as a helpful foresight tool to identify those UICs that are more likely to continue over time