University-industry collaboration as a driver of digital transformation: Types, benefits and enablers

University-Industry collaboration as a driver of digital transformation ultimately depends on the engagement and relationship between individual academics and industry practitioners as partners in a trust relationship. This paper describes the personal ('lived') experiences of academics and industry practitioners. The types, benefits, and enablers of the collaboration were investigated during semi-structured face-to-face interviews with industry partners and academic staff from the Science, Technology, Engineering and Mathematics (STEM) unit at the University of Enterprise in Australia. The types of collaboration included research activities, sharing facilities and equipment, innovation and commercialisation, collaboration involving students projects, collaborative teaching and learning, and sharing facilities and equipment. The benefits of the collaboration include access to valuable resources, validation of work, learning and teaching opportunities, financial benefits, improved reputation and career progression. The enablers of collaboration have been clustered into institutional, output, framework, and relationship factors. In this paper we focus on relationship factors and institutional enablers such as stakeholder engagement. The research is represented in a UA-IP Collaboration Model indicating the types (what), benefits (why), and enablers (how) of academic-practitioner collaboration. Universities and businesses from a variety of industries can benefit from the results of the presented research.

A dataset from a population-wide-scale survey of academics in Iceland on barriers to collaboration with industry and community

In early 2021, a quantitative survey was conducted among academics in Icelandic universities to gather information about their involvement in Third Mission (TM) activities and barriers to collaboration [1]. The target group consisted of all academics working at any of the seven Icelandic universities. The majority of participants (n = 674) were affiliated with the University of Iceland, while the remaining (n = 360) were associated with other universities. The survey was designed based on previous studies on barriers to university-industry collaboration [2]. Before it was administered, it underwent a pre-test phase involving various academics and university staff members. Email addresses of potential participants were obtained from the universities' public websites. The survey was conducted using QuestionPro over a period of 21 days, with two reminders being sent following the initial invitation to participate. A total of 183 responses were collected, although not all participants completed the entire questionnaire. Consequently, the response rate amounted to 17.7%. It is worth noting that the study is a census, i.e., it targeted all academics in Iceland that satisfied the participation criteria rather than targeting only a sample of the population. Furthermore, the data extends its focus to academic disciplines that have previously received limited attention in third mission research.

The Demola model as a public policy tool boosting collaboration in innovation: A comparative study between Finland and Spain

The next generation of Science, Technology & Innovation (STI) policies will be defined by mission orientation and co-creation processes and implemented by dynamic public-private partnerships. However, the experience of European countries up to now in attempting to boost cooperation in innovation reveals a very different story. Beyond some contextual factors, the characteristics of the agents involved, the dynamics of their relationships and the design of the collaboration tools also have a critical influence at organisational level. This paper aims to identify these organisational factors in different contexts through the analysis of the Demola model, a university-industry innovation platform created in Finland which has spread to other countries, including Spain. Demola applies a standardised model but it has differences in its functioning depending on the national levels of collaboration in the innovation systems. In our case study, we have compared Finland and Spain, bearing in mind the features of the agents involved and their relationships, through a content analysis of primary and secondary information. The results of the study show that the institutional structure of the collaboration and the organisational culture of each institution are key factors in its functioning. These results can be useful for innovation managers, university leaders, educational experts and policy makers.

Forms and varieties of research and industry collaboration across disciplines

Academic scientists' engagement with industry is a central mechanism in university-industry knowledge transfer and the development of collaborative research. However, most empirical studies are limited to researchers in technical disciplines. We extend the analysis beyond engineers to include broader disciplinary fields, including humanists, economists, medicine, biosciences and cross-disciplinary scientists. Our findings suggest that cross-disciplinary researchers and researchers in technical sciences engage in more industry interaction than their peers. The motivations for the choice of research area play an important role in industry collaboration. Furthermore, we identify three types of industry interaction (interaction modes) among researchers: 1. educational interaction, consisting of conferences or seminars, corporate training programs, or supervising thesis work; 2. research interaction, consisting of shared publications, research-related consulting, public research programs and contract research; 3. integrated interaction, consisting of joint research in shared premisesand employment contracts with companies. Of these, the educational and research interaction modes (1 and 2) are motivated by the possibility of individual academic advancement. Integrated interaction (3) is rare and significantly correlates with only one of the three types of industry cooperation motivations: commercialization of research findings. We conclude by identifying future research needs, opportunities for methodological improvement and policy interventions.

The motivations, institutions and organization of university-industry collaborations in the Netherlands

This study builds on the economics and organization literatures to explore whether and how institutions and organizational structure complement or substitute each other to create specific spaces of alignment where specific individual actors' motivations co-exist. Focusing on university-industry collaborations, the study examines whether and how different axes of alignment of university and industry motivations are integrated in projects with specific technological objectives and organizational structures, benefitting from the presence of specific institutions designed to facilitate collaboration. Empirically, the study relies on in-depth data on 30 university-industry collaborations in the Netherlands, and provides preliminary evidence that the technological objective and organizational structure of collaboration are malleable variables allowing the integration of both partners' objectives and expectations. Different institutional incentives for university-industry collaboration favor specific axes of alignment of motivations and certain types of collaborative projects' design. Hence, our exploratory results suggest that specific organizational and technological structures tend to prevail in the presence of specific institutions.

Measuring efficiency of university-industry Ph.D. projects using best worst method

A collaborative Ph.D. project, carried out by a doctoral candidate, is a type of collaboration between university and industry. Due to the importance of such projects, researchers have considered different ways to evaluate the success, with a focus on the outputs of these projects. However, what has been neglected is the other side of the coin—the inputs. The main aim of this study is to incorporate both the inputs and outputs of these projects into a more meaningful measure called efficiency. A ratio of the weighted sum of outputs over the weighted sum of inputs identifies the efficiency of a Ph.D. project. The weights of the inputs and outputs can be identified using a multi-criteria decision-making (MCDM) method. Data on inputs and outputs are collected from 51 Ph.D. candidates who graduated from Eindhoven University of Technology. The weights are identified using a new MCDM method called Best Worst Method (BWM). Because there may be differences in the opinion of Ph.D. candidates and supervisors on weighing the inputs and outputs, data for BWM are collected from both groups. It is interesting to see that there are differences in the level of efficiency from the two perspectives, because of the weight differences. Moreover, a comparison between the efficiency scores of these projects and their success scores reveals differences that may have significant implications. A sensitivity analysis divulges the most contributing inputs and outputs.