Trust and distrust in university-industry research collaboration

Beryl Morris
University of Queensland

University-industry collaborations emerged in the second half of last century as key forces in the economy, particularly in the science-based industries. Collaborations are defined as the creation of new value together by the parties (Kanter 1994). Collaborations between industry and academia now abound and are acknowledged as offering competitive advantage to the parties (National Innovation & Science Agenda, 2016). However, collaborative relationships can be difficult to manage, particularly where cultural differences are not acknowledged and managed. Problems in the collaborative relationship may result in failure or a sub-optimal outcome and distrust ensuing from a single bad experience has potential to affect future relationships between a sector of industry and universities for long periods of time. Recognition has been given to the complex inter-relationships amongst the parties in a collaboration and an evolution of relationships amongst the parties, producing a rich diversity of trust as different tasks, settings and parties become involved in the evolving collaborative process (Rousseau, Sitkin et al. 1998). Furthermore, it is acknowledged that it is the individuals, not organisations, who really define the levels of expectation forming, risk-taking and vulnerability in collaborations (Six 2005). While the word ‘trust’ is well known and frequently believed to be understood, trust has been defined in many different ways, as described by Morton et al. (2006). Vlaar et al. (2007) confirm that trust and distrust are separate but related constructs and as such, this study defines trust according to Mayer et al. (1995) as a willingness to be vulnerable to the actions of another party and distrust according to Sitkin and Roth (1993) as negative expectations regarding another’s conduct.

Anyone managing relationships where there are cultural differences, whether they arise from the divide between industry and academia, culturally different countries, multidisciplinary collaboration spanning several industry sectors or just different scholarly disciplines within the one institution, knows that such collaborations can be challenging and often do not generate/maintain trust or lead to satisfaction for either party. At a time when universities and industry are being incentivised by government to work more closely in the interests of driving an innovative economy, how does one manage for future collaborative success? The question is of importance because successful collaboration is crucial for harnessing key, cross-disciplinary know-how into commercially and economically valuable science-driven industry projects.

To find an answer, this study took a mixed methods approach, comprising three strands. The first was a researcher-delivered survey to provide demographic and attitudinal information. Second was application of an innovative technique, Multi-criteria Mapping (MCM), which gathered quantitative and qualitative data with the aid of a computer program to map differences of opinion on collaboration. Last were three case studies involving paired interviews to gain the perspectives of the university and industry individuals who had experienced failure in sustaining their respective university-industry collaborations. The survey and MCM instruments involved 52 participants drawn from across Australia and encompassing four equal groups: 1. university researchers; 2. university administrators; 3. industry researchers; and 4. industry administrators.

Using mainly descriptive statistics and establishment of patterns and themes to analyse the combined data, it was apparent that the participants in university-industry collaborations held different opinions of which attributes would satisfy their expectations of the conduct and outcomes of research collaboration. These differences related particularly to issues surrounding relationships, intellectual property, transaction costs and collaboration products. The industry participants showed a preference for interactions through low-risk collaboration with universities, such as using university facilities without the involvement of university researchers. First choice for university administrators was consultancies whilst university researchers preferred to collaborate with industry via grant-funded projects. In the case studies, by contrasting success and failure, noise was stripped away leaving only the presence or absence of matters important to the longevity of the collaboration. As the participants failed to demonstrate, or sustain one or more of competence, integrity, and cross-cultural communication, distrust replaced trust, the participants became dissatisfied and the collaborations broke down. By integrating data from the surveys, MCM exercise and case studies, five prerequisites to trust were identified and incorporated into a model along with the consequence of trust – satisfaction. Together, these five factors were seen to be the critical elements that should be monitored and managed to ensure successful research collaborations, particularly in science-related disciplines. Testing the model in ongoing collaborations between university and industry partners demonstrates its value throughout the life cycle of a collaborative relationship and even as an effective tool for restoring relationships which have started to move along the continuum away from trust and towards mistrust.