Many businesses seeking enhanced innovation have corporate research teams that engage in collaborative research projects (CRPs), with external entities such as universities, public organizations, or customers. Anecdotal evidence, however, suggests mixed outcomes of CRPs in terms of corporate research impact, which implies successful transfer of novel knowledge generated within CRPs to company-internal business networks to develop radically innovative products. We use the multiple regression quadratic assignment procedure (MRQAP) and meta-analysis to analyze six CRP networks. Our findings indicate that the network’s relational characteristics (tie strength) and structural characteristics (network range) are important determinants of knowledge transfer at the fuzzy front end of innovation.

Trust is fragile. The 2018 Facebook and Cambridge Analytica debacles highlighted how data harvested from social media platforms can be used not only for commercial purposes but also for political manipulation. This incident and the widespread discussion around it further demonstrated the following issues: unethical data collection enabled by a platform; unethical use of data for corporate and political interest; and unethical data sharing by an academic. Research needs to be credible to maintain social license. Data is the lifeblood of research. For research to remain credible, research needs to remain fundamentally ethical and research methods comprising data collection and data analysis need to be robust, transparent, repeatable, and auditable. Such methods alone cannot create credibility, but research data infrastructure design and implementation can provide a foundation for credibility by addressing these fundamental processes. Social science research has traditionally relied on data collection methods such as surveys, interviews, and ethnographic observations. However, an increasing proportion of human life is being mediated by online platforms, with approximately 2.3 billion active users on Facebook and 326 million active users on Twitter (Statista 2019). Social media data collection and analysis have become imperative for researchers interested in various phenomena playing out in these new media. This paper discusses the current state and issues of social media data collection and describes the Digital Observatory’s approach to establishing a credible and trusted research data infrastructure.

Companies invest in research and development (R&D) with the expectation of boosting their innovation pipeline to achieve growth and profitability. In addition, many companies seeking to enhance their internal R&D capability adopt an open innovation strategy and engage with external entities. One form of external engagement is through collaborative research projects (CRPs) where the company joins a consortium composed of universities, public agencies and other companies to collaboratively work on and tackle complex research problems of common interest. Anecdotal evidence, however, suggests mixed outcomes of collaborative research projects in terms of achieving corporate research impact. Corporate research teams participating in CRPs have the dual tasks of (1) working with diverse collaborative research project team members from different organisations to create novel knowledge and (2) identifying and transferring high-potential knowledge into the company's innovation funnel for further commercial development, through a process called as the "fuzzy front end" (FFE) of innovation. Achievement of corporate research impact requires the achievement of both tasks. This study focuses on how the interplay of network characteristics and knowledge processes within the collaborative research project networks and across the corporate-internal networks influences the achievement of research impact. This study posits that knowledge transfer among network stakeholders is a facilitating mechanism in generating research impact. This study further posits that the structural and relational characteristics of the network influence knowledge transfer, which in turn facilitates research impact. To test these hypotheses, this study examines the social networks of several collaborative research projects in which the corporate research department of a global software company participated. Based on the data collected from collaborative research project teams, their interactions with corporate research team and corporate internal networks and drawing on network theory, this study aims to examine how network characteristics influence the knowledge transfer processes that facilitate research impact. This thesis uses a multi-method and multi-level design. Study 1 investigates the effect of the structural characteristics of the global network of collaborative research projects on research impact. Studies 2 and 3 involve the analyses of collaborative research project networks in Australia and Europe. Both studies aim to understand how the characteristics of project-level Publications during candidature