Introduction to the Special Issue on Open Innovation in Science

KEYWORDS:

• Open innovation in science
• scientific research
• openness in science
• collaboration in science
• inter- and transdisciplinary research

To address the grand challenges of our time, scientific research is increasingly conducted by crossing disciplinary, organisational, and even sectoral boundaries (Dasgupta and David 1994; Stokes 2011; Merton 1973; Sauermann et al. 2020; Nature editorial 2018, 2021; Van Noorden 2015; Fortunato et al. 2018). Scientific fields, however, oftentimes differ in their understanding of openness and collaboration, and some related practices have been discussed without a unified foundation of their open and collaborative aspects. Among others, such practices include interdisciplinary collaborations, data sharing and reuse, open access publishing, university-industry collaborations, as well as crowd science and citizen science. This special issue focuses on ‘Open Innovation in Science’ (OIS) as a novel concept that provides a unifying frame to study openness and collaboration in scientific research.

Article 1 in this special issue provides the conceptual foundation by defining OIS and laying out a framework of OIS practices, antecedents, consequences, and contingency factors. According to Beck et al. (2022, article #1), OIS can be defined as a process of purposively enabling, initiating, and managing inbound, outbound, and coupled knowledge flows and collaboration across organisational and disciplinary boundaries. This may occur along all stages of the scientific research process, from the formulation of research questions and the acquisition of funding to the development of methods, to data collection and analyses to writing and the diffusion of results. An important aspect of this definition is that openness and collaboration in science are considered as means to increase the productivity and impact of scientific research, not as ends in themselves. This first article is also, in itself, an example of an OIS practice: It resulted from a collaborative effort of 47 co-authors from different disciplines who met at the first OIS Research Conference held in 2019 and co-created the OIS research framework.

Based on this integrative framework, the five subsequent articles aim to provide a better understanding of the multilevel antecedents and boundary conditions of open and collaborative practices at different stages of the research process and involving different actors. These articles represent research areas that have often operated in isolation and make contributions to their respective literatures. By bringing them together in a special issue, we hope to also draw attention to interdependencies and perhaps even tensions that may arise between different conceptualisations and implementations of openness and collaboration in science.

Articles 2 and 3 focus on data sharing as a particular open research practice. Barczak et al. (2022, article #2) use a quantitative approach to investigate academic openness by exploring the perceptions and practices of data sharing among innovation management researchers. They seek to explain the propensity of academics to share their data by building upon the resource-based view and social exchange theory. Consistent with the resource-based view, the authors find that many academics see data as a strategic resource that provides them with a competitive advantage over their peers, resulting in both professional and financial security. Management scholars who collect and prepare data also typically bear the entire costs of doing so, further reducing their willingness to share proprietary data. Based on social exchange theory, the authors add to this cost-benefit rationale by positing that researchers may only share data if they perceive that the benefits of having open access to others’ data will outweigh the costs of losing exclusive access to their own. The results of their survey reveal that, despite a high stated willingness to share data openly, actual sharing is relatively rare even amongst scholars studying openness. What is more, only a small percentage of data that are shared openly is ever cited. These findings indicate that there is a lack of both supply of and demand for publicly available data. The authors propose an increase in institutional pressures to share more data openly and to conduct more replication studies as potential solutions.

Pujol Priego, Wareham, and Romasanta (2022, article #3) also examine data sharing, but draw on theories of collective action and epistemic cultures to investigate data sharing attitudes and practices among scientists beyond their own scientific field. The results of their mixed-method study suggest that data sharing is largely community-bound and determined by an ‘epistemic machinery’ (Cetina 2007) that produces differing domain-specific methods, perspectives, beliefs, cultural norms, and economic incentives, as well as differences in the infrastructure, time, and financial resources required to conduct domain-specific experiments. The authors suggest that institutional mechanisms should be more widely used in order to better align personal incentives with the collective good produced by data sharing. In particular, they suggest that data sharing can be enhanced by the use of storage infrastructure that makes use of three specific universal mechanisms: modularity, time delay, and boundary organisations.

Three articles in this special issue focus on collaborations between academics and different types of partners outside of academia. Article 4 (Franzoni, Poetz, and Sauermann 2022) explores how both scientific and non-scientific goals can be advanced by involving ‘crowds’ and non-professional ‘citizen scientists’. In doing so, the authors integrate two largely separate but substantively overlapping streams of literature using the terminology of Crowd Science and Citizen Science, respectively. The authors propose a novel framework that can be used to map projects along four dimensions of crowd contributions: activities, knowledge, resources, and decisions. They further discuss whether and how new technologies, such as Artificial Intelligence, could contribute to greater scope and depth of crowd involvement in scientific research. Franzoni et al. conclude by outlining an agenda for future research, which focusses on four key organisational challenges that need to be tackled when involving crowds and citizens: the division of tasks, allocation of tasks to different actors, provision of rewards to motivate contributions, and provision of information to enable successful collaboration. The authors argue that whether and how academics address these challenges will depend on the degree to which they prioritise productivity versus democratisation goals when involving crowds and citizen scientists.

Audretsch and Belitski (2022, article #5) develop a conceptual framework focusing on knowledge transfer from universities to partners in industry. The authors emphasise the importance of ensuring that knowledge created through research at universities is disseminated to industry and the public at large, and discuss how universities can foster more innovation through a closer strategic alignment with non-academic institutions. The authors conjecture that entrepreneurial universities (i.e. those that prioritise their impacts on regional and national economic development) in particular should seek both functional and strategic congruence in order to achieve alignment at the individual, organisational, and system/entrepreneurial ecosystem level. Their proposed framework, the ‘multi-level strategic alignment model’ (SAM), suggests that congruence between knowledge capital and entrepreneurial capital (i.e. strategic fit) as well as between knowledge production and knowledge dissemination (i.e. functional integration) need to be achieved at all three levels of the university ecosystem in order to maximise the impact that university-generated knowledge can have. Crucially, this framework emphasises that a balance is needed between commercially oriented entrepreneurial activities and the production of knowledge beneficial to society.

The special issue closes with a discussion about the development and productivity of university partnerships with small and medium-sized enterprises. Johnston (2022, article #6) makes the case that small and medium-sized enterprises (SMEs) are particularly impacted by such collaborations themselves and by policy measures designed to encourage university-industry relationships. This is because SMEs are both less likely to have natural links with universities and more likely to face resource constraints that deter university-industry collaborations. The author posits that proximity between SMEs and universities is key to the success of any potential university-industry relationship, and proposes a proximity matrix framework that takes into account spatial proximity, social proximity (existing social connections), technological proximity (similar knowledge and expertise), and organisational proximity (shared knowledge, methods, or culture). While each individual proximity plays an important role in the university-industry relationship, some have stronger impacts than others do and the interactions of two or more proximities can produce complementarities. Ultimately, the success of any university-industry collaboration is dependent upon the access that SMEs have to university actors and their ability to operationalise these relationships via cognitive and relational commonalities.

Taken together, the articles in this special issue highlight the variety of practices and the different levels of analysis that can contribute to a better understanding of the antecedents, effects, and contingencies of openness and collaboration in scientific research. While each of the articles opens numerous avenues for future research on particular OIS practices, considering these articles jointly within the broader OIS framework points to additional promising directions for future research. For instance, the four-dimensional framework to map crowd contributions outlined by Franzoni, Poetz, and Sauermann (2022) could be a useful lens for studying other forms of scientific collaborations such as interdisciplinary or university-industry collaborations by drawing attention to the activities, knowledge, resources, and decisions contributed by different partners. Similarly, the two articles investigating data sharing (Pujol Priego, Wareham, and Romasanta 2022; Barczak et al. 2022) jointly suggest that research on drivers (and deterrents) of open and collaborative practices in science should consider how individual-level cost-benefit considerations intersect with specific epistemic cultures and their particular requirements, affinities, and traditions. Overall, we hope that this special issue provides a focal point for the growing interdisciplinary community of scholars studying Open Innovation in Science.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.