Research Summary This paper examines the creation of entrepreneurial opportunities under coupled technical and demand uncertainty within science-based ventures (SBVs). Whereas opportunity creation theory emphasizes discursive processes, we build on practice theory and pragmatism to explore how SBV opportunities also emerge through entrepreneurs' evolving engagements with indeterminate material artifacts. Through a longitudinal multiple-case study, we identify two patterns of material engagement: epistemic engagement, oriented toward knowledge creation, and pragmatic engagement, oriented toward practical use. We show how opportunity creation unfolds through interweaving cycles of epistemic and pragmatic engagement. By introducing material engagements as constitutive, we specify creation theory for SBVs and highlight the central role of materiality in shaping belief formation, opportunity objectification, stakeholder engagement, and the variation-selection-retention process.Managerial Summary This paper explores how entrepreneurs create science-based ventures (SBVs) by engaging with evolving material artifacts, like sensors and prototypes. Based on a study of SBV initiatives supported by the European Commission's ATTRACT program, we identify a process in which entrepreneurs repeatedly alternate between epistemic engagement-focused on scientific understanding-and pragmatic engagement-focused on usability and implementation. This material engagement cycle plays a central role in venture development under coupled technical and demand uncertainty. We highlight tensions inherent in this process and the strategies through which they are accommodated, offering practical insights for entrepreneurs building ventures from frontier science and policymakers supporting science commercialization.

The articles in this issue suggest that the future of effective innovation does not lie in the dominance of algorithmic efficiency over human cognition, nor in rejecting automation. Instead, success depends on a "Hybrid Intelligence" model where formal innovation processes are rigorously applied to speed up execution and reduce risk, while simultaneously leveraging linguistic and contextual diversity to create the "cognitive friction" necessary for high-quality decision-making. Consequently, the challenge for leadership is to design organizations that are "ambidextrous"—capable of balancing the "closing behaviors" required for efficiency and execution with the "opening behaviors" needed for exploration and creativity. Together, these aspects provide a comprehensive perspective on the journey of an idea, from a simple spark of cognitive potential to a transformative force that reshapes our world, starting with the most fundamental element: the innovator's mind.

The papers collected in this issue of the CERN IdeaSquare Journal of Experimental Innovation do not merely describe innovation; they map its geography. They do not describe a safe, linear process; they describe a tension between opposing forces. Collectively, they suggest that innovation is an act of "boundary spanning"—a persistent effort to navigate the cognitive, structural, and institutional frontiers that separate scientific inquiry from societal impact.

In a world overshadowed by uncertainty, where technologically mediated connectedness can leave us feeling isolated and powerless, it is crucial to recognize that innovation seems our sole response.

While the scientific output of research infrastructures is well documented, the broader effects of their secondary outputs, such as computational resources and datasets, remain poorly understood. To better understand the benefits of these public resources, this study explores the AlphaFold (AFDB) database, a collaboration between DeepMind and the European Molecular Biology Laboratory (EMBL) that democratizes access to protein structure data. Employing a quantitative case study strategy using bibliometric analysis, this study compares publications indexed in the Web of Science Core Collection citing the original AF paper (Jumper et al., 2021) with those citing the AlphaFold database (Varadi et al., 2022), covering publications up to August 2024. We examine the impact of the EMBL AlphaFold database on research themes, collaboration patterns, and scientific impact. Our exploratory analysis identifies several impacts: studies leveraging the AF database investigate application-focused themes and require collaboration between fewer institutions. This research highlights the wide-ranging impacts of research infrastructures, emphasizing the need for comprehensive impact assessments to inform future research policy and funding decisions.

Datafication is driving organizations to invest in data commons, not only to share the costs of data generation, analysis, and curation but, more importantly, to realize synergies in precompetitive research collaborations where private and public motives interact (i.e., semicommons). The fanfare surrounding datafication often hails the sophisticated algorithms used to develop large quantities of data toward greater insight, naively assuming that more data equals better data. Yet for datafication in general and precompetitive research specifically, less attention is awarded to what actually constitutes data and evidence in the first place—that is, to its genesis, construction, and interpretation by heterogeneous scientific and commercial entities. We present the case of Open Targets, a precompetitive collaboration in the life sciences, where publicly funded research, nonprofit foundations, and for-profit pharma collaborate to generate and share data in genomics, proteomics, and bioinformatics. We theorize about the process of data commoning, a political activity in the semicommons where data are created, evidential value is assembled, and scientific meaning converges as data travels, or journeys, across creators, validators, and users. Our findings highlight the effects of relational dynamics and the political nature of data journeys: why these dynamics form, how they are manifested in a precompetitive semicommons, and what implications this can have for the mobility of data as a shared, public good.

Purpose In the context of rewards-based crowdfunding, this study aims to examine the role of project backers as providers of knowledge inputs beyond just financial capital. Design/methodology/approach This study uses binomial regression to study the relationship between project creators’ and backers’ knowledge sharing, and the relationship of these two knowledge-sharing elements with achieving above-goal funding levels. Findings This study finds that the project creator’s knowledge sharing is significantly and positively related to backers’ knowledge sharing and that this relationship is moderated by the type of project. Furthermore, backers’ knowledge sharing is positively related to above-goal funding outcomes for a project. Research limitations/implications This study established the link between creators’ and backers’ knowledge sharing in rewards-based crowdfunding, which has been underexplored in the literature. This study’s direct attention to the role of knowledge as a key resource in rewards-based crowdfunding and crowdsourcing in general. Practical implications For entrepreneurs seeking crowdfunding, this study highlights the importance of knowledge sharing with their project backers to attain above-goal funding. Furthermore, eliciting backers’ knowledge input acts as a signaling mechanism that increases the crowd’s confidence in the project. It also endows entrepreneurs with knowledge resources that can improve project outcomes and achieve broader market success postcrowdfunding. Originality/value To the best of the authors’ knowledge, this study is one of the first to focus on knowledge content as a critical element in project backer-creator communication in rewards-based crowdfunding. This study also delineate the various knowledge types shared between the project creator and backers in both rewards-based crowdfunding projects.

Abstract Big Science Research Infrastructures (RIs) have a strong track record of producing “deep-tech,” which has transformed industries and society. Yet, cultivating novel applications from these RIs is not straightforward due to often misaligned priorities with industry partners and the inherent technical complexity and market uncertainties in their technologies. Open Innovation (OI) provides a framework to address these challenges and nourish a mutually beneficial relationship between RIs and entrepreneurial industry actors. Showcasing the potential of applying OI mechanisms to facilitate the commercialization of deep tech, this chapter highlights ATTRACT, a novel initiative funded by the European Commission’s Horizon 2020 program, to facilitate the commercialization of early-stage technologies from Big Science RIs. The findings suggest that many open innovation practices can be useful in getting these sophisticated deep technologies into the market: ATTRACT facilitates project development by offering financial resources for risk absorption, brokering relationships with industrial partners, and facilitating the applications of technologies in diverse domains outside their immediate purview

Lying in the space of human curiosity, this issue of CIJ experiments with the boundaries of scientific exploration to foster technological development. To cultivate experimental innovation, it is imperative to translate research into tangible action, explore multifaceted problems, offer support for implementation, and effectuate meaningful changes.

The papers in this issue offer novel insights and tools to recraft and extend innovation beyond its traditional domains to focus on complex global challenges, driving innovation frontiers toward transformative and impactful outcomes. In an era where economic growth, societal progress and social equity are focal for policy makers, this issue explores the power of structure and experimentation to surface the complexities of university and industry collaboration.

Although considered a relatively recent phenomenon of the past decade, open source hardware (OSH) is already influencing commercial hardware development. However, a common belief is that the greater economic cost and complexity of hybrid digital objects (i.e., digital objects with both hardware and software) precludes their development with open source methods traditionally used for software. We study a sophisticated OSH named White Rabbit initiated at CERN and developed through a vibrant and heterogenous open source community. Our findings show that the assumption that hardware and software require fundamentally distinctive development and production modes should be replaced with a more nuanced differentiation characterized by three main attributes describing an object's composition: embodiment, modularity, and granularity. Taken together, these three attributes determine how a hybrid object is developed throughout its evolution in an open source community. Our research offers several contributions. First, we provide a more nuanced view of the consequences of the material embodiment of hardware. Once considered a simple deterrent to open source development, we describe how economic cost is subordinate to more influential aspects of an object's physical layers: as the open source community modifies the object to accommodate the operating requirements of diverse physical instantiations, such modifications can be incorporated in the logical design covered by the open source license. Additionally, we show how embodiment, modularity, and granularity progress through the object's evolution and how this maturation subsequently affects development modes. We trace the implications of our findings for hybrids and digital object conceptualizations in IS research, open source development and, more broadly, normative implications for OSH in scientific and commercial computing.

Developing new pharmaceutical products has become increasingly challenging due to rising costs, increased complexity in the types of drugs, and changing patient demands. To address these issues, this chapter identifies the emerging management practices shaping the pharmaceutical industry from early drug discovery to supply chains. Synthesizing industry reports from top consulting firms and academic publications in drug development journals, the chapter explores the internal and external management needed to adapt to the changing landscape in developing and manufacturing innovative pharmaceutical products. With respect to the internal activities to aid firms’ innovation processes, agile methods were highlighted; with respect to engaging with external organizations, FAIR data management was explored. The implications of these trends were then identified for different actors within the life science ecosystem, including large pharmaceutical companies, biotech startups, and academia.

Scholars across disciplines increasingly hear calls for more open and collaborative approaches to scientific research. The concept of Open Innovation in Science (OIS) provides a framework that integrates dispersed research efforts aiming to understand the antecedents, contingencies, and consequences of applying open and collaborative research practices. While the OIS framework has already been taken up by science of science scholars, its conceptual underpinnings require further specification. In this essay, we critically examine the OIS concept and bring to light two key aspects: 1) how OIS builds upon Open Innovation (OI) research by adopting its attention to boundary-crossing knowledge flows and by adapting other concepts developed and researched in OI to the science context, as exemplified by two OIS cases in the area of research funding; 2) how OIS conceptualises knowledge flows across boundaries. While OI typically focuses on well-defined organisational boundaries, we argue that blurry and even invisible boundaries between communities of practice may more strongly constrain flows of knowledge related to openness and collaboration in science. Given the uptake of this concept, this essay brings needed clarity to the meaning of OIS, which has no particular normative orientation towards a close coupling between science and industry. We end by outlining the essay's contributions to OI and the science of science, as well as to science practitioners.

Government funding entities have placed data sharing at the centre of scientific policy. While there is widespread consensus that scientific data sharing benefits scientific progress, there are significant barriers to its wider adoption. We seek a deeper understanding of how researchers from different fields share their data and the barriers and facilitators of such sharing. We draw upon the notions of epistemic cultures and collective action theory to consider the enablers and deterrents that scientists encounter when contributing to the collective good of data sharing. Our study employs a mixed-methods design by combining survey data collected in 2016 and 2018 with qualitative data from two case studies sampled within two scientific communities: high-energy physics and molecular biology. We describe how scientific communities with different epistemic cultures can employ modularity, time delay, and boundary organisations to overcome barriers to data sharing.

The potential of big science research infrastructures to make contributions far beyond their scientific purview has long been acknowledged. However, less consensus exists about the specific mechanisms with which such value can realised. This paper describes the ATTRACT project. A novel approach funded with €20 million by the European Commission Horizon 2020 programme, ATTRACT represents a consortium of leading European scientific centres, academic institutions, and industry associations formed to harness their world-class scientific instrumentation technologies towards entrepreneurship within European economies. ATTRACT will award 170 projects centred on breakthrough imaging and detection technologies €100,000 each to develop a proof-of-concept within one year. With the goal of scaling a select few of the most promising projects, ATTRACT will facilitate additional iterations of public and private funding along with relevant commercial and legal support to bridge the gap between supply-push and demand-pull innovation policy instruments. The paper describes the ATTRACT project: its motivation, philosophy, design, and results to date.

ABSTRACT Openness and collaboration in scientific research are attracting increasing attention from scholars and practitioners alike. However, a common understanding of these phenomena is hindered by disciplinary boundaries and disconnected research streams. We link dispersed knowledge on Open Innovation, Open Science, and related concepts such as Responsible Research and Innovation by proposing a unifying Open Innovation in Science (OIS) Research Framework. This framework captures the antecedents, contingencies, and consequences of open and collaborative practices along the entire process of generating and disseminating scientific insights and translating them into innovation. Moreover, it elucidates individual-, team-, organisation-, field-, and society‐level factors shaping OIS practices. To conceptualise the framework, we employed a collaborative approach involving 47 scholars from multiple disciplines, highlighting both tensions and commonalities between existing approaches. The OIS Research Framework thus serves as a basis for future research, informs policy discussions, and provides guidance to scientists and practitioners.