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.

There is certainly a lot of discussion about digital technologies, their transformative nature, and their potentially disruptive impact on business and society. The number of publications on digital technologies and their impact on business and management have risen dramatically. This paper's main objective is to draw attention to practical and research‐related views on what we know and what we still need to learn about business and management in the digital era. We do so by combining the insights obtained from interviews with senior managers in charge of their firm's digital transformation activities in 2017 with the results of a systematic literature review covering a decade of practice‐oriented, academic literature on the impact of digital technologies. We identify the challenges that firms face at the beginning of their digital transformation efforts and summarize the managerial guidance offered by 242 publications over the years, 133 of which have been published since 2017. Based on the analysis conducted, we discuss the emerging solutions for a number of the key challenges identified in 2017, flag the remaining ones, and identify new themes that require attention. This leads us to propose an agenda for future, practice‐oriented research on digital transformation.

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This article presents an extreme crowdsourcing case to tackle grand challenges such as Covid‐19. Researchers became more interested in crowds’ involvement to deal with grand challenges and scholars reported the use of crowdsourcing in producing innovative solutions to solve these challenges. Driven by recent calls for more research to examine forms of socially motivated interaction options in order to support crowds in dealing with these ill‐defined problems, this research conducts a qualitative study of how an extreme crowdsourcing program – Open Covid‐19 launched by Just One Giant Lab to develop and test low‐cost solutions to tackle the pandemic – was organized. By examining conditions for coordination and knowledge exchange in the Open Covid‐19 case, we present an open structure where the challenges of predictability and common understanding have to be managed continuously and the participants themselves gradually build accountability; multidisciplinary knowledge integration is achieved by exchanging efforts and skills, and extreme transparency contributes positively in building a collective project memory. These findings are theoretically important because they clarify how extreme crowdsourcing can be organized to deal with grand challenges.

Regulating how digital platforms use algorithms to determine and control content displayed to their users has become a controversial topic and an important societal challenge. Despite the acknowledgement of institutional tussles around regulation of algorithmic control, we lack research on how the development of regulation takes place. This research examines the process of institutional work by actors to develop regulation. We study two cases of algorithmic control regulation in Australia: one involving algorithmic control for content display, the other for moderation. We build on data from a longitudinal discourse analysis of 410 media articles and 447 policy and industry documents. We have found that the institutional work of regulators and digital platforms is critical to creating a new institutional arrangement, while third parties (media, academia, etc.) play a supporting or mediating role. We develop a process model of institutional work for regulation of algorithmic control. This model captures the institutional tussles between the main stakeholders as they express their perspective on legitimate forms of algorithmic control, and shape the process and outcome of regulation. Building on our model, we discuss the dynamics of regulation development in light of the constellations of actors and their power positions in the process. We further consider the outcome of regulation and highlight future research questions that build on our findings.

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Firms' organization of exploratory research has interested scholars of both research policy and organizational theory, yet we still know too little about how firms undertake organizational changes to shift to more-exploratory strategies. Adopting a process perspective, we explore this question through a longitudinal, comparative case study of a Danish pharmaceutical firm and a French-Italian semiconductor firm. We demonstrate how firms adjust their organizational structures to increase exploration, a process of constantly addressing countervailing organizational and interorganizational demands by deploying, combining, and changing balancing mechanisms at the organizational and managerial levels. Moreover, our findings show that firms' different organizational structures affect their adaptations to exploratory outcomes. These findings advance theory because they illuminate the dynamic interplay between firms' adjustments of organizational structures and their movements toward more exploration. We use a recursive process model to theorize our findings.