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Prior research has traditionally understood the dynamic nature of coopetition as shifts between relational states of coopetition over time and has treated coopetition interplay and coopetitive dynamics tautologically. This understanding is problematic because it fails to capture the continuous flux of changes in coopetitive relationships based on the finer details of partners' situated interactions on the micro-level. To overcome this limitation, this conceptual paper introduces the interaction perspective on coopetition, which allows us to uncover the microfoundations of the cooperation-competition interplay that bring about shifts in coopetitive relationships. By focusing researchers' attention on managers' reflecting-in-action, coopetition work that propels temporal interactions, and the relational embeddedness of their interactions, the interaction perspective helps us better understand the dynamic nature of coopetition and uncover the coopetitive dynamics feeding into the coopetition interplay and triggering moves towards new relational states. This paper makes four key contributions to the coopetition literature, outlines methodological implications of the suggested interaction perspective, and identifies avenues for further research.

Digital platforms are arguably instrumental for the Circular Economy (CE), yet they frequently fail to deliver. An increasingly popular strategy for developing digital platforms is corporate incubation, where corporations invest in startups. Prior research has nonetheless paid scant attention to the role of corporate incubation in the evolution of digital platforms over time. To shed light on this, we conducted a qualitative case study of a digital platform incubated by one of the largest construction firms in Europe in the context of corporate incubation. Building on prior research suggesting that platform growth and platform scaling are distinct but often conflated phenomena, our analysis reveals four mechanisms through which corporate incubation enables platform growth and (ii) unpacks its role in transitioning to platform scaling. Drawing on boundary work theory, we offer a model explaining how the boundary work frames of organizational actors involved in corporate incubation influence the evolution of digital platforms over time. Our study contributes to the literature on digital platforms for CE by establishing corporate incubation as a promising yet challenging strategy for achieving platform growth, by deciphering the relational and temporal dynamics that condition platform scaling, and by conceptualizing digital platforms as entities performing boundary work.

While data are often depicted as raw, neutral, and mere inputs to algorithms, we build on an emerging stream of research on data work viewing data as ambivalent, performative, and embedded entities, interwoven with organizing. We argue that in the process of developing AI, where epistemic uncertainty prevails as a key organizing challenge, data work serves as an organizing principle providing the logic through which behaviors are adopted, interpretations are made, and the collective efforts of domain experts and AI experts are coordinated. Prior research suggests that active involvement of both AI and domain experts is required for developing AI. Yet, domain experts and AI experts have distinct knowledge and understandings of domain specificities, meanings of data, and AI’s possibilities and limitations. Consequently, in AI initiatives, a key organizing challenge is epistemic uncertainty, i.e., ignorance of pertinent knowledge that is knowable in principle. We build a conceptual model deciphering three key mechanisms through which data work serves as an organizing principle supporting organizations to cope with epistemic uncertainty: cultivating knowledge interlace, triggering data-based effectuation, and facilitating multi-faceted delegations. These three mechanisms emerge when domain experts and AI experts work with and on data to define and shape trajectories of an AI initiative and make decisions about AI. This chapter contributes to the nascent body of research on data work by expounding the performative role of data as a relational entity, by providing a processual view on data’s interweaving with organizing, and by deciphering data work as a collectively accomplishment.

Crafting ground truth data labels is instrumental but challenging in AI development. In contrast to the prevailing dominant objective view on ground truth labels and human-centered data labeling approaches, we adopt a conjoined agency perspective to theorize how the complementarities between humans and AI play out in organizing the data labeling process for AI development. We conceptualize ground truth data labeling as a highly iterative process involving reflection in action between human agency and AI agency. We propose that the level of ground truth uncertainty determines the composition of conjoined agency and the degree of reflection in action necessary to get the appropriate labels, which can lead to two different organizing principles emphasizing either accuracy or divergence. Our theoretical framework and propositions are expected to contribute to unpacking the composition and interactive dynamics of humans and AIs in constructing ground truth data labels and how learning occurs within human-AI interactions.

Data flows across organizational boundaries are vital for creating and capturing value from data-intensive digital technologies, such as Artificial Intelligence. To achieve this, organizations increasingly engage in digital innovation networks, i.e., constellations of relations among dispersed, loosely coupled actors, who seek to collaborate for combining heterogeneously distributed domain expertise to train and leverage emerging digital technologies that learn from data. Yet, data flows remain stalled within digital innovation networks, and organizations fail to achieve sought-after benefits from data-intensive digital technologies. To date, research has paid limited attention to what contributes to stalled data flows and what strategies are required to facilitate seamless data flows. Our in-depth qualitative study of a digital innovation network within the Swedish forestry identified four key mechanisms underlying stalled data flows and hampering firms in leveraging value from data-intensive digital technologies and revealed the key role of brokerage functions in digital innovation networks for establishing what we call related variety.

This study presents a metaverse flywheel model providing insights into how the emerging layered modular architecture of the metaverse can enable new types of value-creation opportunities for organizations. Based on interviews with early metaverse adopters and innovators, this article identifies three key metaverse affordances: prospection of future conditions, persistence of editable and evolving virtual spaces, and integration between virtual and physical worlds. The findings enhance the nascent metaverse literature by highlighting that new organizing logics are required for metaverse-specific value creation, which goes beyond the previous generation’s isolated 3D models and other interfaces.

Entrepreneurs traditionally use 'learning-by-doing' and 'learning-by-thinking' as alternative approaches to iteratively build business models for their new ventures. However, both approaches face criticism in how they address novelty and uncertainty, which are crucial to successful entrepreneurship. While Generative AI (GenAI) is increasingly used in entrepreneurial tasks, the practices through which it becomes a learning resource for entrepreneurs remain unexplored. Based on a qualitative study, we present a process model that illustrates how entrepreneurs incorporate GenAI into business model design through five resourcing practices. These practices transform GenAI into a valuable resource for facilitating learning during the design process. This approach, which we term 'Learning-by-Conversing,' introduces a generative startup methodology to complement the lean startup model. We distinguish two modes of learning by conversing - reflexive learning and confirmatory learning - based on how novice and experienced entrepreneurs engage with it. By proposing a learning approach that integrates GenAI with entrepreneurial efforts, we bridge the 'thinking' versus 'doing' debate in business model generation and deepen our understanding of GenAI's role in entrepreneurship

Trust and distrust are two distinct organizing principles that play a critical role in interorganizational projects where highly interdependent organizations collaborate to build tailor-made and technologically complex solutions. Whereas an emerging body of research has debated the conceptual distinction between trust and distrust, this paper emphasizes the processual nature of trusting and distrusting and the interplay between them. Drawing upon insights from project-based collaboration in a complex products and systems industry, we explore the distinct cognitive and behavioral mechanisms through which trust and distrust work and orient firms towards optimism and watchfulness in the interaction. Our findings show that trust and distrust can act both as substitutes and complements through three interconnected dynamics—undermining, enabling, and compensating. These dynamics develop and recursively interrelate through interfirm interactions within single projects and in the broader network. We conclude by presenting our contributions to interorganizational trust literature and by proposing that the interplay of trust and distrust can have both positive and negative effects on the pursuit of project-based relationships.