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Digital innovation is an open collaborative process that involves many contributors for creating digital products and services. Entrepreneurs continuously engage with various external actors during their venture's lifecycle, utilizing these interactions to source opportunities, knowledge and resources, while shaping the project vision. However, the mechanisms governing digital innovation sourcing remain unclear. This paper proposes an entrepreneurial storytelling perspective to bridge this gap. We study the case of digital innovation sourcing by analyzing the crowdfunding story of Pebble Time, the most successful Kickstarter campaign to date. Using digital archival sources, we trace Pebble's approach over the course of the campaign. Our findings contribute to the digital innovation literature by demonstrating how the company's efforts allowed diverse actors to participate collectively and affect the entrepreneurial story over time. We identify four modes of actions that digital ventures employ in the collective construction of entrepreneurial narratives: nudging, pushing, scanning, and highlighting. We suggest that the modes of digital action enable digital innovation sourcing when crafting a compelling narrative in the digital age.

Digital platforms are increasingly integrating Generative AI (GenAI) tools as a boundary resource to enhance the quantity and quality of content with the ultimate goal of improving platform viability. As GenAI tools hold unique characteristics compared to traditional boundary resources, platform owners need to adapt their governance mechanisms accordingly. To understand how platform governance evolves over time in response to this novel boundary resource, we draw on the distributed tuning framework and build on insights from an in-depth qualitative study of a digital content platform in the educational sector. We find that the platform owner deployed different logics of GenAI integration over time that were enacted through specific governance mechanisms. The shift in logics and respective governance mechanisms was triggered by a dialectic process of resistance and accommodation between platform actors. In this process, the GenAI-enabled boundary resource not only changed over time but also served as a means for the power dynamics between platform owner and complementors to be reshaped. Our study contributes to both the platform governance literature as well as recent debates around GenAI.

Despite the crucial role of generative AI in organizations and society, our understanding of its evolution is limited. Prior research has focused on isolated examinations into the layers of generative AI. However, the synergistic interactions between these layers are often overlooked, resulting in examinations that may ultimately be limited. There is a pressing need for theory to better understand the emergence of generative AI, especially considering how Digital Infrastructure Theory is well-positioned to critically examine these layers not as separate silos but as interwoven strands in a complex web. Drawing on Digital Infrastructure Theory, this essay theorizes the dynamics of generative AI evolution through a six-layered framework. These layers—Technology, Data, Contents, Network, User and Interaction, and Regulation and Ethics—are integral to understanding and navigating the complexities of generative AI as it becomes. The essay proposes that each layer influences and is influenced by the others, forming a complex interdependent system that guides AI’s evolution and integration into various domains. The study’s insights aim to inform how we manage and foster AI’s development, ensuring ethical and effective use within organizational structures.

Background: AI management has attracted increasing interest from researchers rooted in many disciplines, including information systems, strategy, and economics. In recent years, scholars with interests in these diverse fields have formulated similar research questions, investigated similar research contexts, and even often adopted similar methodologies when studying AI. Despite these commonalities, the AI management literature has largely evolved in an isolated fashion within specific fields, thereby impeding the development of cumulative knowledge. Moreover, views of AI’s anticipated trajectory have often oscillated between unjustifiably optimistic assessments of its benefits and extremely pessimistic appraisals of the risks it poses for organizations and society.

Method: To move beyond the polarized discussion, this work offers a systematic review of the vast, interdisciplinary AI management literature, based on analysis of a large sample of articles published between 2010 and 2022. Results: We identify four main research streams in the AI management literature and associated, conflicting discussion, concerning four (data, labor, critical, and value) dimensions.

Conclusion: The review conceptually and practically contributes to the IS field by documenting the literature’s evolution and highlighting avenues for future research trajectories. We believe that by outlining four key themes and visualizing them in an organized framework the study promotes a holistic and broader understanding of AI management research as a cross-disciplinary effort, for both researchers and practitioners, and provides suggestions that extend the framing of AI beyond myth and hype.

Citizen-centricity is a common concept in digital government research and policy. However, there is little clarity regarding the concept in previous literature. To address this shortcoming, and build theoretical foundations for addressing both citizen-centricity and associated phenomena, we have examined how citizen-centricity is characterized in digital government research. This study is based on literature review of 66 journal articles. A combination of narrative analysis and ideal-type methodology identified themes concerning four modes of government, designated traditionalist, service-dominant, participatory, and transformative. Further analysis of associated types and research streams provides an overview of the theoretical understandings of citizen-centricity and methodological approaches applied to explore it in the literature. The findings contribute to contemporary theory on citizens in digital government by outlining an integrative framework of citizen-centricity. The paper concludes with proposals for further research, including efforts to enhance conceptual clarity and develop more dynamic theories.

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.

Machine learning (ML) offers widely-recognized, but complex, opportunities for both public and private sector organizations to generate value from data. A key requirement is that organizations must find ways to develop new knowledge by merging crucial ‘domain knowledge’ of experts in relevant fields with ‘machine knowledge’, i.e., data that can be used to inform predictive models. In this paper, we argue that understanding the process of generating such knowledge is essential to strategically develop ML. In efforts to contribute to such understanding, we examine the generation of new knowledge from domain knowledge through ML via an exploratory study of two cases in the Swedish public sector. The findings reveal the roles of three mechanisms – dubbed consolidation, algorithmic mediation, and naturalization – in tying domain knowledge to machine knowledge. The study contributes a theory of knowledge production related to organizational use of ML, with important implications for its strategic governance, particularly in the public sector.

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.

Artificial intelligence (AI) is poised to have a profound influence on businesses across all sectors. Specifically, generative AI is set to underpin the development of potent and novel capabilities, ushering in a new wave of innovation. For example, ChatGPT has seen massive hype surrounding its launch, with growing speculation regarding its disruptive nature for organizations and society. The ongoing debate argues that ChatGPT will lead to far-reaching innovation. However, it is less clear whether such innovation can be managed. We seek to close this gap by identifying distinctive innovation strategies in terms of two key dimensions: automation and augmentation (high or low). This results in a typology of four generic innovation strategies: Traditional Tool (low automation, low augmentation), Basic Automation (high automation, low augmentation), Automated Assistance (low automation, high augmentation), and Assisted Augmentation (high automation, high augmentation). The strategies and typology essentially differ in relation to automation and augmentation for innovation, risks, and challenges faced in the process, and available tactics for managing the process. Building upon this framework, our insights suggest that practitioners can harness ChatGPT effectively by aligning their innovation objectives with the appropriate strategy, whether it be enhancing creative processes or streamlining operational efficiency, thereby navigating the complexities of innovation with a more structured and strategic approach.