Information and Organization最新文献

This study undertakes a hermeneutic analysis of the growing literature on algorithmic management. Algorithmic management is a subset of algorithmic decision-making, also referred to as algorithmic work. To date, the underlying norms, and assumptions of researchers, and how assumptions shape understandings of algorithmic management, have been under investigated. Using a hermeneutic methodology, we uncover four different onto-epistemological positions in the literature based on two overarching worldviews. The first is techno-human dualism, rooted in dualist ontological assumptions foregrounding entities. The second is techno-human entanglement, grounded in relational perspectives that view the social and material as inseparable. The worldviews are comprised of four meta-understandings that form our framework: (1) the ‘techno-centric’ view gives primacy to the technology, with humans seen as a secondary feature; (2) the ‘techno-mediated control’ view focuses on managerial power with technology a tool for control and the organization of labor; (3) the ‘techno-human enactment’ view focuses on the performative aspects of algorithmic management; and (4) the ‘techno-human being’ view explores how algorithmic management affects identity (re)formation and meaning-making. We demonstrate how onto-epistemological assumptions configure interpretations of algorithmic management. We focus on algorithms (as a foundational and integral characteristic), organizational control (a core function), and human-in-the-loop configurations (as a possible safeguard). By surfacing the plurality of assumptions in algorithmic management research, we seek to foster more engaged scholarship and encourage the virtue of choosing a research position rather than inheriting it.

This study offers a nuanced exploration into the intersection of expertise and AI-powered decision-making, particularly within the realm of high-volume recruitment. It leverages theory from the evolving discourse on relational expertise and human-AI interaction to examine how experts navigate, interpret, and sometimes challenge AI tool outputs. Through in-depth interviews with 42 recruitment experts, the study focuses on the concept of algorithmic folk theories—the interpretive frameworks through which experts engage with algorithmic recommendations. Central to the study's findings is the range of perceptions among experts toward AI technologies, viewed through the lens of expert-AI pairings. These perceptions oscillate between viewing AI as either a complementary ally or a challenging rival, significantly shaped by organizational contexts. Factors influencing these views include oversight levels, trust in AI outputs, and the prioritization of AI tools in decision-making processes. Findings also reveal instances of algoactivism, where experts actively resist or workaround AI outputs to align with their professional judgment. In turn, algorithmic folk theories are interpretive frameworks informed by and situated within organizational structures.

Theoretically, this study deepens our understanding of the relational dynamics between human expertise and AI systems in professional settings. It highlights the critical role of context-specific factors in shaping these interactions and offers new perspectives on the complexities of AI integration for workplace decision-making. I explain my work's findings in relation to our broader discourse around artificial intelligence use at work. Finally, I offer theoretical and practical considerations for future research and practice.

Digital platforms contribute significantly to sustainable development yet pose specific risks to developing countries. Using a World Bank global database of antitrust actions complemented by secondary data, we empirically analyze developing countries' regulatory responses to threats to competition and innovation associated with digital platforms. We ask: (1) Which types of anticompetitive agreements and abuse of dominance practices were associated with various platform types? (2) For mergers, which salient characteristics of the acquiring platform drove the antitrust investigations, and what actions were taken by the enforcement authorities? We find that two types of platforms (transaction and hybrid) give rise to distinct competitive concerns and elicit different responses from enforcement authorities. We then discuss our findings in the broader context of policy responses from developing countries to challenges related to digital platforms. We offer recommendations for policymakers and suggest avenues for future research.

Consider the massive recovery response that included over 25,000 professionals and volunteers representing more than 120 organizations tasked with locating both human remains and vehicle debris following the Columbia Space Shuttle tragedy. Despite the daunting scope of the initial search area – 2.28 million acres of land – participating members were successful in their efforts to achieve the collective's goals. We contend that the response effort was effective because relatively disparate organizations and governmental agencies came together and ultimately exemplified the hallmarks of high reliability organizing (HRO). Our study explores how the transition in boundaries made this possible. Using interview and secondary data from our case study, we explore how individuals engaged in boundary work that facilitated boundary transformation. Specifically, we document how individuals interacted with a data visualization system to temper the physical, social, temporal, and scope boundary tensions initially present following the disaster. Amidst an emergent, messy, and complex setting, the interaction with a boundary object allowed for unity in diversity of participating organizations, a common language through mapping, a form of trichordal temporal and rapid sensemaking, and a foundation for dynamic decision making. Therefore, our study yields critical insights into how organizational members engage in boundary work to aid HRO collaborations.

Despite the importance of digital platforms in the global economy, there has been little systematic or quantitative analysis of how investors value platforms and the scope of their business models in private or public markets. This paper seeks to fill this gap in part by analyzing how unicorn valuations are affected by “platformness” (the degree to which a firm incorporates at least some elements of a multisided business model with the potential to generate network effects). We investigated 959 unicorns (private companies valued at $1 billion or more) existing as of December 31, 2021, to assess whether investors placed a higher value on firms in different regions of the world and operating with platform businesses rather than offering only “standalone” products or services. We found that companies with some elements of a platform business model commanded a significantly higher average valuation compared to non-platform companies. These higher average valuations also varied by location: North America 129%, Europe 68%, and Asia-Pacific (APAC) 39%. The geographical variations are likely due to greater investor interest in platform businesses in the United States as well as other characteristics more common among North American unicorn platforms. More than half of the unicorn sample and more than half of platform unicorns originated in North America. We also found that investors paid 34% more for “innovation platforms” (these enable third-party complementary innovations through application programming interfaces) versus “transaction platforms” (these bring together two market sides as in product or service marketplaces, financial exchanges, or social media and messaging websites). Platform unicorns with the potential to generate and exploit global network effects also had approximately 26% higher valuations than platforms limited to non-global network effects.