A Relational View on Artificial Intelligence Business Value Manifestation

Abstract

The rapid advancement of Artificial Intelligence (AI) has prompted many organizations to explore how they can leverage AI. As a result, AI business value has emerged as a central topic in both scholarly and practitioner discourse. As AI technologies grow increasingly complex, organizations often lack the capabilities to develop and deploy them independently and therefore turn to interorganizational collaborations as a means of creating and capturing AI-enabled value. However, interorganizational partnerships are characterized by distinctive dynamics that stem from interactions among heterogeneous organizations, each comprising stakeholders with differing perceptions, interests, and expectations regarding AI business value. Information Systems (IS) literature provides rich insights into how AI business value manifests, yet current knowledge predominantly focuses on intraorganizational settings, which creates an analytical blind spot that obscures how partnership dynamics affect the manifestation of AI business value. Without a clear understanding of these dynamics, it remains difficult to explain the challenges organizations face when partnering around AI, including misaligned expectations, mismatches in partner resources and capabilities, and opportunistic behaviors among partners. This dissertation addresses this need and explores the manifestation of AI business value in interorganizational settings. Adopting a relational view, it sheds light on how partnership dynamics shape the manifestation of AI business value and, ultimately, the business value perceived by different stakeholders. The dissertation consists of four studies. The first study focuses on stakeholders’ perceptions of AI business value, uncovering connections between partnership conditions and how stakeholders perceive the business value. The second study builds on this by examining how stakeholders’ values shape their perceptions of AI business value. The third study advances the analysis by exploring how AI business value is communicated across partner organizations. Finally, the fourth study establishes the relational determinants that shape the manifestation of AI business value in interorganizational partnerships. The dissertation synthesizes the findings of the individual studies into an analytical framework that offers explanatory insights on the manifestation of AI business value in interorganizational settings. Specifically, the framework establishes the relevance of the relational dynamics, the need to examine the manifestation as a phenomenon operating across individual and (inter)organizational levels of analysis, and the importance of accounting for the distinctive characteristics of AI, which enable novel dynamics in interorganizational settings. Through this synthesis, the dissertation contributes to the AI business value literature by offering both novel insights and new analytical directions for future IS research.