Purpose In the context of artificial intelligence (AI), machines may replace humans, thereby disrupting established actor bonds in business-to-business (B2B) relationships and bringing issues of trust to the fore. This paper aims to review and discuss trust across human-to-machine, machine-intermediated and machine-to-machine interactions, exploring how B2B exchanges are being transformed by the advent of AI. Design/methodology/approach This conceptual paper is underpinned by an integrative literature review and discussions of trust in the context of AI. The integrative review synthesises arguments across multiple bodies of literature to generate novel insights. Trust related to the actor dimension in the actor-resource-activity model provides the foundation for the discussion. Findings The paper highlights how prior literature typically positions AI on the supplier side, focusing on decision-making applications related to episodic exchanges and on AI as a complement to human actors. The paper develops a conceptual grid that extends existing knowledge to encompass generative and reasoning AI across human-to-machine, machine-intermediated and machine-to-machine interactions. It addresses the strategic nature of interactions that transcend AI’s involvement in isolated exchanges. Originality/value The focus on socially disrupted exchanges, and the mechanisms through which these are compensated, offers an important conceptual foundation for future research on the implications of AI replacing humans in B2B interactions. Furthermore, the grid helps address future-oriented advancements in AI, extending beyond current research and applications.
Christina Öberg (Thu,) studied this question.