AI Inter-organizational research

As artificial intelligence (AI) increasingly mediates inter-organizational decision-making, concerns arise about how algorithmic opacity reshapes trust, information sharing, and cooperation between firms. This study investigates the relational consequences of AI-supported negotiation, focusing on explainability as a sociotechnical governance mechanism. Using a dyadic negotiation experiment involving 76 professional dyads across three global software providers, we compare human-only negotiation with opaque (black-box) AI and explainable AI (XAI) decision-support systems. Results show that black-box AI significantly reduces partner-directed trust, voluntary information sharing, and subjective satisfaction, despite improving negotiation efficiency. In contrast, explainable AI restores—and in some cases enhances—relational outcomes, yielding higher trust, earlier cooperative gestures, and greater willingness to disclose strategic information. Moderated mediation analyses reveal that explainability mitigates the negative indirect effect of AI on satisfaction through trust. These findings demonstrate that explainability is not merely a technical usability feature but a relational requirement that shapes how organizations interpret intentions and evaluate fairness in AI-mediated collaboration. By articulating explainability as a relational governance mechanism, this study advances theory on AI-enabled cooperation and offers practical guidance for the responsible design of negotiation and procurement systems in digitally mediated inter-organizational environments