Effective human-AI collaboration in buyer-supplier negotiations

Contact person: Jonas Fränzl

Generative artificial intelligence (genAI) has proven to be a crucial technology for the next generation of decision support systems (DSS). Its ability to interpret large amounts of unstructured data, generate knowledge and mimic human behaviour (Berente et al., 2021) promises to transform various strategic decision-making processes, such as buyer-supplier negotiations. In strategic B2B negotiation processes, decision-makers must analyse counterparties and devise negotiation strategies. By delegating negotiation activities to genAI, genAI-based DSS could fundamentally reshape the development of negotiation strategies in organisations and the conduct of negotiations (Herold, 2025).

However, fully automating strategic negotiations appears problematic, as these interactions involve high financial risks and shape long-term business relationships. On the other hand, purely manual approaches are often time-consuming and limited by cognitive biases and insufficient calibration of one's own negotiating position (Simon, 1960; Kahneman & Tversky, 1979; Bazerman & Neale, 1983). This often leads to inefficient deals and economic losses (Bazerman & Neale, 1983; Thompson, 2006). This discrepancy points to a configuration in which the complementary skills of humans and genAI are utilised, rather than relying on one of the two actors alone.

Complementarity describes a synergistic relationship in which humans and GenAI contribute different but mutually reinforcing strengths (Jarrahi, 2018), so that their joint performance exceeds the individual performance of each actor (Donahue et al., 2022; Hemmer et al., 2025). GenAI is characterised by analytical, data-driven competence, while humans contribute intuition, implicit knowledge and contextual judgement (Fügener et al., 2022; Jarrahi, 2018). Human-AI collaboration (HAIC) refers to the intentional combination of these capabilities in joint workflows to achieve defined goals (Vössing et al., 2022).

Previous research shows that HAIC systems often perform worse than humans or AI alone (Vaccaro et al., 2024). This suggests that the specific design of the collaboration is fundamental (Hemmer et al., 2025). Previous studies have focused primarily on structured tasks such as classifications (Bansal et al., 2021) or predictions (Revilla et al., 2023), where solution spaces and success metrics are clearly defined. In addition, the focus is often on generic chatbot interfaces, while task-specific and workflow-embedded GenAI applications receive little attention. As a result, there is a lack of fine-grained understanding of how task characteristics and system properties shape HAIC in unstructured, dynamic, and especially adversarial contexts such as negotiations. Specifically, it remains unclear through which mechanisms and under what conditions actual complementary performance is achieved or inhibited in these complex settings (Benbya et al., 2024).

Against this backdrop, this dissertation project examines how genAI can support strategic decision-making and, in particular, group-based decision-making processes such as negotiations. Within the framework of constructivist research, design principles for genAI-augmented negotiation support systems are also to be developed that productively shape the complementarity between humans and AI.

The project is divided into four phases: (1) Qualitative study to understand human-AI collaboration in strategic and unstructured knowledge work. (2) Analysis of the status quo of GenAI in buyer-supplier negotiations; (3) Technical instantiation of agent simulations to map realistic negotiation dynamics; (4) Empirical evaluation of the artefact in controlled experiments. The aim is to explain the underlying mechanisms of action (e.g. reduction of anchoring effects, fixed-sum bias, increase in planning effectiveness) and to identify the boundary conditions (e.g. user experience, AI literacy, algorithmic fidelity) under which complementarity between humans and AI is successful.

Cooperative doctoral project between FernUniversität Hagen and Mainz University.

Sources:

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Bazerman, M. H., & Neale, M. A. (1983). Heuristics in negotiation: Limitations to effective dispute resolution. In M. H. Bazerman & R. J. Lewicki (Eds.), Negotiating in Organizations (pp. 51–67). Sage.

Benbya, H., Strich, F., & Tamm, T. (2024). Navigating generative artificial intelligence promises and perils for knowledge and creative work. Journal of the Association for Information Systems, 25(1), 23–36.

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Hemmer, P., Schemmer, M., Kühl, N., Vössing, M., & Satzger, G. (2025). Complementarity in human–AI collaboration: Concept, sources, and evidence. European Journal of Information Systems. Advance online publication.

Herold, S., Heller, J., Rozemeijer, F., & Mahr, D. (2025). Brave new procurement deals: An experimental study of how generative artificial intelligence reshapes buyer–supplier negotiations. Journal of Purchasing and Supply Management, 31(4), 101012.

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