Conflict Management Styles in AI and Robot-Led Organisations
By Dr Suhair Mereish and Dr Mona Abdelhady.
As artificial intelligence technologies continue to advance rapidly, robots characterised by efficiency and rational decision-making have increasingly been applied in organisational management contexts to perform tasks, allocate work, and serve in leadership roles. In team environments, conflict remains inevitable, often arising from interdependent working relationships, divergent objectives, and individual differences. How a leader manages conflict shapes team coordination, emotional climate, and ultimately work performance.
This raises a pressing question for Business Psychologists and HR practitioners:
Does it matter how AI and robots handle conflict with employees?
Emerging evidence suggests it matters considerably and that the conflict management styles exhibited by intelligent systems have measurable consequences for employee performance. This article explores what the emerging evidence on robot leadership reveals about conflict management style and employee performance, and offers evidence-based interventions that Business Psychologists can embed in their practice: (1) designing cooperative conflict management into AI and robot systems, and (2) building conflict style literacy among employees.
The Dual Concern Model for Conflict Management
A useful framework for understanding how leaders approach conflict is the dual-concern model (Pruitt and Rubin, 1986), which proposes that conflict management behaviour is shaped by two underlying motivations: concern for one's own outcomes and concern for others' outcomes. The intersection of these two dimensions gives rise to distinct conflict management orientations, with cooperative and competitive styles being the most widely recognised.
A cooperative orientation reflects high concern for both parties, prioritising dialogue, mutual understanding and integrative solutions. A competitive orientation, by contrast, reflects high self-concern at the expense of others, characterised by the assertion of one's own position and the use of authority to drive outcomes.
These distinctions are particularly relevant in the context of human–robot interaction, where reduced understanding, emotional reciprocity, and nonverbal communication from robot leaders may influence employee perceptions and responses to different conflict management styles.
The Emerging Evidence
Empirical investigation into this precise intersection of robot leaders' conflict management styles and employee performance remains in its earliest stages. Chen and Cheng (2026) offer one of the first experimental attempts to address this gap directly. The research used the Lunar Survival Task, a well-validated group decision-making exercise that elicits task conflict. It included 72 participants who worked alongside either a robot or a human leader, each exhibiting a cooperative or competitive conflict-management style. The key findings were:
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Cooperative conflict management produced significantly higher work performance than competitive approaches, regardless of whether the leader was a robot or human.
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Dyadic emotional climate mediated the relationship, meaning the emotional tone of the human-robot interaction directly shaped how well employees performed.
Interventions for Practitioners
1. Designing Cooperative Conflict Management into AI and Robot Systems
One of the most direct levers available to practitioners is influencing how robot leaders are designed and configured before they ever interact with an employee. Just as we invest in leadership development for human managers, organisations deploying AI or robotic leaders should work with designers and vendors to ensure cooperative conflict management behaviours are embedded by default – not treated as optional features.
In practice, this means advocating for robot systems programmed to:
- Invite dialogue
- Acknowledge multiple perspectives
- Seek integrative solutions during disagreements
Rather than defaulting to directive, authority-based responses.
Research by Babel et al. (2021) on conflict resolution strategies specifically within human-robot interaction supports this position: cooperative and polite approaches are significantly more acceptable and effective than commanding or threatening ones. Thus, Business Psychologists can translate these findings into design briefs using the dual-concern model (Pruitt & Rubin, 1986), specifying that AI systems should reflect cooperative conflict management by balancing employee and task outcomes, rather than prioritising task completion alone.
Given that dyadic emotional climate emerged as a key mediating mechanism in Chen and Cheng's (2026) findings, there is a compelling case for ensuring robot leaders communicate in relationally attuned ways:
- Using affirming language
- Acknowledging emotional context
- Signalling genuine interest in resolution
This is not about making robots seem artificially human; it is about ensuring the style of conflict management is fit for purpose.
2. Building Conflict Style Literacy Among Employees
Even the most cooperatively designed robot leader will struggle to resolve conflict effectively if employees are not equipped to engage constructively in return. A second intervention, therefore, focuses on the human side of the interaction: building what might be called conflict-style literacy – a practical awareness of one's own default conflict-management tendencies and how they interact with the behaviour of others, including non-human others.
Structured workshops drawing on the dual concern model (Pruitt & Rubin, 1986) can help employees identify whether they naturally lean toward competitive or cooperative responses when in conflict with a leader – human or robotic.
Importantly, self-awareness and self-regulation have been identified as among the strongest individual predictors of cooperative conflict management: those higher in these capacities are more likely to seek collaborative rather than avoidant or competitive responses (Jordan & Troth, 2002). While this research predates the emergence of robot leadership, the principle applies directly – employees who understand their own conflict style are better placed to make deliberate, constructive choices when disagreements arise with a robot leader, precisely because they cannot rely on emotional reciprocity or nonverbal cues to guide them.
Further, role-play scenarios using simulated AI-led conflict situations can provide a low-stakes environment in which employees practice cooperative responses, build confidence, and develop the self-awareness needed to engage productively. This kind of preparation may, in turn, enhance organisational performance by strengthening interpersonal effectiveness in conflict situations.
Conclusion
As robots take on leadership roles, the question is no longer simply whether they can manage – but how they manage. Evidence suggests that cooperative conflict management, even when delivered by a robot, significantly enhances employee performance.
For Business Psychologists, this is a call to act on both fronts: shaping how intelligent systems are designed, and equipping employees to meet them constructively. The style of conflict management still matters – perhaps now more than ever.
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About the Authors
Dr. Suhair Mereish is a Senior Lecturer in Organisational Psychology at the University of Westminster and Fellow of the Higher Education Academy. Her research focuses on individual differences, social identity, and national culture. She also brings industry experience spanning telecoms and the NHS, and is an award recipient from the International Conference on Organisational Psychology, New York.
Dr. Mona Abdelhady is a Fellow of the Higher Education Academy (FHEA) and Lecturer in Management at the University of Westminster. Holding a PhD in International Management from Loughborough University, she teaches across International Management, Leadership, HRM, Organisational Behaviour, and Change, bringing a global perspective to management education.
References
Babel, F., Kraus, J. M., & Baumann, M. (2021). Development and testing of psychological conflict resolution strategies for assertive robots to resolve human–robot goal conflict. Frontiers in Robotics and AI, 7, 591448. https://doi.org/10.3389/frobt.2020.591448
Chen, N., & Cheng, J. (2026). Influence of robot leaders' conflict management styles on work performance. International Journal of Conflict Management, 37(2), 341–371. https://doi.org/10.1108/IJCMA-02-2025-0063
Jordan, P. J., & Troth, A. C. (2002). Emotional intelligence and conflict resolution: Implications for human resource development. Advances in Developing Human Resources, 4(1), 62–79. https://doi.org/10.1177/1523422302004001005
Mereish, S. (2020). Investigating how individual differences in organisations are associated with employee performance, job satisfaction and climate for innovation: A Quantitative Study in Jordan’s Middle Eastern Context (Doctoral dissertation, University of Westminster).
Pruitt, D. G., & Rubin, J. Z. (1986). Social conflict: Escalation, stalemate, and settlement. Random House.