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Título

THE ALGORITHMIC LEADERSHIP PARADOX: BALANCING AUTONOMY AND ACCEPTANCE IN HUMAN-ROBOT TEAMS

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Palavras-chave

Algorithmic leadership Hybrid teams Robotics
Agradecimento: This work was supported by the Foundation for Support to Research and Innovation of the State of Santa Catarina (FAPESC).

Autores

  • Ricardo Pereira
    UNIVERSIDADE FEDERAL DE SANTA CATARINA (UFSC)
  • Karla Cristina Rocha Botão
    UNIVERSIDADE FEDERAL DE SANTA CATARINA (UFSC)
  • Maurício Fernandes Pereira
    UNIVERSIDADE FEDERAL DE SANTA CATARINA (UFSC)
  • Cristiano José Castro de Almeida Cunha
    UNIVERSIDADE FEDERAL DE SANTA CATARINA (UFSC)

Resumo

Introdução

The adoption of autonomous robots in organizational settings has transformed collaborative dynamics and challenged traditional leadership models. This context has led to the emergence of human-robot teams and the concept of algorithmic leadership, wherein intelligent systems coordinate tasks and influence the social dynamics of teams. This study seeks to map the concepts, mechanisms, and impacts of algorithmic leadership on effectiveness and acceptance in organizational settings.

Problema de Pesquisa e Objetivo

How does algorithmic leadership impact the effectiveness, acceptance, and dynamics of human-robot teams? This study aims to: map the concepts and definitions of algorithmic leadership; identify mechanisms of robotic leadership; analyze the psychosocial factors that influence acceptance and performance; and delineate research gaps to propose future directions for research in organizational contexts.

Fundamentação Teórica

HRTs integrate humans and robots in interdependent collaboration. Robotic autonomy and social capabilities impact team coordination, performance, and acceptance. Algorithmic leadership redefines role allocation by incorporating automated decisions. Robotic leadership, in turn, enacts classic styles, such as transformational and facilitative, adapted to the interaction between humans and intelligent systems in organizational settings.

Discussão

The analysis reveals the paradox of algorithmic leadership: while greater robotic autonomy enhances efficiency, it can diminish human acceptance. Team effectiveness is contingent upon the balance between automated decisions and human intervention, a dynamic influenced by psychosocial factors such as perceived fairness, trust, and an understanding of the algorithms. Organizational settings demand dynamic adaptations environments thus require dynamic adaptations contingent on the specific tasks and contexts.

Conclusão

Algorithmic leadership transforms the dynamics of hybrid teams, driving efficiency gains but posing significant acceptance challenges. The balance between algorithmic control and human interaction is essential to maximize both performance and satisfaction. This study highlights the importance of calibrating autonomy and transparency, proposing future research directions for complex organizational settings.

Contribuição / Impacto

This study contributes by consolidating definitions and evidence on algorithmic leadership in hybrid teams, integrating technical and psychosocial perspectives. It highlights research gaps concerning acceptance, effectiveness, and practical application in organizational settings. Its findings inform future research and the development of more effective algorithmic leadership strategies tailored to real-world organizational contexts.

Referências Bibliográficas

Few, D., Bruemmer, D., & Walton, M. C. (2006a). Dynamic leadership for human-robot teams. Proceedings of the ACM/IEEE International Conference on Human-Robot Interaction, 24–31.
Levac, D., Colquhoun, H., & O’Brien, K. K. (2010). Scoping studies: Advancing the methodology. Implementation Science, 5(1), 69. https://doi.org/10.1186/1748-5908-5-69
Wolf, F. D., & Stock-Homburg, R. M. (2025). One size does not fit all: Mechanisms of employees’ acceptance of robotic lower-level managers. Group & Organization Management. Advance online publication. https://doi.org/10.1177/10596011251313568

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