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Organisational and individual determinants of human-AI collaboration

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  • Panasiewicz Leszek

    (Faculty of Management, Lublin University of Technology Nadbystrzycka 38, 20-618, Lublin,Poland)

Abstract

The article examines organisational and individual factors influencing employees’ openness to collaborating with artificial intelligence (AI). The adopted understanding of “collaboration” refers to current practices in this field, where distinctions between human persons and AI are blurring. The study concerns the determinants of readiness to work within a hybrid, biological-silicon cooperation network. The theoretical framework draws on socio-technical systems theory, the socio-cognitive model of trust (SCMT), task-technology fit theory (TTF), and self-determination theory (SDT), which enabled a multidimensional analysis of attitudes towards AI. The research data consisted of survey results covering organisational practices, respondents’ personal attitudes and beliefs regarding digitalisation and work organisation, and their digital competencies. Three complementary methods were applied: exploratory factor analysis, cluster analysis, and linear regression, analysing responses from 614 respondents. The results revealed a difference between the strength of influence on openness to AI of individual factors - personal attitudes, beliefs, and digital competencies - and organisational factors. The bidirectional influence of formalisation on openness to AI was also confirmed. For the presented research, an original concept of “openness to AI collaboration” was developed, encompassing readiness to accept AI in a relatively broad range of organisational roles. In this regard, the study offers new insight into the conditions for an effective human-AI relationship. Based on the findings, recommendations were formulated for business practitioners interested in the effective implementation of AI.

Suggested Citation

  • Panasiewicz Leszek, 2026. "Organisational and individual determinants of human-AI collaboration," Engineering Management in Production and Services, Sciendo, vol. 18(2), pages 15-27.
  • Handle: RePEc:vrs:ecoman:v:18:y:2026:i:2:p:15-27:n:1002
    DOI: 10.2478/emj-2026-0009
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