Human–AI Collaboration for Organisational Innovation in Times of Unrest

Liu, Rebecca and Mouzas, Stefanos (2026) Human–AI Collaboration for Organisational Innovation in Times of Unrest. In: EIASM-IPDMC 2026 :. UNSPECIFIED, SWE, pp. 1-7.

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Abstract

Objectives and Theoretical and Practical Relevance This project examines how human–AI collaboration fosters organisational innovation (changes in structures, practices, governance, and collaboration) and reshapes autonomy, responsibility, and legitimacy by redistributing agency between human and algorithmic actors. In times of economic, social, and technological unrest, organisations increasingly adopt AI to stabilise decision-making, enhance resilience, and introduce new organisational practices. Yet research still focuses on technical efficiency, bias mitigation, or ethical compliance (Rahwan et al., 2019; Beer, 2017; Batool et al., 2023), offering limited insight into how human–AI collaboration influences organisational behaviour, governance, and pathways to organisational innovation under uncertainty. Drawing on Amartya Sen’s Entitlement Theory and Martha Nussbaum’s Capability Approach, this study develops a human-centred framework to examine how algorithmic systems mediate decision-making and accountability in ways that shape organisational innovation in volatile environments. Brief Literature Mapping and Theoretical Positioning Existing scholarship highlights AI’s role in decision support, workflow optimisation, and algorithmic management (Elish & Boyd, 2018; Foster & Heeks, 2020; Rahman, 2023). However, empirical work on human–AI collaboration remains fragmented, often neglecting employees’ experiences, critical engagement, and role in co-producing decisions—key mechanisms for organisational innovation. These gaps intensify during periods of unrest, when organisations face heightened demands for adaptability, trust, and clarity. Sen’s Entitlement Theory explains how institutional arrangements shape legitimate claims to act, while Nussbaum’s Capability Approach emphasises individuals’ real opportunities to achieve meaningful goals (Sen, 1982; Nussbaum, 2005). Together, they illuminate how AI reshapes authority, discretion, and practical agency, accelerating organisational innovation when uncertainty disrupts power structures. This lens bridges human-centred normative theories with organisational behaviour research, offering a foundation for understanding responsible AI integration under instability. Research Model and Questions The project operationalises an Entitlement–Capability framework to examine three dimensions of AI integration: 1.AI and authority in organisational innovation 2.Employee perceptions and interactions that influence organisational innovation 3.Critical engagement with AI in organisational innovation The core research question asks: How does human–AI collaboration reshape organisational authority, responsibility, and legitimacy, and through which mechanisms does it drive adaptive organisational innovation? Six sub-questions explore behavioural norms, perceived agency, and the influence of roles, identity, and context in AI-mediated decision-making under conditions of unrest. Figure 1 illustrates the proposed research model. Methodology A mixed-methods design integrates ethnography, semi-structured interviews, think-aloud protocols, surveys, and behavioural measures (eye-tracking and clickstream analysis). Fieldwork in UK-based organisations captures how human–AI interactions unfold under pressure, including how AI-enabled processes facilitate or constrain organisational innovation. Qualitative data will be thematically coded to identify patterns of reasoning, agency, and critical engagement. Quantitative and behavioural measures assess relationships between perceived autonomy, decision quality, innovation-related behaviours, and evaluative engagement. Convergent analysis enables a holistic understanding of AI’s influence across structural and experiential levels. Expected Contribution and Managerial Implications Conceptually, the study reframes AI as a constitutive organisational actor shaping authority, agency, and organisational innovation. Empirically, it offers new insight into how employees co-produce decisions, negotiate responsibility, and develop innovative practices in algorithmically mediated environments. Practically, it provides guidance for managers and policymakers on fostering responsible, human-centred AI adoption—enhancing autonomy, critical engagement, organisational legitimacy, and adaptive organisational innovation. REFERENCES Batool, A., Zowghi, D., & Bano, M. (2023). Responsible AI governance: A systematic literature review. arXiv. Beer, D. (2017). The social power of algorithms. Information, Communication & Society, 20(1), 1 13. Elish, M. C., & Boyd, D. (2018). Situating methods in the magic of Big Data and AI: Algorithmic imaginaries and sociotechnical visions. (Note: full article title, journal, pages to be verified) Foster, C., & Heeks, R. (2020). [Title of article on AI, workflow optimisation and/or algorithmic management]. (Note: full details to be verified) Rahman, S. (2023). Towards multifaceted human centred AI workflows. arXiv preprint. Rahwan, I., Cebrian, M., Obradovich, N., Bongard, J., Bonnefon, J.-F., Breazeal, C., Crandall, J. W., Christakis, N. A., Couzin, I. D., Jackson, M. O., Jennings, N. R., Kamar, E., Kloumann, I. M., Larochelle, H., Lazer, D., McElreath, R., Mislove, A., Parkes, D. C., Pentland, A., Roberts, M. E., Shariff, A., Tenenbaum, J. B., & Wellman, M. (2019). Machine behaviour. Nature, 568(7753), 477 486. Sen, A. (1982). Rights and capabilities: Reflections on welfare economics. In S. McMurrin (Ed.), The quality of life (pp. 153 169). Oxford University Press. (Note: verify exact chapter reference) Nussbaum, M. C. (2005). Frontiers of justice: Disability, nationality, species membership. Harvard University Press.