ENHANCING INNOVATION THROUGH HUMAN-AI COLLABORATION IN INDUSTRY 5.0: THE MEDIATING ROLE OF TRUST
DOI:
https://doi.org/10.35631/IJIREV.825002Keywords:
Human–AI Collaboration, Industry 5.0, Innovation Performance, Trust in AI & TrustAbstract
Industry 5.0 envisions a human-centric industrial future where collaboration between humans and artificial intelligence (AI) enables sustainable innovation. However, despite the transformative potential of Human-AI Collaboration (HAIC), its impact on innovation remains inconsistent. This variability stems largely from insufficient attention to the psychological and relational mechanisms that facilitate effective partnership, particularly trust. The present research addresses this gap by proposing a conceptual framework that positions trust as the critical mediator between HAIC and innovation performance. Using a structured integrative review approach, we analyzed 28 peer-reviewed articles from the Scopus database (2020–2025) through thematic analysis to synthesize current knowledge and identify theoretical linkages. Our findings reveal that trust is not a static outcome but a dynamic, context-dependent construct shaped by transparency, explainability, reliability, and ethical AI design. Trust enables human operators to engage in risk-taking, knowledge sharing, and co-creative behaviors that directly enhance innovation in products, processes, and business models. This work provides a dual contribution: it unifies socio-technical systems theory with Industry 5.0 principles into a coherent mediation framework and offers actionable insights for designing “trustworthy-by-design” AI systems. The proposed framework underscores that technological sophistication alone is insufficient; fostering calibrated trust is essential to unlock the full innovative potential of human–AI symbiosis in next-generation manufacturing.
Downloads
References
Ahangar, M. N., Farhat, Z. A., & Sivanathan, A. (2025). AI trustworthiness in manufacturing: Challenges, toolkits, and the path to Industry 5.0. Sensors, 25(14), 4357. https://doi.org/10.3390/s25144357
Bunte, A., Wunderlich, P., Moriz, N., Li, P., Mankowski, A., Rogalla, A., & Niggemann, O. (2019). Why symbolic ai is a key technology for self-adaption in the context of cpps. 2019 24th IEEE International Conference On Emerging Technologies.
Campagna, G., Chrysostomou, D., & Rehm, M. (2024). Analysis of facial features for trust evaluation in industrial human-robot collaboration. In 2024 IEEE International Conference on Advanced Robotics and its Social Impacts (ARSO) (pp. 1–6). IEEE. https://doi.org/10.1109/ARSO60199.2024.10557748
Campagna, G., Dadgostar, M., Chrysostomou, D., & Rehm, M. (2024). A data-driven approach utilizing body motion data for trust evaluation in industrial human-robot collaboration. In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (RO-MAN) (pp. 1984–1990). IEEE. https://doi.org/10.1109/RO-MAN60168.2024.10731437
Campagna, G., Lagomarsino, M., Lorenzini, M., Chrysostomou, D., Rehm, M., & Ajoudani, A. (2025). Estimating trust in human-robot collaboration through behavioral indicators and explainability. IEEE Robotics and Automation Letters, 10(10), 10218–10225. https://doi.org/10.1109/LRA.2025.3600170
Charalambous, G., Fletcher, S., & Webb, P. (2016). The development of a scale to evaluate trust in industrial human-robot collaboration. International Journal of Social Robotics, 8(2), 193–209. https://doi.org/10.1007/s12369-015-0333-8
Chesbrough, H. &. (2014). Explicating open innovation: clarifying an emerging paradigm for understanding innovation. New Frontiers in Open Innovation. Oxford: Oxford University Press, Forthcoming, 3–28.
Golovianko, M., Terziyan, V., Branytskyi, V., & Malyk, D. (2022). Industry 4.0 vs. Industry 5.0: Co-existence, transition, or a hybrid. Procedia Computer Science, 217, 102–113. https://doi.org/10.1016/j.procs.2022.12.206
Katuk, N., Gilal, A. R., & Safdar, S. (2024). Artificial intelligence in Industry 5.0: Revolutionising human–computer synchronisation. In The future of human–computer integration: Industry 5.0 technology, tools, and algorithms (pp. 12–24). CRC Press. https://doi.org/10.1201/9781003479727-2
Nahavandi, S. (2019). Industry 5.0—A human-centric solution. Sustainability, 11(16), 4371. https://doi.org/10.3390/su11164371
Pawar, P. P., Hemamalini, V., Kumari, S., & Tyagi, A. K. (2025). Industry 5.0: A new industrial revolution and technological applications. In Computational intelligence for analysis of trends in Industry 4.0 and 5.0 (pp. 426–457). CRC Press. https://doi.org/10.1201/9781003533023-17
Rožanec, J. M., Novalija, I., Zajec, P., Kenda, K., Tavakoli Ghinani, H., Suh, S., Veliou, E., Papamartzivanos, D., Giannetsos, T., Menesidou, S. A., Alonso, R., Cauli, N., Meloni, A., Recupero, D. R., Kyriazis, D., Sofianidis, G., Theodoropoulos, S., Fortuna, B., Mladenić, D., & Soldatos, J. (2023). Human-centric artificial intelligence architecture for Industry 5.0 applications. International Journal of Production Research, 61(20), 6847–6872. https://doi.org/10.1080/00207543.2022.2138611
Shabur, M. A., Shahriar, A., & Ara, M. A. (2025). From automation to collaboration: Exploring the impact of Industry 5.0 on sustainable manufacturing. Discover Sustainability, 6(1), 341. https://doi.org/10.1007/s43621-025-01201-0
Xu, X., Lu, Y., Vogel-Heuser, B., & Wang, L. (2021). Industry 4.0 and Industry 5.0—Inception, conception and perception. Journal of Manufacturing Systems, 61, 530–535. https://doi.org/10.1016/j.jmsy.2021.10.006
Yanytska, L. (2025). The rise of human-centric manufacturing in the Industry 5.0 era. International Journal of Advanced Manufacturing Technology, 139(9–10), 5067–5077. https://doi.org/10.1007/s00170-025-16192-5
Zywiołek, J. (2024). Trust-building in AI–human partnerships within Industry 5.0. System Safety: Human – Technical Facility – Environment, 6(1), 89–98. https://doi.org/10.2478/czoto-2024-0011
