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Paving the future: the role of responsible artificial intelligence, hybrid intelligence and leaders' symbolisation in leveraging breakthrough innovations

Shahan Bin Tariq et al.

International Journal of Internet Manufacturing and Services2025https://doi.org/10.1504/ijims.2025.144366article
AJG 1
Weight
0.41

What the paper says

Artificial intelligence (AI) integration within businesses has significantly altered human resource management techniques. Despite AI's expected advantages, employees harbour legitimate concerns and challenges. However, a notable dearth of discourse surrounding responsible AI exists. To bridge this gap, this study draws on signalling theory (ST) and social exchange theory (SET) to present a model outlining responsible AI's (RAI) effect on employees' breakthrough innovation engagement. Using 344 employees' valid survey responses from Pakistan's high-tech sector, Hayes Process Macro was employed to examine the moderated-mediation model. The results illustrate RAI enhances breakthrough innovation engagement through hybrid intelligence utilisation. Moreover, the findings showed that leaders' RAI symbolisation strengthens the relationship between RAI, hybrid intelligence use, and breakthrough innovation engagement. By demonstrating the influential role of RAI in breakthrough innovations, this research offers critical insights for both theory and practice.

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https://doi.org/https://doi.org/10.1504/ijims.2025.144366

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@article{shahan2025,
  title        = {{Paving the future: the role of responsible artificial intelligence, hybrid intelligence and leaders' symbolisation in leveraging breakthrough innovations}},
  author       = {Shahan Bin Tariq et al.},
  journal      = {International Journal of Internet Manufacturing and Services},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1504/ijims.2025.144366},
}

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.