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Lookup NU author(s): Professor Natalia YannopoulouORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
Manufacturing firms are increasingly recognizing the critical importance of responsible artificial intelligence (AI)—the ethical and conscientious integration of AI into business processes. While scholars have highlighted the role of responsible AI in advancing responsible innovation, conceptualized as a transparent, reflexive, and inclusive process that engages all stakeholders, existing research has concentrated on initiatives at the AI design phase. As such, the processes through which responsible AI can be collaboratively implemented with external stakeholders to facilitate responsible innovation remain underexplored. To address this gap, this study employs a mixed-methods approach, comprising in-depth interviews (N = 26) and surveys (N = 618), to examine the relationship between responsible AI and responsible innovation. Drawing upon affordance–actualization theory, our findings reveal that responsible AI catalyzes three external stakeholder affordances: joint planning, joint problem-solving, and ethical climate. These affordances lead to immediate outcomes—stakeholder engagement and collective ethical efficacy—that ultimately foster responsible innovation. This study contributes to the literature on responsible AI by identifying three affordances related to the external stakeholders and one contextual factor, organizational mindfulness. It enriches the literature on responsible innovation by advancing the digital technology view and empirically examining the stepwise process through which responsible AI affects responsible innovation.
Author(s): Ye D, Yuan R, Liu JM, Yannopoulou N
Publication type: Article
Publication status: Published
Journal: Technological Forecasting & Social Change
Year: 2025
Volume: 221
Print publication date: 01/12/2025
Online publication date: 12/09/2025
Acceptance date: 01/09/2025
Date deposited: 08/09/2025
ISSN (print): 0040-1625
ISSN (electronic): 1873-5509
Publisher: Elsevier
URL: https://doi.org/10.1016/j.techfore.2025.124349
DOI: 10.1016/j.techfore.2025.124349
ePrints DOI: 10.57711/ykyy-6v16
Data Access Statement: Data will be made available on request.
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