Jul 2026· Humanities and Social Sciences Communications· 0 citations
TL;DR
It is demonstrated that ethical AI encourages knowledge sharing most effectively when synchronized with a supportive, rather than perfectionistic, work culture, and that ethical AI encourages knowledge sharing most effectively when synchronized with a supportive, rather than perfectionistic, work culture.
Abstract
As artificial intelligence (AI) permeates modern workplaces, the need for ethical governance, specifically corporate-responsible AI (CRAI), has become paramount. However, empirical research has been hampered by the lack of a validated instrument to assess CRAI from the perspective of employees. To bridge this gap, this study first creates and validates a novel measurement scale for CRAI through a rigorous multi-stage development process. Using this newly developed instrument, we subsequently investigate how CRAI fosters employees’ knowledge-sharing behavior (KSB) by integrating organizational behavior theories and knowledge-based perspectives. Data were collected using a three-wave, time-lagged design from 405 working professionals in South Korea. The findings indicate that CRAI positively influences KSB, with psychological safety serving as a partial mediator. This outcome suggests that employees’ sense of psychological safety acts as a key mechanism for translating ethical AI governance into collaborative knowledge exchange. Furthermore, the results reveal that organizationally prescribed perfectionism (OPP) moderates the relationship between CRAI and psychological safety; specifically, rigid performance demands undermine the trust-building potential of responsible AI practices. This study contributes to the literature by providing a psychometrically sound tool for future CRAI research and by demonstrating that ethical AI encourages knowledge sharing most effectively when synchronized with a supportive, rather than perfectionistic, work culture.
It is found that AI dependence does not have a significant direct negative effect on innovative behavior, and inhibits innovation exclusively through a full mediation pathway by eroding employee self-efficacy, indicating that the suppression of innovation is caused not by the technology itself, but by the “deprivation of mastery experiences” that accompanies over-dependence.
Byung‐Jik Kim, Yeon-Jun Choi, Julak Lee· Humanities and Social Scienc...· 0 citations
This study investigates how organizational members concurrently perceive the benefits of artificial intelligence (AI) for knowledge management processes (KMPs) and the challenges involved in implementing AI within knowledge management systems (KMSs). Based on survey data from 378 respondents across diverse sectors and roles, the research employs validated instruments measuring perceptions of AI’s contribution to knowledge acquisition, documentation, sharing, and application, as well as perceived human, technological, financial, and ethical‑regulatory barriers. The results show a consistent positive relationship between perceived AI usefulness and perceived implementation barriers: individuals who attribute greater value to AI-enhanced knowledge processes also express heightened awareness of the complexities required to integrate AI into organizational systems. Knowledge documentation presents the strongest associations with all barrier categories, while knowledge sharing exhibits the weakest. Human‑related barriers emerge as the most pervasive across all processes, indicating the central role of employee readiness and organizational culture in shaping AI-enabled KM. These findings reveal a dual perception in which optimism regarding AI’s potential coexists with recognition of the organizational adjustments it demands. The study contributes to a more integrated understanding of AI adoption in KM, emphasizing that effective implementation requires aligning technological capabilities with human, cultural, and governance considerations.
M. Nakash, E. Bolisani· European Conference on Knowl...· 0 citations
Artificial Intelligence (AI) often suffers from a "science-to-service gap," where high-performing models fail to translate into effective real-world decision-making. This systematic literature review investigates this divide, identifying three critical barriers: inadequate technical reasoning, organizational resistance, and stringent regulatory compliance. To bridge this gap, we propose a holistic analytical framework anchored in three interconnected pillars: the human–AI relationship, predicated on mutual trust and complementarity; organizational preparedness, necessitating comprehensive cultural transformation and workforce reskilling; and ethical regulation, prioritizing process transparency and robust accountability. Our findings reveal that successful AI integration extends beyond technical optimization, requiring cross-disciplinary strategies such as participative design and collaborative human–AI audits. By synthesizing these dimensions, this study provides a strategic roadmap for enterprises to navigate systemic challenges, fostering a transition from theoretical AI potential to actionable, empowered, and human-centric decision-making systems in complex operational environments. Research indicates that proper application of MCDM techniques can relate AI outputs to real-life decision-making by structuring, enforcing transparency, and justifying AI-scoring results for use within an MCDA (Multi-Criteria Decision Analysis) framework, as demonstrated in complex use cases such as transportation planning.
Karzan Ismael, Ali Mohammed Salih, Zryan Najat Rashid· Knowledge and Decision Syste...· 0 citations
AI does not replace the role of executive leaders; instead, it serves as a cognitive aid that frees up a leader's capacity from routine operational tasks, yet it still requires the contextual intuition and ethical governance of human leaders.
Alifah Widya Rachmawati, Syamsul Hadi, Eni Purnasari et al.· INTERNATIONAL JOURNAL OF ECO...· 0 citations
