This study aims to examine an unresolved issue in artificial intelligence (AI) driven customer service: Why transparency and bias mitigation do not automatically translate into customer trust across organizational contexts. Specifically, this study investigates the effects of bias mitigation strategies and AI transparency on customer trust, and the moderating role of organizational ethical culture, in the context of China.
A cross-sectional survey of 1,450 customers using generative AI services in banking and e-commerce was conducted. Adapted scales measured perceptions of AI transparency, bias mitigation, customer trust and organizational ethical culture. Data were analyzed using Partial Least Squares Structural Equation Modeling via a two-step approach: assessment of the measurement model (reliability and validity), followed by the structural model to test hypothesized relationships. Multiple regression served as a supplementary robustness check.
The study found that both bias mitigation strategies (β = 0.30, p < 0.001) and AI transparency (β = 0.35, p < 0.001) significantly enhance customer trust. Additionally, organizational ethical culture moderated these effects, amplifying them (β = 0.20 and 0.18, p < 0.01). These results emphasize the importance of transparency and fairness in AI, particularly in organizations with strong ethical cultures.
This study reframes AI transparency and bias mitigation as governance and organizational control mechanisms, rather than mere technical features. It demonstrates that organizational ethical culture acts as a key informal context, shaping whether customers view these mechanisms as credible signals of fairness and accountability. In the Chinese setting, the study explains why transparency and bias mitigation do not automatically build customer trust.
M. Ehsan, Abid Hussain, Jing Song et al.· International Journal of Eth...· 0 citations
This study aims to explore the ethical challenges of integrating Artificial Intelligence (AI) into leadership decision-making, focusing on the shift from emotion-based to data-driven approaches. It examines the impact of AI on ethical considerations in leadership, with an emphasis on the role of emotional sensitivity as a moderator.
A quantitative approach was employed, utilizing a survey of 300 leaders and managers across various industries in China. The study introduced two novel scales: “AI-supported leadership decisions (AISLDS)” and “emotional sensitivity of leadership decision contexts (ESLDC).” Data was analyzed using exploratory and confirmatory factor analyses, along with regression analysis to test relationships between AI decision-making, emotional sensitivity and ethical considerations.
The study found a significant positive relationship between AISLDS and ethical considerations. Emotional sensitivity was found to moderate this relationship, highlighting that AI struggles with ethically consistent decisions in emotionally charged situations, suggesting that human judgment is crucial in these contexts.
The findings offer guidance for organizations on when AI is suitable for decision-making and when human empathy is essential. It suggests the need for leadership training that emphasizes the integration of AI with human judgment, ensuring that AI complements rather than replaces human compassion and moral reasoning. Additionally, the study informs the development of policies for ethically integrating AI into leadership.
This study advances AI leadership ethics by demonstrating that emotional sensitivity moderates the relationship between AI-supported decisions and ethical outcomes. Through two newly developed and validated scales (AISLDS and ESLDC), it provides a context-sensitive framework that clarifies when AI can be used ethically and when human empathy is indispensable.
Abid Hussain, Muhammad Ehsan, Jing Song· Management Research Review· 0 citations
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