Be ethical then proficient: navigating employees’ ethical adoption of generative AI in the workplace

Dongya Wang · Jo-Yun Li · Yeunjae Lee · Weiting Tao

6 citations0/0 files storedJournal of Communication Management

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Ethics in Business and Education Ethics and Social Impacts of AI Technostress in Professional Settings

Abstract

Purpose As the use of generative artificial intelligence (Gen AI) grows in the workplace, organizations prioritize employee training due to ethical concerns. Employing the social-cognitive perspective of moral action, this project examined how organizations’ communication strategies and employees’ ethical orientations collectively impact the Gen AI training outcomes. Design/methodology/approach An online survey was conducted in February 2024 with 500 full-time employees in the United States whose organizations had implemented any form of Gen AI training. Structural equation modeling (SEM) and Hayes’s PROCESS Model 1 were employed for the data analysis. Findings The results revealed that change leadership and transparent communication during the training fostered ethical Gen AI adoption. Such enhanced ethical adoption of Gen AI subsequently enhanced employees’ proficient use of this technology. Employees’ ethical orientations (deontology and consequentialism) moderated this effect: those with lower procedural ethics (higher scores in consequentialism and lower scores in deontology) were more likely to be influenced by their leaders’ change-related actions. Originality/value The study provides theoretical insights and practical advice for utilizing organizations’ efforts to navigate employees’ Gen AI usage at work. Theoretically, the study presents an organizational communication framework connecting leadership, internal communication, ethical readiness, and work empowerment outcomes to guide employees’ ethical decision-making during new implementations and training. Practically, it provides organizations with data-proven Gen AI ethical training suggestions, such as enhancing leaders’ change-specific guidance and building transparent training environments.

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References

1998Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification.2023Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning 2007A New Model of Work Role Performance: Positive Behavior in Uncertain and Interdependent Contexts 2008The effects of transformational and change leadership on employees' commitment to a change: A multilevel study.2008Give the Emperor a Mirror: Toward Developing a Stakeholder Measurement of Organizational Transparency 2023The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGPT 2019Bridging transformational leadership, transparent communication, and employee openness to change: The mediating role of trust 2008The Influence of Leadership on Innovation Processes and Activities

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