From employee–ai collaboration to career sustainability: Exploring the roles of ai trust, approach motivation, and avoidance motivation

Abstract

Drawing upon person-environment (PE) fit theory, protection motivation theory (PMT), and the emergence of career sustainability in the fast-paced changing global economic context, which is significantly driven by the adoption of AI technologies in corporate environments, our study proposes a research model to examine how AI-related skills, knowledge and the faith in AI as well as different types of motivation and avoidance contribute to effective collaboration process between employees and AI. Moreover, we also assess the impacts of this collaboration on the sustainability of the career, which involves happiness, health, and productivity. According to our proposed research model, employee-AI collaboration serves as a mediator, bridging the gap between AI-related factors and career sustainability. Meanwhile, protean career orientation acts as a moderating role, influencing the effect of AI reliance on employees’ collaboration with AI. Building on gaps identified in prior studies, our research incorporates additional variables, such as AI-related competencies to improve the quality and accuracy of the findings in the current context. It also examines the interactions between AI and human collaboration through the lens of approach motivation and avoidance motivation, as outlined by protection motivation theory (PMT). 500 valid responses stemming from individuals working in various jobs attributed to very different industries were captured with a primary-based research method. The data was then analyzed using the SEM technique to calculate and assess the relationships between variables proposed in the study. The authors highlight that employee-AI collaboration is positively affected by trust in AI, which is also positively influenced by knowledge of AI skills, understanding of AI, and a deep comprehension of the role of AI within the workplace. Additionally, we also found that protean career orientation also occupies a key position in moderating the interaction between AI trust and employee-AI collaboration in a positive way. Moreover, our findings suggest that different types of motivation also bring significantly different impacts on the collaboration, which culminates in exerting a positive influence on factors of career sustainability to some extent. The study also offers practical insights for employees and the future workforce to have a basic understanding of AI. Besides, the findings also help managers accumulate knowledge about the factors influencing collaboration and improve them to further integrate AI into the organization, leading to better business and employee performance.