Earticle

초록

영어

The dynamics of employees’ career growth have undergone significant and intricate changes due to the deep integration of AI into contemporary workplaces. The precise influence of AI on employees’ perceived career growth opportunities (CGO) is still a contentious issue in academic circles, even though AI is well known for its ability to create new job positions and reshape existing occupational roles. Among various theoretical approaches, the technology acceptance model (TAM) is relevant to addressing the current research gap. Likewise, career construction theory (CCT) provides a distinct and valuable lens through which to examine the phenomenon in question. Finally, the conservation of resources (COR) theory serves as a well-established theoretical basis to address the identified gap. Although each of these theories has been extensively applied in its respective field of study, their combined applicability to the research on AI and workers’ career development has not been thoroughly investigated. From a career development standpoint, exploring AI’s impact on employees’ CGO holds great theoretical and practical significance: theoretically, it can enrich the digital-era research system of human resource management as well as organizational behavior, and compensate for the lack of in-depth discussion on the connection between AI and individual career development. Practically, it can provide guidance for organizations to implement AI effectively and support employees in navigating and thriving within AI-driven work environments, thereby achieving a win-win situation for organizational digital transformation and individual career development. Using a three-wave time-lagged survey approach, this study collected reliable research data from 499 workers across a range of Chinese sectors. Preliminary empirical findings indicate a robust, positive correlation between employees’ perceived career growth prospects and their AI attitudes (AIA), with perceived AI adaptability (PAIA) acting as a partial mediating factor in this relationship. This perception, in turn, enhances their ability to identify potential CGOs. This result is consistent with the empirical conclusion that AI can supplement and improve job capabilities, opening new avenues for human-AI collaboration and facilitating employees’ career development. In addition, job insecurity (JI) was identified as a critical boundary condition in this study, as it significantly weakens the positive correlation between PAIA and CGO. Thus, the mediating effect of PAIA between AI attitude and CGO is most pronounced among employees with low JI. For employees with a strong sense of job security, AI is perceived as a valuable resource for CGO. Conversely, employees experiencing heightened JI are more inclined to perceive the impact of AI’s adaptive attributes on their career advancement. It should also be noted that the influence mechanism of AI on employees’ CGO may vary across different types of organizations, indicating that the workplace context significantly shapes the interplay between AI and individual career paths. In summary, the findings provide an initial in-depth insight into how AI is reshaping employees’ career trajectories. They indicate that the mere introduction of AI technologies in the workplace is insufficient to achieve positive organizational outcomes for all employees. Organizations must ensure a sufficiently secure work environment to mitigate uncertainty regarding job loss, as such fears may otherwise hinder employees’ career development.

목차

Abstract
1. Introduction
2. Theoretical Foundations and Hypothesis
2.1 AIA and CGO
2.2 The Mediating Role of PAIA
2.3 The Moderating Role of JI
3. Methodology
3.1 Data Sources
3.2 Variable Description
4. Empirical Analysis
4.1 Preliminary Analyses
4.2 Confirmatory Factor Analysis
4.3 Hypothesis Testing
5. Discussion
5.1 Theoretical Implications
5.2 Practical Implications
5.3 Limitations
6. Conclusion
References

키워드

저자

• Yang, Lei [ Seoul School of Integrated Sciences and Technologies, Korea. ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  Journal of China Studies

• 간기

  계간

• pISSN

  1975-5902

• eISSN

  3022-5590

• 수록기간

  2006~2026

• 등재여부

  KCI 등재

• 십진분류

  KDC 912 DDC 951