AI驱动的工作非常规化对员工工作意义感的双路径影响：基于AI工作重塑的调节作用
The Dual-Path Effects of AI-Enabled Job Non-Routinization on Employees’ Work Meaningfulness: The Moderating Role of AI-Based Crafting
摘要
在AI广泛接管组织常规性任务的背景下，员工所承担工作的非常规化程度显著上升，形成AI驱动的工作非常规化(AI-enabled Job Non-Routinization, AIJNR)。现有研究主要将AIJNR理解为员工任务类型的变化，并据此考察其对创造力的影响。然而，AIJNR也在改变员工整体工作的价值来源以及角色与任务边界，使员工需要重新理解自身的工作意义。基于工作特征模型，本研究提出，AIJNR一方面通过提升任务重要性增加员工工作意义感，另一方面通过降低任务完整性减少员工工作意义感；同时，员工基于AI工作重塑会调节AIJNR对其工作特征的影响。研究一基于GitHub协作日志事件(N = 286,773)、案件数据(N = 31,037)和用户层面数据(N = 1,891; N = 764)，初步发现AI出现后工作非常规化程度上升，且AIJNR与任务重要性正相关、与任务完整性负相关。研究二通过视频情景实验(N = 180)，研究三通过三阶段问卷调查(N = 224)进一步验证了上述双路径机制：AIJNR通过任务重要性正向影响工作意义感，通过任务完整性负向影响工作意义感。此外，基于AI工作重塑显著强化AIJNR与任务重要性之间的正向关系，但对AIJNR与任务完整性之间关系的调节作用不显著。最后，本研究讨论了研究发现的理论贡献、实践启示以及未来研究方向。
Abstract
As artificial intelligence (AI) increasingly serves as an automation tool that takes over routine work in organizations, employees are left with a greater proportion of non-routine tasks. This shift gives rise to AI-enabled job non-routinization (AIJNR). Prior research has mainly understood AIJNR as a change in job content, whereby employees move from routine tasks to non-routine tasks, and has examined its implications for creativity. However, AIJNR is not merely a process of making job content more non-routine. It may also restructure the value attributes and boundary structure of employees work, requiring employees to reconsider the meaning of their work.Drawing on the Job Characteristics Model (JCM), we propose that AIJNR influences work meaningfulness through two opposing pathways. On the one hand, when AI absorbs repetitive and rule-based tasks, employees may devote more attention to work that is consequential, judgment-intensive, and valuable for the organization, thereby enhancing perceived task significance and, in turn, work meaningfulness. On the other hand, AI may also fragment workflows and reduce employees opportunities to complete an identifiable whole piece of work, thereby undermining perceived task identity and, in turn, work meaningfulness. In addition, we examine AI-based crafting as a moderator that captures employees proactive adjustment to AI-enabled changes in work.We tested these ideas through three studies. Study 1 used archival collaboration data from the GitHub repository huggingface/transformers, including 286,773 event logs and 31,037 cases, to provide descriptive evidence of increasing job non-routinization after the diffusion of AI tools. User-level analyses based on 1,891 collaborators further indicated that AIJNR became more salient in the post-2022 period. Focusing on 764 collaborators in 20232024, we found that AIJNR was positively associated with indicators of task significance and negatively associated with indicators of task identity. Study 2 employed a video-based scenario experiment with 180 participants and showed that higher AIJNR increased task significance but decreased task identity, which in turn produced opposite indirect effects on work meaningfulness. Study 3, a three-wave field survey of 224 employees, replicated this dual-path pattern in a field setting. Moreover, AI-based crafting strengthened the positive relationship between AIJNR and task significance, whereas its moderating effect on the relationship between AIJNR and task identity was not significant.This research makes three theoretical contributions. First, it extends the job characteristics model by showing that AIJNR can serve as an important source of changes in core job characteristics. Second, it reveals the boundary role of AI-based crafting in shaping how AIJNR is translated into employees perceptions of job characteristics. Third, it incorporates technology-driven work structure change into research on the sources of work meaningfulness, offering a new perspective for understanding work design and employees meaning experience in the AI era.关键词
AI驱动的工作非常规化/基于AI工作重塑/工作意义感/工作特征模型Key words
AI-enabled job non-routinization/AI-based crafting/work meaningfulness/job characteristics model引用本文复制引用
黄泽杰,龙立荣,黄世英子,祝养浩.AI驱动的工作非常规化对员工工作意义感的双路径影响：基于AI工作重塑的调节作用[EB/OL].(2026-09-23)[2026-09-26].https://chinaxiv.org/abs/202609.00305.学科分类
经济计划、经济管理
