AI感知对于员工创新绩效的“双刃剑”影响机制研究

Research on the "double-edged sword" impact mechanism of AI Awareness on Employee Innovation Performance

成果信息

DC信息

传播影响力

归属院系：

商学院（监察审计学院）

作者：

许伟奇

导师：

任静

导师单位：

商学院（监察审计学院）

学位：

硕士

语种：

中文

关键词：

AI感知, 趋近型工作重塑, 回避型工作重塑, 学习目标导向, 员工创新绩效

摘要：

在数字化转型与智能化加速的背景下，人工智能（AI）技术的广泛应用正在深刻改变员工的工作方式与行为模式，会在人机协作情境中对员工创新绩效产生“双刃剑”影响。一方面，AI技术通过提高工作效率、优化资源分配与推动技术协作，为员工创新行为创造了新的机遇，激发了更多的创新动力；另一方面，AI技术的引入也可能带来负面影响，如岗位替代性担忧与职业发展不确定性，这些因素可能导致员工心理压力增加，进而抑制创新行为。因此，全面探讨人机协作模式下AI技术对员工创新绩效的双重影响机制，以及在这一过程中可能发挥作用的边界条件，对学术理论研究与企业实践管理均具有重要参考价值。本研究理论基础为工作要求-资源模型（JD-R模型）和趋近-回避应对理论，并从“动机-行为-结果”路径出发，重点探讨了AI感知对员工创新绩效的双重作用机理，并引入中介变量：趋近型工作重塑和回避型工作重塑。同时，本研究将学习目标导向作为调节变量，揭示了AI感知与员工创新绩效之间复杂关系的边界条件。AI感知具体分为AI机遇感知与AI阻碍感知两个维度，分别反映员工对AI技术的积极认知与消极认知。AI机遇感知指员工将AI视为提升工作效率与职业发展的机遇，表现为更高的工作动机和积极行为倾向；而AI阻碍感知则代表员工对AI技术的威胁性认知，可能带来工作不安全感，诱发防御性行为。在使用问卷星设计与发放问卷后，本研究共回收568份有效问卷。通过分析软件SPSS26.0和AMOS26.0对数据进行分析，研究结果表明，AI感知对员工创新绩效的作用存在显著的双路径效应。在正向路径中，AI机遇感知通过趋近型工作重塑显著促进员工创新绩效。员工在AI机遇感知的驱动下，会主动进行任务、关系与认知层面的工作重塑；在负向路径中，AI阻碍感知通过回避型工作重塑显著抑制员工创新绩效。当员工将AI视为潜在威胁时，他们更可能采取回避型工作重塑行为，通过减少任务范围、削弱人际互动与固守现有认知来降低心理负担。这些消极行为最终阻碍了员工创新绩效的提升。此外，学习目标导向在AI感知与创新绩效的关系中起到了显著的调节作用。研究发现，学习目标导向较高的员工能够更积极地适应AI驱动的工作变化，并通过不断学习新技能与积累经验有效应对AI技术带来的压力与挑战。这一特质不仅能够缓解AI阻碍感知的负面影响，还能增强AI机遇感知的正向作用，为员工创造更多创新行为的机会。因此，培养学习目标导向成为企业管理者优化员工创新绩效的重要抓手。本研究的理论贡献在于揭示了AI感知通过趋近型与回避型工作重塑影响员工创新绩效的双路径机制，并强调了学习目标导向作为边界条件的关键作用。这一发现丰富了工作要求-资源模型在AI应用场景下的理论内涵。同时，本研究也为实践提供了重要启示。企业管理者应通过正确引导员工对AI的认知，强化其对AI的机遇感知，帮助员工将AI视为支持工具而非威胁来源。同时，组织应注重心理安全感的建设，关注员工的情绪状态并提供相应支持。此外，企业可以通过制定学习与培训计划，提升员工的学习目标导向与数字技能，以更好地应对AI技术带来的挑战与机遇，最终推动组织的整体创新能力。

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学科：

工商管理

提交日期：

2025-05-28

引用参考：

许伟奇. AI感知对于员工创新绩效的“双刃剑”影响机制研究[D]. 西南政法大学,2025.

全文附件授权许可：

知识共享许可协议-署名

dc.title
AI感知对于员工创新绩效的“双刃剑”影响机制研究

dc.title
Research on the "double-edged sword" impact mechanism of AI Awareness on Employee Innovation Performance

dc.contributor.schoolno
20221202002237

dc.contributor.author
许伟奇

dc.contributor.affiliation
商学院（监察审计学院）

dc.contributor.degree
硕士

dc.contributor.childdegree
管理学硕士学位

dc.contributor.degreeConferringInstitution
西南政法大学

dc.identifier.year
2025

dc.contributor.direction
人力资源

dc.contributor.advisor
任静

dc.contributor.advisorAffiliation
商学院（监察审计学院）

dc.language.iso
中文

dc.subject
AI感知,趋近型工作重塑,回避型工作重塑,学习目标导向,员工创新绩效

dc.subject
AI Awareness; Approach-Oriented Job Crafting; Avoidance-Oriented Job Crafting; Learning Goal Orientation; Employee Innovation Performanc

dc.description.abstract
在数字化转型与智能化加速的背景下，人工智能（AI）技术的广泛应用正在深刻改变员工的工作方式与行为模式，会在人机协作情境中对员工创新绩效产生“双刃剑”影响。一方面，AI技术通过提高工作效率、优化资源分配与推动技术协作，为员工创新行为创造了新的机遇，激发了更多的创新动力；另一方面，AI技术的引入也可能带来负面影响，如岗位替代性担忧与职业发展不确定性，这些因素可能导致员工心理压力增加，进而抑制创新行为。因此，全面探讨人机协作模式下AI技术对员工创新绩效的双重影响机制，以及在这一过程中可能发挥作用的边界条件，对学术理论研究与企业实践管理均具有重要参考价值。本研究理论基础为工作要求-资源模型（JD-R模型）和趋近-回避应对理论，并从“动机-行为-结果”路径出发，重点探讨了AI感知对员工创新绩效的双重作用机理，并引入中介变量：趋近型工作重塑和回避型工作重塑。同时，本研究将学习目标导向作为调节变量，揭示了AI感知与员工创新绩效之间复杂关系的边界条件。AI感知具体分为AI机遇感知与AI阻碍感知两个维度，分别反映员工对AI技术的积极认知与消极认知。AI机遇感知指员工将AI视为提升工作效率与职业发展的机遇，表现为更高的工作动机和积极行为倾向；而AI阻碍感知则代表员工对AI技术的威胁性认知，可能带来工作不安全感，诱发防御性行为。在使用问卷星设计与发放问卷后，本研究共回收568份有效问卷。通过分析软件SPSS26.0和AMOS26.0对数据进行分析，研究结果表明，AI感知对员工创新绩效的作用存在显著的双路径效应。在正向路径中，AI机遇感知通过趋近型工作重塑显著促进员工创新绩效。员工在AI机遇感知的驱动下，会主动进行任务、关系与认知层面的工作重塑；在负向路径中，AI阻碍感知通过回避型工作重塑显著抑制员工创新绩效。当员工将AI视为潜在威胁时，他们更可能采取回避型工作重塑行为，通过减少任务范围、削弱人际互动与固守现有认知来降低心理负担。这些消极行为最终阻碍了员工创新绩效的提升。此外，学习目标导向在AI感知与创新绩效的关系中起到了显著的调节作用。研究发现，学习目标导向较高的员工能够更积极地适应AI驱动的工作变化，并通过不断学习新技能与积累经验有效应对AI技术带来的压力与挑战。这一特质不仅能够缓解AI阻碍感知的负面影响，还能增强AI机遇感知的正向作用，为员工创造更多创新行为的机会。因此，培养学习目标导向成为企业管理者优化员工创新绩效的重要抓手。本研究的理论贡献在于揭示了AI感知通过趋近型与回避型工作重塑影响员工创新绩效的双路径机制，并强调了学习目标导向作为边界条件的关键作用。这一发现丰富了工作要求-资源模型在AI应用场景下的理论内涵。同时，本研究也为实践提供了重要启示。企业管理者应通过正确引导员工对AI的认知，强化其对AI的机遇感知，帮助员工将AI视为支持工具而非威胁来源。同时，组织应注重心理安全感的建设，关注员工的情绪状态并提供相应支持。此外，企业可以通过制定学习与培训计划，提升员工的学习目标导向与数字技能，以更好地应对AI技术带来的挑战与机遇，最终推动组织的整体创新能力。

dc.description.abstract
With the acceleration of digital transformation and intelligentization, the widespread application of artificial intelligence (AI) technology has profoundly influenced employees' work methods and behavioral patterns, exhibiting a "double-edged sword" effect on their innovation performance. On one hand, AI technology enhances work efficiency, optimizes resource allocation, and promotes technological collaboration, creating new opportunities for employees' innovative behaviors and stimulating greater motivation for innovation. On the other hand, the introduction of AI may lead to negative impacts, such as concerns over job displacement and uncertainties in career development, increasing psychological stress and suppressing innovative actions. Therefore, comprehensively exploring the dual effects of AI technology on employees' innovation performance, as well as the boundary conditions influencing these effects, holds significant importance for theoretical research and management practices.This study is grounded in the Job Demands-Resources (JD-R) model and the Approach-Avoidance Coping Theory, investigating the dual mechanisms of AI Awareness on employees' innovation performance through a "motivation-behavior-outcome" pathway. It identifies approach-oriented job crafting and avoidance-oriented job crafting as key mediating variables. Additionally, the study incorporates learning goal orientation as a moderating variable to explore the boundary conditions in the relationship between AI Awareness and employees' innovation performance. AI Awareness is divided into two dimensions: AI Challenge Appraisal and AI Hindrance Appraisal, representing employees' positive and negative perceptions of AI technology, respectively. AI Challenge Appraisal refers to employees perceiving AI as an opportunity to enhance work efficiency and career development, characterized by higher work motivation and proactive behaviors. Conversely, AI Hindrance Appraisal reflects employees' perception of AI as a potential threat, leading to job insecurity and defensive behaviors.Using a questionnaire distributed via Questionnaire Star, the study collected 568 valid responses. Data were analyzed with SPSS 26.0 and AMOS 26.0, revealing significant dual-pathway effects of AI Awareness on employees' innovation performance. In the positive pathway, AI Challenge Appraisal significantly enhances innovation performance through approach-oriented job crafting. Specifically, driven by AI Challenge Appraisal, employees proactively engage in task, relational, and cognitive job crafting. For example, in tasks, they redefine task boundaries and introduce new activities to optimize workflows, stimulating novel and valuable ideas. In relationships, employees seek higher-quality interactions with AI or team members, acquiring more resources essential for innovation. In cognition, employees adopt a more positive perspective toward work goals and significance, fostering innovation-oriented cognitive processing.In the negative pathway, AI Hindrance Appraisal suppresses innovation performance through avoidance-oriented job crafting. When employees perceive AI as a threat, they are more likely to adopt avoidance-oriented behaviors, such as reducing task scope, weakening interpersonal interactions, and adhering to existing cognitive frameworks to alleviate psychological burdens. For instance, in tasks, employees may avoid innovative responsibilities or decline new assignments. In relationships, they reduce collaboration with AI or team members, limiting access to innovation-related resources. In cognition, they narrow their perspective on AI technology, diminishing work meaningfulness and active engagement in innovative thinking. These avoidance behaviors ultimately hinder the enhancement of innovation performance.Additionally, learning goal orientation significantly moderates the relationship between AI Awareness and innovation performance. Employees with a strong learning goal orientation can adapt more effectively to AI-driven workplace changes by continuously acquiring new skills and experiences to address challenges and opportunities presented by AI technology. This trait not only mitigates the negative effects of AI Hindrance Appraisal but also amplifies the positive effects of AI Challenge Appraisal, creating more opportunities for innovative behaviors. Thus, fostering learning goal orientation is crucial for managers seeking to optimize employees' innovation performance.The theoretical contribution of this study lies in uncovering the dual-pathway mechanisms through which AI Awareness influences employees' innovation performance via approach- and avoidance-oriented job crafting. It also highlights the critical role of learning goal orientation as a boundary condition, enriching the JD-R model within the context of AI applications. Practically, this study provides important insights for management. Organizations should guide employees to develop a positive perception of AI, strengthening AI Challenge Appraisal and helping employees view AI as a supportive tool rather than a threat. Moreover, organizations should build psychological safety, address employees' emotional needs, and provide adequate support. Finally, companies should design training and development programs to enhance employees' learning goal orientation and digital skills, enabling them to better navigate the challenges and opportunities brought by AI, ultimately driving organizational innovation.

dc.date.issued
2025-05-28

dc.date.oralDefense
2025-05-16

dc.relation.citedreferences
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