Amid the global digital wave, artificial intelligence (AI) technology, with its powerful data processing and intelligent decision-making capabilities, is increasingly becoming the core driving force for innovation and transformation across various industries. However, the introduction of AI transcends mere tool replacement; it reshapes the working modes of organizations and employees, giving rise to a series of management challenges. A prominent challenge is how to effectively leverage AI to empower employees and stimulate their innovative behavior. A review of the literature reveals that scholars' research on stimulating AI-driven employee innovation behavior mainly focuses on two perspectives: exploration of the double-edged sword effect on AI-driven employee innovation behavior from individuals' perception of AI, and examination of how AI collaborates with employees to promote individual innovation. Nevertheless, these studies overlook the significant role of leadership in stimulating AI-driven employee innovation behavior. Leaders' AI symbolization refers to leaders' explicit expression of support, acceptance, and promotion of AI by taking actions closely related to AI and displaying items that reflect their preference for AI. In light of this, this study investigates the mechanism and applicable boundaries of leaders' AI symbolization on AI-driven employee innovation behavior, aiming to provide guidance for both theory and practice.
Drawing on social cognitive theory, this study reveals the mediating mechanisms of leaders' AI symbolization on AI-driven employee innovation behavior through two aspects: technical cognitive trust (AI trust) and self-cognitive efficacy (AI innovation self-efficacy). Leaders' AI symbolization reflects their recognition, support, and trust in AI. This influences employees to trust AI more, increasing their willingness to accept AI and boosting AI-driven innovation. Additionally, employees' experience with leaders' AI symbolization helps them recognize their own innovative abilities. They gain confidence in solving complex problems and completing tasks innovatively with AI assistance. This enhances their AI innovation self-efficacy and provides psychological support for AI-driven innovation. Furthermore, individuals' cognitive processes towards leadership behavior are not only influenced by leaders' traits and behaviors themselves but also depend on how individuals understand and interpret these traits and behaviors. The inherent complexity of AI has left many leaders with insufficient expertise to fully grasp its implications, often resulting in a tendency to offer only surface-level support without the critical resources, training programs, or strategic direction needed to effectively implement AI solutions and address genuine organizational requirements. This can easily trigger employees' attribution analysis of leaders' AI symbolization motives. Therefore, this study explores the boundary conditions of leaders' AI symbolization influencing AI-driven employee innovation behavior through the dual-mediating cognitive mechanism from the perspective of employees' attribution of leaders' AI symbolization motives.
The analysis of matched data from 488 employees in two stages indicates that leaders' AI symbolization positively affects AI-driven employee innovation behavior through AI trust and AI innovation self-efficacy. Moreover, when employees attribute leaders' AI symbolization motives to performance improvement, the positive impact of leaders' AI symbolization on employees' AI trust is enhanced, thereby boosting AI-driven employee innovation behavior. Conversely, if attributed to impression management, the positive effects of leaders' AI symbolization on employees' AI trust and AI innovation self-efficacy are weakened, thereby reducing AI-driven employee innovation behavior. However, when employees attribute leaders' AI symbolization motives to performance improvement, the moderating effect of leaders' AI symbolization on AI innovation self-efficacy is not significant, and the moderated mediation hypothesis is also not significant.
The theoretical contributions of this study are as follows: First, it enriches the research on leadership factors in the antecedent mechanism of AI-driven employee innovation behavior and expands the influence of leaders' AI symbolization, providing a new perspective for understanding how leaders can effectively stimulate employees' innovative potential through AI symbolization. Second, this study innovatively analyzes how leaders' AI symbolization influences AI-driven employee innovation behavior and its effects through the dual-mediating paths of technical cognitive trust (i.e., AI trust) and self-cognitive efficacy (i.e., AI innovation self-efficacy), offering insights from a social cognitive perspective. Third, drawing on attribution theory, this study explores the boundary conditions of leaders' AI symbolization influencing AI-driven employee innovation behavior from the perspective of employees' attribution of leaders' motives, making an important supplement to the research on leaders' AI symbolization.
[1] JIA N, LUO X M, FANG Z, et al. When and how artificial intelligence augments employee creativity[J]. Academy of Management Journal, 2024, 67(1): 5-32.
[2] KONG H Y, YIN Z H, CHON K, et al. How does artificial intelligence (AI) enhance hospitality employee innovation? the roles of exploration, AI trust, and proactive personality[J]. Journal of Hospitality Marketing & Management, 2024, 33(3): 261-287.
[3] 蒋建武, 龙晗寰, 胡洁宇. 工作场所人工智能应用对员工影响的元分析[J]. 心理科学进展, 2024, 32(10): 1621-1639.
[4] 洪贝尔, 毛江华, 郭紫俊, 等. 领导的AI时尚: AI符号化与领导效能[J]. 外国经济与管理, 2024, 46(10): 121-134.
[5] HE G H, LIU P, ZHENG X N, et al. Being proactive in the age of AI: exploring the effectiveness of leaders' AI symbolization in stimulating employee job crafting[J]. Management Decision, 2023, 61(10): 2896-2919.
[6] 马璐, 李思柔. 人工智能焦虑与新生代员工创新行为: 组织依恋及工作重塑的作用[J]. 科技进步与对策, 2025, 42(1): 132-140.
[7] LIANG X D, GUO G X, SHU L L, et al. Investigating the double-edged sword effect of AI awareness on employee′s service innovative behavior[J]. Tourism Management, 2022, 92: 104564.
[8] 桂橙林, 赵旭宏, 张鹏程, 等. 数智化背景下员工AI意识对其创新绩效的影响机制[J]. 中国人力资源开发, 2024,34(8): 6-22.
[9] YIN M, JIANG S Y, NIU X Y. Can AI really help? the double-edged sword effect of AI assistant on employees′ innovation behavior[J]. Computers in Human Behavior, 2024, 150: 107987.
[10] 张恒, 高中华, 李慧玲. 增益还是损耗: 人工智能技术应用对员工创新行为的“双刃剑” 效应[J]. 科技进步与对策, 2023, 40(18): 1-11.
[11] 刘云硕, 刘园园, 张帆, 等. 威胁还是挑战: 人工智能使用对员工创新绩效的双刃剑效应[J]. 财经论丛, 2024,40 (9): 91-102.
[12] 韩明燕, 赵静幽, 李志. 员工—AI合作与越轨创新: 一个被调节的双路径模型[J]. 外国经济与管理, 2024, 46(10): 89-104.
[13] BANDURA A. Social foundations of thought and action: a social cognitive theory [M]. Englewood Cliffs: Prentice- Hall, 1986.
[14] LYTHREATIS S, MOSTAFA A M S, WANG X J. Participative leadership and organizational identification in SMEs in the MENA region: testing the roles of CSR perceptions and pride in membership [J]. Journal of Business Ethics, 2019, 156(3): 635-650.
[15] 尹萌, 牛雄鹰. 与AI“共舞”: 系统化视角下的AI—员工协作[J]. 心理科学进展, 2024, 32(1): 162-176.
[16] BARRICK M R,MOUNT M K,LI N.The theory of purposeful work behavior: the role of personality, higher-order goals, and job characteristics[J]. Academy of Management Review, 2013, 38(1): 132-153.
[17] HUSTON T L. Foundations of interpersonal attraction [M]. New York: Elsevier, 2013.
[18] 毛江华, 廖建桥, 韩翼, 等. 谦逊领导的影响机制和效应: 一个人际关系视角[J]. 心理学报, 2017, 49(9): 1219-1233.
[19] 刘美玉, 王季. 谦逊领导如何影响员工创造力——员工归因和心理安全的双重视角[J]. 经济管理, 2020, 42(3): 102-116.
[20] HEIDER F. The psychology of interpersonal relations [M]. New York: John Wiley and Sons, 1958.
[21] MARTINKO M J, HARVEY P, DASBOROUGH M T. Attribution theory in the organizational sciences: a case of unrealized potential[J]. Journal of Organizational Behavior, 2011, 32(1): 144-149.
[22] 宋瑶, 王震. 领导过程中的下属归因: 一个整合性理解框架[J]. 外国经济与管理, 2023, 45(6): 84-100.
[23] MURRAY A,RHYMER J,SIRMON D G.Humans and technology:forms of conjoined agency in organizations [J]. Academy of Management Review, 2021, 46(3): 552-571.
[24] LAM W, HUANG X, SNAPE E. Feedback-seeking behavior and leader-member exchange: do supervisor-attributed motives matter[J]. Academy of Management Journal, 2007, 50(2): 348-363.
[25] OWENS B P, HEKMAN D R. How does leader humility influence team performance? exploring the mechanisms of contagion and collective promotion focus[J]. Academy of Management Journal, 2016, 59(3): 1088-1111.
[26] CHOWDHURY S, BUDHWAR P, DEY P K, et al. AI-employee collaboration and business performance: integrating knowledge-based view, socio-technical systems and organisational socialisation framework[J]. Journal of Business Research, 2022, 144: 31-49.
[27] 齐玥, 陈俊廷, 秦邵天, 等. 通用人工智能时代的人与AI信任[J]. 心理科学进展, 2024, 32(12): 2124-2136.
[28] GLIKSON E, WOOLLEY A W. Human trust in artificial intelligence: review of empirical research[J]. Academy of Management Annals, 2020, 14(2): 627-660.
[29] BANG H, REIO T G. Personal accomplishment, mentoring, and creative self-efficacy as predictors of creative work involvement: the moderating role of positive and negative affect[J]. The Journal of Psychology, 2017, 151(2): 148-170.
[30] 陈启山, 温忠麟. 印象整饰的测量及其在人力资源管理中的应用[J]. 心理科学, 2005, 28(1): 178-179.
[31] DESAI S D, KOUCHAKI M. Moral symbols: a necklace of garlic against unethical requests[J]. Academy of Management Journal, 2017, 60(1): 7-28.
[32] BHARANITHARAN D K,LOWE K B,BAHMANNIA S,et al. Seeing is not believing: leader humility, hypocrisy, and their impact on followers' behaviors[J]. The Leadership Quarterly, 2021, 32(2): 101440.
[33] PARK J, WOO S E, KIM J. Attitudes towards artificial intelligence at work: scale development and validation[J]. Journal of Occupational and Organizational Psychology, 2024, 97(3): 920-951.
[34] YAM K C, GOH E Y, FEHR R, et al. When your boss is a robot: workers are more spiteful to robot supervisors that seem more human[J]. Journal of Experimental Social Psychology, 2022, 102: 104360.
[35] XIA Q, CHIU T K F, LEE M, et al. A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education[J]. Computers & Education, 2022, 189: 104582.
[36] EDWARDS J R, LAMBERT L S. Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis [J]. Psychological Methods, 2007, 12(1): 1-22.
[37] 张志鑫,郑晓明,钟杰.基于人机交互视角的领导力研究[J].科技进步与对策,2025,42(10): 116-126.
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