In recent years, a growing number of organizations have rapidly adopted artificial intelligence (AI) technology, aiming to build a sustainable competitive advantage through automation and intelligence. Despite substantial investments in AI, many organizations have yet to realize satisfactory returns. A key reason for this shortfall lies in the disproportionate emphasis on the technical implementation of AI, while neglecting the management of AI use at the employee level. Consequently, AI often fails to be fully integrated into business processes or to realize its full potential. Although AI can effectively reduce costs and improve organizational efficiency, its growing role in tasks previously performed by humans has led to a squeeze on employees' professional value and livelihood. This has given rise to a phenomenon known as “AI disruption awareness”which refers to employees' perception of the threats posed by AI applications. Such awareness may trigger resistance to AI and become a significant barrier to its adoption and effective use. Therefore, as organizations undergo AI-driven transformation, it is essential to understand and address the impact of AI disruption awareness on employees' use of AI.
While academic discussions around AI disruption awareness have grown in recent years, relatively little attention has been paid to its influence on employees' use of AI. Existing research has primarily focused on employees' willingness to use AI, with little attention paid to how AI disruption awareness affects employees' actual AI usage behavior. Moreover, the majority of prior studies have emphasized the negative effects of AI disruption awareness on AI usage, neglecting its potential positive effects. Against this backdrop, this study focuses on employees' AI usage behavior in AI application scenarios, exploring how they adjust their use of AI in response to AI disruption awareness, in order to expand the study of the impact of AI disruption awareness on AI usage. Drawing on the cognitive appraisal theory of stress, the study constructs a model to explore how AI disruption awareness differentially impacts employees' innovative and avoidant use of AI, and the moderating role of strengths-based leadership in this process.
By analyzing two-wave survey data collected from 317 employees, the study yields the following conclusions: AI disruption awareness triggers two distinct strategies of innovative use and avoidant use of AI, and the choice of strategy is influenced by strengths-based leadership. Under the influence of strengths-based leadership, employees tend to make a challenge appraisal of AI disruption awareness, which drives them to adopt innovative usage strategies toward AI. Conversely, in the absence of strengths-based leadership, employees will make a threat appraisal of AI disruption awareness, leading them to adopt avoidant usage strategies toward AI.
The theoretical contributions of this study are as follows: First,it shifts the analytical lens from intention to actual behavior, foregrounding employee agency in AI application. By addressing the key question of “how AI disruption awareness influences employees' AI usage behavior”, this study offers new insights into how employees use AI under the influence of AI disruption awareness. Additionally, by revealing the impact of AI disruption awareness on creative use—a positive AI usage behavior—this study addresses the limitations of previous research, which often adopted a singularly negative perspective. Second, unlike previous studies that mostly explored employees' behavioral performance under AI disruption awareness from a single positive or negative perspective, this study integrates previous research perspectives based on a dialectical perspective, incorporates employees' positive and negative responses into the same framework, and proposes a dual behavioral mechanism of employees' AI disruption awareness, which provides a more comprehensive theoretical explanation for understanding the effects of AI disruption awareness. Third, this study proposes that strengths-based leadership is an important conditioning factor in determining the effect of AI disruption awareness. This not only bridges the gap of past studies' understanding of the boundaries of the differential impact effects of AI impact awareness from a leader's perspective, but also provides effective clues to reconcile the controversy of existing studies on the differential impact effects of AI disruption awareness.
Wang Hongyu
,
Kou Xianliu
,
Zhao Di
,
Gu Yu
. The Double-Edged Sword Effect of AI Disruption Awareness on AI Usage Behavior[J]. Science & Technology Progress and Policy, 2025
, 42(23)
: 1
-11
.
DOI: 10.6049/kjjbydc.D6202502010RJ
[1] MAKARIUS E E, MUKHERJEE D, FOX J D, et al. Rising with the machines: a sociotechnical framework for bringing artificial intelligence into the organization[J].Journal of Business Research, 2020, 120: 262-273.
[2] 张恒, 高中华, 徐燕. AI技术替代感对工作场所人与AI合作意愿的影响机制[J].软科学, 2024, 38(3): 107-114.
[3] BROUGHAM D, HAAR J. Smart technology, artificial intelligence, robotics, and algorithms (STARA): employees' perceptions of our future workplace[J].Journal of Management & Organization, 2018, 24(2): 239-257.
[4] LIU S, CHENG P. The double-edged sword effect of artificial intelligence awareness among hotel employees[J].International Journal of Contemporary Hospitality Management, 2025, 37(3): 997-1015.
[5] YIN M, JIANG S, NIU X. Can AI really help? the double-edged sword effect of AI assistant on employees' innovation behavior[J].Computers in Human Behavior, 2024, 150: 107987.
[6] WANG X, LIN X, SHAO B. How does artificial intelligence create business agility? evidence from chatbots[J].International Journal of Information Management, 2022, 66: 102535.
[7] GKINKO L, ELBANNA A. The appropriation of conversational AI in the workplace: a taxonomy of AI chatbot users[J].International Journal of Information Management, 2023, 69: 102568.
[8] YIN Z, KONG H, BARUCH Y, et al. Interactive effects of AI awareness and change-oriented leadership on employee-AI collaboration: the role of approach and avoidance motivation[J].Tourism Management, 2024, 105: 104966.
[9] DING L. Employees' challenge-hindrance appraisals toward STARA awareness and competitive productivity: a micro-level case[J].International Journal of Contemporary Hospitality Management, 2021, 33(9): 2950-2969.
[10] LAZARUS R S, FOLKMAN S. Transactional theory and research on emotions and coping[J].European Journal of Personality, 1987, 1(3): 141-169.
[11] YU L, CHEN Y, GONG M. The duality of ICT-mediated overload:its nature and consequences[J].Information & Management, 2023, 60(8): 103864.
[12] 朱永跃, 张明凤. 技术冲击意识对制造业员工主动技能发展的双刃剑效应——基于SEM与fsQCA的研究[J].科技进步与对策, 2024, 23(41): 149-160.
[13] LIANG X, GUO G, SHU L, et al. Investigating the double-edged sword effect of AI awareness on employee's service innovative behavior[J].Tourism Management, 2022, 92: 104564.
[14] GOETZ T M, BOEHM S A. Am I outdated? the role of strengths use support and friendship opportunities for coping with technological insecurity[J].Computers in Human Behavior, 2020, 107: 106265.
[15] 汪海霞, 王娜娜. 员工优势使用如何影响创新行为——组织自尊与积极情绪的链式中介作用[J].科技进步与对策, 2021, 38(17): 125-133.
[16] 孟祥婧, 徐鹏. 人工智能警觉对员工工作倦怠的影响研究——基于科技公司的调查数据[J].经济与管理研究, 2024, 45(10): 99-110.
[17] 彭息强, 田喜洲. 优势领导对员工知识共享行为的“双刃剑”效应——基于个体—人际二元情感机制[J].经济管理, 2025, 47(6): 187-208.
[18] WANG J, VAN WOERKOM M, BREEVAART K, et al. Strengths-based leadership and employee work engagement: a multi-source study[J].Journal of Vocational Behavior, 2023, 142: 103859.
[19] VAN WOERKOM M, OERLEMANS W, BAKKER A B. Strengths use and work engagement: a weekly diary study[J].European Journal of Work and Organizational Psychology, 2016, 25(3): 384-397.
[20] MATSUO M. The role of supervisor support for strengths use in promoting perceived employability and career satisfaction[J].Personnel Review, 2022, 51(5): 1587-1603.
[21] BAKKER A B, VAN WOERKOM M. Strengths use in organizations: a positive approach of occupational health[J].Canadian Psychology / Psychologie Canadienne, 2018, 59(1): 38-46.
[22] LI X, HSIEH J J P A, RAI A. Motivational differences across post-acceptance information system usage behaviors: an investigation in the business intelligence systems context[J].Information Systems Research, 2013, 24(3): 659-682.
[23] YU H, SHUM C, ALCORN M, et al. Robots can't take my job: antecedents and outcomes of Gen Z employees' service robot risk awareness[J].International Journal of Contemporary Hospitality Management, 2022, 34(8): 2971-2988.
[24] LU X, KLUEMPER D, TU Y. When does hindrance appraisal strengthen the effect of challenge appraisal? the role of goal orientation[J].Journal of Organizational Behavior, 2023, 44(9): 1464-1485.
[25] BALA H, VENKATESH V. Adaptation to information technology: a holistic nomological network from implementation to job outcomes[J].Management Science, 2016, 62(1): 156-179.
[26] ZHAO J, HU E, HAN M, et al. That honey, my arsenic: the influence of advanced technologies on service employees' organizational deviance[J].Journal of Retailing and Consumer Services, 2023, 75: 103490.
[27] CHIU Y T, ZHU Y Q, CORBETT J. In the hearts and minds of employees: a model of pre-adoptive appraisal toward artificial intelligence in organizations[J].International Journal of Information Management, 2021, 60: 102379.
[28] PODSAKOFF P M, MACKENZIE S B, LEE J Y, et al. Common method biases in behavioral research: a critical review of the literature and recommended remedies[J].Journal of Applied Psychology, 2003, 88(5): 879-903.
[29] SPECTOR P E, BRANNICK M T. Methodological urban legends: the misuse of statistical control variables[J].Organizational Research Methods, 2011, 14(2): 287-305.
[30] LI W, QIN X, YAM K C, et al. Embracing artificial intelligence (AI) with job crafting: exploring trickle-down effect and employees' outcomes[J].Tourism Management, 2024, 104: 104935.
WeChat Service Account
WeChat Official Account
WeCom Customer Service