When AI enables and undermines: dual mechanisms linking AI usage to task performance

When AI enables and undermines: dual mechanisms linking AI usage to task performance | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article When AI enables and undermines: dual mechanisms linking AI usage to task performance Wenhui Zhang, Po-Chien Chang, Xinqi Geng This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7168956/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 9 You are reading this latest preprint version Abstract Purpose. The growing adoption of AI is reshaping organizational workflows and employee experiences, delivering benefits while presenting novel challenges, reflecting a double-edged sword effect. Although prior studies have identified potential impacts of AI usage on employees, the underlying mechanisms remain insufficiently examined. Guided by self-determination theory, this study investigates the dual pathways—facilitative and inhibitive—by which AI usage impacts employee task performance via motivational and behavioral mechanisms. It further investigates the boundary role of core self-evaluations (CSE) in moderating these effects. Methodology. A three-wave, multi-source field study was conducted involving 409 employee-supervisor pairs from AI-intensive industries in China. Data were collected using leader–employee matched questionnaires. Key constructs—AI usage, motivation types, job crafting, task performance, and CSE—were measured using validated scales. Hypotheses were tested via hierarchical regression, bootstrapping, and moderated mediation analyses using SPSS and Mplus. Findings. Results revealed a dual-chain mediation mechanism: AI usage enhances task performance via autonomous motivation and promotion-focused job crafting, but simultaneously impairs it through controlled motivation and prevention-focused job crafting. Furthermore, CSE significantly moderates both pathways, amplifying the positive and buffering the negative effects. Originality. This study provides understanding of AI usage’s “double-edged sword” effect by identifying parallel motivational-behavioral pathways and the boundary condition of core self-evaluations. The findings enrich self-determination theory in the digital context and offer actionable insights for designing inclusive and personalized AI integration strategies. Business and commerce/Business and management Social science/Business and management Business and commerce/Information systems and information technology Biological sciences/Psychology Social science/Psychology Social science/Science technology and society Figures Figure 1 Figure 2 Figure 3 Introduction As digital transformation accelerates, AI has become a central force redefining organizational operations and employee work experiences (Haenlein & Kaplan, 2019 ). The incorporation of AI into innovation, operations, and decision-making has had profound and wide-ranging implications (Belanche et al., 2019 ; Duan et al., 2019 ; Gregory et al., 2021 ; Paschen et al., 2020 ), prompting organizations to embed AI into their core operations to enhance efficiency, productivity, performance, and innovation (Braganza et al., 2021 ; Cheng et al., 2023 ; Nam et al., 2021 ; Noy & Zhang, 2023 ). Consequently, more organizations are implementing AI to enhance performance and strengthen their competitive advantage (Li et al., 2019 ). However, while AI is redefining the nature of work, it has also raised a fundamental question in both academia and practice: How does AI usage influence employee performance? Task performance—defined as employees’ effectiveness in executing core responsibilities and delivering expected outputs—is central to organizational functioning (Janssen & Van Yperen, 2004 ; Williams & Anderson, 1991), yet the mechanisms through which AI affects it remain insufficiently understood. Although organizations often adopt AI usage to alleviate workload and empower high-value tasks (Wilson & Daugherty, 2018 ), its implementation may also restructure workflows and impose new technical demands, introducing uncertainty, role ambiguity, and perceived threats of technological replacement—factors that can undermine task performance. Existing research has preliminarily revealed contradictory results regarding AI usage and its impact on employee task performance. AI usage has been shown to foster positive psychological and behavioral responses, such as increased thriving at work, heightened engagement and autonomy, enriched task challenge, and ultimately improved performance (Friedman et al., 2024 ; Kemp, 2024 ; Marimon et al., 2024 ; Tang et al., 2023 ). Conversely, AI usage may also trigger adverse emotional and behavioral responses—such as AI anxiety, fear, and insecurity—that undermine job security, organizational commitment, and turnover retention, ultimately impairing performance (Chui et al., 2015 ; Fan et al., 2020 ; Suseno et al., 2023 ). This coexistence of enabling and inhibiting effects reveals the inherently paradoxical nature of AI usage (Jones, 2024 ; Tang et al., 2023 ; Wu et al., 2025 ). However, prior studies mostly focused on the single effect of AI usage and lacked a systematic explanation of the dual impacts (Kellogg et al., 2020 ). Self-determination theory (SDT) offers a compelling lens through which to examine the paradoxical impact of AI usage on task performance (Deci & Ryan, 1985 ). Essentially, this theory posits that the same external context may elicit different types of motivation by either fulfilling or hindering individuals’ basic psychological needs that give rise to divergent behavioral and performance outcomes (Ryan & Deci, 2000a , 2000b ). AI usage, as a new type of external context, may influence employees’ psychological needs by affecting the degree to which these needs are satisfied. This shift can trigger various kinds of work motivation. For instance, there is autonomous motivation, which stems from internal passion and beliefs, and controlled motivation, which is fueled by external stimuli like pressure or incentives (Ryan & Deci, 2000a ). When AI is perceived as a tool for fostering growth and expanding capabilities, it helps stimulate autonomous motivation, encouraging employees to voluntarily engage and proactively seek change. Conversely, if AI is interpreted as external control or a threat to resources, it is more likely to trigger controlled motivation, forcing employees to respond out of pressure or reward/punishment. Furthermore, these two distinct motivational pathways drive employees to reshape their behavior through work to respond to external environmental changes. Lichtenthaler and Fischbach ( 2019 ) identify two main forms of job crafting. The first, promotion-focused job crafting emphasizes actively acquiring resources and expanding task boundaries, which can enhance positive affect and performance. Conversely, prevention-focused job crafting centers on risk avoidance and role contraction, which often results in resource depletion and performance decline (Bindl et al., 2019 ; Bruning & Campion, 2018 ). Accordingly, in an AI usage context, employees with autonomous motivation will actively adjust their work strategies based on their recognition of work value and intrinsic interest, triggering promotion-focused job crafting and thereby enhancing task performance. In contrast, employees with controlled motivation will avoid risks due to external pressure and narrow their work scope, tending to trigger prevention-focused job crafting and thereby impairing task performance. Moreover, SDT suggests that individual traits influence how individuals perceive external contexts and engage motivational processes (Ryan & Deci, 2000a ). Core self-evaluations (CSE)—a higher-order personality construct encompassing self-esteem, generalized self-efficacy, emotional stability, and locus of control (Judge et al., 1998 , 2002 ). High-CSE employees often perceive AI usage as a developmental opportunity, thereby triggering autonomous motivation and prompting promotion-focused job crafting. Nevertheless, low-CSE employees may perceive AI usage as threatening, leading to controlled motivation and prevention-focused job crafting. Thus, CSE functions as a pivotal boundary condition. In summary, this study introduces a two-path framework to clarify how AI usage influences task performance through motivation and behavior. Specifically, AI usage activates either autonomous or controlled motivation, which subsequently drives promotion-focused or prevention-focused job crafting, ultimately shaping task performance. Furthermore, core self-evaluations serve as a boundary condition moderating this motivational-behavioral pathway. This study makes the following contributions. First, it integrates the positive and negative effects within a unified framework, thereby offering a comprehensive explanation of how AI usage impacts task performance. Second, it identifies a dual-chain mediation mechanism—via motivational types and job crafting strategies—that clarifies the process through which AI usage affects task performance. Finally, it validates the moderating effect of CSE, providing a new dimension for research on individual heterogeneity. Practically, the findings provide managerial implications for organizations aiming to optimize AI implementation, foster positive motivational states, and mitigate potential risks to employee performance. Theoretical background and hypothesis development AI usage and autonomous/controlled motivation. According to self-determination theory, motivation comprises two fundamental forms—autonomous and controlled (Ryan & Deci, 2000b). Autonomous motivation stems from an individual’s interest, value, and meaning in an activity itself, manifesting as employees actively, voluntarily, and enthusiastically engaging in work; whereas controlled motivation originates from external pressure, reward and punishment mechanisms, or self-coercion, manifesting as employees passively completing tasks (Deci & Ryan, 2000). SDT posits that external environments trigger varying motivations by either satisfying or hindering individuals’ three basic psychological needs—autonomy, competence, and relatedness. Accordingly, AI usage, as an external organizational context, may influence the emergence of autonomous and controlled motivation depending on how well these needs are met. First, AI usage may foster autonomous motivation when it satisfies employees’ core psychological needs. When AI autonomously handles repetitive, complex, or cognitively demanding tasks and provide timely, valuable decision-support information, they can enhance employees’ work flexibility and decision-making discretion, thereby fulfilling the need for autonomy (Davenport et al., 2019). In addition, AI usage may assign meaningful and challenging tasks, deliver positive feedback and recognition, and support employees’ career development, all of which strengthen their confidence and sense of competence, thus satisfying their competence needs (Prentice et al., 2020). Simultaneously, AI usage can promote cross-departmental collaboration, knowledge sharing, and emotional support, enabling employees to feel accepted and valued, thereby satisfying their relatedness needs (Cordery et al., 2010). The fulfillment of basic psychological needs fosters autonomous motivation among employees. However, AI usage may also trigger controlled motivation by hindering these psychological needs. When AI applications reduce employees’ autonomy, ignore employees’ work preferences or increase the skill requirements and transition costs, employees may feel helpless and insecure due to their inability to meet job requirements, thereby undermining employees’ autonomy and competence needs (Mirbabaie et al., 2022; Wu et al., 2025; Yam et al., 2023). Additionally, AI usage may diminish opportunities for face-to-face interaction, weaken team emotional bonds, and reduce organizational connectedness, thereby impeding the fulfillment of relatedness needs (Ryan et al., 2021). When these needs go unmet, employees often respond with controlled motivation. Therefore, we put forward the following hypotheses: Hypothesis 1a: AI usage is positively related to employees’ autonomous motivation. Hypothesis 1b: AI usage is positively related to employees’ controlled motivation. Autonomous/controlled motivation and promotion-/prevention-focused job crafting. Self-determination theory categorizes individual behavioral motivation into autonomous motivation and controlled motivation. This framework suggests that different forms of motivation lead to distinct behavioral outcomes—autonomous motivation typically encourages constructive behaviors, whereas controlled motivation often leads to negative consequences (Ryan & Deci, 2000a). Within organizational contexts, motivation functions as a critical internal force that drives employees to actively reshape their roles—a concept known as job crafting (Shin & Jung, 2021). This proactive behavior involves workers deliberately modifying their tasks and redefining professional boundaries (Wrzesniewski & Dutton, 2001). It encompasses two distinct forms: promotion-focused crafting, where employees seek out growth opportunities by expanding resources and tackling challenges, and prevention-focused crafting, which aims to minimize obstacles that hinder performance (Lichtenthaler & Fischbach, 2019). Different types of motivation prompt employees to adopt different job crafting strategies. Specifically, employees driven by autonomous motivation are more likely to embrace AI-driven workplace changes, proactively seek challenges, and pursue personal growth. When confronted with demanding tasks, they tend to exhibit higher levels of psychological vitality and respond with greater initiative (Rich et al., 2010). Such individuals often engage in self-directed learning, process optimization, and cross-functional collaboration to accumulate resources, enhance their sense of control, and achieve developmental goals (Laurence et al., 2020; Lazazzara et al., 2020; Parker & Grote, 2022). Therefore, employees with autonomous motivation are inclined to actively expand their skills and social resources through ambitious, growth-oriented approaches to job crafting. Conversely, when employees are driven by controlled motivation due to external pressures or reward and punishment mechanisms, they are more likely to adopt conservative strategies to reduce risks when faced with the increased skill barriers brought about by AI usage. Such individuals tend to maintain existing routines and minimize task challenges to avoid errors and emotional strain (Bindl et al., 2019; Jia et al., 2024). This defensive orientation leads them to avoid new technologies and uncertain tasks, reduce learning investments, and perceive greater hindrance demands. Such behavior aligns with prevention-focused job crafting, as described by Tims et al. (2012). Therefore, employees with controlled motivation tend to preserve the status quo and avoid potential threats through prevention-focused job crafting. Hypothesis 2a: Autonomous motivation is positively related to promotion-focused job crafting. Hypothesis 2b: Controlled motivation is positively related to prevention-focused job crafting. Promotion-focused and prevention-focused job crafting and task performance. Job crafting, as a self-initiated form of work redesign, has a significant impact on employee task performance (Grant & Parker, 2009). Prior research suggests that distinct forms of job crafting yield varying workplace results (Harju et al., 2021; Petrou et al., 2018; Tims & Bakker, 2010). Specifically, promotion-focused job crafting tends to foster positive outcomes, including greater engagement, commitment, and task performance; on the flip side, a prevention-focused strategy is typically linked to decreased work involvement, satisfaction, and performance (Bindl et al., 2019; Bruning & Campion, 2018; Demerouti et al., 2015; Rudolph et al., 2017; Weseler & Niessen, 2016; Zhu et al., 2024). Self-determination theory suggests that both types of job crafting are closely linked to satisfying individuals’ basic psychological needs and enhancing task performance (Lichtenthaler & Fischbach, 2019). Those who lean towards promotion-focused job crafting proactively broaden task boundaries to gain work autonomy, take on challenging tasks to develop competence, and strengthen social ties through active collaboration—all of which satisfy employees’ basic psychological needs and ultimately improve task performance (Zhang & Parker, 2019). In contrast, employees engaging in prevention-focused job crafting tend to avoid task responsibilities and challenging tasks, thereby limiting opportunities for growth and weakening relational connections, which collectively hinder need satisfaction and ultimately impair their performance (Petrou et al., 2018). When it comes to AI usage, employees who engage in promotion-focused job crafting tend to proactively manage AI tasks, enhancing work autonomy through optimized resource allocation and skills adaptation (Jia et al., 2024). This positive behavior not only gains organizational recognition but also enhances work meaningfulness, increases work engagement, and ultimately improves task performance (Qin et al., 2025). Conversely, prevention-focused job crafting prompts employees to reduce task demands, avoid AI use, and disengage from skill development to minimize failure risk—leading to conservative and passive behavior patterns (Petrou et al., 2012). Such avoidance behaviors diminish confidence, increase feelings of frustration and insecurity, and trigger negative emotions—factors that ultimately undermine task performance (Demerouti et al., 2015; Rudolph et al., 2017). Therefore, we propose the following hypothesis Hypothesis 3a: Promotion-focused job crafting is positively related to task performance. Hypothesis 3b: Prevention-focused job crafting is negatively related to task performance. The chain-mediating effect of AI usage on task performance. Self-determination theory posits that individuals’ evaluations of whether organizational environments satisfy their basic psychological needs influence their underlying motivational orientations, which in turn shape their behavioral tendencies and outcomes (Ryan & Deci, 2000a, 2000b). Building on the logic established in Hypotheses 1 through 3, we propose a dual-chain mediation model that delineates how AI usage influences task performance. This model posits that employees’ cognitive appraisals of AI usage can activate different types of motivation, which subsequently trigger differentiated job crafting behaviors, ultimately affecting task performance (Ryan & Deci, 2000a). Specifically, when employees view AI as resources for growth and opportunities for development, it is more likely to alleviate their burden of repetitive and transactional tasks, provide space for continuous learning and skill upgrading, and enhance their perceived autonomy and control—thereby satisfying basic psychological needs (Chuang et al., 2024; Huang et al., 2024; Parker et al., 2006). This need satisfaction fosters autonomous motivation, encouraging employees to actively acquire AI-related skills, optimize workflows, and take on challenging tasks—manifesting as promotion-focused job crafting (Jia et al., 2024; Li & Yeo, 2024). These proactive behaviors help accumulate valuable resources, enhance the sense of meaning at work, and improve efficiency and task performance (LePine et al., 2016; Shin et al., 2018). Conversely, when employees appraise AI as a potential threat, concerns about external control and uncertainty may reduce their sense of problem-solving efficacy and perceived control, triggering anxiety over their perceived skill inadequacy (Tang et al., 2023). When these basic psychological needs are unmet, controlled motivation is more likely to arise, accompanied by tension and negative affect. Employees may respond defensively by adopting prevention-focused job crafting strategy, such as narrowing task scope, avoiding technical challenges, or minimizing human–AI interaction (Bindl et al., 2019; Qin et al., 2025; Vansteenkiste et al., 2020). Over time, these behaviors limit learning and growth opportunities, weaken self-efficacy, and ultimately erode task performance (Gagné & Deci, 2005; Shin et al., 2018) . Consequently, we hypothesize: Hypothesis 4a: The positive association between AI usage and task performance is sequentially mediated by autonomous motivation and promotion-focused job crafting. Hypothesis 4b: The negative association between AI usage and task performance is sequentially mediated by controlled motivation and prevention-focused job crafting. The moderating role of core self-evaluations. Core self-evaluations (CSE) refer to individuals’ appraisals of their competence, value, and potential. It is a stable and widely applicable personality trait (Judge et al., 1997). Individual differences in personality may condition the extent to which external environments shape motivational outcomes (Ryan & Deci, 2000a). Prior studies suggest that CSE can function as a moderator in relational models by either amplifying or attenuating the strength of associations between constructs (Chang et al., 2012). Employees’ CSE shape their motivational response to AI usage, thereby conditioning the AI usage–motivation relationship (Tang et al., 2023). More specifically, CSE may positively moderate the link between AI usage and autonomous motivation. Employees high in CSE tend to interpret environmental changes as chances to improve rather than risks to avoid (Bono & Judge, 2003). When exposed to AI implementation, these individuals generally perceive it as an opportunity for skill enhancement and capability expansion rather than as a looming threat. This optimistic cognitive framing enhances their ability to internalize external goals and fosters a strong learning orientation (Judge et al., 2005). This drives them to pursue skill enhancement, fulfilling autonomy and competence needs, and reinforcing autonomous motivation (Joo et al., 2010). Conversely, employees low in CSE often doubt their control over resources and the environment, view AI as an external threat, magnify perceived risks and failures, and experience heightened tension and anxiety—conditions that intensify controlled motivation (Khudozhnikova et al., 2025). At the same time, CSE may negatively moderate the connection between AI usage and controlled motivation. Those with high CSE possess ample psychological resources and self-efficacy, which buffer technology-related anxiety and anticipated loss (Chang et al., 2024; Judge & Bono, 2001). Accordingly, they treat AI as a manageable tool, reducing its coercive pull and weakening controlled motivation (Zahoor et al., 2024). In contrast, low-CSE employees often lack confidence in their ability to regulate external resources, view AI as an imposed threat, and fixate on potential failure and loss of control. These appraisals elevate stress and anxiety, thus reinforcing controlled motivation (Judge & Kammeyer-Mueller, 2011). Consequently, we propose that CSE moderates the effects of AI usage on both autonomous and controlled motivation. Hypothesis 5a: Core self-evaluations moderate the positive relationship between AI usage and autonomous motivation; this link is amplified at higher levels of CSE. Hypothesis 5b: Core self-evaluations moderate the positive relationship between AI usage and controlled motivation; this link is attenuated at higher levels of CSE. The self-determination theory offers a fascinating viewpoint for examining how the interaction between individual dispositions and situational contexts jointly shapes motivation, behavior, and outcomes (Mischel & Shoda, 1995). Building on the chain mediation effects proposed in Hypothesis 4 and the moderating effect in Hypothesis 5, we further propose a dual-path moderated mediation model. We predict that CSE not only moderate the impact of AI usage on motivation but also condition the extent to which such motivational responses translate into distinct job crafting behaviors and ultimately influence task performance. Specifically, employees with high CSE typically view AI usage as an opportunity for growth and challenge. They tend to perceive their work as meaningful and valuable activities, which enhances their autonomous motivation. This motivational orientation encourages employees toward promotion-focused job crafting, which subsequently boosts their task performance. Conversely, employees with low CSE are more inclined to focus on the risks and obligations associated with AI. This threat-oriented appraisal may activate controlled motivation, leading them to adopt defensive behaviors such as prevention-focused job crafting—avoiding technological challenges and reducing task responsibilities—which can ultimately impair task performance. Consequently, we present these moderated mediation hypotheses: Hypothesis 6a: Core self-evaluations moderate the indirect effect of AI usage on task performance via autonomous motivation and promotion-focused job crafting; this indirect effect is amplified at higher levels of CSE. Hypothesis 6b: Core self-evaluations moderate the indirect effect of AI usage on task performance via controlled motivation and prevention-focused job crafting; this indirect effect is attenuated at higher levels of CSE. Fig.1 illustrates the proposed theoretical framework. --------------------------------------------- Insert Fig.1 here ---------------------------------------------- Methodology Sampling and Data Collection. This study focuses on widely adopted organizational applications of AI, including technologies such as facial and voice recognition, chatbots, and intelligent recommendation algorithms. Following Tang et al.(2022), we prefaced the survey with a clear definition of AI usage and industry-specific examples to anchor participants’ responses in authentic AI interaction experiences. This was designed to ensure accurate comprehension and valid responses. We selected participants from organizations located in China’s major AI development clusters. The sample comprised employees and managers from information technology, financial services, smart manufacturing, healthcare, education, and related sectors. We employed a leader-employee matched-pair survey design, combining convenience sampling and snowball sampling techniques. For the offline sample, data were collected through field visits to participating firms. Employees and their direct supervisors completed separate questionnaires that were later linked via the final four digits of their mobile phone numbers. Questionnaires were returned onsite by the HR department or the research team. To encourage accurate and complete responses, participants received token gifts as a gesture of gratitude. For the online sample, data were gathered via the Wenjuanxing platform, where supervisors distributed the questionnaire links or QR codes to their subordinates and used social networks to expand the sample size. Additionally, monetary incentives ranging from 5 to 10 RMB were provided to promote engagement and data quality. To mitigate common method bias, a multi-wave, multi-source design was utilized, aligning with procedural solutions outlined in Podsakoff et al. (2003). Data were gathered over three time points, spaced approximately 1.5 months apart, between July and November 2024. At T1, employees reported on AI usage, core self-evaluations, and demographics. At T2, employees reported their autonomous and controlled motivation. At T3, employees reported job crafting, and supervisors rated their task performance. In total, we distributed 563 questionnaires (500 to employees and 63 to supervisors). For the offline sample, we collected 207 valid employee responses out of 250 (82.8%) and 24 valid supervisor responses out of 28 (85.7%). For the online sample, we obtained 202 valid employee surveys out of 250 (80.8%) and 29 valid supervisor surveys out of 35 (82.8%). This yielded a total of 409 employee and 53 supervisor surveys, with overall response rates of 81.8% and 84.1%, respectively. Table 1 indicates that among the 409 valid employee respondents, the gender distribution was relatively balanced (53.79% male, 46.21% female). Most respondents were in the 26-35 age range (49.14%). Participants also tended to have substantial work experience, with 82.88% reporting between 3 to 10 years on the job. Industry representation was diverse and well-balanced, covering IT/internet (19.07%), finance (18.09%), manufacturing (17.85%), and education (15.16%), suggesting the sample had broad representativeness. --------------------------------------------- Insert Table 1 here ---------------------------------------------- Measurement. We assessed all constructs using well-established, previously validated scales. Items were rated on five-point Likert scales (1 = “strongly disagree,” 5 = “strongly agree”) . Operational definitions and measurement instruments for each variable are presented below: AI Usage. AI usage was assessed with a three-item scale adapted by Tang et al. (2022) from Medcof (1996), including items such as “I use artificial intelligence to accomplish most of my core job functions.” The reliability coefficient (Cronbach’s α) was 0.857. Autonomous and Controlled Motivation. Work motivation was assessed with the Multidimensional Work Motivation Scale (Gagné et al., 2015), adapted by Gillet et al. (2016). Autonomous motivation was assessed with six items, including three for intrinsic motivation and three for identified regulation (e.g., “I find my job interesting”). Controlled motivation was measured using ten items, comprising six for external regulation and four for introjected regulation (e.g., “Because I want to be recognized by others”). The Cronbach’s alpha values were 0.916 for autonomous motivation and 0.946 for controlled motivation. Promotion-Focused and Prevention-Focused Job Crafting. Job crafting was measured using the scale developed by Bindl et al. (2019), which includes 16 items for promotion-focused job crafting (e.g., “I actively seek to broaden my skill set at work”) and 12 items for prevention-focused job crafting (e.g., “I develop skills to avoid negative work outcomes”). The Cronbach’s alpha values were 0.964 and 0.950, respectively. Task Performance. Task performance was assessed using a four-item scale developed by Van Dyne & LePine (1998). One item states, “This employee fulfills all responsibilities specified in their job description.” The Cronbach’s alpha value was 0.868. Core Self-Evaluations (CSE). CSE was measured with the 12-item scale developed by Judge et al. (2003). A sample item is: “I am confident I will achieve the success I deserve in life.” The measure demonstrated excellent internal consistency, with a Cronbach’s alpha coefficient of 0.958. Control Variables. Consistent with prior studies, we controlled for gender, age, education, and job tenure to address demographic heterogeneity (Cheng et al., 2023). Results Common Method variance (CMV) and Confirmatory Factor Analysis (CFA). We followed the guidelines outlined by Podsakoff et al. (2003) and employed Harman’s single-factor test to detect any potential CMV. The results showed the dominant factor explained merely 27.85% of total variance—substantially below the 40% cutoff and less than half of the overall explained variance—suggesting that CMV was not a significant concern. To ensure our measurement model accurately captures the constructs, we performed a CFA using Mplus 8.3. As indicated in Table 1, our seven-factor theoretical model exhibited a notably better fit compared to alternative configurations, with fit indices such as χ²/df = 1.28, CFI = 0.97, TLI = 0.97, RMSEA = 0.03, and SRMR = 0.03. These results meet the standard benchmarks for an acceptable model fit—namely, χ²/df 0.90, RMSEA/SRMR < 0.08 (Kyndt & Onghena, 2014)—providing strong evidence of discriminant validity. --------------------------------------------- Insert Table 2 here ---------------------------------------------- Correlation analysis. Correlation analysis. Table 3 illustrates statistics and correlations among the variables. As shown, AI usage is positively correlated with both autonomous motivation ( r = 0.49, p < 0.001) and controlled motivation ( r = 0.53, p < 0.001). Autonomous motivation shows a strong positive connection with promotion-focused job crafting ( r = 0.60, p < 0.001), while controlled motivation is positively associated with prevention-focused job crafting ( r = 0.58, p < 0.001). Furthermore, promotion-focused job crafting demonstrates a favorable impact on task performance ( r = 0.36, p < 0.001), whereas prevention-focused job crafting shows an inverse relationship with task performance ( r = –0.28, p < 0.001). The observed correlations among the key variables offer preliminary empirical support for the hypothesized relationships. --------------------------------------------- Insert Table 3 here ---------------------------------------------- Hypothesis tests. First, we conducted hierarchical regression analysis using SPSS 25.0 (Table 4). The findings indicated that AI usage was positively linked to autonomous motivation ( β = 0.51, p < 0.001, M1) and controlled motivation ( β = 0.49, p < 0.001, M3), confirming H1a and H1b. Autonomous motivation showed a strong connection to promotion-focused job crafting ( β = 0.59, p < 0.001, M5), supporting H2a, whereas controlled motivation was tied to prevention-focused job crafting ( β = 0.58, p < 0.001, M6), supporting H2b. Promotion-focused job crafting was found to significantly enhance task performance ( β = 0.33, p < 0.001, M7), supporting H3a, whereas prevention-focused job crafting actually undermined performance ( β = -0.30, p < 0.001, M8), corroborating H3b. Second, following Preacher et al.’s (2007) procedure, we performed a bootstrapping analysis with 5,000 resamples to examine the proposed dual-chain mediation pathways. The results, outlined in Table 4, revealed a statistically significant positive sequential mediation effect for the chain AI usage → autonomous motivation → promotion-focused job crafting → task performance ( β = 0.04, 95% CI = [0.01, 0.07]), confirming H4a. Likewise, the sequential indirect effect of AI usage → controlled motivation → prevention-focused job crafting → task performance showed a significant negative relationship ( β = –0.10, 95% CI = [–0.13, –0.07]), lending support to Hypothesis 4b. When considering the overall impact, the total indirect effect via the positive route was notably favorable ( β = 0.24, 95% CI = [0.17, 0.32]), while the negative pathway’s total effect was significantly adverse ( β = –0.37, 95% CI = [–0.44, –0.30]). Taken together, these findings reveal that AI usage exerts opposing influences on employees’ task performance via distinct motivational and job-crafting mechanisms. --------------------------------------------- Insert Table 4 here ---------------------------------------------- Third, hierarchical regression analyses (SPSS 25.0) were performed to assess the moderating effect of CSE. To reduce multicollinearity, all predictors were mean-centered prior to computing interaction terms. Table 4 illustrates that there was a significant positive interaction between AI usage and CSE on autonomous motivation ( β = 0.35, p < 0.001, Model 2), supporting H5a, and a negative and significant interaction for controlled motivation ( β = –0.35, p < 0.001, Model 4), supporting H5b. To further interpret these interactions, we plotted moderation effect diagrams (see Fig. 2 and 3) for employees with high and low CSE levels (M±SD). Fig. 2 indicated that AI usage had a substantially stronger impact on autonomous motivation among employees with higher CSE (β = 0.83, t = 11.95, p < 0.001) compared to those with lower CSE (β = 0.18, t = 2.98, p < 0.01). Similarly, Fig. 3 demonstrated that the positive association between AI usage and controlled motivation was weaker when CSE was high ( β = 0.16, t = 2.61, p < 0.01) but more pronounced when CSE was low ( β = 0.80, t = 15.31, p < 0.001). These results further substantiate Hypotheses 5a and 5b. --------------------------------------------- Insert Fig. 2 here ---------------------------------------------- --------------------------------------------- Insert Fig. 3 here ---------------------------------------------- --------------------------------------------- Insert Table 5 here ---------------------------------------------- Finally, the bias-corrected bootstrap analysis (5,000 resamples) revealed a contingent pattern of sequential mediation (see Table 5). When core CSE was high, the pathway from AI usage to task performance—mediated by autonomous motivation and promotion-focused job crafting—showed a statistically significant indirect effect (0.06, 95% CI = [0.02, 0.11]). This effect diminished, though remained significant, at lower CSE levels (0.01, 95% CI = [0.00, 0.03]). Notably, the disparity between these conditional effects was statistically meaningful (difference = 0.05, 95% CI [0.01, 0.09]), providing support for H6a. In contrast, the mediated pathway through controlled motivation and prevention-focused job crafting revealed a stronger negative association under low CSE conditions (indirect effect = –0.16, 95% CI = [–0.21, –0.11]) compared to high CSE (indirect effect = –0.03, 95% CI = [–0.06, –0.01]). This divergence was also statistically robust (difference = 0.12, 95% CI = [0.08, 0.18]), thereby corroborating H6b. Discussion Grounded in self-determination theory, this study illuminates the paradoxical effects of AI usage on task performance and its underlying mechanisms. Three key conclusions emerged: First, AI usage exerts both facilitative and impeding effects on employee task performance, revealing a double-edged sword effect that extends prior research by demonstrating that AI’s performance implications are not unidirectional but rather contingent on the underlying processes it activates. Second, AI usage influences employee task performance through sequential mediation involving motivational and behavioral mechanisms—specifically, the motivational processes it triggers and the subsequent forms of job crafting that emerge. The facilitative pathway operates through autonomous motivation and promotion-focused job crafting, whereas the impeding pathway functions through controlled motivation and prevention-focused job crafting. Third, CSE emerges as a key boundary condition that strengthens the positive pathway while weakening the negative one. Employees with high CSE more readily convert AI usage into autonomous motivation and promotion-focused job crafting to strengthen task performance. Simultaneously, high CSE buffers against AI-related pressures, mitigating the adverse effects of controlled motivation and prevention-focused job crafting on task performance. Theoretical Contributions. Theoretical Contributions. First, it expands the discourse on the dual nature of AI’s impact. Previous research has largely highlighted the bright side of AI’s usage (Gregory et al., 2021 ; Jarrahi, 2018 ), while paying relatively little attention to its potential dark side (Brougham & Haar, 2018 ). To remedy this imbalance, we develop a dual-path model illustrating how AI usage affects task performance through two distinct mechanisms. By integrating both routes, the model rectifies the neglect of AI’s darker side and offers an explanatory framework for prior inconsistent findings (Tang et al., 2023 ). Second, it deepens comprehension of the processes by which AI usage influences task performance. Previous studies have typically examined this relationship through a single-path lens, often overlooking the mediating roles of motivation and behavior. By drawing on the “situation–motivation–behavior–outcome”process model, we propose and validate a dual-chain mediation framework. This framework explicates how AI usage affects employee performance through distinct psychological and behavioral channels. This framework furnishes the field with an integrated lens for understanding how technology applications translate into outcomes through motivation and behavior (Gagné & Deci, 2005 ; Higgins, 1997 ). Third, it delineates key boundary conditions of AI’s impact by introducing CSE as a critical moderator. Our findings indicate that employees high in CSE capitalize on the facilitative path of AI usage, whereas those low in CSE are more vulnerable to its inhibitive path. This insight underscores the significance of personal characteristics in AI contexts, extending the theoretical scope of core self-evaluation (Tang et al., 2023 ). Practical Implications. First, organizations should foster autonomous motivation. Drawing on self-determination theory, managers can satisfy employees’ essential psychological needs (Deci et al., 2017 ). To begin with, design work tasks with a high degree of freedom, grant employees autonomy in using AI tools, and thereby optimize human–AI collaboration (Hou et al., 2024; Lee et al., 2022; Friedman et al., 2024 ). Additionally, provide structured training and AI integration support to alleviate replacement anxiety and bolster perceptions of competence (Jia et al., 2024 ; Shah et al., 2024 ). Finally, cultivate a supportive team climate by offsetting AI-induced declines in peer interaction through cross-departmental projects and team-building activities, thereby strengthening social belonging and emotional connection (Schmutz et al., 2024 ; Simón et al., 2024 ). Second, organizations should actively shape employee job crafting behavior by encouraging promotion-focused job crafting and minimizing prevention-focused job crafting. Recommended practices include: supporting autonomous task redesign that enables human-AI complementarity (Vaccaro et al., 2024 ); ensuring adequate training and timely feedback to bolster self-efficacy and proactivity (Verma & Singh, 2022 ); building psychological safety and an organizational support culture to reduce technological anxiety through transparent communication and participatory decision-making (Albrecht et al., 2023 ). Third, organizations should implement personalized management strategies grounded in individual differences in core self-evaluations (Judge & Kammeyer-Mueller, 2011 ). For employees with lower CSE, managers should provide emotional support, psychological counseling, and collaborative opportunities to build confidence, reduce anxiety, and enhance their sense of belonging and value through social connection (Gong et al., 2024 ; Kinias & Sim, 2016 ). For those with higher CSE, organizations can assign challenging tasks and innovation-driven roles, while reinforcing motivation and self-efficacy through performance feedback and developmental opportunities (Demır, 2020 ). Limitations and Future Research. First, although prior studies indicate that gender, age, industry, and tenure are salient predictors of employees’ AI usage, we treated these factors merely as control variables. Future studies could focus on specific industries (e.g., service operations), targeted populations (e.g.,, older employees), or compare differences across gender, tenure, and occupational groups to provide a richer account of how AI usage impacts work outcomes. Second, guided by self-determination theory, this study identified work motivation and job crafting as pivotal intermediaries connecting AI usage with task performance. However, other mechanisms—especially those involving cognitive appraisals or affective responses—may also serve as important explanatory pathways. Scholars are encouraged to explore these additional mechanisms to provide a more comprehensive account. Finally, this study adopted an individual-level lens. A more complete picture would benefit from multi-level designs that examine contextual influences such as leadership styles, peer interactions, or team contexts, thereby offering a more holistic understanding of how AI usage affects task performance. Declarations Funding Statement This research did not receive any external funding. Competing interests The authors declare no competing interests. Ethical Approval This study received ethical clearance and was approved on June 25, 2024 by the Academic (Ethics) Committee of the School of Public Administration, Guangdong University of Finance, China (Approval No. 20240625). The review process ensured that all research procedures complied with institutional guidelines, the principles outlined in the 1964 Declaration of Helsinki and its subsequent amendments, and comparable ethical standards. Informed Consent During the data collection period from July to November 2024, participants provided informed consent electronically. The online questionnaire included a comprehensive information section detailing the study’s purpose, data handling procedures, and participant rights. Respondents were required to actively confirm their understanding and willingness to participate by checking a consent box. This approach ensured that participants had the opportunity to review all relevant information before granting consent. It was explicitly stated that all data collected through the survey would remain anonymous. Additional information Correspondence and requests for materials should be addressed to the author. Data Availability All data generated or analyzed during this study are included in this published article and its supplementary information file. Author Contribution Conceptualization: W.Z, P.C and X.G; Methodology: W.Z, P.C and X.G; Data Collection: W.Z and X.G; Data Analysis: W.Z and X.G; Writing—Original Draft: W.Z and X.G; Writing—Review & Editing: W.Z and P.C. All authors contributed to the article and approved the submitted version. References Albrecht, S. L., Furlong, S., & Leiter, M. P. (2023). 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Demographics Category Frequency ( N ) Percentage (%) Gender Male 220 53.79 Female 189 46.21 Age 25 years and below 98 23.96 26–35 years 201 49.14 36–45 years 86 21.03 Above 45 years 24 5.87 Education High school or below 14 3.42 Associate degree 88 21.52 Bachelor's degree 196 47.92 Master's degree or above 111 27.14 Job tenure 2 years or less 53 12.96 3–5 years 145 35.45 6–10 years 194 47.43 More than 10 years 17 4.16 Industry type IT / Software & Hardware / E-commerce / Internet Services 78 19.07 Banking / Insurance / Securities / Investment 74 18.09 Manufacturing / Machinery / Equipment / Heavy Industry 73 17.85 Education / Training / Research / Academic Institutions 62 15.16 Food / Entertainment / Tourism / Hospitality / Services 42 10.27 Healthcare / Nursing / Public Health / Pharma / Biotech 53 12.96 Others 27 6.60 Note: N = 409 Table 2 Results of confirmatory factor analysis. Model χ² df χ²/ df CFI TLI SRMR RMSEA Seven-factor model (AIU, CSE, AM, OJC, CM, EJC, TP) 2398.23 1869 1.28 0.97 0.97 0.03 0.03 Six-factor model (AIU, CSE, AM+OJC, CM, EJC, TP) 3407.98 1875 1.82 0.92 0.92 0.05 0.05 Five-factor model (AIU, CSE, AM+ OJC, CM+ EJC, TP) 5110.69 1880 2.72 0.84 0.83 0.07 0.07 Four-factor model (AIU, CSE, AM+ OJCC+ CM+EJC, TP) 9610.41 1884 5.10 0.61 0.60 0.16 0.10 Three-factor model (AIU, CSE, AM+ OJC+ CM+ EJC+TP) 10330.17 1887 5.47 0.58 0.56 0.17 0.11 Two-factor model (AIU, CSE+AM+OJC+ CM+ EJC+TP) 14095.76 1889 7.46 0.39 0.37 0.20 0.13 One-factor model (AIU+ CSE+AM+ OJC+ CM+ EJC+TP) 14414.20 1890 7.63 0.37 0.35 0.20 0.13 Note: N = 409; AIU AI Usage, CSE Core Self-Evaluations, AM Autonomous Motivation, OJC Promotion-Focused Job Crafting, CM Controlled Motivation, EJC Prevention-Focused Job Crafting, TP Task Performance. Table 3 Means, standard deviations (SD), and correlations of variables. Variable Mean SD 1 2 3 4 5 6 7 8 9 10 1 Gender 0.46 0.50 2 Age 2.09 0.82 -0.02 3 Education 2.99 0.79 0.01 -0.05 4 Tenure 2.43 0.77 -0.07 0.49 *** -0.03 5 AIU 3.51 0.87 0.09 0.15 ** 0.13 ** 0.02 6 CSE 3.31 0.92 0.07 0.06 -0.05 -0.02 0.17 *** 7 AM 3.49 0.92 0.02 0.17 *** 0.08 0.14 ** 0.49 *** 0.33 *** 8 CM 2.81 0.83 0.01 0.17 *** 0.14 ** 0.04 0.53 *** -0.11 * 0.18 *** 9 OJC 3.48 0.88 0.09 0.03 0.06 -0.00 0.50 *** 0.23 *** 0.60 *** 0.28 *** 10 EJC 2.78 0.81 0.10 * 0.07 -0.00 0.00 0.55 *** 0.04 0.20 *** 0.58 *** 0.28 *** 11 TP 3.44 0.83 0.00 0.10 * 0.11 * 0.06 0.23 *** 0.23 *** 0.44 *** -0.18 *** 0.36 *** -0.28 *** Note: N = 409; AIU AI Usage, CSE Core Self-Evaluations, AM Autonomous Motivation, OJC Promotion-Focused Job Crafting, CM Controlled Motivation, EJC Prevention-Focused Job Crafting, TP Task Performance; *** p < 0.001, ** p < 0.01, * p < 0.05. Table 4 Result of hierarchical regressions. AM CM OJC EJC TP Path M1 M2 M3 M4 M5 M6 M7 M8 intercept 1.24 *** 0.17 0.73 *** 1.58 *** 1.62 *** 1.41 *** 1.74 *** 3.57 *** Gender -0.02 -0.00 -0.05 -0.07 0.13 0.15 * 0.01 0.12 Age 0.05 0.08 0.11 * 0.07 -0.04 -0.03 0.08 0.13 * Education 0.02 0.04 0.08 0.07 0.01 -0.09 * 0.10 * 0.12 * Tenure 0.13 0.10 -0.03 0.00 -0.08 -0.00 0.03 0.01 AIU 0.51 *** 0.50 *** 0.49 *** 0.48 *** AM 0.59 *** CM 0.58 *** OJC 0.33 *** EJC -0.30 *** CSE 0.29 *** -0.22 *** AIU*CSE 0.35 *** -0.35 *** R 2 0.26 0.39 0.30 0.43 0.37 0.35 0.15 0.11 F 27.69 *** 36.54 *** 34.66 *** 42.44 *** 47.85 *** 43.06 *** 14.18 *** 9.86 *** Note: N = 409; AIU AI Usage, CSE Core Self-Evaluations, AM Autonomous Motivation, OJC Promotion-Focused Job Crafting, CM Controlled Motivation, EJC Prevention-Focused Job Crafting, TP Task Performance; *** p < 0.001, ** p < 0.01, * p < 0.05. Table 5 Tests of chain mediation and moderated chain mediation effects. Path Bootstrap results 95%CI M SE LLCI ULCI AIU→AM→OJC→TP Chain Mediation Effects Total Indirect Effect 0.24 0.04 0.17 0.32 Indirect Effect 0.04 0.02 0.01 0.07 AIU→AM→OJC→TP Chain Mediation Effects Total Indirect Effect -0.37 0.04 -0.44 -0.30 Indirect Effect -0.10 0.02 -0.13 -0.07 AIU→AM→OJC→TP moderated chain mediation effect Low CSE(-1SD) 0.01 0.01 0.00 0.03 High CSE(+1SD) 0.06 0.02 0.02 0.11 Difference 0.05 0.02 0.01 0.09 AIU→AM→OJC→TP moderated chain mediation effect Low CSE(-1SD) -0.16 0.03 -0.21 -0.11 High CSE(+1SD) -0.03 0.01 -0.06 -0.01 Difference 0.12 0.03 0.08 0.18 Note: N = 409; AIU AI Usage, CSE Core Self-Evaluations, AM Autonomous Motivation, OJC Promotion-Focused Job Crafting, CM Controlled Motivation, EJC Prevention-Focused Job Crafting, TP Task Performance. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7168956","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":506645733,"identity":"647646fb-ea77-4bf2-be82-c6946e63015c","order_by":0,"name":"Wenhui Zhang","email":"","orcid":"","institution":"Guangdong University of Finance","correspondingAuthor":false,"prefix":"","firstName":"Wenhui","middleName":"","lastName":"Zhang","suffix":""},{"id":506645734,"identity":"31a59d8d-d46b-404b-a06b-4fc116b65b56","order_by":1,"name":"Po-Chien Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYHACNgYGAzBiYPhgYGNHmhbGGQVpyURqYYBoYeb5cIixgZB6c/bmZw8+FNyRN2c/e/i1jcEBZgb2w0c34NNi2XPM3HCGwTPDnT15adY5Bnf4GHjS0m7g02JwI4dNmsfgcILBgRwz4xyDZ8wMEjxmhLX8AWk5/8bM2MLgMGMDUVoYQFpu5Bg/ZiBGC9AvZpI9BocNN9x4Y8bYY5CWzEbIL6AQk/jx57C8wfkc4w8//tjY8bMfPobfYUhsNgkwiU85uhbmD4RUj4JRMApGwcgEAKY+SqOiQroZAAAAAElFTkSuQmCC","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Po-Chien","middleName":"","lastName":"Chang","suffix":""},{"id":506645735,"identity":"81f4e56d-77d2-4fda-983c-ce39c402d48f","order_by":2,"name":"Xinqi Geng","email":"","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Xinqi","middleName":"","lastName":"Geng","suffix":""}],"badges":[],"createdAt":"2025-07-20 10:08:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7168956/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7168956/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90598259,"identity":"195b81cc-a89c-4a96-b805-7237db8e233b","added_by":"auto","created_at":"2025-09-04 14:05:49","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":212683,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Fig.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7168956/v1/047e47066843368321ba09a3.jpg"},{"id":90597870,"identity":"f2125828-6e99-41a3-9a32-11aa717a79fd","added_by":"auto","created_at":"2025-09-04 13:57:49","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":127099,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Fig.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7168956/v1/b1e51dc26eb1da6aa655c708.jpg"},{"id":90597869,"identity":"30f706a9-1279-4fc4-a649-7ffe86bddd65","added_by":"auto","created_at":"2025-09-04 13:57:49","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":125082,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"Fig.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7168956/v1/3f4984da2ef4dedc7fc8a91f.jpg"},{"id":90599702,"identity":"0cac3125-2487-44c7-91b6-4607acd159ab","added_by":"auto","created_at":"2025-09-04 14:21:54","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1735484,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7168956/v1/6c3c0a79-af78-4ab1-9a5a-a5ee83bb0d93.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"When AI enables and undermines: dual mechanisms linking AI usage to task performance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAs digital transformation accelerates, AI has become a central force redefining organizational operations and employee work experiences (Haenlein \u0026amp; Kaplan, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The incorporation of AI into innovation, operations, and decision-making has had profound and wide-ranging implications (Belanche et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Duan et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Gregory et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Paschen et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), prompting organizations to embed AI into their core operations to enhance efficiency, productivity, performance, and innovation (Braganza et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Cheng et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Nam et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Noy \u0026amp; Zhang, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Consequently, more organizations are implementing AI to enhance performance and strengthen their competitive advantage (Li et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, while AI is redefining the nature of work, it has also raised a fundamental question in both academia and practice: How does AI usage influence employee performance? Task performance\u0026mdash;defined as employees\u0026rsquo; effectiveness in executing core responsibilities and delivering expected outputs\u0026mdash;is central to organizational functioning (Janssen \u0026amp; Van Yperen, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Williams \u0026amp; Anderson, 1991), yet the mechanisms through which AI affects it remain insufficiently understood. Although organizations often adopt AI usage to alleviate workload and empower high-value tasks (Wilson \u0026amp; Daugherty, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), its implementation may also restructure workflows and impose new technical demands, introducing uncertainty, role ambiguity, and perceived threats of technological replacement\u0026mdash;factors that can undermine task performance.\u003c/p\u003e\u003cp\u003eExisting research has preliminarily revealed contradictory results regarding AI usage and its impact on employee task performance. AI usage has been shown to foster positive psychological and behavioral responses, such as increased thriving at work, heightened engagement and autonomy, enriched task challenge, and ultimately improved performance (Friedman et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kemp, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Marimon et al., \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Conversely, AI usage may also trigger adverse emotional and behavioral responses\u0026mdash;such as AI anxiety, fear, and insecurity\u0026mdash;that undermine job security, organizational commitment, and turnover retention, ultimately impairing performance (Chui et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Fan et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Suseno et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This coexistence of enabling and inhibiting effects reveals the inherently paradoxical nature of AI usage (Jones, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Tang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Wu et al., \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, prior studies mostly focused on the single effect of AI usage and lacked a systematic explanation of the dual impacts (Kellogg et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSelf-determination theory (SDT) offers a compelling lens through which to examine the paradoxical impact of AI usage on task performance (Deci \u0026amp; Ryan, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Essentially, this theory posits that the same external context may elicit different types of motivation by either fulfilling or hindering individuals\u0026rsquo; basic psychological needs that give rise to divergent behavioral and performance outcomes (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2000a\u003c/span\u003e, \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2000b\u003c/span\u003e). AI usage, as a new type of external context, may influence employees\u0026rsquo; psychological needs by affecting the degree to which these needs are satisfied. This shift can trigger various kinds of work motivation. For instance, there is autonomous motivation, which stems from internal passion and beliefs, and controlled motivation, which is fueled by external stimuli like pressure or incentives (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2000a\u003c/span\u003e). When AI is perceived as a tool for fostering growth and expanding capabilities, it helps stimulate autonomous motivation, encouraging employees to voluntarily engage and proactively seek change. Conversely, if AI is interpreted as external control or a threat to resources, it is more likely to trigger controlled motivation, forcing employees to respond out of pressure or reward/punishment. Furthermore, these two distinct motivational pathways drive employees to reshape their behavior through work to respond to external environmental changes.\u003c/p\u003e\u003cp\u003eLichtenthaler and Fischbach (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) identify two main forms of job crafting. The first, promotion-focused job crafting emphasizes actively acquiring resources and expanding task boundaries, which can enhance positive affect and performance. Conversely, prevention-focused job crafting centers on risk avoidance and role contraction, which often results in resource depletion and performance decline (Bindl et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bruning \u0026amp; Campion, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Accordingly, in an AI usage context, employees with autonomous motivation will actively adjust their work strategies based on their recognition of work value and intrinsic interest, triggering promotion-focused job crafting and thereby enhancing task performance. In contrast, employees with controlled motivation will avoid risks due to external pressure and narrow their work scope, tending to trigger prevention-focused job crafting and thereby impairing task performance. Moreover, SDT suggests that individual traits influence how individuals perceive external contexts and engage motivational processes (Ryan \u0026amp; Deci, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2000a\u003c/span\u003e). Core self-evaluations (CSE)\u0026mdash;a higher-order personality construct encompassing self-esteem, generalized self-efficacy, emotional stability, and locus of control (Judge et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1998\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). High-CSE employees often perceive AI usage as a developmental opportunity, thereby triggering autonomous motivation and prompting promotion-focused job crafting. Nevertheless, low-CSE employees may perceive AI usage as threatening, leading to controlled motivation and prevention-focused job crafting. Thus, CSE functions as a pivotal boundary condition.\u003c/p\u003e\u003cp\u003eIn summary, this study introduces a two-path framework to clarify how AI usage influences task performance through motivation and behavior. Specifically, AI usage activates either autonomous or controlled motivation, which subsequently drives promotion-focused or prevention-focused job crafting, ultimately shaping task performance. Furthermore, core self-evaluations serve as a boundary condition moderating this motivational-behavioral pathway. This study makes the following contributions. First, it integrates the positive and negative effects within a unified framework, thereby offering a comprehensive explanation of how AI usage impacts task performance. Second, it identifies a dual-chain mediation mechanism\u0026mdash;via motivational types and job crafting strategies\u0026mdash;that clarifies the process through which AI usage affects task performance. Finally, it validates the moderating effect of CSE, providing a new dimension for research on individual heterogeneity. Practically, the findings provide managerial implications for organizations aiming to optimize AI implementation, foster positive motivational states, and mitigate potential risks to employee performance.\u003c/p\u003e"},{"header":"Theoretical background and hypothesis development","content":"\u003cp\u003e\u003cstrong\u003eAI usage and autonomous/controlled motivation.\u0026nbsp;\u003c/strong\u003eAccording to self-determination theory, motivation comprises two fundamental forms\u0026mdash;autonomous and controlled (Ryan \u0026amp; Deci, 2000b). Autonomous motivation stems from an individual\u0026rsquo;s interest, value, and meaning in an activity itself, manifesting as employees actively, voluntarily, and enthusiastically engaging in work; whereas controlled motivation originates from external pressure, reward and punishment mechanisms, or self-coercion, manifesting as employees passively completing tasks (Deci \u0026amp; Ryan, 2000). SDT posits that external environments trigger varying motivations by either satisfying or hindering individuals\u0026rsquo; three basic psychological needs\u0026mdash;autonomy, competence, and relatedness. Accordingly, AI usage, as an external organizational context, may influence the emergence of autonomous and controlled motivation depending on how well these needs are met.\u003c/p\u003e\n\u003cp\u003eFirst, AI usage may foster autonomous motivation when it satisfies employees\u0026rsquo; core psychological needs. When AI autonomously handles repetitive, complex, or cognitively demanding tasks and provide timely, valuable decision-support information, they can enhance employees\u0026rsquo; work flexibility and decision-making discretion, thereby fulfilling the need for autonomy (Davenport et al., 2019). In addition, AI usage may assign meaningful and challenging tasks, deliver positive feedback and recognition, and support employees\u0026rsquo; career development, all of which strengthen their confidence and sense of competence, thus satisfying their competence needs (Prentice et al., 2020). Simultaneously, AI usage can promote cross-departmental collaboration, knowledge sharing, and emotional support, enabling employees to feel accepted and valued, thereby satisfying their relatedness needs (Cordery et al., 2010). The fulfillment of basic psychological needs fosters autonomous motivation among employees.\u003c/p\u003e\n\u003cp\u003eHowever, AI usage may also trigger controlled motivation by hindering these psychological needs. When AI applications reduce employees\u0026rsquo; autonomy, ignore employees\u0026rsquo; work preferences or increase the skill requirements and transition costs, employees may feel helpless and insecure due to their inability to meet job requirements, thereby undermining employees\u0026rsquo; autonomy and competence needs (Mirbabaie et al., 2022; Wu et al., 2025; Yam et al., 2023). Additionally, AI usage may diminish opportunities for face-to-face interaction, weaken team emotional bonds, and reduce organizational connectedness, thereby impeding the fulfillment of relatedness needs (Ryan et al., 2021). When these needs go unmet, employees often respond with controlled motivation. Therefore, we put forward the following hypotheses:\u003c/p\u003e\n\u003cp\u003eHypothesis 1a: AI usage is positively related to employees\u0026rsquo; autonomous motivation.\u003c/p\u003e\n\u003cp\u003eHypothesis 1b: AI usage is positively related to employees\u0026rsquo; controlled motivation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutonomous/controlled motivation and promotion-/prevention-focused job crafting.\u0026nbsp;\u003c/strong\u003eSelf-determination theory categorizes individual behavioral motivation into autonomous motivation and controlled motivation. This framework suggests that different forms of motivation lead to distinct behavioral outcomes\u0026mdash;autonomous motivation typically encourages constructive behaviors, whereas controlled motivation often leads to negative consequences (Ryan \u0026amp; Deci, 2000a). Within organizational contexts, motivation functions as a critical internal force that drives employees to actively reshape their roles\u0026mdash;a concept known as job crafting (Shin \u0026amp; Jung, 2021). This proactive behavior involves workers deliberately modifying their tasks and redefining professional boundaries (Wrzesniewski \u0026amp; Dutton, 2001). It encompasses two distinct forms: promotion-focused crafting, where employees seek out growth opportunities by expanding resources and tackling challenges, and prevention-focused crafting, which aims to minimize obstacles that hinder performance (Lichtenthaler \u0026amp; Fischbach, 2019).\u003c/p\u003e\n\u003cp\u003eDifferent types of motivation prompt employees to adopt different job crafting strategies. Specifically, employees driven by autonomous motivation are more likely to embrace AI-driven workplace changes, proactively seek challenges, and pursue personal growth. When confronted with demanding tasks, they tend to exhibit higher levels of psychological vitality and respond with greater initiative (Rich et al., 2010). Such individuals often engage in self-directed learning, process optimization, and cross-functional collaboration to accumulate resources, enhance their sense of control, and achieve developmental goals (Laurence et al., 2020; Lazazzara et al., 2020; Parker \u0026amp; Grote, 2022). Therefore, employees with autonomous motivation are inclined to actively expand their skills and social resources through ambitious, growth-oriented approaches to job crafting. Conversely, when employees are driven by controlled motivation due to external pressures or reward and punishment mechanisms, they are more likely to adopt conservative strategies to reduce risks when faced with the increased skill barriers brought about by AI usage. Such individuals tend to maintain existing routines and minimize task challenges to avoid errors and emotional strain (Bindl et al., 2019; Jia et al., 2024). This defensive orientation leads them to avoid new technologies and uncertain tasks, reduce learning investments, and perceive greater hindrance demands. Such behavior aligns with prevention-focused job crafting, as described by Tims et al. (2012). Therefore, employees with controlled motivation tend to preserve the status quo and avoid potential threats through prevention-focused job crafting.\u003c/p\u003e\n\u003cp\u003eHypothesis 2a: Autonomous motivation is positively related to promotion-focused job crafting.\u003c/p\u003e\n\u003cp\u003eHypothesis 2b: Controlled motivation is positively related to prevention-focused job crafting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePromotion-focused and prevention-focused job crafting and task performance.\u0026nbsp;\u003c/strong\u003eJob crafting, as a self-initiated form of work redesign, has a significant impact on employee task performance (Grant \u0026amp; Parker, 2009). Prior research suggests that distinct forms of job crafting yield varying workplace results (Harju et al., 2021; Petrou et al., 2018; Tims \u0026amp; Bakker, 2010). Specifically, promotion-focused job crafting tends to foster positive outcomes, including greater engagement, commitment, and task performance; on the flip side, a prevention-focused strategy is typically linked to decreased work involvement, satisfaction, and performance\u0026nbsp;(Bindl et al., 2019; Bruning \u0026amp; Campion, 2018; Demerouti et al., 2015; Rudolph et al., 2017; Weseler \u0026amp; Niessen, 2016; Zhu et al., 2024). Self-determination theory suggests that both types of job crafting are closely linked to satisfying individuals\u0026rsquo; basic psychological needs and enhancing task performance (Lichtenthaler \u0026amp; Fischbach, 2019). Those who lean towards promotion-focused job crafting proactively broaden task boundaries to gain work autonomy, take on challenging tasks to develop competence, and strengthen social ties through active collaboration\u0026mdash;all of which satisfy employees\u0026rsquo; basic psychological needs and ultimately improve task performance (Zhang \u0026amp; Parker, 2019). In contrast, employees engaging in prevention-focused job crafting tend to avoid task responsibilities and challenging tasks, thereby limiting opportunities for growth and weakening relational connections, which collectively hinder need satisfaction and ultimately impair their performance (Petrou et al., 2018).\u003c/p\u003e\n\u003cp\u003eWhen it comes to AI usage, employees who engage in promotion-focused job crafting tend to proactively manage AI tasks, enhancing work autonomy through optimized resource allocation and skills adaptation (Jia et al., 2024). This positive behavior not only gains organizational recognition but also enhances work meaningfulness, increases work engagement, and ultimately improves task performance (Qin et al., 2025). Conversely, prevention-focused job crafting prompts employees to reduce task demands, avoid AI use, and disengage from skill development to minimize failure risk\u0026mdash;leading to conservative and passive behavior patterns (Petrou et al., 2012). Such avoidance behaviors diminish confidence, increase feelings of frustration and insecurity, and trigger negative emotions\u0026mdash;factors that ultimately undermine task performance (Demerouti et al., 2015; Rudolph et al., 2017). Therefore, we propose the following hypothesis\u003c/p\u003e\n\u003cp\u003eHypothesis 3a: Promotion-focused job crafting is positively related to task performance.\u003c/p\u003e\n\u003cp\u003eHypothesis 3b: Prevention-focused job crafting is negatively related to task performance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe chain-mediating effect of AI usage on task performance.\u0026nbsp;\u003c/strong\u003eSelf-determination theory posits that individuals\u0026rsquo; evaluations of whether organizational environments satisfy their basic psychological needs influence their underlying motivational orientations, which in turn shape their behavioral tendencies and outcomes (Ryan \u0026amp; Deci, 2000a, 2000b). Building on the logic established in Hypotheses 1 through 3, we propose a dual-chain mediation model that delineates how AI usage influences task performance. This model posits that employees\u0026rsquo; cognitive appraisals of AI usage can activate different types of motivation, which subsequently trigger differentiated job crafting behaviors, ultimately affecting task performance (Ryan \u0026amp; Deci, 2000a). Specifically, when employees view AI as resources for growth and opportunities for development, it is more likely to alleviate their burden of repetitive and transactional tasks, provide space for continuous learning and skill upgrading, and enhance their perceived autonomy and control\u0026mdash;thereby satisfying basic psychological needs (Chuang et al., 2024; Huang et al., 2024; Parker et al., 2006). This need satisfaction fosters autonomous motivation, encouraging employees to actively acquire AI-related skills, optimize workflows, and take on challenging tasks\u0026mdash;manifesting as promotion-focused job crafting (Jia et al., 2024; Li \u0026amp; Yeo, 2024). These proactive behaviors help accumulate valuable resources, enhance the sense of meaning at work, and improve efficiency and task performance (LePine et al., 2016; Shin et al., 2018).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConversely, when employees appraise AI as a potential threat, concerns about external control and uncertainty may reduce their sense of problem-solving efficacy and perceived control, triggering anxiety over their perceived skill inadequacy (Tang et al., 2023). When these basic psychological needs are unmet, controlled motivation is more likely to arise, accompanied by tension and negative affect. Employees may respond defensively by adopting prevention-focused job crafting strategy, such as narrowing task scope, avoiding technical challenges, or minimizing human\u0026ndash;AI interaction (Bindl et al., 2019; Qin et al., 2025; Vansteenkiste et al., 2020). Over time, these behaviors limit learning and growth opportunities, weaken self-efficacy, and ultimately erode task performance (Gagn\u0026eacute; \u0026amp; Deci, 2005; Shin et al., 2018) . Consequently, we hypothesize:\u003c/p\u003e\n\u003cp\u003eHypothesis 4a: The positive association between AI usage and task performance is sequentially mediated by autonomous motivation and promotion-focused job crafting.\u003c/p\u003e\n\u003cp\u003eHypothesis 4b: The negative association between AI usage and task performance is sequentially mediated by controlled motivation and prevention-focused job crafting.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe moderating role of core self-evaluations.\u0026nbsp;\u003c/strong\u003eCore self-evaluations (CSE) refer to individuals\u0026rsquo; appraisals of their competence, value, and potential. It is a stable and widely applicable personality trait (Judge et al., 1997). Individual differences in personality may condition the extent to which external environments shape motivational outcomes (Ryan \u0026amp; Deci, 2000a). Prior studies suggest that CSE can function as a moderator in relational models by either amplifying or attenuating the strength of associations between constructs (Chang et al., 2012). Employees\u0026rsquo; CSE shape their motivational response to AI usage, thereby conditioning the AI usage\u0026ndash;motivation relationship (Tang et al., 2023). More specifically, CSE may positively moderate the link between AI usage and autonomous motivation. Employees high in CSE tend to interpret environmental changes as chances to improve rather than risks to avoid (Bono \u0026amp; Judge, 2003). When exposed to AI implementation, these individuals generally perceive it as an opportunity for skill enhancement and capability expansion rather than as a looming threat. This optimistic cognitive framing enhances their ability to internalize external goals and fosters a strong learning orientation (Judge et al., 2005). This drives them to pursue skill enhancement, fulfilling autonomy and competence needs, and reinforcing autonomous motivation (Joo et al., 2010). Conversely, employees low in CSE often doubt their control over resources and the environment, view AI as an external threat, magnify perceived risks and failures, and experience heightened tension and anxiety\u0026mdash;conditions that intensify controlled motivation (Khudozhnikova et al., 2025).\u003c/p\u003e\n\u003cp\u003eAt the same time, CSE may negatively moderate the connection between AI usage and controlled motivation. Those with high CSE possess ample psychological resources and self-efficacy, which buffer technology-related anxiety and anticipated loss (Chang et al., 2024; Judge \u0026amp; Bono, 2001). Accordingly, they treat AI as a manageable tool, reducing its coercive pull and weakening controlled motivation (Zahoor et al., 2024). In contrast, low-CSE employees often lack confidence in their ability to regulate external resources, view AI as an imposed threat, and fixate on potential failure and loss of control. These appraisals elevate stress and anxiety, thus reinforcing controlled motivation (Judge \u0026amp; Kammeyer-Mueller, 2011). Consequently, we propose that CSE moderates the effects of AI usage on both autonomous and controlled motivation.\u003c/p\u003e\n\u003cp\u003eHypothesis 5a: Core self-evaluations moderate the positive relationship between AI usage and autonomous motivation; this link is amplified at higher levels of CSE.\u003c/p\u003e\n\u003cp\u003eHypothesis 5b: Core self-evaluations moderate the positive relationship between AI usage and controlled motivation; this link is attenuated at higher levels of CSE.\u003c/p\u003e\n\u003cp\u003eThe self-determination theory offers a fascinating viewpoint for examining how the interaction between individual dispositions and situational contexts jointly shapes motivation, behavior, and outcomes (Mischel \u0026amp; Shoda, 1995). Building on the chain mediation effects proposed in Hypothesis 4 and the moderating effect in Hypothesis 5, we further propose a dual-path moderated mediation model. We predict that CSE not only moderate the impact of AI usage on motivation but also condition the extent to which such motivational responses translate into distinct job crafting behaviors and ultimately influence task performance. Specifically, employees with high CSE typically view AI usage as an opportunity for growth and challenge. They tend to perceive their work as meaningful and valuable activities, which enhances their autonomous motivation. This motivational orientation encourages employees toward promotion-focused job crafting, which subsequently boosts their task performance. Conversely, employees with low CSE are more inclined to focus on the risks and obligations associated with AI. This threat-oriented appraisal may activate controlled motivation, leading them to adopt defensive behaviors such as prevention-focused job crafting\u0026mdash;avoiding technological challenges and reducing task responsibilities\u0026mdash;which can ultimately impair task performance. Consequently, we present these moderated mediation hypotheses:\u003c/p\u003e\n\u003cp\u003eHypothesis 6a: Core self-evaluations moderate the indirect effect of AI usage on task performance via autonomous motivation and promotion-focused job crafting; this indirect effect is amplified at higher levels of CSE.\u003c/p\u003e\n\u003cp\u003eHypothesis 6b: Core self-evaluations moderate the indirect effect of AI usage on task performance via controlled motivation and prevention-focused job crafting; this indirect effect is attenuated at higher levels of CSE.\u003c/p\u003e\n\u003cp\u003eFig.1 illustrates the proposed theoretical framework.\u003c/p\u003e\n\u003cp\u003e---------------------------------------------\u003c/p\u003e\n\u003cp\u003eInsert Fig.1 here\u003c/p\u003e\n\u003cp\u003e----------------------------------------------\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003e\u003cstrong\u003eSampling and Data Collection.\u0026nbsp;\u003c/strong\u003eThis study focuses on widely adopted organizational applications of AI, including technologies such as facial and voice recognition, chatbots, and intelligent recommendation algorithms. Following Tang et al.(2022), we prefaced the survey with a clear definition of AI usage and industry-specific examples to anchor participants\u0026rsquo;\u0026nbsp;responses\u0026nbsp;in authentic AI interaction\u0026nbsp;experiences. This was designed to ensure accurate comprehension and valid responses. We selected participants from organizations located in China\u0026rsquo;s major AI development clusters. The sample comprised employees and managers from information technology, financial services, smart manufacturing, healthcare, education, and related sectors.\u003c/p\u003e\n\u003cp\u003eWe employed a leader-employee matched-pair survey design, combining convenience sampling and snowball sampling techniques. For the offline sample, data were collected through field visits to participating firms. Employees and their direct supervisors completed separate questionnaires that were later linked via the final four digits of their mobile phone numbers. Questionnaires were returned onsite by the HR department or the research team. To encourage accurate and complete responses, participants received token gifts as a gesture of gratitude. For the online sample, data were gathered via the Wenjuanxing platform, where supervisors distributed the questionnaire links or QR codes to their subordinates and used social networks to expand the sample size. Additionally, monetary incentives ranging from 5 to 10 RMB were provided to promote engagement and data quality. To mitigate common method bias, a multi-wave, multi-source design was utilized, aligning with procedural solutions outlined in Podsakoff et al. (2003). Data were gathered over three time points, spaced approximately 1.5 months apart, between July and November 2024. At T1, employees reported on AI usage, core self-evaluations, and demographics. At T2, employees reported their autonomous and controlled motivation. At T3, employees reported job crafting, and supervisors rated their task performance.\u003c/p\u003e\n\u003cp\u003eIn total, we distributed 563 questionnaires (500 to employees and 63 to supervisors). For the offline sample, we collected 207 valid employee responses out of 250 (82.8%) and 24 valid supervisor responses out of 28 (85.7%). For the online sample, we obtained 202 valid employee surveys out of 250 (80.8%) and 29 valid supervisor surveys out of 35 (82.8%). This yielded a total of 409 employee and 53 supervisor surveys, with overall response rates of 81.8% and 84.1%, respectively.\u003c/p\u003e\n\u003cp\u003eTable 1 indicates that among the 409 valid employee respondents, the gender distribution was relatively balanced (53.79% male, 46.21% female). Most respondents were in the 26-35 age range (49.14%). Participants also tended to have substantial work experience, with 82.88% reporting between 3 to 10 years on the job. Industry representation was diverse and well-balanced, covering IT/internet (19.07%), finance (18.09%), manufacturing (17.85%), and education (15.16%), suggesting the sample had broad representativeness.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e---------------------------------------------\u003c/p\u003e\n\u003cp\u003eInsert Table 1 here\u003c/p\u003e\n\u003cp\u003e----------------------------------------------\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurement.\u0026nbsp;\u003c/strong\u003eWe assessed all constructs using well-established, previously validated scales. Items were rated on five-point Likert scales (1 = \u0026ldquo;strongly disagree,\u0026rdquo; 5 = \u0026ldquo;strongly agree\u0026rdquo;) . Operational definitions and measurement instruments for each variable are presented below:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAI Usage.\u0026nbsp;\u003c/strong\u003eAI usage was assessed with a three-item scale adapted by Tang et al. (2022) from Medcof (1996), including items such as \u0026ldquo;I use artificial intelligence to accomplish most of my core job functions.\u0026rdquo; The reliability coefficient (Cronbach\u0026rsquo;s \u0026alpha;) was 0.857.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAutonomous and Controlled Motivation.\u0026nbsp;\u003c/strong\u003eWork motivation was assessed with the Multidimensional Work Motivation Scale (Gagn\u0026eacute; et al., 2015), adapted by Gillet et al. (2016). Autonomous motivation was assessed with six items, including three for intrinsic motivation and three for identified regulation (e.g., \u0026ldquo;I find my job interesting\u0026rdquo;). Controlled motivation was measured using ten items, comprising six for external regulation and four for introjected regulation (e.g., \u0026ldquo;Because I want to be recognized by others\u0026rdquo;). The Cronbach\u0026rsquo;s alpha values were 0.916 for autonomous motivation and 0.946 for controlled motivation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePromotion-Focused and Prevention-Focused Job Crafting.\u0026nbsp;\u003c/strong\u003eJob crafting was measured using the scale developed by Bindl et al. (2019), which includes 16 items for promotion-focused job crafting (e.g., \u0026ldquo;I actively seek to broaden my skill set at work\u0026rdquo;) and 12 items for prevention-focused job crafting (e.g., \u0026ldquo;I develop skills to avoid negative work outcomes\u0026rdquo;). The Cronbach\u0026rsquo;s alpha values were 0.964 and 0.950, respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTask Performance.\u0026nbsp;\u003c/strong\u003eTask performance\u0026nbsp;was assessed using a\u0026nbsp;four-item scale developed by Van Dyne \u0026amp; LePine (1998). One item states, \u0026ldquo;This employee fulfills all responsibilities specified in their job description.\u0026rdquo; The Cronbach\u0026rsquo;s alpha value was 0.868.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCore Self-Evaluations (CSE).\u003c/strong\u003e CSE was measured with the 12-item scale developed by Judge et al. (2003). A sample item is: \u0026ldquo;I am confident I will achieve the success I deserve in life.\u0026rdquo; The measure demonstrated excellent internal consistency, with a Cronbach\u0026rsquo;s alpha coefficient of 0.958.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eControl Variables.\u003c/strong\u003e Consistent with prior studies, we controlled for gender, age, education, and job tenure to address demographic heterogeneity (Cheng et al., 2023).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eCommon Method variance (CMV) and Confirmatory Factor Analysis (CFA).\u0026nbsp;\u003c/strong\u003eWe followed the guidelines outlined by Podsakoff et al. (2003) and employed Harman\u0026rsquo;s single-factor test to detect any potential CMV. The results showed the dominant factor explained merely 27.85% of total variance\u0026mdash;substantially below the 40% cutoff and less than half of the overall explained variance\u0026mdash;suggesting that CMV was not a significant concern.\u003c/p\u003e\n\u003cp\u003eTo ensure our measurement model accurately captures the constructs, we performed a CFA using Mplus 8.3. As indicated in Table 1, our seven-factor theoretical model exhibited a notably better fit compared to alternative configurations, with fit indices such as \u0026chi;\u0026sup2;/df = 1.28, CFI = 0.97, TLI = 0.97, RMSEA = 0.03, and SRMR = 0.03. These results meet the standard benchmarks for an acceptable model fit\u0026mdash;namely, \u0026chi;\u0026sup2;/df \u0026lt; 3, CFI/TLI \u0026gt; 0.90, RMSEA/SRMR \u0026lt; 0.08 (Kyndt \u0026amp; Onghena, 2014)\u0026mdash;providing strong evidence of discriminant validity.\u003c/p\u003e\n\u003cp\u003e---------------------------------------------\u003c/p\u003e\n\u003cp\u003eInsert Table 2 here\u003c/p\u003e\n\u003cp\u003e----------------------------------------------\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorrelation analysis.\u003c/strong\u003e Correlation analysis. Table 3 illustrates statistics and correlations among the variables. As shown, AI usage is positively correlated with both autonomous motivation (\u003cem\u003er\u003c/em\u003e = 0.49, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) and controlled motivation (\u003cem\u003er\u003c/em\u003e = 0.53, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Autonomous motivation shows a strong positive connection with promotion-focused job crafting (\u003cem\u003er\u0026nbsp;\u003c/em\u003e= 0.60, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), while controlled motivation is positively associated with prevention-focused job crafting (\u003cem\u003er\u003c/em\u003e = 0.58, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). Furthermore, promotion-focused job crafting demonstrates a favorable impact on task performance (\u003cem\u003er\u003c/em\u003e = 0.36, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), whereas prevention-focused job crafting shows an inverse relationship with task performance (\u003cem\u003er\u003c/em\u003e = \u0026ndash;0.28, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). The observed correlations among the key variables offer preliminary empirical support for the hypothesized relationships.\u003c/p\u003e\n\u003cp\u003e---------------------------------------------\u003c/p\u003e\n\u003cp\u003eInsert Table 3 here\u003c/p\u003e\n\u003cp\u003e----------------------------------------------\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHypothesis tests.\u003c/strong\u003e First, we conducted hierarchical regression analysis using SPSS 25.0 (Table 4). The findings indicated that AI usage was positively linked to autonomous motivation (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.51, \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.001, M1) and controlled motivation (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.49, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, M3), confirming H1a and H1b. Autonomous motivation showed a strong connection to promotion-focused job crafting (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.59, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, M5), supporting H2a, whereas controlled motivation was tied to prevention-focused job crafting (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.58, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, M6), supporting H2b. Promotion-focused job crafting was found to significantly enhance task performance (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.33, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, M7), supporting H3a, whereas prevention-focused job crafting actually undermined performance (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.30, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, M8), corroborating H3b.\u003c/p\u003e\n\u003cp\u003eSecond, following Preacher et al.\u0026rsquo;s (2007) procedure, we performed a bootstrapping analysis with 5,000 resamples to examine the proposed dual-chain mediation pathways.\u0026nbsp;The results, outlined in Table 4, revealed a statistically significant positive sequential mediation effect for the chain AI usage \u0026rarr; autonomous motivation \u0026rarr; promotion-focused job crafting \u0026rarr; task performance (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.04, 95% CI = [0.01, 0.07]), confirming H4a. Likewise, the sequential indirect effect of AI usage \u0026rarr; controlled motivation \u0026rarr; prevention-focused job crafting \u0026rarr; task performance showed a significant negative relationship (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026ndash;0.10, 95% CI = [\u0026ndash;0.13, \u0026ndash;0.07]), lending support to Hypothesis 4b. When considering the overall impact, the total indirect effect via the positive route was notably favorable (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.24, 95% CI = [0.17, 0.32]), while the negative pathway\u0026rsquo;s total effect was significantly adverse (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026ndash;0.37, 95% CI = [\u0026ndash;0.44, \u0026ndash;0.30]). Taken together, these findings reveal that AI usage exerts opposing influences on employees\u0026rsquo; task performance via distinct motivational and job-crafting mechanisms.\u003c/p\u003e\n\u003cp\u003e---------------------------------------------\u003c/p\u003e\n\u003cp\u003eInsert Table 4 here\u003c/p\u003e\n\u003cp\u003e----------------------------------------------\u003c/p\u003e\n\u003cp\u003eThird, hierarchical regression analyses (SPSS 25.0) were performed to assess the moderating effect of CSE. To reduce multicollinearity, all predictors were mean-centered prior to computing interaction terms. Table 4 illustrates that there was a significant positive interaction between AI usage and CSE on autonomous motivation (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.35,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, Model 2), supporting H5a, and a negative and significant interaction for controlled motivation (\u003cem\u003e\u0026beta;\u003c/em\u003e = \u0026ndash;0.35, p \u0026lt; 0.001, Model 4), supporting H5b. To further interpret these interactions, we plotted moderation effect diagrams (see Fig. 2 and 3) for employees with high and low CSE levels (M\u0026plusmn;SD). Fig. 2 indicated that AI usage had a substantially stronger impact on autonomous motivation among employees with higher CSE (\u0026beta; = 0.83, \u003cem\u003et\u003c/em\u003e = 11.95,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) compared to those with lower CSE (\u0026beta; = 0.18, \u003cem\u003et\u003c/em\u003e = 2.98,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01). Similarly, Fig. 3 demonstrated that the positive association between AI usage and controlled motivation was weaker when CSE was high (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.16, \u003cem\u003et\u003c/em\u003e = 2.61, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) but more pronounced when CSE was low (\u003cem\u003e\u0026beta;\u003c/em\u003e = 0.80, \u003cem\u003et\u003c/em\u003e = 15.31,\u0026nbsp;\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). These results further substantiate Hypotheses 5a and 5b.\u003c/p\u003e\n\u003cp\u003e---------------------------------------------\u003c/p\u003e\n\u003cp\u003eInsert Fig. 2 here\u003c/p\u003e\n\u003cp\u003e----------------------------------------------\u003c/p\u003e\n\u003cp\u003e---------------------------------------------\u003c/p\u003e\n\u003cp\u003eInsert Fig. 3 here\u003c/p\u003e\n\u003cp\u003e----------------------------------------------\u003c/p\u003e\n\u003cp\u003e---------------------------------------------\u003c/p\u003e\n\u003cp\u003eInsert Table 5 here\u003c/p\u003e\n\u003cp\u003e----------------------------------------------\u003c/p\u003e\n\u003cp\u003eFinally, the bias-corrected bootstrap analysis (5,000 resamples) revealed a contingent pattern of sequential mediation (see Table 5). When core CSE was high, the pathway from AI usage to task performance\u0026mdash;mediated by autonomous motivation and promotion-focused job crafting\u0026mdash;showed a statistically significant indirect effect (0.06, 95% CI = [0.02, 0.11]). This effect diminished, though remained significant, at lower CSE levels (0.01, 95% CI = [0.00, 0.03]). Notably, the disparity between these conditional effects was statistically meaningful (difference = 0.05, 95% CI [0.01, 0.09]), providing support for H6a. In contrast, the mediated pathway through controlled motivation and prevention-focused job crafting revealed a stronger negative association under low CSE conditions (indirect effect = \u0026ndash;0.16, 95% CI = [\u0026ndash;0.21, \u0026ndash;0.11]) compared to high CSE (indirect effect = \u0026ndash;0.03, 95% CI = [\u0026ndash;0.06, \u0026ndash;0.01]). This divergence was also statistically robust (difference = 0.12, 95% CI = [0.08, 0.18]), thereby corroborating H6b.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eGrounded in self-determination theory, this study illuminates the paradoxical effects of AI usage on task performance and its underlying mechanisms. Three key conclusions emerged:\u003c/p\u003e\u003cp\u003eFirst, AI usage exerts both facilitative and impeding effects on employee task performance, revealing a double-edged sword effect that extends prior research by demonstrating that AI\u0026rsquo;s performance implications are not unidirectional but rather contingent on the underlying processes it activates. Second, AI usage influences employee task performance through sequential mediation involving motivational and behavioral mechanisms\u0026mdash;specifically, the motivational processes it triggers and the subsequent forms of job crafting that emerge. The facilitative pathway operates through autonomous motivation and promotion-focused job crafting, whereas the impeding pathway functions through controlled motivation and prevention-focused job crafting. Third, CSE emerges as a key boundary condition that strengthens the positive pathway while weakening the negative one. Employees with high CSE more readily convert AI usage into autonomous motivation and promotion-focused job crafting to strengthen task performance. Simultaneously, high CSE buffers against AI-related pressures, mitigating the adverse effects of controlled motivation and prevention-focused job crafting on task performance.\u003c/p\u003e\u003cp\u003e\u003cb\u003eTheoretical Contributions.\u003c/b\u003e Theoretical Contributions. First, it expands the discourse on the dual nature of AI\u0026rsquo;s impact. Previous research has largely highlighted the bright side of AI\u0026rsquo;s usage (Gregory et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jarrahi, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), while paying relatively little attention to its potential dark side (Brougham \u0026amp; Haar, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). To remedy this imbalance, we develop a dual-path model illustrating how AI usage affects task performance through two distinct mechanisms. By integrating both routes, the model rectifies the neglect of AI\u0026rsquo;s darker side and offers an explanatory framework for prior inconsistent findings (Tang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Second, it deepens comprehension of the processes by which AI usage influences task performance. Previous studies have typically examined this relationship through a single-path lens, often overlooking the mediating roles of motivation and behavior. By drawing on the \u0026ldquo;situation\u0026ndash;motivation\u0026ndash;behavior\u0026ndash;outcome\u0026rdquo;process model, we propose and validate a dual-chain mediation framework. This framework explicates how AI usage affects employee performance through distinct psychological and behavioral channels. This framework furnishes the field with an integrated lens for understanding how technology applications translate into outcomes through motivation and behavior (Gagn\u0026eacute; \u0026amp; Deci, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Higgins, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Third, it delineates key boundary conditions of AI\u0026rsquo;s impact by introducing CSE as a critical moderator. Our findings indicate that employees high in CSE capitalize on the facilitative path of AI usage, whereas those low in CSE are more vulnerable to its inhibitive path. This insight underscores the significance of personal characteristics in AI contexts, extending the theoretical scope of core self-evaluation (Tang et al., \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePractical Implications.\u003c/b\u003e First, organizations should foster autonomous motivation. Drawing on self-determination theory, managers can satisfy employees\u0026rsquo; essential psychological needs (Deci et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). To begin with, design work tasks with a high degree of freedom, grant employees autonomy in using AI tools, and thereby optimize human\u0026ndash;AI collaboration (Hou et al., 2024; Lee et al., 2022; Friedman et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, provide structured training and AI integration support to alleviate replacement anxiety and bolster perceptions of competence (Jia et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shah et al., \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Finally, cultivate a supportive team climate by offsetting AI-induced declines in peer interaction through cross-departmental projects and team-building activities, thereby strengthening social belonging and emotional connection (Schmutz et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Sim\u0026oacute;n et al., \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, organizations should actively shape employee job crafting behavior by encouraging promotion-focused job crafting and minimizing prevention-focused job crafting. Recommended practices include: supporting autonomous task redesign that enables human-AI complementarity (Vaccaro et al., \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e2024\u003c/span\u003e); ensuring adequate training and timely feedback to bolster self-efficacy and proactivity (Verma \u0026amp; Singh, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e2022\u003c/span\u003e); building psychological safety and an organizational support culture to reduce technological anxiety through transparent communication and participatory decision-making (Albrecht et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThird, organizations should implement personalized management strategies grounded in individual differences in core self-evaluations (Judge \u0026amp; Kammeyer-Mueller, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For employees with lower CSE, managers should provide emotional support, psychological counseling, and collaborative opportunities to build confidence, reduce anxiety, and enhance their sense of belonging and value through social connection (Gong et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Kinias \u0026amp; Sim, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). For those with higher CSE, organizations can assign challenging tasks and innovation-driven roles, while reinforcing motivation and self-efficacy through performance feedback and developmental opportunities (Demır, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eLimitations and Future Research.\u003c/b\u003e First, although prior studies indicate that gender, age, industry, and tenure are salient predictors of employees\u0026rsquo; AI usage, we treated these factors merely as control variables. Future studies could focus on specific industries (e.g., service operations), targeted populations (e.g.,, older employees), or compare differences across gender, tenure, and occupational groups to provide a richer account of how AI usage impacts work outcomes.\u003c/p\u003e\u003cp\u003eSecond, guided by self-determination theory, this study identified work motivation and job crafting as pivotal intermediaries connecting AI usage with task performance. However, other mechanisms\u0026mdash;especially those involving cognitive appraisals or affective responses\u0026mdash;may also serve as important explanatory pathways. Scholars are encouraged to explore these additional mechanisms to provide a more comprehensive account.\u003c/p\u003e\u003cp\u003eFinally, this study adopted an individual-level lens. A more complete picture would benefit from multi-level designs that examine contextual influences such as leadership styles, peer interactions, or team contexts, thereby offering a more holistic understanding of how AI usage affects task performance.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any external funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received ethical clearance and was approved on June 25, 2024 by the Academic (Ethics) Committee of the School of Public Administration, Guangdong University of Finance, China (Approval No. 20240625). The review process ensured that all research procedures complied with institutional guidelines, the principles outlined in the 1964 Declaration of Helsinki and its subsequent amendments, and comparable ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the data collection period from July to November 2024, participants provided informed consent electronically. The online questionnaire included a comprehensive information section detailing the study\u0026rsquo;s purpose, data handling procedures, and participant rights. Respondents were required to actively confirm their understanding and willingness to participate by checking a consent box. This approach ensured that participants had the opportunity to review all relevant information before granting consent. It was explicitly stated that all data collected through the survey would remain anonymous.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdditional information\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence and requests for materials should be addressed to the author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article and its supplementary information file.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization: W.Z, P.C and X.G; Methodology: W.Z, P.C and X.G; Data Collection: W.Z and X.G; Data Analysis: W.Z and X.G; Writing\u0026mdash;Original Draft: W.Z and X.G; Writing\u0026mdash;Review \u0026amp; Editing: W.Z and P.C. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAlbrecht, S. L., Furlong, S., \u0026amp; Leiter, M. P. (2023). The psychological conditions for employee engagement in organizational change: Test of a change engagement model. \u003cem\u003eFrontiers in Psychology\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e, 1071924. https://doi.org/10.3389/fpsyg.2023.1071924\u003c/li\u003e\n \u003cli\u003eBelanche, D., Casal\u0026oacute;, L. V., \u0026amp; Flavi\u0026aacute;n, C. (2019). Artificial Intelligence in FinTech: Understanding robo-advisors adoption among customers. \u003cem\u003eIndustrial Management \u0026amp; Data Systems\u003c/em\u003e, \u003cem\u003e119\u003c/em\u003e(7), 1411\u0026ndash;1430.\u003c/li\u003e\n \u003cli\u003eBindl, U. K., Unsworth, K. L., Gibson, C. B., \u0026amp; Stride, C. B. (2019). 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The double-edged sword effects of perceived algorithmic control on platform workers\u0026rsquo; service performance. \u003cem\u003eHumanities \u0026amp; Social Sciences Communications, 11\u003c/em\u003e(1), 316. https://doi.org/10.1057/s41599-024-02812-0\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Respondents\u0026rsquo; demographic profiles.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"86%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eDemographics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eCategory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003eFrequency (\u003cem\u003eN\u003c/em\u003e)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003ePercentage (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e53.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e46.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 21px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e25 years and below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e23.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e26\u0026ndash;35 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e49.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e36\u0026ndash;45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e21.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eAbove 45 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 21px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eHigh school or below\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eAssociate degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e21.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eBachelor\u0026apos;s degree\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e47.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMaster\u0026apos;s degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e27.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width: 21px;\"\u003e\n \u003cp\u003eJob tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e2 years or less\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e12.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e3\u0026ndash;5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e145\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e35.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e6\u0026ndash;10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e194\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e47.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eMore than 10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e4.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"7\" style=\"width: 21px;\"\u003e\n \u003cp\u003eIndustry type\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eIT / Software \u0026amp; Hardware / E-commerce / Internet Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e19.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eBanking / Insurance / Securities / Investment\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e18.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eManufacturing / Machinery / Equipment / Heavy Industry\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e17.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eEducation / Training / Research / Academic Institutions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e15.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eFood / Entertainment / Tourism / Hospitality / Services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e10.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eHealthcare / Nursing / Public Health / Pharma / Biotech\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e12.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003eOthers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 19px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 18px;\"\u003e\n \u003cp\u003e6.60\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 409\u003c/p\u003e\n\u003cp\u003eTable 2 \u0026nbsp;Results of confirmatory factor analysis.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026chi;\u0026sup2;/\u003c/em\u003edf\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003eTLI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003eSRMR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003eRMSEA\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSeven-factor model (AIU, CSE, AM, OJC, CM, EJC, TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e2398.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1869\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eSix-factor model (AIU, CSE, AM+OJC, CM, EJC, TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e3407.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1875\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eFive-factor model (AIU, CSE, AM+ OJC, CM+ EJC, TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e5110.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1880\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eFour-factor model (AIU, CSE, AM+ OJCC+ CM+EJC, TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e9610.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1884\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e5.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eThree-factor model (AIU, CSE, AM+ OJC+ CM+ EJC+TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e10330.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e5.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eTwo-factor model (AIU, CSE+AM+OJC+ CM+ EJC+TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e14095.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e7.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 227px;\"\u003e\n \u003cp\u003eOne-factor model (AIU+ CSE+AM+ OJC+ CM+ EJC+TP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003e14414.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45px;\"\u003e\n \u003cp\u003e1890\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 40px;\"\u003e\n \u003cp\u003e7.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 39px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 53px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 63px;\"\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: \u003cem\u003eN\u0026nbsp;\u003c/em\u003e= 409; \u003cem\u003eAIU\u003c/em\u003e AI Usage, \u003cem\u003eCSE\u003c/em\u003e Core Self-Evaluations, \u003cem\u003eAM\u003c/em\u003e Autonomous Motivation, \u003cem\u003eOJC\u003c/em\u003e Promotion-Focused Job Crafting, \u003cem\u003eCM\u003c/em\u003e Controlled Motivation, \u003cem\u003eEJC\u003c/em\u003e Prevention-Focused Job Crafting, \u003cem\u003eTP\u0026nbsp;\u003c/em\u003eTask Performance.\u003c/p\u003e\n\u003cp\u003eTable 3 Means, standard deviations (SD), and correlations of variables.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e1 Gender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e2 Age\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e3 Education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e4 Tenure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e5 AIU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.15\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.13\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e6 CSE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e-0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e7 AM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.33\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e8 CM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.17\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.14\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e-0.11\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e9 OJC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.50\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.60\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e10 EJC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e-0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.55\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.20\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e0.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e11 TP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 49px;\"\u003e\n \u003cp\u003e3.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 38px;\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 34px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.10\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.11\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.23\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 41px;\"\u003e\n \u003cp\u003e0.36\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 44px;\"\u003e\n \u003cp\u003e-0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: \u003cem\u003eN\u003c/em\u003e= 409; \u003cem\u003eAIU\u003c/em\u003e AI Usage, \u003cem\u003eCSE\u003c/em\u003e Core Self-Evaluations, \u003cem\u003eAM\u003c/em\u003e Autonomous Motivation, \u003cem\u003eOJC\u003c/em\u003e Promotion-Focused Job Crafting, \u003cem\u003eCM\u003c/em\u003e Controlled Motivation, \u003cem\u003eEJC\u003c/em\u003e Prevention-Focused Job Crafting, \u003cem\u003eTP\u0026nbsp;\u003c/em\u003eTask Performance; \u003csup\u003e***\u003c/sup\u003ep \u0026lt; 0.001, \u003csup\u003e**\u003c/sup\u003ep \u0026lt; 0.01, \u003csup\u003e*\u003c/sup\u003ep \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eTable 4 \u0026nbsp;Result of hierarchical regressions.\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"70%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 19px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eAM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eCM \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e\u0026nbsp; OJC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003e\u0026nbsp;EJC \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cu\u003eTP \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/u\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eintercept\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.24\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.73\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e1.58\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.62\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e1.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1.74\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n 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style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.59\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n 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10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.30\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eCSE\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n 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\u003cp\u003e0.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e-0.35\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 15px;\"\u003e\n \u003cp\u003eF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e27.69\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e36.54\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e34.66\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e42.44\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e47.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9px;\"\u003e\n \u003cp\u003e43.06\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e14.18\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 11px;\"\u003e\n \u003cp\u003e9.86\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: \u003cem\u003eN\u003c/em\u003e= 409; \u003cem\u003eAIU\u003c/em\u003e AI Usage, \u003cem\u003eCSE\u003c/em\u003e Core Self-Evaluations, \u003cem\u003eAM\u003c/em\u003e Autonomous Motivation, \u003cem\u003eOJC\u003c/em\u003e Promotion-Focused Job Crafting, \u003cem\u003eCM\u003c/em\u003e Controlled Motivation, \u003cem\u003eEJC\u003c/em\u003e Prevention-Focused Job Crafting, \u003cem\u003eTP\u0026nbsp;\u003c/em\u003eTask Performance; \u003csup\u003e***\u003c/sup\u003ep \u0026lt; 0.001, \u003csup\u003e**\u003c/sup\u003ep \u0026lt; 0.01, \u003csup\u003e*\u003c/sup\u003ep \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003eTable 5 \u0026nbsp;Tests of chain mediation and moderated chain mediation effects.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003ePath\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 29px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003eBootstrap results\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21px;\"\u003e\n \u003cp\u003e95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eSE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eLLCI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003eULCI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAIU\u0026rarr;AM\u0026rarr;OJC\u0026rarr;TP\u003c/p\u003e\n \u003cp\u003eChain Mediation Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eTotal Indirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eIndirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAIU\u0026rarr;AM\u0026rarr;OJC\u0026rarr;TP\u003c/p\u003e\n \u003cp\u003eChain Mediation Effects\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eTotal Indirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eIndirect Effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAIU\u0026rarr;AM\u0026rarr;OJC\u0026rarr;TP\u003c/p\u003e\n \u003cp\u003emoderated chain mediation effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eLow CSE(-1SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eHigh CSE(+1SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width: 25px;\"\u003e\n \u003cp\u003eAIU\u0026rarr;AM\u0026rarr;OJC\u0026rarr;TP\u003c/p\u003e\n \u003cp\u003emoderated chain mediation effect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eLow CSE(-1SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.11\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eHigh CSE(+1SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e-0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 29px;\"\u003e\n \u003cp\u003eDifference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 12px;\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eNote: \u003cem\u003eN\u003c/em\u003e= 409; \u003cem\u003eAIU\u003c/em\u003e AI Usage, \u003cem\u003eCSE\u003c/em\u003e Core Self-Evaluations, \u003cem\u003eAM\u003c/em\u003e Autonomous Motivation, \u003cem\u003eOJC\u003c/em\u003e Promotion-Focused Job Crafting, \u003cem\u003eCM\u003c/em\u003e Controlled Motivation, \u003cem\u003eEJC\u003c/em\u003e Prevention-Focused Job Crafting, \u003cem\u003eTP\u0026nbsp;\u003c/em\u003eTask Performance.\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7168956/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7168956/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose.\u003c/h2\u003e\u003cp\u003eThe growing adoption of AI is reshaping organizational workflows and employee experiences, delivering benefits while presenting novel challenges, reflecting a double-edged sword effect. Although prior studies have identified potential impacts of AI usage on employees, the underlying mechanisms remain insufficiently examined. Guided by self-determination theory, this study investigates the dual pathways\u0026mdash;facilitative and inhibitive\u0026mdash;by which AI usage impacts employee task performance via motivational and behavioral mechanisms. It further investigates the boundary role of core self-evaluations (CSE) in moderating these effects.\u003c/p\u003e\u003ch2\u003eMethodology.\u003c/h2\u003e\u003cp\u003eA three-wave, multi-source field study was conducted involving 409 employee-supervisor pairs from AI-intensive industries in China. Data were collected using leader\u0026ndash;employee matched questionnaires. Key constructs\u0026mdash;AI usage, motivation types, job crafting, task performance, and CSE\u0026mdash;were measured using validated scales. Hypotheses were tested via hierarchical regression, bootstrapping, and moderated mediation analyses using SPSS and Mplus.\u003c/p\u003e\u003ch2\u003eFindings.\u003c/h2\u003e\u003cp\u003eResults revealed a dual-chain mediation mechanism: AI usage enhances task performance via autonomous motivation and promotion-focused job crafting, but simultaneously impairs it through controlled motivation and prevention-focused job crafting. Furthermore, CSE significantly moderates both pathways, amplifying the positive and buffering the negative effects.\u003c/p\u003e\u003ch2\u003eOriginality.\u003c/h2\u003e\u003cp\u003eThis study provides understanding of AI usage\u0026rsquo;s \u0026ldquo;double-edged sword\u0026rdquo; effect by identifying parallel motivational-behavioral pathways and the boundary condition of core self-evaluations. The findings enrich self-determination theory in the digital context and offer actionable insights for designing inclusive and personalized AI integration strategies.\u003c/p\u003e","manuscriptTitle":"When AI enables and undermines: dual mechanisms linking AI usage to task performance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-04 13:57:44","doi":"10.21203/rs.3.rs-7168956/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-23T15:34:27+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-17T09:41:56+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-12T06:54:40+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"226405854609522782021556598346828933766","date":"2025-08-28T03:17:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"321210332515336323756641463746260620866","date":"2025-08-28T01:51:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-28T01:17:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-20T19:01:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-07T17:34:14+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-08-07T17:24:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"e2e0d2ff-01d9-4e87-9a80-13a307fd82a7","owner":[],"postedDate":"September 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[{"id":53812956,"name":"Business and commerce/Business and management"},{"id":53812957,"name":"Social science/Business and management"},{"id":53812958,"name":"Business and commerce/Information systems and information technology"},{"id":53812959,"name":"Biological sciences/Psychology"},{"id":53812960,"name":"Social science/Psychology"},{"id":53812961,"name":"Social science/Science technology and society"}],"tags":[],"updatedAt":"2026-05-14T13:24:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-04 13:57:44","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7168956","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7168956","identity":"rs-7168956","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}