The emergence of artificial intelligence (AI) has ushered in a new era of strategic opportunities, leveraging the digital revolution’s vast potential to profoundly transform all sectors (Bag et al., 2021). AI involves machines programmed to exhibit human-like intelligence and has been applied across diverse fields, including finance, education, and retail (Huang & Rust, 2018). AI technologies, such as smart robots and big data analytics, can boost corporate efficiency, enhance services, and reduce labor costs, prompting their adoption by numerous service-oriented enterprises  (Borges et al., 2021). As intellectual capital influences competitive advantage, innovative talent is crucial in driving digital transformation and achieving high-quality development (Ferreira et al., 2021). As a result, maximizing employees’ capacity for innovation and fostering innovative behavior within workflow intelligence processes have emerged as critical issues requiring urgent exploration.

 

Introducing AI tools in the workplace provides many job resources for employees, making the job demands–resources (JD-R) model an applicable theoretical framework. The JD-R model illuminates individuals’ resource status and work engagement by dividing job characteristics into job demands and resources (Bakker & Demerouti, 2007). Demands are the elements of a job requiring continuous effort, contributing to physical and mental strain, while resources are aspects of a job that facilitate the accomplishment of work goals, alleviate job demands, and foster personal growth and development, significantly enhancing employee motivation and producing positive results (Schaufeli, 2017). As AI expands the availability of work resources, employees dynamically adapt to these changes by engaging in JD-R matching. Petrou et al. (2012) found that this process, often occurring spontaneously, facilitates innovation through job crafting, which allows employees to align their roles more closely with the evolving work environment.

 

The JD-R model and job crafting theories are closely interconnected. Job crafting encompasses the proactive actions undertaken by workers to tailor their professional roles to better align with their preferences, motivations, and interests (Tims et al., 2012), which can enhance their engagement, well-being, and performance. Job crafting involves workers both leveraging and optimizing their resources as well as mitigating the negative impacts of their job demands per the principles outlined in the JD-R model (Tims et al., 2013). This may be accomplished by augmenting work resources, intensifying job demands (to ensure they are seen as challenging), or reducing job demands (through assistance and support from supervisors and colleagues).

 

Despite the potential of AI to enhance employee efficiency and engagement, few studies have explored the role of mediating mechanisms such as job crafting in the relationship between artificial intelligence usage (AIU) and innovative behavior in the workplace. Instead, previous research on AIU has primarily focused on how it directly affects individual behaviors (e.g., Cheng et al., 2023; Jia et al., 2023; Yin et al., 2024). Among the few scholars who have investigated intermediary mechanisms, Liang et al. (2022) examined emotional exhaustion and intrinsic motivation as mediators, while Yin et al. (2024) incorporated two mediators into the transactional model of stress to explore the positive influence of AI assistance on AI-enabled innovation behavior: creative self-efficacy and awareness of smart technology, artificial intelligence, robotics, and algorithms. Although these studies have provided a solid foundation, expanding understanding of the mechanisms and specific circumstances that enhance the positive effect of AIU on innovative behavior in the workplace holds significant practical and theoretical value.

 

Therefore, we used the JD-R model to explore the mediating role of job crafting in the relationship between AIU and innovative behavior, and introduced two moderators: creative self-efficacy and a strengths-based psychological climate. This dual-moderation perspective will advance understanding of the interplay between individual and organizational factors, offering practical implications for managers seeking to foster innovation in AI-enhanced work environments. By addressing these complex dynamics, this study will contribute to the broader literature on organizational behavior and technology management, thus providing a foundation for future research on optimizing AI integration.

AI involves sophisticated computers programmed to execute cognitive tasks traditionally associated with human intelligence, including learning, interacting, and problem solving (Liang et al., 2022). Given AI’s capacity to significantly enhance worker productivity (Brynjolfsson et al., 2021), organizations have increasingly incorporated AIU into their operations to enhance staff capabilities and streamline various tasks. According to West and Farr (1989), innovative behavior refers to individuals’ drive to conceive, articulate, and execute novel ideas to improve work processes and relationships, thus enhancing productivity. From this standpoint, innovation involves several steps: identifying a problem, generating ideas or solutions, pursuing sponsorship or support, and finally implementing the initial solution (Scott & Bruce, 1994). The catalyst for innovation is idea generation, which is intrinsically tied to creativity—a fundamental prerequisite for innovation (West, 2002).

 

Previous studies have suggested that despite its inability to completely replace workers in innovation-related jobs, AI has the potential to enhance employees’ creativity and innovation. For example, Jia et al. (2023) found that using AI support in sales generation significantly enhanced employees’ ability to respond creatively to clients’ inquiries. In addition, AI has been shown to contribute to reshaping work design by amplifying employee engagement with increasingly demanding consumers (Yin et al., 2024). This modification allows highly skilled individuals to create unique scripts and cultivate a pleasant working environment, thus promoting innovation. A hybrid workplace motivates talented individuals to accomplish objectives through autonomous systems and cutting-edge tasks. They are drawn to the organizational and management issues associated with implementing AI technology for data interpretation, believing they are undertaking tasks of significant value (Neubert & Montañez, 2020). Employees utilizing AI therefore exhibit more self-motivation, achieve exceptional performance, persistently tackle intricate jobs that require cognitive talent, and display heightened creativity. Thus, we proposed the following hypothesis:

Hypothesis 1: Artificial intelligence usage will be positively associated with innovative behavior.

The widespread implementation of organizational AI has created a dynamic work environment characterized by unpredictability and complexity (Cheng et al., 2023), which provides employees with opportunities to adjust their work duties and roles in response to organizational changes (Petrou et al., 2018). Self-determination theory posits that individuals who strive for autonomy while adjusting to a changing work environment will participate in activities that align with their interests and promote competence development (Deci et al., 2017). For these individuals, using AI technology frees up time and energy, enabling them to engage in more demanding tasks to develop their skills and abilities, including job-crafting behaviors centered on promotion (Cheng et al., 2023). Therefore, we proposed the following hypothesis:

Hypothesis 2: Artificial intelligence usage will positively predict job crafting.

 

Prior studies have posited that job crafting can enhance workers’ overall creativity and innovative behavior (Afsar et al., 2019). Employees who engage in job crafting have both sufficient resources and appropriate demands, which enables them to generate, introduce, and implement innovative ideas to enhance work relations and processes, ultimately improving organizational productivity (West & Farr, 1989). Demerouti et al. (2015) found that equitable employment resources and demands enhanced enthusiasm and dedication among employees for engaging in innovative activities, including adopting novel approaches and adapting to new concepts. Lin et al. (2017) identified a positive correlation between individuals’ energy and enthusiasm and their tendency to exhibit innovative behavior. Therefore, we proposed the following hypothesis:

Hypothesis 3: Job crafting will have a positive effect on innovative behavior.

 

As previously noted, the adoption of AI can create an inventive and positive working environment, fostering innovative behavior and engagement (Afsar et al., 2019; Jia et al., 2023). Therefore, it is reasonable to posit that workers who engage in job crafting would leverage new AI resources, communicate effectively with colleagues, and implement novel techniques and technologies, all of which foster inventive behavior. Therefore, we proposed the following hypothesis:

Hypothesis 4: Job crafting will mediate the positive effect of artificial intelligence usage on innovative behavior.

Creative self-efficacy refers to individuals’ perception of their capacity to generate creative outputs (Tierney & Farmer, 2002). Bandura (1977) emphasized that high self-efficacy is a crucial aspect of creativity and knowledge acquisition. Tierney and Farmer (2004, 2011) found that individuals with high creative self-efficacy exhibit a strong propensity for innovation, which enables them to leverage available resources by using AI to perform routine tasks such as repetitive activities, compliance, and system processing. They proactively manage their work schedules and tasks, actively engage in learning, and apply new knowledge (Yang & Lee, 2019). These proactive behaviors would attract such workers to the ability of AI technology to enhance autonomy, which would subsequently fuel their enthusiasm for job crafting and ultimately foster innovative behaviors. In contrast, individuals with low self-efficacy are not interested in innovating and do not seek to enhance their abilities or acquire work-related resources. Therefore, these individuals would perceive the adoption of AI as a daunting challenge and lack enthusiasm for innovation. Thus, we proposed the following hypothesis:

Hypothesis 5: Creative self-efficacy will positively moderate the relationship between artificial intelligence usage and job crafting.

In a strengths-based psychological climate, organizations seek to elevate employee engagement by introducing initiatives aimed at developing their strengths, which significantly influences their attitudes and behaviors (Chang et al., 2022; van Woerkom et al., 2016). In such an atmosphere, employees feel valued and recognized for their unique contributions, which enhances their sense of self-worth, respect, and competence (van Woerkom & Meyers, 2015). Cultivating employees’ talents and strengths in a supportive and open environment also allows organizations to create further growth and development opportunities (i.e., gain spirals). According to the JD-R model, job resources characterized by gain spirals generate additional resources that enhance positive employee outcomes through psychological motivation (Bakker & Demerouti, 2007; Hakanen et al., 2008). With these additional resources, employees are more likely to engage in job-crafting activities, such as increasing job resources and enhancing job demands, to promote their personal growth and development, which can improve their innovation performance (Afsar et al., 2019). Moreover, a strengths-based psychological climate can amplify the positive effects of AI on innovation performance, enabling employees to better utilize their strengths, engage in job crafting, and achieve higher levels of creativity and problem solving. Thus, we proposed the following hypothesis:

Hypothesis 6: A strengths-based psychological climate will positively moderate the relationship between job crafting and innovation behavior, such that this type of climate will enhance the indirect effect of artificial intelligence usage on innovation behavior.

 

Figure 1 depicts our research model.

To efficiently gather accurate data from a large pool of respondents within a restricted timeframe, we conducted a web survey over 4 weeks in December 2023, targeting Chinese employees with expertise in AI. Surveys were distributed through a well-known Chinese questionnaire distribution platform (https://www.wjx.cn/), which employs a systematic and randomized approach to ensure the reliability and robustness of the collected data. Prior to the survey, we obtained informed consent from all participants, with clear communication that participation was voluntary and that they could withdraw at any time without consequence. Although the survey was voluntary, we offered a small incentive valued at RMB 9 (USD 1.25) to acknowledge the time and effort of the participants. Following a rigorous review for completeness and accuracy, 519 responses out of the 640 surveys we collected were deemed valid and included in the analysis (effective rate of response = 81.09%). Table 1 contains the demographic statistics of the participants.

The measures used in this investigation were slightly modified from prior studies. To ensure that the original meaning of the items was preserved, we employed professional translators to convert all scales into Chinese and then back into English for use in this study. Items were rated using a 7-point Likert scale ranging from 1 = strongly disagree to 7 = strongly agree.

Innovative behavior was measured using nine items divided evenly across three dimensions (Janssen, 2001): idea generation (e.g., “I create new ideas for improvements”), idea promotion (e.g., “I mobilize support for innovative ideas”), and idea realization (e.g., “I transform innovative ideas into useful applications”).

 

We measured AIU using three items adapted from Tang et al. (2022). A sample item is “I use artificial intelligence to carry out most of my job functions.”

 

Job crafting was measured using 21 items across three dimensions adapted from Tims et al. (2012). Seven items were deleted, as their factor loadings were less than .60. Increasing structural job resources is represented by four items (e.g., “I try to develop my capabilities”), increasing social job resources is assessed with five items (e.g., “I ask my supervisor to coach me”), and increasing challenging job demands is represented by five items (e.g., “When an interesting project comes along, I offer myself proactively as a project coworker”).

 

We measured creative self-efficacy using four items adapted from Tierney and Farmer (2002). A sample item is “I have confidence in my ability to solve problems creatively.”

 

Strengths-based psychological climate was assessed using five items adapted from van Woerkom and Meyers (2015). A sample item is “I am facilitated to recognize my strengths.”

 

In addition to controlling for the demographic variables of gender, age, level of education, position, and work experience, we controlled for personal initiative, as previous studies (e.g., Mustafa et al., 2023) have found that employees’ proactive behavior can influence innovative behavior. We measured personal initiative using six items from Li et al. (2014). A sample item is “I am particularly good at realizing ideas.”

We conducted a hierarchical regression analysis to investigate the incremental validity of key predictive factors in our model and tested for mediation and moderation effects using the PROCESS macro version 4.1 for SPSS 27.0. In addition, we employed Amos 26.0 for structural equation modeling to further ensure the validity of our measurement model.

We evaluated the convergent validity of the measures through confirmatory factor analysis using Amos 26.0. To ensure adequate reliability and convergent validity, composite reliability and Cronbach’s alpha values must exceed .70, factor loadings must be above .50, and average variance extracted values must surpass .50 (see Table 2). All of the measurement model’s fit indices were acceptable, χ² = 881.677, df = 764, p < .001; χ²/df = 1.154. We also conducted a discriminant validity test (see Table 3). The average variance extracted values exceeded all correlations with related constructs, indicating that each construct under investigation exhibited uniqueness.

We employed regression analysis utilizing Model 4 of the PROCESS 4.1 macro for SPSS 27.0. As shown in Table 4, the correlation coefficients between the variables were below .70, thus ruling out the possibility of multicollinearity.

We conducted mediating tests according to the four conditions proposed by Baron and Kenny (1986) to examine the mediating effects. First, Model 1 in Table 5 shows that AIU had a significant and positive effect on innovation behavior, supporting Hypothesis 1. Second, according to Model 6, AIU had a significant and positive effect on job crafting; thus, Hypothesis 2 was supported. The results of Model 3 show that job crafting had a significant and positive effect on innovative behavior, supporting Hypothesis 3. Finally, comparing the results of Models 1, 3, and 6, we found that the effect of AIU on innovative behavior decreased from .356 (p < .001) to .272 (p < .001), indicating that job crafting served as a partial mediator of the influence of AIU on inventive behavior. To further examine the mediating effect of job crafting, we used the bootstrapping approach via Model 4 of the PROCESS macro. The results also showed that job crafting partially mediated the association between AIU and innovative behavior. As the indirect effect did not include 0, the result was significant (see Table 6). Thus, Hypothesis 4 was supported.

We used regression models to examine the moderating effects of creative self-efficacy and a strengths-based psychological climate. As shown in Table 5, after controlling for demographic variables, the moderating effect of creative self-efficacy on the link between AIU and job crafting was significant (see Model 5), and the moderating effect of a strengths-based psychological climate on the link between job crafting and innovative behavior was significant (see Model 4). As shown in Figure 2, the slope value of the regression straight line between AIU and job crafting was larger under conditions of high creative self-efficacy than under conditions of low creative self-efficacy, indicating that creative self-efficacy positively moderated the relationship between AIU and job crafting. As shown in Figure 3, the slope value of the regression line between job crafting and innovative behavior was higher for a high strengths-based psychological climate than a low strengths-based psychological climate. In addition, the promotive effect of job crafting and innovative behavior was stronger, indicating that a strengths-based psychological climate positively moderated the relationship between AIU and innovative behavior.

 

We tested the two-stage moderated mediation using Model 21 of the PROCESS macro. The results in Table 7 show that with high creative self-efficacy and a high strengths-based psychological climate, the indirect effect of AIU on innovative behavior through job crafting was significant. Thus, Hypotheses 5 and 6 were supported.

This study examined the internal impact mechanism of AIU on employees’ innovative behavior based on the JD-R model, with the following key findings: (a) job crafting partially mediated the association between AIU and innovative behavior; (b) creative self-efficacy enhanced the relationship between AIU and job crafting, such that there was a stronger impact on job crafting when employees possessed a higher level of creative self-efficacy; and (c) a strengths-based psychological climate played a positive moderating role in the relationship between job crafting and innovative behavior, such that there was a stronger positive effect in a stronger strengths-based psychological climate.

 

This study aligns with various theoretical perspectives regarding the impact of AIU on employee behavior, as well as offering fresh insights for future investigations into the relationship between AIU and innovative behavior. Although prior research has highlighted the significant impact of organizational interventions on the implementation and integration of AI systems (Seeber et al., 2020), the specific effect of these interventions on workers’ use of AI remained poorly understood. This study addressed this research gap by introducing job crafting as a mediator based on the JD-R model and demonstrating the moderating effect of a strengths-based psychological climate on the indirect relationship between AIU and innovative behavior through job crafting, highlighting the specific circumstances in which the positive effect of AIU on innovative behavior can be enhanced. Moreover, we found that creative self-efficacy enhanced the pathway for gaining job crafting through AIU. These discoveries contribute to the existing research on organizational interventions, expanding understanding of the significance of such interventions in facilitating collaboration between employees and AI.

Our findings have several practical implications. Although numerous firms have devoted significant time, energy, and money to incorporating AI into their current work environment, many have experienced difficulties or outright failure (Fountaine et al., 2019). Organizations must recognize the internal impact mechanism of AIU on innovative behavior, particularly whether it operates via the job-crafting path. On the basis of our findings, we recommend that managers implement strategies that promote workers’ job crafting, for example, by enhancing their creative self-efficacy. Furthermore, our results suggest that the circumstances in which AIU positively impacts innovative behavior may be enhanced by a strengths-based psychological climate. Therefore, we recommend that companies implement programs that develop and utilize employees’ strengths to help maximize their potential and encourage positive attitudes and behaviors, as these practices will help to establish an environment conducive to continuous learning and proactive AIU.

This research has limitations. Although the initial research model was based on the JD-R model, several other aspects may influence the correlation between AIU and inventive behavior. This study also analyzed a single mediator, job crafting, yet several other potential mediators could influence their interaction. Therefore, subsequent investigations could explore additional mediators, such as perceived ease of use and perceived usefulness from the technology acceptance model, and investigate other pathways. Furthermore, all study participants were from China, which limits the generalizability of our results. As this topic has significant potential for further investigation in various contexts, further research that tests this model in other cultures could broaden the applicability of our findings.

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