The proactive motivation model, introduced by Parker et al. (2010), explains how individuals cultivate proactive behaviors through a motivated, deliberate and goal-directed process. This process is governed by three key motivational states: “Can Do,” “Reason To,” and “Energized To.” Engaging in proactive behavior often entails setting ambitious goals to challenge the status quo, necessitating a strong belief in one’s ability to succeed (“Can Do”). First, the “Can Do” motivation reflects employees’ confidence in their abilities and their assessment of task feasibility, primarily rooted in self-efficacy and control appraisals (Bandura, 1997; Frese and Fay, 2001). Second, the “Reason To” motivation emphasizes the intrinsic and extrinsic drivers behind employees’ proactive pursuit of specific goals. This motivation is grounded in self-determination theory, intrinsic motivation and integrated motivation (Deci and Ryan, 2000). Finally, the “Energized To” motivation underscores the role of positive emotional states, such as enthusiasm and excitement, in fuelling employees’ proactive behaviors.

The proactive motivation model elucidates how digital leadership shapes employees’ innovation performance via job crafting, highlighting the distinct motivational mechanisms it provides. As a vital organizational resource, digital leaders act as key drivers of motivation by strengthening employees’ “Can Do” motivation (offering technological support and training), stimulating “Reason To” motivation (enhancing autonomy and instilling new values) and fostering “Energized To” motivation (cultivating an innovative environment and facilitating team collaboration) (Sun et al., 2024; Alvarez-Torres and Schiuma, 2022; Hanelt et al., 2021). These three motivational states interact synergistically to drive employees’ proactive behaviors (i.e., job crafting) within digital transformation, ultimately boosting their innovation performance.

The new generation of young knowledge workers make up a larger proportion of the organizational workforce; they are more self-centered than their predecessors, giving more attention not only to pay, but also to work experience and the value of work for themselves and the community; they also aspire to perform meaningful work. They have started to realize that within the prescribed framework of work, they can take the initiative to make some changes to their original work according to their needs, so that the work is more in line with their preferences and strengths, and better aligns with their skills, values, and motivations (Rousseau et al., 2006). Against this background, changing the traditional top-down approach to work and the behavior of employees in redesigning their work based on their initiative and spontaneity has received increasing attention from organizational managers and researchers (Kulik et al., 1987).

The concept of job crafting was first introduced by Wrzesniewski and Dutton (2001), who defined it from a role perspective as employees proactively changing their job boundaries and making substantive or cognitive changes in tasks and relationships to better match their work with their skills, preferences, and values, thereby meeting their individual needs. They analyzed job crafting based on three dimensions. Task remodeling refers to the initiative of an individual to alter the number, scope, and type of tasks at work. Relational remodeling refers to the initiative of an individual to modify the quality and quantity of interpersonal interactions at work. Cognitive remodeling refers to the initiative of individuals to alter their original views and ideas at work (Wrzesniewski and Dutton, 2001).

As job crafting is an employee initiative, it is described as a personalized, bottom-up, and proactive design method, as opposed to the top-down and “one size fits all” work design methods initiated by enterprises (Grant and Parker, 2009; Parker, 2014). Through job crafting, employees can change the task, cognitive, and relational boundaries of their work (Petrou et al., 2018). Employees who change any of these elements change the job design and social context of their work. These behaviors change the meaning of work and the identity of an employee at work (Lyons, 2008). Therefore, this study adopted the definition of job crafting provided by Wrzesniewski and Dutton and examined each of the three dimensions.

Job crafting has important characteristics. First, it is self-focused and designed to benefit the individual for whom job crafting occurs (Tims et al., 2012; Wrzesniewski and Dutton, 2001). Second, it is a self-directed, bottom-up behavior that is an employee-initiated self-transformation (Tims and Bakker, 2010; Wrzesniewski and Dutton, 2001). Third, job crafting behaviors are sustainable and not one-off or temporary changes (Bruning and Campion, 2018; Wrzesniewski and Dutton, 2001). Finally, the purpose of individual work redesign is to achieve a match between the individual and the environment, thereby enhancing work meaning, motivation, wellbeing, and performance (Tims et al., 2013; Wrzesniewski and Dutton, 2001).

With the rise in digital technologies and digital economy, most enterprises that compete globally have become digital companies (Abollado and Shehab, 2018; Barchiesi and Colladon, 2021), and traditional processes and business models are changing (Holzmann et al., 2020; Wesseling et al., 2020). The digital age requires new capabilities to create a sense of digital urgency to drive this vision and implement appropriate leadership models (Kohnke, 2017). Digital leadership is defined as a leader’s ability to integrate competencies with digital technologies to generate organizational value (Rudito and Sinaga, 2017). Digital leaders are pivotal in steering organizational digital transformation. They confer competitive advantages to organizations by adopting a strategic perspective and flexibly adapting to diverse leadership styles, including transformational and transactional leadership (Sow and Aborbie, 2018). The leadership style of digital leaders is fast-paced, team-centric, collaborative, cross-functional and highly innovation-focused (Oberer and Erkollar, 2018). Strong digital leaders will possess new skills to help their organization effectively navigate the uncertainty and complexity surrounding them, assisting the business in defining a digital strategy and enhancing business performance (de Araujo et al., 2021).

Employee innovation performance results from innovation in the work of employees and refers to the process in which employees proactively propose, promote and implement new ideas, methods or processes within the organization (Janssen, 2000). Digital technology brings new business models and has a borderless impact on innovation (Zhu, 2015). Digital leaders play an important role in nurturing the creativity of employees and possess creativity and innovative thinking to turn ideas into reality for the enterprise. Digital leaders drive innovation and digitisation in enterprises, where information and knowledge are shared rapidly, so that every employee can access, process and apply them (Wasono and Furinto, 2018). Digital leaders demonstrate adaptability and openness to new ideas and technologies (Bennis, 2013), enabling rapid dissemination of beneficial new concepts generated by employees and expeditious application of new products and ideas.

Digitalization has transformed the working environment and needs of enterprises. Leaders no longer merely assign tasks to subordinates and monitor their completion; they also create space for employees to realize their creative potential through collaboration and continuous learning (Bass and Riggio, 2006). Digital leaders should empower employees to engage in innovative activities and create an innovative atmosphere (Zhu et al., 2022) to promote the implementation of innovative ideas for achieve innovative outcomes, thereby enhancing employee innovative performance. Existing research indicates that digital leadership enhances innovation performance (Benitez et al., 2022), strengthens service innovation capability (Brunner et al., 2023) and promotes open innovation (Fatima and Masood, 2024). Furthermore, digital leadership positively moderates the relationship between digital technology utilization and innovation capability (Borah et al., 2022). Hence, we propose that digital leadership positively influences employee innovation performance.

Enterprises facing a rapidly changing external environment and increasing complexity in internal tasks need more flexibility to manage complex and challenging team tasks. It is unlikely that one person can do this (Edelmann et al., 2020). Therefore, enterprises need digital leaders who encourage employees to participate in the overall process. This form of leadership enables employees to express different ideas (Abbasi et al., 2020). In addition, the increasing number of knowledge-based employees in enterprises is changing the employee hierarchy (Nurhidayati and Zaenuri, 2023). Most of them believe that they can adjust the order, manner or scope of their tasks according to the specifics of their work, rather than just carrying out their duties per the inherent requirements of enterprises (Berg et al., 2010). Job crafting breaks the previous passive management approach, where employees proactively adjust job tasks, cognition and relationships to adapt to the new environment.

As digital leadership and digital technologies change how employees work and the scope of their work (Trenerry et al., 2021), employees need to change their traditional ways of working and the work processes, adjust the definition of digital work boundaries and perform digital work tasks effectively for adapting to digital transformation (Davison and Ou, 2017). Digital leadership inspires employees to explore emerging technologies and embrace innovative problem-solving approaches (Dery et al., 2017). Digital leaders are responsible for providing essential resources, implementing structural changes and guiding employees in adapting to evolving work practices (Trenerry et al., 2021; Chanias et al., 2019; Selimović et al., 2021). In line with the proactive motivation model, digital leadership fosters employees’ self-efficacy and controls appraisals by offering technological support and resources, thereby reinforcing their “Can Do” motivation and bolstering their confidence in job crafting. Employees can achieve task crafting by changing the quantity, type, scope or method of digitized tasks, thereby aligning their interests and motivations more closely with the requirements of their jobs. Digital leaders are adept at using digital resources, such as digital devices and services, to establish relationships among organizational members (Van Wart et al., 2019). Employees can build and maintain quality relationships by increasing the frequency of interactions with others in the workplace. High-quality collegiality promotes the exchange of experiences in using digital technologies to help each other adapt to digital transformation and enhancing employees’ positive emotions, thereby strengthening their “Energized To” motivation and changing employee relations. Digitalisation is not just a technological change, but it is a revolution in cognitive thinking. The attitude of employees toward digital technology affects their use of digital technology and job performance (Khin and Ho, 2019). Digital leaders have a strong willingness to engage in role learning and role sensitisation to improve their digital skills and literacy, articulate knowledge related to digital technologies, develop employee trust in the virtual environment and improve organizational resilience to the digital environment (Larjovuori et al., 2016). Changing employee perceptions and value judgements about the relationship between digital technologies and digital work demands as well as personal preferences can increase positive perceptions of digital technologies, and stimulate intrinsic and integrated motivation, thereby strengthening their “Reason To” motivation (Deci and Ryan, 2000) and fundamentally changing the cognitive attitudes of employees toward digital technologies and digital work demands (Kane et al., 2019).

Employee job crafting can also positively impact employee innovation performance. First, employee involvement in job crafting can stimulate creativity, job satisfaction, positive job identity, work-related wellbeing and job performance (Tims et al., 2012). An increase in work resources, engagement, and satisfaction enhances employees’ intrinsic motivation, encouraging them to engage in innovative behaviors to perform their jobs efficiently (Demerouti et al., 2015). Second, job crafting reshapes the content and boundaries of work. Employees gain more opportunities to identify new challenges and contradictions in their work environment, which can spark innovative ideas (Wang, 2021). Additionally, job crafting transforms employees’ perceptions of their work, helping them find meaning in their roles (Wrzesniewski et al., 2013) and influencing their innovative performance (Tuan, 2018). Finally, job crafting also involves changing relationships by expanding interpersonal resources and interactions. These resources support employees in promoting new ideas and foster an environment that encourages innovative activities (Afsar et al., 2019). Therefore, we propose the following hypothesis:

Person–job fit is an important aspect of person–environment fit (Yu, 2016). Person–job fit refers to the degree of fit between the person and the tasks and goals of the job (Caldwell and O’Reilly, 1990). Person–job fit can be divided into two broad categories: demands–abilities (D–A) fit and needs–supplies (N–S) fit (Edwards, 1991). D–A fit refers to the match between environmental (i.e., job) demands and personal abilities, focusing on whether employees can meet the requirements of their job positions. N–S fit refers to the fit between the needs of a person and the ability of the environment to meet those needs, and focuses on how well the organization meets the needs of its employees and whether, for example, the reward offered by the company matches the contribution of the employee (Cable and DeRue, 2002).

Based on Holland’s (1996) theory of vocational interests, the alignment between an individual’s vocational interests and their work environment is a critical factor influencing work behaviors and performance. The theory suggests that when employee vocational interests align with their work environment, they are more likely to demonstrate positive work attitudes and engage in effective behaviors, ultimately improving performance. Therefore, with a high level of person–job fit, the working styles, skills, and competencies of employees after job crafting will be better aligned with the needs of the organization. When employees feel that their new ways of working, skills, and abilities can be activated in the work environment, they will achieve higher work performance (Lee et al., 2008). Second, a higher person–job fit is conducive to higher job satisfaction and organizational commitment as well as lower employee turnover (Saks and Ashforth, 1997). In such an atmosphere, employees involved in job crafting will gain a strong sense of job security, which will be more conducive to generating innovative ideas and implementing them, thus increasing innovative performance. Finally, a higher person–job fit leads to a better match between the cognitive abilities of the employee and the characteristics of the job itself, thus increasing the likelihood of the employee showing innovative behavior (Huang et al., 2019). Therefore, person–job fit affects the relationship between job crafting and employee innovation performance, resulting in the following hypothesis.

Prior studies have highlighted that person-job fit enhances proactive behavior effectiveness by aligning employees’ competencies with job demands (Cable and DeRue, 2002; Huang et al., 2019). H2 and H3 together form a mediation model with moderating, which is based on the moderating mediator inference method (Edwards and Lambert, 2007). Person–job fit moderates the relationship between job crafting and employee innovative performance. This study further predicted that person–job fit positively moderates the mediating effects of digital leadership on employee creativity via job crafting. Therefore, the following hypothesis is proposed.

Figure 1 shows the theoretical model developed in this study.

Data for this study were collected from March 2024 to April 2024. The research sample mainly included employees from manufacturing firms that are implementing digital transformation in the Shandong province of China. Among the 20 companies approached a total of 6 entities accorded their consent to participate in the study. A formal survey was conducted using a web-based questionnaire. Approval was guaranteed by the relevant human resource heads of the companies, who willingly participated in the surveys. This study separated the independent variables from the dependent variables in survey waves to mitigate common method bias (Podsakoff et al., 2012). The questionnaire survey comprised two stages: During Time 1 (T1), employees completed questionnaires regarding a predictor variable (digital leadership), a mediating variable (job crafting), and demographic variables (age, gender, education, and seniority). After a month, during Time 2 (T2), the same participants completed questionnaires regarding a moderating variable (person–job fit) and a dependent variable (employee innovative performance). To match the responses obtained during T1 and T2, participants were asked to enter the last four digits of their ID numbers in the questionnaires.

A total of 455 questionnaires were distributed in this study and 370 questionnaires were collected. After filtering out incomplete responses and removing outliers, 306 valid questionnaires remained with a response rate of 67.3% In terms of the sample distribution, 187 (61.11%) respondents were males and 119 (38.89%) were females. Most of the respondents were aged between 26 and 35 years (35.62% of the total survey population). The majority possessed a bachelor’s degree (43.46% of the total survey population) and seniority ranged mostly between 4 and 6 years (27.45% of the total survey population) (Table 1). The sample size was adequate to analyze the model. Using the rule of thumb of (no. of items in questionnaire X 5 = 48 X5) which is 240 (Faul et al., 2009). Therefore, the sample of 306 is adequate because this is more than the required number of 240 responses.

The scales used in this study were mainly derived from mature scales used in the academic community, with proven reliability and validity in domestic and foreign studies. All scales used the 5-point rating like the Likert scale, where 1 means strongly disagree and 5 means strongly agree. The specific measurement of each variable is as follows.

Digital leadership was measured with the 17-item scale adapted by Wang et al. (2022) based on the measure developed by Zhu (2015), containing five dimensions: creativity, thinking and inquisition, curiosity, deep knowledge, and global vision and collaboration. However, consistent with our contextual theoretical framework, we considered the scale as a single construct measuring organizational empowerment. The items were as follows: “The top leader of our company has the capability to implement the learning and digital capability,” “The top leader of our company has creativity and an innovative mindset,” etc. Cronbach’s alpha for this scale was 0.946.

Job crafting was measured with the 15-item scale developed by Slemp and Vella-Brodrick (2013), containing 3 dimensions: task crafting, cognitive crafting, and relational crafting. The items are as follows: “Introduce new approaches to improve your work,” “Think about how your job gives your life purpose,” and “Make an effort to get to know people well at work.” Cronbach’s alpha values for this scale were 0.860, 0.879, and 0.897, respectively, for the three dimensions.

Employee innovative performance was measured with the 10-item scale developed by Janssen (2000). The items are as follows: “I will provide new ideas to improve the current situation,” “I can transform innovative ideas into reality application” etc. Cronbach’s alpha for this scale was 0.921.

Person–job fit was measured with the 6-item scale developed by Cable and DeRue (2002), The items are as follows: “There is a good fit between what my job offers me and what I am looking for in a job,” “The attributes that I look for in a job are fulfilled very well in my present job” etc. Cronbach’s alpha for this scale was 0.892.

To assess the reliability of the constructs, we first calculated Cronbach’s alpha to test the reliability. The Cronbach’s alpha value of each construct ranged from 0.860 to 0.946 (see Table 2), which was greater than the recommended threshold value of 0.70, indicating adequate reliability (Gliem and Gliem, 2003).

Second, CFA was conducted to calculate the overall measurement model’s convergent and discriminant validity. The model fulfilled (Hair et al., 2019) criteria for convergent validity, with factor loadings ranging from 0.687 to 0.906 (all exceeding 0.6; p < 0.001). Additionally, CR values ranged from 0.899 to 0.952 (surpassing 0.7), and AVE values ranged from 0.538 to 0.708 (exceeding 0.5). AVE, CR, and Cα-values for each construct are presented in Table 2.

For discriminant validity, compared to other competition models, the theoretical six-factor model (digital leadership, task crafting, cognitive crafting, relational crafting, person–job fit, and employee innovative performance) had a better fit to the data [χ²/df = 1.152, (CFI) = 0.980, (TLI) = 0.979, (RMSEA) = 0.022, and (SRMR) = 0.046 (see Table 3). The CFA results showed that the theoretical six-factor model had satisfactory discriminant validity.

Furthermore, the heterotrait–monotrait ratio of correlations (HTMT) criteria were employed to test the discriminant validity. Different recommendations exist for confirming the HTMT criterion, with the conservative criterion suggesting that the HTMT value should be below 0.85 (Tabri and Elliott, 2012), and the classical criterion indicating that the HTMT value should be below 0.90 (Henseler et al., 2015). The HTMT ratio table demonstrates that all values fall within the range of 0.083-0.554, which is lower than the specified criterion, thus confirming discriminant validity (Table 4).

Common method variance (CMV) may affect the empirical results because our study data were collected through self-report questionnaires. Podsakoff et al. (2003) showed that procedural and statistical techniques can be adopted for CMV. In the statistical technique, the possibility of common method bias was tested using Harman’s one factor test (Podsakoff and Organ, 1986). A principal component factor analysis with varimax rotation was used on the items of digital leadership, job crafting, P–J fit, and employee innovative performance. This result revealed multiple factors with eigenvalues greater than 1. The first factor accounted for 19.35% ( < 50%) loading, which proved the absence of CMV (Woszczynski and Whitman, 2004). Further, we conducted the unmeasured latent method factor (Podsakoff et al., 2003), to test CMV.

A comparison of the latent method factor model (χ²/df = 1.059, CFI = 0.993, TLI = 0.992, RMSEA = 0.014, SRMR = 0.037) and the six-factor model (χ²/df = 1.152, CFI = 0.980, TLI = 0.979, RMSEA = 0.022, SRMR = 0.046) indicated no significantly changes in CFI (Cheung and Rensvold, 2002). Thus, CMV was not a major problem for the data (Podsakoff et al., 2003).

The descriptive statistics and correlation analysis results presented in Table 5 indicate that digital leadership is positively correlated to task crafting (r = 0.398**), cognitive crafting (r = 0.429**), relational crafting (r = 0.161**), and employee innovative performance (r = 0.414**). Task crafting and cognitive crafting are positively correlated to employee innovative performance (r = 0.412** and r = 0.495**). The correlation between the key variables supports our hypotheses on the direct and indirect effects of digital leadership on employee innovative performance.

Before testing the structural model, we first examined the R²-value, which indicates the model’s predictive power by showing the endogenous variable’s variance that the exogenous variables can explain. The R²-value for EIP (0.337) indicates that all the constructs combined explain 33.7% of the variance in EIP. The R²-values for the other variables were TC (0.166), CC (0.193), and RC (0.028). Further, we checked the Q²-values to assess the predictive relevance values generated by the variables. The Q²-values for EIP (0.171), TC (0.156), CC (0.184), and RC (0.021) were above 0, which means that the model has predictive relevance.

To examine the hypotheses, bootstrapping was carried out using SmartPLS 4.1 with 5000 subsamples based upon percentile bootstrapping with a two-tailed test type and a significance level of 0.05. The PLS-SEM bootstrapping approach statistically determined the structural mode coefficients representing the hypothesized relationships.

Figure 2 and Table 6 portray the results of the structural path analysis. The results show that digital leadership has a significant positive impact on employee innovative performance (B = 0.188; P < 0.001; 95% CI: 0.084–0.291), supporting H1. Further, the indirect effect through task crafting (B = 0.082; P < 0.001; 95% CI: 0.041–0.132) and the indirect effect through cognitive crafting (B = 0.148; P < 0.001; 95% CI: 0.100–0.205) are significant. The indirect effect through relational crafting is insignificant (B = 0.004; P = 0.670; 95% CI: -0.014–0.024). Therefore, H2a and H2b are supported, whereas H2c is not.

We examined the moderating effect of person–job fit on the relationship between job crafting (task crafting, cognitive crafting, and relational crafting) and employee innovative performance. As shown in Figure 3 and Table 7, the interaction between cognitive crafting and person–job fit is significantly and positively related to employee innovative performance (B = 0.111; P < 0.05; 95% CI: 0.018–0.208), indicating that person–job fit positively moderates the relationship between cognitive crafting and employee innovative performance. Hence, H3b is supported. The interaction between task redesign, relational crafting, and person–job fit is not significantly related to employee innovative performance (B = -0.017; P = 0.727; 95% CI: -0.112–0.084, B = 0.035; P = 0.496; 95% CI: -0.070–0.134). Hence, H3a and H3c are not supported. According to the suggestions of Toothaker (1994), this study further analyzed the moderating effect by testing the simple slopes at high and low levels of person–job fit, and the moderating effect diagram was drawn (see Figure 4).

As it was concluded that H3a and H3c were not valid when evaluating the moderating effect, H4a and H4c were directly rejected and only H4b was tested. The index of moderated mediation (Hayes, 2015) presented in Table 8 indicates the total moderated mediation effect. The effect was significant (B = 0.062; P < 0.01; 95% CI: 0.026–0.102), indicating that the indirect effect of digital leadership on employee innovative performance through cognitive crafting was moderated by person–job fit.

The conditional indirect effect on the values of the moderator was calculated, namely the mean, one standard deviation above, and one standard deviation below. The results are shown in Table 8. The model of digital leadership influencing employee innovative performance via cognitive crafting shows that at low levels of person–job fit, the mediating effect of cognitive crafting is significant (B = 0.112; P < 0.01; 95% CI: 0.044–0.181). At high levels of person–job fit; the mediating effect of employee cognitive crafting is significant (B = 0.229; P < 0.001; 95% CI: 0.157–0.310). The analysis results show that the higher the person–job fit, the stronger the mediating role of cognitive crafting in the relationship between digital leadership and employee innovative performance, thus supporting H4b.

First, this study examined the relationship between digital leadership and employee innovative performance. Existing literature shows that the impact of digital leadership on employees is mainly in terms of performance (Turyadi et al., 2023), capability (Retnowati and Santosa, 2023), and creativity (Zhu et al., 2022). This study is a step toward filling this gap by exploring the relationship between digital leadership and employee innovative performance. Results show that digital leadership does improve employee innovative performance, thus enriching the literature on the antecedents of employee innovative performance.

Second, this study is grounded in the Proactive Motivation Model (Parker et al., 2010) and develops a mediation model linking digital leadership, job crafting, and innovative performance from a role motivation perspective. This model explains how digital leadership influences employee innovative performance through job crafting and highlights the unique motivational mechanisms provided by digital leadership. Few studies have focused on the mechanisms by which digital leadership mediates job crafting. Based on the foundational research of Wrzesniewski and Dutton (2001), this study categorized job crafting into three dimensions and examined the mediating role of each of these dimensions in the relationship between digital leadership and employee innovation performance. Results show that task crafting and cognitive crafting mediate the relationship between digital leadership and employee innovative performance, but relational crafting does not show the same effect. This finding provides a more nuanced view of job crafting, revealing the differential impact of different types of job crafting on employee innovation performance.

Third, this study used person–job fit as a moderating variable in the relationship between job crafting and employee innovation performance. A high person–job fit enhanced the positive impact of cognitive crafting on innovation performance but had no significant effect on the outcomes of task crafting and relational crafting. Further, the results highlight the important role of person–job fit in organizational behavior, suggesting that person–job fit may contribute to the positive impact of cognitive crafting on innovation performance by enhancing the intrinsic motivation of employees. This study also revealed that person–job fit moderates the mediating role of cognitive crafting in the relationship between digital leadership and employee innovative performance. It deepens the understanding of the boundaries of digital leadership affecting employee innovative performance through cognitive crafting.

First, in the digital economy, employee innovation is a key enabler of business growth. Companies and managers should recognize the unique ability of digital leadership to foster innovation. Managers can strive to improve their digital literacy and skills in various ways, such as deliberate learning and active participation in relevant trainings and forums.

Second, the findings suggest that task crafting and cognitive crafting mediate the relationship between digital leadership and employee innovation performance. Overall, although job crafting is an employee-initiated activity, it can be influenced to some extent by managers. Specifically, managers can enhance the positive impact of digital leadership on employees by focusing on the task and cognitive aspects of job crafting and providing opportunities for employees to engage in these activities. Therefore, managers should prioritize the creation of flexible work environments that allow for task autonomy and support and encourage employees to cognitively redefine their work roles. For example, managers should encourage employees to set personal development goals and regularly seek their feedback on work-related matters. This alignment of work tasks, personal values, and career aspirations can in turn enhance employee innovation performance.

Third, the results indicate that person–job fit positively moderates the mediating effect of cognitive crafting in the relationship between digital leadership and employee innovation performance. A high person–job fit facilitates full exploitation of the benefits of cognitive crafting. Therefore, organizations should provide ongoing training and development opportunities to help employees improve their skills and maintain high levels of alignment with their evolving job roles. In addition, reward mechanisms encouraging employees to propose innovative solutions during the cognitive crafting process are essential. Involving employees in practical projects can enable them to apply the results of cognitive crafting and test their innovative ideas in real-life scenarios.

First, this study collected data at different time points to mitigate the issue of common method variance (CMV) and to somewhat capture the causal relationships between variables over time. However, as all variables were self-reported by employees, CMV could still be a concern. Future research should employ multi item point and multisource data collection methods to address this issue more robustly. Additionally, more rigorous experimental designs, such as matched-pair studies, longitudinal designs, and experimental methods, should be considered to strengthen the validity of the findings.

Second, this study confirms that task crafting and cognitive crafting mediate the relationship between digital leadership and employee innovation performance at the individual level. However, the study does not provide a thorough explanation for the non-significant mediating effect of relational crafting. This result may be attributed to the characteristics of the study sample, as manufacturing firms often feature highly centralized, multi-tiered structures. In these organizations, communication channels and collaboration rules are tightly controlled. While digital leadership facilitates cross-departmental collaboration, organizational structural constraints may restrict employee autonomy in relational crafting, potentially diminishing or even negating its impact on innovative performance. Future research could compare various organizational types or hierarchical structures and apply multilevel analysis to explore the varying effects of employees’ relational crafting on innovative performance.

Third, this study considers the moderating effect of person–job fit on the relationship between cognitive crafting and employee innovative performance, but it did not confirm its moderating effect on the relationship between task crafting and relational crafting with respect to employee innovative performance. However, person–job fit is only one of the boundary conditions for determining how job crafting affects innovative performance. In reality, many other factors may influence this process. Future research should explore the boundary conditions that influence the impact of job crafting on innovative performance from different perspectives to gain a more comprehensive understanding of this relationship.

Fourth, although this study used Janssen (2000) Innovative Work Behavior (IWB) scale, which was designed to capture key behaviors in the innovation process (e.g., idea generation, promotion, and implementation), the scale primarily emphasizes the process dimension of innovative behavior, rather than directly assessing innovation outcomes. While this measurement choice aligns with much of the literature (Anderson et al., 2014), it may not fully capture the conversion of innovative behavior into ultimate performance outcomes. Future research could integrate multi-source data (e.g., number of innovation projects, patent outputs, qualitative evaluations from supervisors or peers) or adopt longitudinal designs to track the lagged effects between behavior and outcomes, thereby systematically revealing the dynamic relationship between innovative behavior and performance.