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Abstract

Digital leadership, a hallmark of the digital era, is a novel leadership style that leverages digital technologies to empower organizations and drive transformation and innovation. Grounded in self-determination theory and person-environment fit theory, this study investigates the mediating mechanisms and contextual factors that explain how digital leadership influences employee innovation. We propose a sequential mediation model in which job crafting and innovation efficacy transmit this influence, and identify organizational innovation climate as a key moderator. An analysis of 312 valid questionnaires from employees in Chinese digital enterprises reveals that: (a) digital leadership positively influences employee innovation behavior; (b) job crafting and innovation efficacy partially mediate this relationship; and (c) job crafting enhances innovation efficacy, forming a sequential mediation chain between digital leadership and employee innovation. Furthermore, organizational innovation climate moderates the relationship between digital leadership and employee innovation. Based on these findings, we offer practical recommendations for stimulating employee innovation and fostering effective enterprise development.

Plain Language Summary

How does Digital Leadership Drive Employee Innovation? The Sequential Mediation of Job Crafting and Innovation Efficacy with the Moderating Role of Organizational Innovation Climate

Study Overview This research addresses a critical gap in leadership and organizational psychology by investigating how digital leadership—a transformative approach leveraging digital technologies—shapes employee innovation. Grounded in self-determination theory (SDT) and person-environment (PE) fit theory, we propose and test a dual-path model that identifies: 1) The sequential mediation pathway through job crafting and perceived innovation efficacy 2) The boundary condition imposed by organizational innovation climate Key Contributions - Identifies a novel mechanism linking digital leadership to innovation behavior via two sequential mediators (job crafting → innovation efficacy) - Validates organizational innovation climate as a critical contextual enhancer of digital leadership's effectiveness - Integrates SDT (autonomy support) and PE fit (environmental alignment) frameworks to explain digital-era leadership dynamics Empirical Evidence Analysis of 312 valid responses from digital enterprise employees reveals: 1) Digital leadership significantly predicts employee innovation behavior 2) Job crafting and innovation efficacy serve as partial mediators 3) A sequential mediation chain (digital leadership → job crafting → innovation efficacy → innovation behavior) 4) Organizational innovation climate positively moderates the leadership-innovation relationship.

Keywords

digital leadership employee innovation behavior job crafting innovation efficacy organizational innovation climate

Introduction

The digital era necessitates a fundamental evolution in organizational leadership, transitioning from traditional command-and-control models toward approaches centered on empowerment and motivation (Ready et al., 2020). In response to this paradigm shift, digital leadership has emerged as a critical competency, representing a new leadership paradigm that proactively leverages digital technologies to enhance organizational management and maintain competitiveness in the global digital economy. This leadership approach transcends conventional leadership boundaries by positioning leaders as both facilitators of digital transformation and cultivators of innovation culture (Sainger, 2018), thereby generating substantial interest among academics and practitioners alike. The growing significance of digital leadership is evidenced by the sustained expansion of research in this domain since 2015, with a particularly remarkable surge during the 2022 to 2024 period as organizations worldwide intensified their digital transformation efforts in the post-pandemic context (Zuriati et al., 2024). Notably, the research scope of digital leadership has expanded beyond its initial focus on information systems to encompass diverse sectors, including education, public governance, tourism, finance, and infrastructure. It employs varied methodological approaches, ranging from quantitative analysis to case studies (Zam et al., 2024). Through the strategic utilization of digital platforms and tools, this leadership style fosters open, flexible, and supportive work environments that are inherently conducive to innovation (Howell & Shea, 2015). While existing research has established valuable foundations by examining the direct impact of digital leadership on employee innovative behavior (e.g., Sacolick, 2017), current studies remain predominantly focused on direct-effect relationships. Consequently, the underlying mediating mechanisms through which digital leadership operates—the essential “black box”explaining how its influence is transmitted—remain inadequately understood and require further scholarly investigation.

Self-Determination Theory (SDT) posits that intrinsic motivation is a primary driver of innovative behavior and highlights the critical role leaders play in fostering this motivation (Ryan & Deci, 2000). As a modern leadership style, digital leadership is uniquely positioned to drive digital work and support innovation (Adie et al., 2022), as it aims to ignite and sustain employees’ intrinsic motivation for innovation. Grounded in SDT, we argue that digital leadership influences innovative behavior through several psychological pathways. First, it provides environmental support that fulfills employees’ core psychological needs for autonomy, competence, and relatedness, thereby facilitating the translation of innovative motivation into action (Brunner et al., 2023). Second, it encourages employees to proactively reshape their job content and goals—a process known as job crafting—which can lead to innovative activities (Chen et al., 2024). Third, by providing digital resources and innovation support, it helps build employees’ confidence in their innovative capabilities, thereby enhancing their innovation efficacy and promoting innovative behavior (Yücebalkan et al., 2018). Ultimately, by enhancing autonomy and competence, digital leadership motivates employees to reshape their work and strengthen their innovation efficacy, which collectively foster innovative behavior. This theoretical reasoning leads to our core research questions: Through what mechanisms does digital leadership influence employee innovative behavior? Specifically, do job crafting and innovation efficacy serve as sequential mediators in this relationship?

Furthermore, Person-Environment Fit Theory (French et al., 1974) suggests that digital leadership fulfills employees’ needs for innovative resources and behavioral stimulation by providing essential digital tools, platforms, and support (Schuster et al., 2023). A supportive organizational innovation climate is expected to strengthen this alignment, thereby amplifying the effectiveness of digital leadership. Consequently, this study also investigates the moderating role of the organizational innovation climate. Our research aims to elucidate the psychological and behavioral pathways linking digital leadership to employee innovation, while accounting for this key contextual factor. In doing so, this study seeks to advance the theoretical understanding of digital leadership and offer evidence-based strategies for fostering organizational innovation.

Theoretical Foundations and Research Hypotheses

The Impact of Digital Leadership on Employee Innovation Behavior

Employee innovative behavior encompasses the generation, promotion, and implementation of new ideas within a work role, group, or organization to benefit performance (Wu & Shi, 2007). Such behavior is pivotal for enhancing organizational performance, and leadership is widely recognized as a key determinant (Bass & Riggio, 2006). Digital leadership, defined as leveraging digital platforms to foster innovation and autonomy during technological transformation (Klus & Müller, 2021), is characterized by a creative vision (Zhu, 2015), digital acumen, and collaborative networking (Araujo et al., 2021). These distinctive competencies enable leaders to anticipate trends and play a critical role in driving employee innovation (Adie et al., 2022).

From the perspective of Self-Determination Theory (SDT), the satisfaction of the three innate psychological needs—autonomy, competence, and relatedness—is a fundamental driver of intrinsic motivation and innovative engagement (Vallerand, 2000). We contend that digital leadership is particularly effective in fulfilling these needs in the context of innovation. It provides autonomy by advocating for an open management philosophy, offering flexible work arrangements, and encouraging self-management, thereby allowing employees to pursue innovation according to their own ideas (Mihardjo et al., 2019). It fosters competence by deploying cutting-edge digital tools and platforms that provide the resources necessary to develop and execute innovative ideas, thereby enhancing employees’confidence in their innovative capabilities (Howell & Shea, 2015). Finally, it strengthens relatedness by utilizing digital communication and collaboration tools to build broader social networks and enhance team cohesion, offering social support that transcends traditional geographical or departmental boundaries (Araujo et al., 2021; Ryan & Deci, 2000). By satisfying these core psychological needs, digital leadership stimulates employees’ intrinsic motivation for innovation. Therefore, we hypothesize:

H1: Digital leadership positively affects employee innovative behavior.

The Mediating Role of Job Crafting

We propose that job crafting serves as a key mediator in the relationship between digital leadership and employee innovative behavior. Job crafting refers to the self-initiated actions employees take to reshape their job boundaries, aligning their knowledge, skills, and psychological resources with work demands (Meijerink et al., 2020). First, we argue that digital leadership promotes such job crafting. By introducing cutting-edge technologies and innovative work practices, digital leadership alters the work environment, creating both the necessity and the opportunity for employees to proactively adjust their tasks, methods, and skill sets (Zhu et al., 2022; Zhuang et al., 2024). This is achieved through several mechanisms: (a) providing advanced digital tools and infrastructure that enable new ways of working (Petry, 2018); (b) advocating flexible work models that increase autonomy and opportunities for role adjustment; and (c) fostering a culture of continuous learning that supports skill development and adaptation (Zeike et al., 2019).

Job crafting embodies employee autonomy, a crucial catalyst for innovation. As employees reshape their jobs, they bolster their innovative capabilities to confront digital challenges and intensify their motivation and willingness to innovate through knowledge and skill enhancement (Pradana & Suhariadi, 2020). Concurrently, job crafting empowers employees to utilize organizational resources more effectively, including technological, informational, and human support. This efficient use of resources lays the groundwork for innovative activities, increasing the likelihood of participation in innovation initiatives (Rafiq et al., 2023). Digital leadership ignites innovative thinking and encourages innovative behaviors in the workplace by prompting employees to autonomously tailor their job content and methods based on their individual circumstances (Zhu & Zhang, 2023). Drawing from this reasoning, the subsequent hypothesis is presented:

H2: Job crafting mediates the relationship between digital leadership and employee innovative behavior.

The Mediating Role of Innovative Self-Efficacy

We further propose that innovation efficacy serves as another mediating mechanism. Grounded in self-determination theory, innovation efficacy refers to an employee's belief in their capability to produce and execute innovative outcomes at work (Tierney & Farmer, 2002). This belief is a critical source of intrinsic motivation, directly influencing an individual's willingness to pursue and persist in challenging innovative tasks. We posit that digital leadership is instrumental in building this efficacy. First, by promoting the acquisition of new digital skills, it directly enhances employees' perceived competence to innovate (Sun et al., 2024). Second, by providing essential resources for innovation—such as technical tools, data, and experimental platforms—it equips employees with the means to succeed, thereby strengthening their belief in their abilities. Third, through digital platforms that facilitate rapid feedback and recognition, it affirms employees’ innovative efforts and achievements, reinforcing their sense of competence and belonging (Yücebalkan et al., 2018).

We further contend that higher innovation efficacy subsequently leads to increased innovative behavior. Extensive research supports the direct, positive influence of innovation efficacy on employees’ engagement in innovative activities (Grosser et al., 2017). Employees with strong efficacy beliefs are more likely to view challenges as opportunities, exert greater effort, and persist in the face of obstacles when pursuing novel ideas. While digital leadership creates a supportive environment, it is the enhanced personal belief in one’s own innovative capabilities—the sense of efficacy—that more proximally drives employees to initiate and follow through with innovative actions (Santoso et al., 2019; Susanti & Ardi, 2022). In summary, by building employees' confidence in their ability to innovate, digital leadership empowers them to translate creative potential into tangible innovative behaviors.

H3: Innovation efficacy mediates the relationship between digital leadership and employee innovative behavior.

The Chain Mediating Role of Job Crafting and Innovative Self-Efficacy

Building on the separate mediating roles of job crafting and innovation efficacy, we propose a sequential mediation model in which these two factors form a causal chain. This model posits that digital leadership encourages job crafting, which in turn enhances innovation efficacy, ultimately leading to increased innovative behavior.

The first link in this chain is from job crafting to innovation efficacy. The very process of job crafting serves as a powerful source of mastery experiences and competence feedback, which are fundamental to building self-efficacy (Bandura, 1997). When employees proactively acquire new digital skills or redesign their work processes to adapt to technological changes, they are not merely adapting; they are actively engaging in behaviors that demonstrate and reinforce their capability to handle novel situations and solve problems (Zhu & Zhang, 2023). Successfully navigating these self-initiated changes provides direct evidence of their growing competence, thereby strengthening their belief in their ability to succeed in future innovative endeavors (Liu et al., 2023). Furthermore, by gaining greater control over their tasks and resources through job crafting, employees enhance their sense of autonomy and mastery, which further fuels their confidence—or efficacy—for innovation (Xu & Jin, 2023).

In sum, we argue that digital leadership initiates a positive psychological sequence: it fosters an environment that promotes job crafting, and the successful experiences gained from crafting one's job subsequently build a robust sense of innovation efficacy, which is a critical direct antecedent of innovative action.

H4: Job crafting and innovation efficacy serially mediate the relationship between digital leadership and employee innovative behavior.

The Moderating Role of Organizational Innovation Climate

Beyond the internal psychological mechanisms, we incorporate the organizational context by proposing the moderating role of the organizational innovation climate. This climate refers to employees’ shared perceptions of the organization’s practices, procedures, and behaviors that encourage and reward innovation (Ren & Zhang, 2015). Grounded in Person-Environment Fit Theory (Edwards et al., 1998), we argue that a strong innovation climate creates an environment that is highly congruent with the objectives and means provided by digital leadership, thereby amplifying its effectiveness.

This amplifying effect operates through several channels. First, while digital leadership provides the technological means for innovation (e.g., tools and platforms), a strong innovation climate provides the social and motivational impetus to use them. It encourages exploration and risk-taking, signaling that using these digital resources for experimentation is not only allowed but valued (Liu et al., 2023). This synergy between digital tools and a supportive climate enhances the fit between the employee’s innovative actions and the organizational environment.

Second, a strong innovation climate, characterized by tolerance for failure and an emphasis on creative problem-solving, reduces the perceived risks associated with innovation (Elsayed et al., 2023). This cultivated psychological safety liberates employees to fully act upon the autonomy and empowerment granted by digital leaders, thereby strengthening the leadership’s impact, a phenomenon also observed in frontline service innovation contexts (Nieder-Heitmann & Malan, 2023).

Third, the organizational innovation climate reinforces the strategic alignment of innovative efforts. Digital leadership may guide employees toward innovative tasks, but the overarching climate ensures that these tasks are perceived as congruent with organizational goals and supported by systemic rewards (Schein, 1992; Wang et al., 2022). This clarity and alignment increase the likelihood that the behaviors prompted by digital leadership will be sustained and effectively channeled.

In essence, a supportive organizational innovation climate acts as a catalyst that enhances the effectiveness of digital leadership in fostering employee innovation.

H5: Organizational innovation climate moderates the relationship between digital leadership and employee innovative behavior, such that the relationship is stronger when the organizational innovation climate is high.

Accordingly, we propose a moderated serial mediation model to elucidate how digital leadership fosters employee innovative behavior. The research framework, which illustrates these proposed relationships, is presented in Figure 1.

Figure 1.

Research framework.

Research Methodology

Measurement Tools

This study employed established scales from international literature to measure the five key variables: digital leadership, job crafting, innovation efficacy, employee innovative behavior, and organizational innovation climate. All items were rated on a five-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”). The reliability and validity of these scales have been well-documented in previous studies.

Digital leadership was measured with the six-item scale developed by Zeike et al. (2019). A sample item is: “My leader possesses extensive knowledge of current digital technologies and can inspire employees to embrace digital transformation.”

Innovation efficacy was assessed using the 8-item scale from Tierney and Farmer (2002). Example items include: “I am capable of creatively achieving most of the goals I set for myself,” and “In general, I believe I can accomplish significant creative outcomes.”

Job crafting was gauged with 15 items selected from the 21-item scale by Tims et al. (2013). Representative items are: “I make efforts to enhance my skills,”“I occasionally determine my own approach to work,” and “When completing a task, I view it as an opportunity for new beginnings.”

Employee innovative behavior was measured using the unidimensional 6-item scale developed by Scott and Bruce (1994). Sample items include: “I frequently generate creative ideas and innovative concepts,” and “I actively formulate suitable plans to implement innovative ideas.”

Organizational innovation climate was evaluated using a 10-item scale adapted to the Chinese context by Wang et al. (2017). This scale covers three dimensions: work autonomy, team collaboration, and organizational incentives. Example items are: “I have the freedom to set my own work pace,”“Colleagues regularly exchange ideas and discuss work-related issues,” and “The company’s reward system stimulates employees' innovative enthusiasm.”

Sample Selection and Data Collection

Data were collected through a structured questionnaire administered to employees in Chinese digital enterprises to test our research model. The target population encompassed staff and managers from firms in key sectors, including high-tech manufacturing, telecommunications and IT, internet services, and new energy, primarily located in the Haixi Western District. To ensure the sample’s representativeness and objectivity, a stratified random sampling method was rigorously implemented. The sampling frame was stratified based on three key criteria relevant to the digital economy landscape: industry type, company size, and geographical location. Within each resultant stratum, potential participating companies and employees were then randomly selected. This approach ensured that our sample captured the inherent diversity of the target population.

Prior to the main survey, a pilot test was conducted with a small group of professionals to assess item clarity, contextual relevance, and the overall reliability of the instrument. The formal survey was carried out over a four-week period in July 2024. A total of 380 questionnaires were distributed via multiple channels, including corporate email, internal communication platforms, and professional online survey tools (e.g., Questionnaire Star). To encourage participation, a token incentive was offered, and respondents were explicitly assured of the complete confidentiality and anonymity of their responses. Following the data collection phase, a rigorous data cleaning procedure was implemented. This involved screening all responses for patterns of straight-lining, inconsistent answers, and a significant proportion of missing values, resulting in the exclusion of 68 questionnaires. This process yielded 312 valid responses for the final analysis, representing a high valid response rate of 82.1%.

The demographic and occupational profile of the final sample (N = 312) is detailed in Table 1. The sample demonstrated a balanced distribution across critical variables. Gender distribution approached parity (54.5% male; 45.5% female). The majority of respondents (86.2%) were concentrated in the 26 to 40 age range, which aligns with the core working-age demographic in the dynamic digital sector. Educational backgrounds were varied, spanning from junior college (37.8%) to advanced degrees (13.8% combined Master's/PhD), with bachelor’s degrees being the most common (39.4%). The sample adequately represented different organizational hierarchies, comprising frontline staff (45.8%), junior and middle management (collectively 48.4%), and senior executives (5.8%). Work tenure was also well-distributed, featuring substantial representation from early-career (25.3%, ≤3 years), mid-career (61.8%, 4–10 years), and long-tenured (12.8%, >10 years) employees. This heterogeneity confirms that the sampling strategy successfully captured a wide spectrum of perspectives, thereby enhancing the generalizability of our findings and supporting the sample’s representativeness of the target population.

Table 1.

Basic Information of the Sample.

Attribute	Demographic Feature	Sample Size	Percentage	Attribute	Demographic Feature	Sample Size	Percentage
Gender	Male	170	54.5	Work Tenure	1 year or less 20	20	6.4
Gender	Female	142	45.5		1–3 years	59	18.9
Age	25 and below	6	1.9		4–7 years	99	31.7
	26–30	126	40.4		8–10 years	94	30.1
	31–40	143	45.8		10+ years	40	12.8
	41–50	31	9.9	Education	High school or less	28	9
	50 and above	6	1.9		Junior college	118	37.8
Job Level	Ordinary staff	143	45.8		Bachelor’s degree	123	39.4
	Junior manager	86	27.6		Master’s degree	35	11.2
	Middle manager	65	20.8		Master’s degree	35	11.2
	Senior manager	18	5.8		PhD	8	2.6

Research Results

Reliability and Validity, and Common Method Bias Test

We assessed the reliability and validity of the measures using SPSS 22.0 and Amos 21.0. As shown in Table 2, all variables demonstrated high internal consistency, with Cronbach's α coefficients and composite reliability (CR) values exceeding .9. The very high Cronbach's alpha coefficients for job crafting and employee innovative behavior indicate excellent internal consistency, which is consistent with the highly reliable, well-validated nature of the original scales. Convergent validity was also supported, as all factor loadings were above .6 and the average variance extracted (AVE) for each construct ranged from .591 to .787, surpassing the recommended threshold of .5.

Table 2.

Reliability and Validity Analysis Results.

Variable	Factor Loadings	Cronbach's α	CR	AVE
Digital Leadership	.721–.906	.914	.916	.647
Job Crafting	.691–.900	.972	.972	.700
Employee Innovative Self-Efficacy	.709–.856	.919	.920	.591
Employee Innovative Behavior	.820–.925	.956	.957	.787
Organizational Innovation Climate	.709–.879	.944	.944	.630

To examine discriminant validity, a series of confirmatory factor analyses (CFA) were conducted. The results, presented in Table 3, indicate that the hypothesized five-factor model (including all distinct constructs) provided a good fit to the data (χ²/df = 1.991, CFI = .927, TLI = .923, IFI = .928, RMSEA = .056). This model demonstrated a significantly better fit than all alternative nested models, thus confirming the discriminant validity of the study variables.

Table 3.

Confirmatory Factor Analysis Results.

Factors Included	χ²/df	CFI	TLI	IFI	RMSEA
DL + JC + EIS + EIB + EIB	8.044	.479	.454	.481	.150
DL + JC + EIS + EIB,EIB	6.06	.626	.608	.627	.128
DL,JC + EIS + EIB,EIB	4.964	.708	.693	.709	.113
DL,JC,EIS + EIB,EIB	3.146	.842	.834	.843	.083
DL,JC,EIS,EIB,EIB	1.991	.927	.923	.928	.056

Note. DL = Digital Leadership, JC = Job Crafting, EIS = Employee Innovative Self-Efficacy, EIB = Employee Innovative Behavior, OIC = Organizational Innovation Climate.

Given that the data were collected from a single source via self-report questionnaires, we employed both procedural and statistical remedies to assess and control for common method bias (CMB). Procedurally, respondent anonymity was emphasized, and items for the dependent and independent variables were placed in different sections of the questionnaire to reduce evaluation apprehension and context-induced mood. Statistically, we conducted two tests. First, Harman's single-factor test was conducted on all measurement items, revealing that the unrotated first factor accounted for 38.35% of the total variance, which is below the critical threshold of 40%. Second, as shown in Table 3, a single-factor confirmatory factor analysis, in which all items were loaded onto a single latent factor, showed a very poor model fit (χ²/df = 8.044, CFI = .479, TLI = .454, IFI = .481, RMSEA = .150). The results of both tests suggest that common method bias is not a serious concern in this study.

Descriptive Statistics and Correlation Analysis of Variables

Table 4 displays the means, standard deviations, and correlations among the key variables. As shown, digital leadership was positively correlated with job crafting (r = .376, p < .01), innovation efficacy (r = .359, p < .01), and innovative behavior (r = .338, p < .01). Job crafting was positively correlated with innovation efficacy (r = .502, p < .01) and innovative behavior (r = .396, p < .01). Innovation efficacy also showed a positive correlation with innovative behavior (r = .483, p < .01). The pattern of these correlations provides preliminary support for our hypothesized relationships. Furthermore, the square roots of the AVEs (shown on the diagonal in Table 4) were greater than the absolute values of the correlations between the respective constructs, supporting discriminant validity. Additionally, all variance inflation factor (VIF) values in the subsequent regression analyses were well below the critical threshold of 10, indicating that multicollinearity was not a concern.

Table 4.

Descriptive Statistics and Correlation Analysis Results.

Variable	M	SD	1	2	3	4	5
1 DL	4.81	1.042	.805
2 JC	5.05	0.926	.376**	.837
3 EIS	5.01	0.875	.359**	.502**	.769
4 EIB	4.46	1.248	.338**	.396**	.483**	.887
5 OIC	4.86	0.923	.407**	.346**	.377**	.376**	.794

Note. DL = Digital Leadership, JC = Job Crafting, EIS = Employee Innovative Self-Efficacy, EIB = Employee Innovative Behavior, OIC = Organizational Innovation Climate. The values on the diagonal represent the square roots of the AVE for the corresponding variables. ** denotes significant correlations at the 0.01 level.

Testing for Main Effects and Mediation Effects

To test the proposed hypotheses, we conducted a series of hierarchical regression analyses followed by bootstrapping procedures for mediation testing. This combination allows for testing direct effects while controlling for covariates, and provides robust confidence intervals for indirect effects without relying on the assumption of normality.

Main Effect Test

The results for testing the direct effect (H1) are presented in Table 5 (Model 6). After controlling for demographic variables (gender, education, age, work tenure, job level), digital leadership demonstrated a significant positive effect on employee innovative behavior (β = .338, p < .001). Thus, H1 was supported.

Table 5.

Test Results of Main Effects and Mediation Effects.

Variable	JC		EIS		EIB
Variable	M1	M2	M3	M4	M5	M6	M7	M8	M9
DL		0.372***		0.356***		0.338***	0.220***	0.188***	0.308***
JC							0.315***
EIS								0.419***
OIC									0.436***
DL* OIC									0.333***
Gender	0.017	0.009	−0.068	−0.075	−0.012	−0.019	−0.022	0.013	−0.035
Education	−0.004	0.01	−0.04	−0.027	−0.013	0	−0.003	0.011	0.04
Age	0.052	0.035	0.039	0.022	0.018	0.003	−0.008	−0.007	0.077
Work Tenure	−0.031	−0.035	0.016	0.012	−0.01	−0.013	−0.002	−0.018	−0.068
Job Level	0.107	0.097	0.097	0.087	0.032	0.023	−0.008	−0.014	0.027
R ²	0.014	0.153	0.016	0.143	0.002	0.115	0.199	0.266	0.280
Adj.R²	−0.002	0.136	0.000	0.126	−0.015	0.098	0.181	0.249	0.261
F	0.897	9.149	1.012	8.458	0.099	6.615	10.797	15.706	15.17

Note. DL = Digital Leadership, JC = Job Crafting, EIS = Employee Innovative Self-Efficacy, EIB = Employee Innovative Behavior, OIC = Organizational Innovation Climate. The values on the diagonal represent the square roots of the AVE for the corresponding variables. ***denotes significant correlations at the 0.001 level.

Simple Mediation Effects Test

We used a two-step approach to test the simple mediation hypotheses (H2 and H3): first, hierarchical regression to establish the relationships between variables, and second, bootstrapping to formally test the significance of the indirect effects.

For H2, which proposed mediation through job crafting (JC), the regression results (Table 5) provided initial support. Digital leadership significantly predicted JC (Model 2: β = .372, p < .001). When both digital leadership and JC were included in the model (Model 7), JC significantly predicted innovative behavior (β = .315, p < .001), and the effect of digital leadership decreased (from β = .338 in Model 6 to β = .220 in Model 7), suggesting a partial mediation. To conclusively test the indirect effect, we employed a bootstrapping procedure with 5,000 resamples. As shown in Table 6, the indirect effect via job crafting was significant, with a point estimate of 0.075 and a 95% bias-corrected confidence interval excluding zero [0.014, 0.152]. Therefore, H2 was supported.

Table 6.

Test Results for Serial Mediation Effects.

Path	Effect Size	S.E.	95% Confidence Interval
Path	Effect Size	S.E.	Lower Bound	UPBound
Total Indirect Effect	0.225	0.046	0.145	0.329
DL-JC-EIB	0.075	0.034	0.014	0.152
DL-EIS-EIB	0.083	0.029	0.033	0.154
DL- JC-EIS-EIB	0.067	0.023	0.033	0.124

Note. Digital Leadership, JC = Job Crafting, EIS = Employee Innovative Self-Efficacy, EIB = Employee Innovative Behavior, OIC = Organizational Innovation Climate.

For H3, which proposed mediation through innovation efficacy (EIS), a similar pattern emerged. Digital leadership significantly predicted EIS (Model 4: β = .356, p < .001). When both were included (Model 8), EIS was a significant predictor (β = .419, p < .001), and the effect of digital leadership attenuated (from β = .338 to β = .188). The bootstrapping results (Table 6) showed a significant indirect effect (effect = .083, 95% CI (0.033, 0.154)). Hence, H3 was supported.

Serial Mediation Effect Test

Hypothesis 4 proposed a serial mediation path via job crafting and then innovation efficacy. The bootstrapping analysis for this specific indirect effect (DL → JC → EIS → EIB) was significant (Effect = 0.067, 95% CI (0.033, 0.124)), as shown in Table 6. Since the confidence interval did not contain zero, Hypothesis 4 was supported. This indicates that digital leadership promotes employee innovative behavior not only through parallel pathways but also by first encouraging job crafting, which in turn enhances employees’ innovation efficacy, ultimately leading to more innovative behavior.

Testing for Moderating Effects

Hypothesis 5 proposed that organizational innovation climate (OIC) moderates the relationship between digital leadership and innovative behavior. To test this moderation effect, we followed a standard hierarchical regression procedure with mean-centering to mitigate potential multicollinearity between the predictor and the interaction term. Specifically, we first mean-centered the variables for digital leadership and organizational innovation climate. We then created an interaction term by multiplying these two mean-centered variables.

As shown in Model 9 of Table 5, after entering the control variables, the mean-centered main effects of digital leadership and OIC were entered, followed by the interaction term (DL × OIC). The results show that the interaction term was positive and statistically significant (β = .333, p < .001). This result provides strong support for Hypothesis 5, indicating that the strength of the relationship between digital leadership and employee innovative behavior depends on the level of the organizational innovation climate.

To interpret the nature of this significant interaction, we plotted the simple slopes at high (mean +1 SD) and low (mean −1 SD) levels of the organizational innovation climate, following the procedure recommended by Aiken and West (1991). As illustrated in Figure 2, the relationship between digital leadership and innovative behavior was positive and stronger when the organizational innovation climate was high (simple slope = .54, p < .001) compared to when it was low (simple slope = .21, p < .01). This pattern visually confirms that a more supportive organizational innovation climate significantly enhances the positive influence of digital leadership on employee innovative behavior.

Figure 2.

The moderating effect of organizational innovation climate.

Conclusion and Suggestions

Research Conclusion

This study set out to elucidate the psychological and behavioral mechanisms through which digital leadership influences employee innovative behavior. Based on empirical data from 312 employees in digital enterprises, we draw the following core conclusions. First, digital leadership exerts a significant positive impact on employee innovative behavior, confirming its direct role as a driver of innovation—a finding that aligns with recent empirical evidence linking core digital leadership competencies to enhanced innovative outcomes across sectors (Fernanda et al., 2024). Second, this influence operates through two significant mediating pathways: job crafting and innovation efficacy. Third, and more importantly, these mediators function sequentially: digital leadership encourages job crafting, which subsequently enhances innovation efficacy, ultimately leading to increased innovative behavior. This serial mediation model provides a more nuanced understanding of the causal chain linking leadership to innovation. Finally, the organizational context proves crucial, as our results demonstrate that organizational innovation climate positively moderates and strengthens the relationship between digital leadership and innovative behavior. A supportive innovation climate significantly amplifies digital leadership's effectiveness in fostering employee innovation, consistent with the recognized importance of digital culture shaping as a core leadership competency (Fernanda et al., 2024).

Research Significance and Implications

Theoretical Significance

Unpacking the “Black Box”: It provides a nuanced, mechanism-based explanation for how digital leadership influences innovation. By identifying job crafting and innovation efficacy as parallel and sequential mediators, we move beyond establishing a direct effect to delineating the specific behavioral and psychological processes that transmit leadership influence.

Theoretical Integration: The research integrates Self-Determination Theory (SDT) and Person-Environment Fit (P-E Fit) Theory into a coherent framework. SDT explains how digital leadership satisfies psychological needs to foster proactive behaviors (job crafting) and confidence (efficacy). P-E Fit theory, in turn, explains how the organizational innovation climate strengthens the alignment between the resources provided by digital leaders and employees’ innovative endeavors, thereby amplifying the overall effect. This integration offers a more comprehensive theoretical lens for understanding innovation drivers.

Contextualizing Digital Leadership: By identifying the organizational innovation climate as a significant moderator, the study adds a critical contingency dimension to digital leadership models. It emphasizes that the effectiveness of digital leadership is not absolute but is contingent upon a supportive organizational environment, thereby bridging micro-level leadership behaviors with macro-level organizational culture.

Practical Implications

The findings offer actionable and strategic insights for managers and organizations:

Cultivate Digital Leadership Holistically: Leadership development programs must extend beyond technical proficiency. They should focus on cultivating leaders who can empower employees, provide psychological support, and actively foster a culture of innovation. This involves mentoring leaders to provide autonomy, recognize efforts, and supply the digital tools and resources that build competence.

Champion Job Crafting Initiatives: Organizations should move away from rigid job descriptions and create fluid environments that encourage proactive role shaping. Managers can be trained to facilitate job crafting by empowering employees to adjust their tasks, acquire new skills, and interact differently with colleagues, thereby unlocking latent innovative potential.

Systematically Build Innovation Efficacy: Confidence is built through mastery. Organizations should implement structured programs that combine skill-building training with opportunities to apply these skills in manageably challenging projects. Creating a feedback culture that celebrates learning from both successes and failures is crucial for reinforcing employees' belief in their innovative capabilities.

Prioritize the Innovation Climate as a Strategic Asset: Senior management must consciously and consistently build an innovation-friendly climate. This requires tangible commitments: allocating dedicated resources (time, budget) for experimentation, formal reward systems that recognize creative attempts, and fostering psychological safety where voicing novel ideas is encouraged and failure is not punished but treated as a learning opportunity.

Research Limitations and Future Directions

Despite its contributions, this study has several limitations that offer avenues for future research. First, the generalizability of the findings may be constrained by the geographical and industrial focus on digital enterprises in the Haixi Western District of china. Future studies could validate and extend this model by collecting data from multiple regions, diverse cultural contexts, and non-digital industries.

Second, the use of cross-sectional data, while appropriate for establishing initial relationships, precludes definitive causal inferences. Although procedural remedies for common method bias were employed, the inherent limitations of self-reported data from a single time point remain. Longitudinal or experimental designs in future work would be invaluable for tracing the dynamic evolution of the proposed mediating mechanisms over time and solidifying the causal claims.

Third, our analysis treated core constructs like job crafting and innovation efficacy as overarching variables. A more nuanced understanding could be achieved by exploring their specific dimensions. For instance, future research could investigate whether different types of job crafting (e.g., increasing structural, social, or challenging job resources) have distinct relationships with digital leadership and innovation efficacy.

Furthermore, while the scales demonstrated high reliability, future studies may consider using shortened versions to improve parsimony and respondent fatigue, provided that conceptual breadth and psychometric validity are preserved.

Lastly, the theoretical framework, though grounded in SDT and P-E Fit theory, could be enriched. Future research could integrate additional theoretical lenses, such as the Job Demands-Resources model, to offer complementary explanations for the relationships explored.

Footnotes

ORCID iDs

Meijiao Huang

Rongrong Zhao

Xiaoxia Chen

Ethical Considerations

This study collects data through a questionnaire survey. It does not involve animal experiments, human clinical trials, invasive procedures, or any interventions that might pose risks to participants. The questionnaire does not contain any sensitive, private, or potentially harmful questions. Participation in the survey does not pose any physical, psychological, or social risks to participants. During the study, participants were only required to complete anonymous questionnaires and were not required to disclose any personally identifiable information, including names, contact information, or other identifying details. In accordance with the ethical principles outlined in the Declaration of Helsinki, participants’ anonymity and confidentiality are safeguarded.

Consent to Participate

Prior to completing the questionnaire, participants were clearly informed of key information such as the purpose of the study, the scope of data use, and protective measures through a detailed explanation at the beginning of the questionnaire. Participants were also informed that submitting the questionnaire constitutes informed consent. All participants have signed the informed consent form to agree to take part in this study and allow their data to be used for publication.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The research and publication of this article are supported by: Natural Science Foundation of Fujian Province (No. 2021J011245); 2024–2025 Basic Theory Research Program of Philosophy and Social Science Disciplines Guided by Marxismin Fujian Universities and Colleges Key Project (No. FJ2025MGCA021).

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

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Digital Leadership and Employee Innovation: A Sequential Mediation Model of Job Crafting and Innovation Efficacy

Abstract

Plain Language Summary

Keywords

Introduction

Theoretical Foundations and Research Hypotheses

The Impact of Digital Leadership on Employee Innovation Behavior

The Mediating Role of Job Crafting

The Mediating Role of Innovative Self-Efficacy

The Chain Mediating Role of Job Crafting and Innovative Self-Efficacy

The Moderating Role of Organizational Innovation Climate

Research Methodology

Measurement Tools

Sample Selection and Data Collection

Research Results

Reliability and Validity, and Common Method Bias Test

Descriptive Statistics and Correlation Analysis of Variables

Testing for Main Effects and Mediation Effects

Main Effect Test

Simple Mediation Effects Test

Serial Mediation Effect Test

Testing for Moderating Effects

Conclusion and Suggestions

Research Conclusion

Research Significance and Implications

Theoretical Significance

Practical Implications

Research Limitations and Future Directions

Footnotes

ORCID iDs

Ethical Considerations

Consent to Participate

Funding

Declaration of Conflicting Interests

Data Availability Statement

References