How Does Digital Knowledge Management Drive Employees’ Innovative Behavior?

Abstract

With AI and other technologies widely applied, knowledge management paradigms are being systemically reconstructed. How to effectively leverage digital technologies to manage knowledge and activate employees’ innovative behaviors has become key for enterprises’ sustainable development. This article explores the influence pathways of digital knowledge management on employees’ innovative behavior, conducting cross-level transmission mechanism research based on digital knowledge management, organizational learning, and employee innovation behavior. Drawing on 325 questionnaires and hierarchical regression, this study finds that: digital knowledge management positively effects employees’ innovative behavior; exploitative learning mediates more strongly than exploratory learning between digital knowledge management and employees’ innovative behavior; challenging technostress weakens the link between organizational learning and innovation. This paper also uses fsQCA analysis to identify three pathways to high employee innovation behavior: exploration-driven innovation based on full knowledge chain collaboration, dual-driven innovation oriented towards knowledge transformation, and dual-driven innovation oriented towards knowledge sharing. The conclusions of this study are intended to promote the application and development of digital knowledge management in enterprises and provide practical insights for enterprises to foster employee innovation and achieve sustainable development.

1. Introduction

With the widespread application and deep integration of technologies such as artificial intelligence and big data, the paradigm of enterprise knowledge management is undergoing a profound and systematic reconstruction [1]. This reconstruction goes beyond mere tool-level innovation, profoundly overturning the core logic of knowledge acquisition, sharing, and application, and driving the evolution of knowledge management towards a paradigm characterized by intelligence, platformization, and ecosystem integration. Increasingly, enterprises are undertaking digital transformation to achieve sustainable development. The essence of digital transformation lies in introducing digital technologies into enterprise production and management processes by innovating information acquisition methods, reshaping R&D and production workflows, and transforming organizational structures and internal management models to improve output efficiency [2]. In this process, digital technology fundamentally changes the integration and innovation logic of organizational knowledge resources by reconstructing the technological architecture, interaction patterns, and application scenarios of knowledge management.

The knowledge-based view holds that knowledge is a key strategic resource determining an enterprise’s long-term performance [3,4,5,6], and that competitive advantage depends on the creation, acquisition, and application of knowledge [7]. The reconstruction of the knowledge management paradigm driven by digital technologies is profoundly changing the existence form, flow logic, and value creation modes of knowledge as a strategic resource, such as intelligent decision support and innovation ecosystem collaboration, posing new challenges and expanded opportunities for the theoretical connotation and practical application of the traditional knowledge-based view. Some studies further highlights that artificial intelligence is emerging as a key driver of the evolution of knowledge management paradigms, reshaping the logic of knowledge management across multiple dimensions-from social interaction and human–machine collaboration to ecosystem construction [8,9,10]. Faced with exponentially increasing volumes of information and increasingly complex decision-making environments, the limitations of individual cognitive abilities make the use of digital technologies for knowledge management inevitable [11,12]. Digital knowledge management refers to the full digitization of knowledge management processes, with its core in the deep application of digital technologies across key stages such as knowledge acquisition, knowledge sharing, and knowledge application, thereby reconstructing the knowledge management process to promote sustainable enterprise development. This reconstruction is far from a simple tool upgrade; it fundamentally alters the essential characteristics of each stage: in knowledge acquisition, it achieves a shift from passive retrieval to intelligent recommendation and data-driven insights, significantly improving information precision and coverage; in knowledge sharing, it promotes a transformation from hierarchical transmission to platform-based real-time collaboration, breaking the constraints of time, space, and organizational boundaries; and in knowledge application, it completes the leap from experience-driven practice to context-aware and intelligent assistance, deeply embedding knowledge into business processes to empower decision execution.

As the primary stage of digital transformation, digital knowledge management centered on paradigm reconstruction serves as the engine of organizational change, driving innovation in business processes, products and services, and business models [13,14]. The new paradigm, by providing convenient and intelligent platforms for knowledge acquisition and sharing, greatly facilitates employees’ absorption and application of knowledge, stimulating innovative thinking [15,16], enabling enterprises to maintain a competitive advantage in fierce market competition [12,17].

However, the construction and application of the new paradigm also come with challenges. The complexity and rapid iteration of information technologies, coupled with high demands on employees’ digital literacy, may trigger technostress [18,19]. This stress can exert a double-edged effect on employees’ innovative behaviors: on one hand, moderate technological pressure may stimulate employees’ learning motivation and innovation awareness, encouraging improvements in work efficiency and innovative capability [20,21]. On the other hand, excessive technological pressure can increase employees’ psychological burden and work stress, thereby suppressing their innovation motivation and creativity [19,22]. Employees, as the ultimate carriers of innovation, have individual innovative behaviors that constitute the cornerstone of a company’s sustainable competitive advantage. Therefore, how to reconstruct organizational learning paths through digital knowledge management to activate employees’ innovative behaviors has become one of the key issues for enterprises to achieve sustainable development.

Although existing research has focused on the impact of knowledge management or digital technologies on innovation, there is still a relative lack of in-depth analysis regarding digital knowledge management and employee’s innovative behavior. In particular, studies are scarce on the cross-level transmission mechanisms involving technology-driven paradigm reconstruction, organizational learning, and employee innovation behavior. Moreover, the moderating role of technostress in the process of innovation influenced by paradigm reconstruction remains underexplored. Based on this, the present study adopts the theoretical perspective of the knowledge-based view to explore the mechanisms and pathways through which digital knowledge management affects employee’s innovative behavior. It provides an in-depth analysis of how the reconstruction of each stage in digital knowledge management influences organizational learning, whether organizational learning mediates its effect on employee innovation behavior, and how different dimensions of technostress moderate the relationship with employee innovation behavior. The exploration of these questions will offer theoretical guidance and practical insights for the transformation of the knowledge management paradigm and sustainable development of enterprises.

2. Literature Review

2.1. Digital Knowledge Management and Employees’ Innovative Behavior

In the context of rapidly advancing information technology, knowledge management is continuously being innovated and has adapted to a new management model: digital knowledge management [23,24]. The concept of digital knowledge management has emerged in recent years, and although there is no clear consensus definition in academia, it is generally recognized as the use of information and communication technologies such as big data, cloud computing, and artificial intelligence to manage semi-structured and unstructured vast amounts of data and knowledge by members through online intelligent communities and platforms [13,25,26]. Compared with traditional knowledge management, enterprise digital knowledge management, as an important factor driving organizational change, fosters innovation in business processes, products and services, and business models [14,27].

In summary, this paper defines digital knowledge management as the process and practical system within or between organizations that utilize digital technologies to manage knowledge, ultimately achieving systematic knowledge management and enhancing the innovative capabilities of individuals and organizations. Digital knowledge management promotes sustainable development by applying digital technologies in the stages of knowledge acquisition, knowledge sharing, and knowledge application [27]. Therefore, this paper will explore the impact of digital knowledge management on employees’ innovative behavior from these three aspects.

The knowledge-based view holds that knowledge creation is an individual activity based on organizational members [28]. Employees create knowledge by actively reorganizing the abundant knowledge resources they have accumulated, providing the uniqueness of resources; enterprises, in turn, provide managerial support for employees’ knowledge innovation activities, thereby achieving improved innovation performance and sustainable competitive advantage [29,30]. Employees’ innovative behavior can be broadly defined as the process in which employees generate valuable new ideas related to their work and put them into practice. However, with the rapid development of technologies such as artificial intelligence and big data, the modes of knowledge acquisition, sharing, and application have undergone profound transformations [8]. AI-driven knowledge management not only extends the applicability of the knowledge-based view but also compels scholars to re-examine the boundaries and potential of classical theories within the digital context. In this study, innovative behavior includes new technologies and new solutions proposed by employees in their work or new ideas regarding work processes [31].

Knowledge acquisition and employees’ innovative behavior. In a competitive environment characterized by rapid technological development and drastic market changes, a vast amount of knowledge exists beyond the traditional boundaries of enterprises [32]. Through systematic technological empowerment, enterprises can significantly lower the barriers to knowledge acquisition, enabling employees to quickly access high-quality internal and external knowledge across multiple domains [12], thus providing critical resource support for innovation. In this process, employees not only improve the efficiency of knowledge acquisition and reduce the time costs invested in information searching, allowing them to focus more on innovation and exploration; they also stimulate innovative inspiration by making tacit knowledge explicit through real-time interactive functions.

Especially, an increasing number of enterprises use big data technologies to automatically identify and extract valuable knowledge [33], accurately pushing industry trends and practical knowledge to employees. This enables them to rapidly acquire knowledge systems highly aligned with current business, providing a solid knowledge foundation and directional guidance for innovation practice. For example, by intelligently mining potential needs and trends from user feedback, employees can precisely identify innovation directions and reduce trial-and-error costs.

These technology-driven knowledge acquisition methods not only expand employees’ cognitive breadth and depth but also strengthen their problem identification and solving abilities, stimulating innovative thinking and thereby better supporting innovation activities. Therefore, leveraging digital technologies to acquire knowledge efficiently helps promote employees’ innovative behavior. In particular, with the support of emerging technologies such as artificial intelligence, knowledge acquisition has become not only more precise and real-time, but has also further reinforced the classical logic of “knowledge as a resource for innovation” within the new technological environment. Therefore, this study posits the following hypothesis:

H1a. 

Knowledge acquisition has a positive impact on employees’ innovative behavior.

Knowledge sharing and employees’ innovative behavior. In this study, knowledge sharing is defined as the intentional and interactive donation and exchange of knowledge among employees [34]. Prior research has shown that such knowledge-sharing behaviors facilitate innovation at the individual, team, and organizational levels [35,36]. Through face-to-face or online interactions, knowledge donation and exchange enable employees to inspire one another and stimulate collaborative innovation. When organizations establish mechanisms and cultures that support such bidirectional exchanges, employees are more willing to engage in knowledge sharing, thereby not only enriching the organizational knowledge base but also providing others with valuable sources of innovative ideas [37]. Leveraging digital tools, firms can rapidly push newly acquired knowledge to relevant employees or teams, who then initiate discussions and interactions based on this information, offering timely knowledge support for innovation practices [8,38,39].

It is important to emphasize that the classification, storage, and retrieval of digital knowledge within knowledge management systems represent the supporting infrastructure for codified knowledge transfer. However, once employees access knowledge through these systems, the subsequent two-way exchanges with colleagues—such as sharing application insights or supplementing with experiential know-how—constitute genuine knowledge sharing. This process not only reduces redundant work but also stimulates employees’ innovative potential, thereby fostering the generation of innovative outcomes [40,41]. Therefore, this study posits the following hypothesis:

H1b. 

Knowledge sharing has a positive impact on employees’ innovative behavior.

Knowledge application and employees’ innovative behavior. Knowledge application refers to putting acquired and shared knowledge into practice [42]. This stage typically involves repackaging available knowledge resources into applicable solutions or delivering new products and services into new environments [43]. The essence of knowledge application is to effectively apply knowledge to solve problems and realize knowledge value [44]. During the process of knowledge application, it is necessary for senior management to provide clear support for knowledge-driven digital business, ensuring knowledge application through technological empowerment and process guarantees, thereby stimulating employees’ innovative behavior. The deep application of big data and artificial intelligence technologies not only lowers the threshold for knowledge application [42,45], but also further expands the boundaries of business innovation, encouraging employees to proactively explore integration paths between new technologies and business. The stronger the employee’s ability to apply knowledge, the higher the efficiency in realizing the value of knowledge, continuously transforming knowledge into innovative products and services, thereby further enhancing enterprise innovation performance. Moreover, the integration of artificial intelligence and big data has significantly enhanced both the efficiency and breadth of knowledge sharing, rendering the traditional relationship between knowledge sharing and innovation more dynamic and time-sensitive within the digital context. Therefore, this study posits the following hypothesis:

H1c. 

Knowledge application has a positive impact on employees’ innovative behavior.

2.2. The Mediating Role of Organizational Learning

Digital knowledge management is the process and practical system within or between organizations that uses digital technologies to achieve knowledge management. It reshapes the organizational cognitive paradigm and lays the foundation for organizational learning and innovation. Organizational learning is generally regarded as the process or capability through which an organization continuously utilizes information and knowledge to improve its behavior in order to adapt to a changing environment and achieve sustainable development [46,47,48]. Organizational learning can be divided into exploitative learning and exploratory learning: the former emphasizes the refinement and expansion of existing organizational knowledge, while the latter stresses the exploration and learning of new external knowledge [49]. With the support of technologies such as artificial intelligence, big data, and cloud computing, the process of organizational learning has not only accelerated but also exhibited distinctive characteristics in terms of knowledge boundary expansion and the depth of knowledge application, compared with traditional contexts. In this paper, digital knowledge management mainly focuses on how the organization uses digital technologies to create, facilitate, and support the flow and application of knowledge through processes and mechanisms, whereas organizational learning centers more on the cognitive and behavioral orientation of organizational members.

Exploitative learning plays a positive mediating role between digital knowledge management and employees’ innovative behavior. Enterprises use digital means to integrate internal and external knowledge, forming a potential resource base for innovation. Exploitative learning adapts and reorganizes based on existing business demands and knowledge systems, helping employees leverage mature methods, experiences, or best practices to optimize business processes, thus driving incremental innovation in existing products and services. The technical capability of knowledge management strengthens employees’ mastery of existing technologies and directly promotes process optimization outcomes within innovative behaviors [31]. Knowledge-sharing systems break down departmental barriers and reduce the cost of repeated trial and error, enabling employees to quickly acquire internal best practices through exploitative learning. Efficient knowledge sharing lowers repetitive trial-and-error costs and accelerates employees’ transformation of mature experience into innovative solutions [50]. In the context of artificial intelligence and big data, exploitative learning is more readily strengthened through mechanisms such as algorithmic recommendations and knowledge graphs, which enhance employees’ absorption and reutilization of existing knowledge. Therefore, exploitative learning builds a bridge between knowledge acquisition, knowledge sharing, knowledge application, and employees’ innovative behavior, deepening existing knowledge and further driving employee innovation. Accordingly, this paper proposes the following hypotheses:

H2a. 

Exploitative learning mediates the relationship between knowledge acquisition and employees’ innovative behavior.

H2b. 

Exploitative learning mediates the relationship between knowledge sharing and employees’ innovative behavior.

H2c. 

Exploitative learning mediates the relationship between knowledge application and employees’ innovative behavior.

Exploratory learning centers on breaking through existing knowledge boundaries and plays a positive mediating role between digitalized knowledge management and employees’ innovative behavior. In the knowledge acquisition phase, exploratory learning drives organizations to adopt an open stance to identify innovative elements in external knowledge that exceed the current business scope. It breaks conventional thinking frameworks and, through interdisciplinary and cross-domain knowledge integration, restructures the new knowledge with existing systems, thus opening up directions for innovation. When employees are exposed to cutting-edge industry technologies and cross-disciplinary knowledge, exploratory learning is more easily promoted. Analyzing external data resources through digital technologies helps employees extend exploratory learning into high-risk, high-potential areas, thereby generating disruptive innovative ideas [50]. Meanwhile, enterprises foster a favorable sharing atmosphere that motivates employees to break through professional barriers and transform heterogeneous knowledge into innovation momentum. Digital knowledge application tools, such as AI decision systems and predictive analytics models, generate anomalous insights in business scenarios, which often trigger exploratory learning. This drives organizations to critically examine existing models, break through business boundaries, attempt to develop new service and product forms, and explore possible innovation frontiers through experimental applications and rapid iterations, thereby driving employees to engage in disruptive innovation. Therefore, the following hypotheses are proposed:

H3a. 

Exploratory learning mediates the relationship between knowledge acquisition and employees’ innovative behavior.

H3b. 

Exploratory learning mediates the relationship between knowledge sharing and employees’ innovative behavior.

H3c. 

Exploratory learning mediates the relationship between knowledge application and employees’ innovative behavior.

2.3. The Moderating Role of Technostress

In recent years, the widespread application of technologies such as artificial intelligence, blockchain, cloud computing, and big data has driven significant changes in employees’ work environments. These new technologies impose higher demands on employees, leading to pervasive technostress [51,52]. Technostress generally refers to modern adaptation difficulties experienced by individuals who fail to meet cognitive and social demands related to new technologies [53,54]. Tarafdar et al. focused on sources of stress and summarized technostress into five dimensions: techno-overload, techno-invasion, techno-complexity, techno-insecurity, and techno-uncertainty [55]; this scale is widely recognized. Building on this, Ramesh et al. classified technostress into challenge technostress (techno-overload and techno-complexity) and hindrance technostress (techno-invasion, techno-uncertainty, and techno-insecurity) based on the job demands-resources theory [56]. This study also analyzes technostress from these two dimensions.

Challenge technostress refers to employees perceiving the stress caused by technology as an opportunity to enhance their abilities and achieve career development rather than as a pure threat [57]. Research indicates that when employees perceive pressure at work, it shows a gap between their expectations or goals and the current reality. Facing such challenge technostress, employees tend to adopt proactive behaviors to reduce this inconsistency [21,31]. Existing studies showed that Challenge technostress positively impacts employees’ individual behaviors, attitudes, and organizational goals [58,59]. During organizational learning, when employees face challenging technostress, they are more willing to consider it a necessary challenge to achieve valuable goals. Therefore, to effectively meet new technical demands and problems, they invest more cognitive resources to understand and master new technologies and knowledge, such as actively participating in related training and collaborating with colleagues. This leads them to more actively explore, integrate, and transform this knowledge to generate novel and useful ideas and behaviors. Therefore, this study posits the following hypothesis:

H4a. 

Challenge technostress positively moderates the relationship between exploitative learning and employees’ innovative behavior.

H4b. 

Challenge technostress positively moderates the relationship between exploratory learning and employees’ innovative behavior.

Hindrance technostress refers to stress caused by technology-related demands that consume individual physical and mental resources and hinder work goal achievement. Studies suggest that excessive technostress beyond employees’ capacity can cause anxiety and pressure, thereby weakening their creativity and innovation ability [19,60]. From the perspective of techno-invasion, boundaries between work and life become blurred due to technological penetration, and a large amount of time is occupied by unnecessary technical tasks, such as continuous work interruptions caused by instant messaging tools. For example, employees’ family life and rest time are invaded by work, leading to work-family role conflicts. This results in difficulty for employees to concentrate on organizational learning activities, weakening the promoting effect of organizational learning on employees’ innovative behavior. Regarding techno-uncertainty, as digital new technologies and tools are continuously applied at work, employees face ongoing challenges in technology adaptation. Frequent iteration of technology requires considerable effort to cope with tool replacements and system switches, reducing employees’ ability to understand and absorb knowledge during organizational learning, thereby weakening the role of organizational learning in promoting innovation behavior. Additionally, the emergence of new technologies and tools may cause employees to worry about being replaced, leading them to hide experiences and avoid collaboration, which hinders the formation of the knowledge network and collaborative atmosphere necessary for innovation, thereby weakening the positive impact of organizational learning on employees’ innovative behavior. In summary, hindrance technostress weakens the positive influence of organizational learning on employees’ innovative behavior and plays a negative moderating role in their relationship. Therefore, this study posits the following hypothesis:

H5a. 

Hindrance technostress negatively moderates the relationship between exploitative learning and employees’ innovative behavior.

H5b. 

Hindrance technostress negatively moderates the relationship between exploratory learning and employees’ innovative behavior.

Based on the proposed hypotheses and theoretical inference, the research framework is as follows (see Figure 1):

3. Methodology

3.1. Research Method

This study employs a mixed-methods approach to explore the impact of digital knowledge management on employees’ innovative behavior. Specifically, it combines hierarchical regression analysis and fuzzy-set qualitative comparative analysis (fsQCA). Hierarchical regression is a typical quantitative research method, while fsQCA is regarded as a case-oriented qualitative comparative research method. These two methods complement and validate each other. Hierarchical regression assumes that each factor independently affects the outcome, discussing the “net effect” of single factors and focusing on the net effect of individual variables on results. This approach struggles to reveal the systemic characteristics emerging from interactions among elements in complex systems [61]. In contrast, fsQCA shifts from studying the “net effect” of single factors to exploring the “combined effect” of multiple factors. It posits that conditions interact rather than act independently, the causal relationship between conditions and outcomes is asymmetric, and specific configurational paths can be interpreted via cases. Thus, combining hierarchical regression with fsQCA offers a novel perspective for analyzing complex causal relationships between conditions and outcomes from the viewpoint of complex causality.

3.2. Questionnaire

Based on the research framework described above, this study involves variables including digital knowledge management, organizational learning, technostress, and employees’ innovative behavior. The measurement scales are adapted from the established domestic and international literature. Digital knowledge management is primarily divided into three dimensions: knowledge acquisition, knowledge sharing, and knowledge application. Drawing upon the research of Martínez-Navalón et al. and Ağan et al. [26,62], adjusted according to the Chinese context and sample characteristics, this study includes 12 items. Organizational learning is divided into exploitative learning and exploratory learning, referencing scales from Zhang and Yang [63], each with 5 items. Technostress encompasses five dimensions: techno-uncertainty, techno-overload, techno-invasion, techno-complexity, and techno-insecurity. This study refers to mature scales from Tarafdar et al. [64] and Martínez-Navalón et al. [26], and adapts them to this study’s context, including a total of 15 items. employees’ innovative behavior is measured mainly with reference to the scales by Bao et al. [31], Zhang and Chen [65], containing 5 items. The questionnaire uses a 5-point Likert scale, where “1” means “strongly disagree” and “5” means “strongly agree.” For control variables, given that employees’ innovative behaviors may be influenced by both organizational and individual characteristics, and following Wang et al. [20] and Martínez-Navalón et al. [26], this study includes seven control variables: firm size, ownership type, employee gender, age, tenure, educational background, and job position, in order to ensure the robustness of the research findings.

3.3. Data Collection Procedure

The questionnaires were distributed and data collected through a commissioned professional research company. The surveyed enterprises are primarily sourced from technology-based firms in the Beijing region, totaling 125 companies. The survey covered both ordinary employees and managers in these firms. Reverse-coded items and screening questions were incorporated into the questionnaire to increase respondent attentiveness. Prior to formal distribution, external experts and some corporate executives were invited to evaluate and revise the items. A pilot survey of 50 questionnaires was conducted, and the survey instrument was adjusted based on the results to avoid relevant bias. Ultimately, 358 questionnaires were collected; after excluding invalid responses, 325 valid questionnaires were obtained, representing an effective response rate of 90.8%. Among the surveyed firms, business sizes of fewer than 100 employees, 101–300, 301–1000, and over 1000 accounted for 25.6%, 36%, 24%, and 14.4%, respectively. Private enterprises, state-owned, Sino-foreign joint ventures, foreign-invested, and others made up 54.4%, 26.4%, 8%, 4%, and 7.2%, respectively. Basic characteristics of respondents are shown in

Table 1. Demographic information of respondents, n = 325.

Variable	Category	No. of Respondents	Percentage
Gender	Male	112	34.46%
	Female	213	65.54%
Age	Under 30 years	10	3.08%
	31–40 years	176	54.15%
	41–50 years	101	31.08%
	51 years and above	38	11.69%
Education	Below College	39	12.00%
	Bachelor’s	214	65.85%
	Master’s and above	72	22.15%
Work Experience	1–3 years	17	5.23%
	4–9 years	166	51.08%
	10 years and above	142	43.69%
Job Position	General Staff	172	52.92%
	Lower Management	81	24.92%
	Middle Management	46	14.16%
	Senior Management	26	8.00%

:

4. Empirical Analysis

4.1. Reliability and Validity Tests

This study used SPSS 29.0 to conduct reliability and validity tests on the data, as shown in

Table 2. Reliability and validity analysis.

Variable		Item	Factor Loadings
Knowledge Acquisition Cronbach’s α = 0.824; AVE = 0.615; CR = 0.864		The enterprise has an effective digital knowledge (e.g., documents, cases, and codes) acquisition mechanism.	0.891
		The enterprise has established effective digital channels (e.g., internal knowledge bases) to facilitate employee access to relevant knowledge.	0.758
		The enterprise supports digital knowledge acquisition and sharing through information technology tools.	0.752
		The enterprise utilizes technologies such as big data analytics to automatically identify and extract valuable knowledge.	0.724
Knowledge Sharing Cronbach’s α = 0.874; AVE = 0.688; CR = 0.898		The enterprise has fostered a culture that encourages employees to share experiences and knowledge via digital platforms.	0.910
		The enterprise has an effective digital knowledge sharing mechanism to promote teamwork and collaboration.	0.813
		The enterprise uses digital tools to ensure newly acquired knowledge is quickly pushed to relevant employees or teams.	0.796
		The enterprise uses knowledge management systems or platforms to effectively classify, store, and retrieve shared digital knowledge (documents, cases, code, data, etc.) for convenient reuse.	0.792
Knowledge Application Cronbach’s α = 0.850; AVE = 0.649; CR = 0.880		Senior management clearly emphasizes and supports decision making and actions driven by digital knowledge.	0.901
		The enterprise provides necessary digital tools (e.g., intelligent decision support systems and embedded knowledge bases) to assist employees in applying relevant knowledge to daily work tasks.	0.783
		The enterprise applies technologies such as big data analytics and artificial intelligence directly in business domains.	0.776
		The enterprise has established processes based on digital knowledge to ensure systematic application of knowledge in business.	0.754
Challenge Technostress Cronbach’s α = 0.735; AVE = 0.699; CR = 0.933	Techno-overload	Using digital tools enables me to perform tasks beyond my capability.	0.790
		Using digital tools makes me feel that I have to work overtime.	0.785
		Using digital tools forces me to change my existing work habits.	0.840
	Techno-complexity	Digital tools are too complex to satisfactorily handle my work.	0.897
		I spend a lot of time learning and managing new technologies and tools.	0.868
		I often find understanding and managing new technologies and tools too complicated.	0.830
Hindrance Technostress Cronbach’s α = 0.747; AVE = 0.712; CR = 0.958	Techno-uncertainty	New digital technologies and tools are continuously introduced into work.	0.893
		The digital technology devices used in work are constantly changing.	0.830
		Digital tools used for work are frequently updated.	0.812
	Techno-invasion	Due to the use of digital tools, my time with family has decreased.	0.904
		I have to sacrifice vacation and weekend time to learn the latest digital tools.	0.818
		I feel my personal life is disturbed due to the use of digital tools.	0.814
	Techno-insecurity	I feel that my job security is continuously threatened by the emergence of new technologies and tools.	0.912
		I rarely share my knowledge with colleagues because I fear being replaced.	0.824
		I feel threatened by colleagues who are more technically proficient.	0.803
Exploitative Learning Cronbach’s α = 0.890; AVE = 0.661; CR = 0.907		The team tends to focus on deeply understanding and optimizing existing products, services, or technologies.	0.901
		The team prioritizes proven, mature, and reliable methods and technologies when solving problems or improving work.	0.794
		The team emphasizes continuously improving efficiency and quality of existing processes through practice and analysis.	0.807
		The main goal of the team is to consolidate and strengthen its position in existing markets and customer bases.	0.784
		The team frequently organizes experience-sharing meetings aimed at distilling and promoting existing best practices.	0.773
Exploratory Learning Cronbach’s α = 0.870; AVE = 0.625; CR = 0.892		The team actively seeks and follows new technologies, trends, and ideas beyond its current business domain.	0.884
		The team enjoys investing time to understand and experiment with potentially promising new knowledge, methods, or tools.	0.772
		The team is willing to take certain risks to explore and develop new products, services, or business models that meet potential or emerging market demands.	0.762
		The team encourages trying new ideas and experiments even if these attempts might fail or involve uncertainty.	0.805
		The team emphasizes learning technologies beyond current experience.	0.719
Employees’ Innovative Behavior Cronbach’s α = 0.882; AVE = 0.681; CR = 0.914		I have improved my ability to solve problems innovatively.	0.908
		I propose new ideas to improve existing situations.	0.801
		I actively support innovative ideas.	0.798
		I transform innovative ideas into practical applications.	0.788
		I have proposed innovative ideas for challenging work tasks.	0.824

. Cronbach’s alpha coefficients and composite reliability (CR) values for all variables exceed 0.8, indicating good internal consistency and reliability of the scales. In exploratory factor analysis, the minimum KMO value was 0.797. The factor loadings for all variables are above 0.7, the average variance extracted (AVE) values exceed 0.5, and the square roots of the AVEs are greater than the correlation coefficients between each variable and other variable. These results demonstrate ideal convergent and discriminant validity for all variables.

4.2. The Common Method Bias Test

Since the data in this study were collected through self-reported surveys, the potential issue of common method bias may arise. To mitigate this concern during questionnaire administration, the order of items across variables was randomized, and respondents were assured of anonymity and confidentiality, with explicit clarification that the data would be used solely for academic research purposes. In addition, Harman’s single-factor test was employed to examine the severity of common method bias. The results show that the total variance explained was 71.865%, with the first factor accounting for 29.69% of the variance, which is below the 50% threshold. Thus, the data do not suffer from serious common method bias.

4.3. Correlation Analysis

Correlation and multicollinearity analyses were conducted using SPSS 29.0.

Table 3. Descriptive statistics and correlation analysis.

Variable	1	2	3	4	5	6	7	8
Knowledge Acquisition 1	0.784
Knowledge Sharing 2	0.345 ***	0.829
Knowledge Application 3	0.379 ***	0.335 ***	0.806
Exploitative Learning 4	0.277 ***	0.425 ***	0.357 ***	0.813
Exploratory Learning 5	0.293 ***	0.334 ***	0.302 ***	0.435 ***	0.791
Challenge Technostress 6	−0.289 ***	−0.425 ***	−0.420 ***	−0.472 ***	−0.407 ***	0.836
Hindrance Technostress 7	−0.277 ***	−0.254 ***	−0.274 ***	−0.219 ***	−0.197 ***	0.276 ***	0.844
Employee Innovation 8	0.359 ***	0.438 ***	0.460 ***	0.493 ***	0.449 ***	−0.480 ***	−0.297 ***	0.825
Mean	4.021	4.025	3.999	3.986	3.983	2.017	2.003	4.084
Standard Deviation	0.566	0.641	0.611	0.642	0.589	0.491	0.353	0.607

Asterisks denote statistical significance (*** p < 0.001). The diagonal represents the square root of the AVE of the variable.

presents the correlation coefficient matrix among variables, along with descriptive statistics including means and standard deviations. The correlation coefficients between knowledge acquisition, knowledge sharing, knowledge application, exploitative learning, exploratory learning, and employees’ innovative behavior range from 0.277 to 0.379, indicating significant associations. The relationship between challenge technostress and hindrance technostress is significantly positive. This is mainly due to the high requirements and complexity of new technologies: on one hand, such technologies may be construed by employees as opportunities to enhance skills and advance their careers, thereby manifesting as challenge stress; on the other hand, they are often accompanied by increased task load, time pressure, and uncertainty, and are therefore perceived as hindrance stress. Consequently, the two types of technostress are positively associated. This finding is consistent with some prior studies, for example Wang et al. [20], Zhu et al. [21], and Ding et al. [66]. Additionally, multicollinearity tests on the independent variables showed variance inflation factor (VIF) values well below the critical threshold of 5, indicating no serious multicollinearity issues in the regression model.

4.4. Hypothesis Testing

4.4.1. Main Effect Tests

Model 1 in

Table 4. Regression model and results (main effects).

Variable	Employees’ Innovative Behavior
	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
Dependent Variable
Knowledge Acquisition		0.378 ***			0.146 **		0.124 *	0.1079 *
Knowledge Sharing			0.420 ***		0.280 ***		0.187 ***	0.218 ***
Knowledge Application				0.455 ***	0.312 ***		0.248 ***	0.268 ***
Mediating Variable
Exploitative Learning						0.347 ***	0.268 ***
Exploratory Learning						0.307 ***		0.269 ***
Controlling Variable
Firm Size	0.005	0.003	0.046	0.010	0.035	−0.033	0.015	0.007
Ownership Type	0.018	0.016	−0.004	0.006	−0.006	0.013	−0.009	0.005
Gender	0.061	0.078	0.042	0.016	0.024	0.004	0.013	0.003
Age	0.057	0.038	0.034	0.059	0.035	0.005	0.020	0.018
Work Experience	−0.028	−0.017	−0.044	−0.012	−0.023	−0.051	−0.033	−0.032
Education	0.135 *	0.091	0.070	0.067	0.028	0.042	0.026	0.000
Job Position	−0.039	−0.026	0.022	0.014	0.043	−0.024	0.025	0.036
R2	0.018	0.141	0.204	0.220	0.327	0.321	0.386	0.380
Adjusted R2	0.003	0.119	0.183	0.201	0.306	0.301	0.365	0.318
F	0.852	6.451	10.099	11.160	15.290	16.492	17.900	17.439

Asterisks denote statistical significance (* p < 0.05, ** p < 0.01, and *** p < 0.001).

is the regression model of control variables on employees’ innovative behavior. Models 2, 3, 4, and 5 add knowledge acquisition, knowledge sharing, and knowledge application to Model 1, respectively. Results show that knowledge acquisition, knowledge sharing, and knowledge application in digital knowledge management exert positive effects on employees’ innovative behavior, with direct effects of 0.378, 0.420, and 0.455, respectively, all significant at p < 0.001. Model 5 also supports the hypotheses. Therefore, H1a, H1b, and H1c are confirmed.

As shown in Table 4, the regression coefficients among the core variables were all significant, whereas the control variables did not exhibit significant effects. One possible explanation for the insignificance of the control variables lies in the particular theoretical and empirical context. For instance, Beijing’s technology-based enterprises, regardless of ownership form, tend to converge in terms of innovation incentives (e.g., R&D subsidies and talent programs), which reduces the differentiation effect of ownership type on innovation behaviors. In the context of digital transformation, employees’ innovative behaviors are more strongly driven by systemic factors such as digital knowledge management and organizational learning, while the direct influence of individual characteristics appears weaker—for example, innovation is not necessarily dependent on accumulated seniority. This indirectly suggests that when organizations provide sufficient knowledge support and learning opportunities, the marginal effects of individual differences on innovation are attenuated.

4.4.2. Mediation Effect Tests

Combining Models 6–8 in Table 4 and Models 11–14 in

Table 5. Regression model (mediator variable).

Variable	Mediator Variable
	Exploitative Learning					Exploratory Learning
	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18
Dependent Variable
Knowledge Acquisition		0.308 ***			0.080 +		0.294 ***			0.141 *
Knowledge Sharing			0.444 ***		0.346 ***			0.324 ***		0.231 ***
Knowledge Application				0.381 ***	0.239 ***				0.288 ***	0.163 ***
Controlling Variable
Firm Size	0.038	0.036	0.081	0.042	0.074	0.081 *	0.080 *	0.11 ***	0.084 ***	0.105 ***
Ownership Type	0.036	0.034	0.012	0.026	0.011	−0.023	−0.025	−0.040	−0.031	−0.041
Gender	0.075	0.090	0.055	0.038	0.040 *	0.100	0.114 +	0.085	0.071	0.080
Age	0.078	0.062	0.053	0.079	0.055	0.083 +	0.068	0.065	0.084 +	0.064
Work Experience	0.040	0.048	0.023	0.053	0.037	0.031	0.039	0.018	0.041	0.032
Education	0.108	0.073	0.039	0.051	0.010	0.180 **	0.146 **	0.130 *	0.137 *	0.104 +
Job Position	−0.013	−0.003	0.050	0.031	0.067	−0.033	−0.023	0.014	0.001	0.024
R2	0.028	0.100	0.213	0.154	0.271	0.059	0.137	0.176	0.145	0.229
Adjusted R2	0.006	0.078	0.193	0.133	0.247	0.038	0.116	0.155	0.123	0.204
F	1.289	4.308	10.669	7.206	11.647	2.834	6.292	8.429	6.695	0.303

Asterisks denote statistical significance (⁺ p < 0.1, * p < 0.05, ** p < 0.01, and *** p < 0.001).

reveals that exploitative learning mediates the relationships between knowledge acquisition and employees’ innovative behavior, knowledge sharing and employees’ innovative behavior, and knowledge application and employees’ innovative behavior, thus supporting hypotheses H2a, H2b, and H2c. Likewise, Models 6–9 in Table 4 and Models 15–19 in Table 5 show exploratory learning mediates the same relationships, confirming hypotheses H3a, H3b, and H3c. Further analysis of control variables indicates that firm size is significant in models where exploratory learning serves as the dependent variable (e.g., Models 16, 17, and 18), suggesting that firm size contributes to explaining exploratory innovation behaviors. A plausible reason is that larger firms are better positioned to allocate resources to exploratory innovation; however, firm size did not show significant effects in models of exploitative learning. Similarly, employee education level was significant in models predicting exploratory learning, but not in those predicting exploitative learning. This may be because employees with higher education levels possess advantages in acquiring and integrating new knowledge across domains, whereas exploitative innovation emphasizes knowledge reuse, where education has less influence. Gender was significant only in Model 13 (exploitative learning), but overall, gender exhibited weak explanatory power, and gender differences did not constitute a key determinant. This finding is consistent with Hosseini et al. [67] and Martínez-Navalón et al. [26], who also reported that gender did not significantly affect the relationships among knowledge management-related variables in their studies.

Furthermore, robustness of the mediation effects was verified using the SPSS PROCESS macro with the Bootstrap method (5000 resamples, 95% confidence intervals). The confidence intervals for both direct and indirect effects of knowledge acquisition, sharing, and application did not include zero, indicating partial mediation effects. In summary, organizational learning mediates the relationship between each dimension of digital knowledge management and employees’ innovative behavior, confirming H2a, H2b, H2c, H3a, H3b, and H3c. Comparison of effect sizes shows that the mediation effect of exploitative learning is greater than that of exploratory learning.

4.4.3. Moderation Effect Tests

According to Models 19 and 20 in

Table 6. The moderating effect test.

Variable	Employees’ Innovative Behavior
	The Moderating Role of Challenge Technostress		The Moderating Role of Hindrance Technostress
	Model 19	Model 20	Model 21	Model 22
Dependent Variable
Exploitative Learning	0.349 ***		0.424 ***
Exploratory Learning		0.311 ***		0.422 ***
Moderator Variable
Challenge Technostress	−0.398 ***	−0.445 ***
Hindrance Technostress			−0.35 ***	−0.383 ***
Interaction Term
Exploitative Learning × Challenge Technostress	−0.143 ***
Exploratory Learning × Challenge Technostress		−0.233 *
Exploitative Learning × Hindrance Technostress			−0.35
Exploratory Learning × Hindrance Technostress				−0.106
Controlling Variable
Firm Size	−0.017	−0.029	−0.003	−0.021
Ownership Type	0.009	0.029	0.003	0.028
Gender	0.023	0.016	0.022	0.011
Age	0.014	0.015	0.027	0.022
Work Experience	−0.025	−0.02	−0.042	−0.036
Education	0.070	0.049	0.085	0.057
Job Position	−0.018	−0.012	−0.044	−0.037
R2	0.337	0.329	0.289	0.257
F	15.95	15.37	12.768	10.864

Asterisks denote statistical significance (* p < 0.05 and *** p < 0.001).

, the interaction terms between challenge technostress and exploitative learning $(\mathsf{\beta } = -0.233, p < 0.001)$and between challenge technostress and exploratory learning ($\mathsf{\beta } = -0.233, p < 0.001$) are both negative, indicating a negative moderating effect of challenge technostress on the relationship between organizational learning and employees’ innovative behavior. Figure 2 illustrates that high challenge technostress negatively moderates the relationships between both types of organizational learning (exploitative and exploratory) and employees’ innovative behavior, contrary to the expected positive moderation. Therefore, H4a and H4b are not supported. This finding diverges from the initial hypotheses and indicates that the role of technological stress in the digital context is more complex. Accordingly, this study draws upon conservation of resources (COR) theory to further interpret these results. COR theory posits that individuals strive to acquire, maintain, protect, and foster their key resources (e.g., time, energy, knowledge, and self-efficacy), while attempting to avoid resource depletion. When employees are confronted with ongoing technological updates and complex system demands, even if these pressures are theoretically categorized as challenge stressors. Once they exceed employees’ resource thresholds, they are perceived as threats rather than opportunities. Employees are thus compelled to allocate their limited resources to meeting basic work requirements and coping with technological tasks, leaving insufficient capacity to translate learning outcomes into innovative behaviors. In other words, under conditions of resource scarcity, challenge-related technostress may trigger a resource depletion effect, thereby weakening the positive impact of organizational learning on innovative behavior and resulting in its negative moderating effect.

Models 21 and 22 in Table 6 show that the interaction terms between hindrance technostress and exploitative learning $(\mathsf{\beta } = -0.350, p > 0.1)$and between hindrance technostress and exploratory learning $(\mathsf{\beta } = -0.106, p > 0.1)$, although negative, are not statistically significant, indicating no moderating effect of hindrance technostress on the relationship between organizational learning and employees’ innovative behavior. Hence, H5a and H5b are not supported. Regarding the non-significant result of hindrance technostress, this study also draws upon COR theory for interpretation. On the one hand, organizations undergoing digital transformation typically provide technological support and training, which help employees mitigate the negative effects of hindrance stress and thereby alleviate resource depletion. On the other hand, through long-term adaptation, employees may have developed tolerance towards common technological obstacles, which reduces the disruptive influence of hindrance stress on organizational learning and employees’ innovative behavior, ultimately leading to its non-significant moderating effect.

5. Configurational Analysis of Employees’ Innovative Behavior

Hierarchical analysis demonstrates that knowledge acquisition, knowledge sharing, knowledge application, exploitative learning, and exploratory learning all have significant positive effects on employees’ innovative behavior. Therefore, based on the prior empirical analysis, this study further employs fuzzy-set qualitative comparative analysis (fsQCA) to conduct configurational analysis of these antecedent variables.

5.1. Variable Calibration

Before conducting fsQCA, calibration was performed for each condition variable and the outcome variable. Drawing on the calibration method of Zhang et al. [68], the direct calibration approach was applied by setting three threshold anchors for full membership, crossover point, and full non-membership at the 95th percentile, mean, and 5th percentile of the case descriptive statistics, respectively. To avoid membership scores exactly at 0.50 and the resulting ambiguity in configurational assignment, a constant of 0.001 was subtracted from the 0.50 membership score. The anchor points for calibration of antecedent conditions and the outcome are presented in

Table 7. Variable calibration.

Conditions and Outcomes	Fuzzy Set Calibration
	Full Non-Membership	Crossover Point	Full Membership
Knowledge Acquisition	3.000	4.250	4.750
Knowledge Sharing	3.000	4.250	5.000
Knowledge Application	3.000	4.000	4.750
Exploitative Learning	3.000	4.200	4.800
Exploratory Learning	3.000	4.000	4.800
Employees’ Innovative Behavior	3.200	4.200	5.000

.

5.2. Necessity Analysis

This study first tested whether single antecedent variables constitute necessary conditions for high employees’ innovative behavior or non-high innovative behavior. Following Ragin’s recommendation that a condition with a consistency score greater than 0.9 indicates necessity, fsQCA 4.1 software was used to analyze the necessary conditions for employee high and non-high innovative behavior. The results are shown in

Table 8. Necessary condition analysis results.

Condition Variables	Outcome Variable
	High Employee’s Innovative Behavior		Low Employee’s Innovative Behavior
	Consistency	Coverage	Consistency	Coverage
Knowledge Acquisition	0.690	0.701	0.502	0.564
~Knowledge Acquisition	0.571	0.509	0.734	0.723
Knowledge Sharing	0.695	0.728	0.480	0.557
~Knowledge Sharing	0.577	0.501	0.766	0.735
Knowledge Application	0.781	0.684	0.556	0.539
~Knowledge Application	0.473	0.491	0.674	0.773
Exploitative Learning	0.722	0.720	0.483	0.533
~Exploitative Learning	0.531	0.482	0.746	0.748
Exploratory Learning	0.779	0.712	0.508	0.514
~Exploratory Learning	0.468	0.462	0.715	0.781

The symbol ~ denotes the absence of the condition.

. The results further show that the consistency of all variables remained below 0.9, indicating that none of them can be strictly defined as a single necessary condition. However, differences in consistency and coverage across variables reveal their varying degrees of importance for innovative behavior. Specifically, knowledge acquisition and knowledge sharing demonstrated relatively high consistency levels in high-innovation cases (0.690 and 0.695, respectively), suggesting their active role in fostering employee innovation. By contrast, their consistency levels were much lower in low-innovation cases, implying that the absence of these factors does not directly lead to reduced innovation. Knowledge application displayed the highest consistency (0.781) among all variables, underscoring its critical role in supporting high innovation; yet it still did not meet the strict threshold for necessity, reflecting the complexity of innovation behavior. From a learning perspective, both exploitative and exploratory learning showed relatively high consistency (0.722 and 0.779, respectively). Notably, exploratory learning played a particularly prominent role in driving high innovation, while its consistency dropped to 0.508 in low-innovation cases, indicating that it primarily functions as a catalyst for high-level innovation. Exploitative learning, by contrast, maintained moderately high levels of consistency across both high-and low-innovation contexts (0.722 and 0.746), suggesting a dual role: it can deepen incremental innovation but, when overemphasized, may also constrain breakthrough innovation.

5.3. Configurational Condition Analysis

Building on the necessity analysis, configurational analysis was conducted on five antecedent variables: knowledge acquisition, knowledge sharing, knowledge application, exploitative learning, and exploratory learning. Consistent with common academic practice, a consistency threshold of 0.8 was set along with a Proportional Reduction in Inconsistency (PRI) threshold of 0.7 to ensure validity. Due to inconclusive prior research regarding the exact relationships between these five antecedents and employees’ innovative behavior, clear counterfactual analyses could not be performed. Hence, for each antecedent variable, both presence and absence conditions were selected in the analysis. Using fsQCA, complex, parsimonious, and intermediate solutions were generated. Guided by the existing literature, the intermediate and parsimonious solutions were used to distinguish core and peripheral conditions within configurations. The final configurational path analysis results are shown in

Table 9. fsQCA findings.

Configurations	High Employee’s Innovative Behavior			Low Employee’s Innovative Behavior
	P1	P2	P3	NP1	NP2	NP3	NP4	NP5	NP6	NP7
Knowledge Acquisition	●	●		⊗			⊗	●	⊗	⊗
Knowledge Sharing	●		●		⊗	⊗	⊗	⊗	●	⊗
Knowledge Application	●	●	●			⊗		⊗	⊗	●
Exploitative Learning		●	●	⊗	⊗			⊗	⊗	⊗
Exploratory Learning	●	●	●	⊗	⊗	⊗	⊗
Consistency	0.920	0.921	0.916	0.898	0.878	0.897	0.871	0.919	0.923	0.874
Raw Coverage	0.48	0.478	0.491	0.451	0.501	0.451	0.479	0.289	0.266	0.321
Unique Coverage	0.04	0.039	0.051	0.024	0.030	0.010	0.012	0.017	0.012	0.026
Overall Solution Consistency	0.884			0.834
Overall Solution Coverage	0.570			0.699

Black circles (●) indicate the presence of a condition, and circles with (⊗) indicate its absence, while blank spaces indicate “don’t care condition.” Large circle: core condition. Small circle: peripheral condition.

.

Regarding high employees’ innovative behavior, Table 9 reveals three distinct configurational paths, each with consistency scores exceeding 0.9 and above the 0.8 threshold, indicating that all three paths can robustly explain the occurrence of high employees’ innovative behavior. Additionally, the overall solution consistency is 0.884, reflecting good consistency, while the overall solution coverage is 0.570, indicating that these paths collectively explain 57% of the variance in factors influencing supply chain resilience. Therefore, the overall solution coverage is considered satisfactory.

Path 1 is exploration-driven innovation through full-chain knowledge collaboration. The core characteristic of this path is the close integration of highly active digital knowledge management throughout the entire process (high acquisition, high sharing, high application) with exploratory learning. When employees are able to efficiently and extensively acquire new internal and external knowledge using digital tools such as web crawlers, APIs, intelligent recommendations, and external knowledge bases; achieve high-efficiency knowledge sharing through internal social platforms, collaboration tools, knowledge repositories, and online communities; and actively apply this knowledge to real work and business scenarios, a solid foundation for innovation is established. When these three aspects combine with high exploratory learning, the breakthrough nature of exploratory learning—characterized by breaking existing frameworks and exploring unknown areas—can drive knowledge to transition from “incremental application” to “disruptive creation.” For example, in a technology R&D team, members broadly acquire cutting-edge industry knowledge, frequently share research progress, rapidly translate theories into experimental plans, and continuously explore new technological pathways, ultimately producing groundbreaking innovations. This path emphasizes that knowledge gains value through flow and achieves qualitative breakthroughs through exploration. It is suitable for complex environments that require disruptive innovation.

Path 2 is a knowledge transformation-oriented dual-driven innovation. This path primarily features the effective synergy of ambidextrous learning (high exploitative learning and high exploratory learning) with knowledge acquisition and transformation/application (high knowledge acquisition and high knowledge application), where high knowledge sharing is not a necessary condition. Employees can deeply cultivate existing domains using digital tools while efficiently exploring unknown areas. Compared with Path 1, this path illustrates that even without intensive internal knowledge sharing, strong capabilities in acquiring internal and external knowledge and efficiently transforming and applying it can effectively drive employees’ innovative behavior. Within this mechanism, employees often rely directly on digital knowledge management tools to access the required information from external databases, collaborative platforms, or open resources, without depending heavily on frequent internal knowledge sharing. Illustrative evidence can be found in Beijing Bayuegua Technology Co., Ltd. (Beijing, China), which has developed the “Innovation Brain” platform to integrate the global patent data and scientific literature. Through cross-linguistic retrieval and AI-enabled analytical tools, the company accelerates the transformation of external knowledge into actionable insights, thereby establishing an “external acquisition–application–innovation” model. Similarly, Beijing Red Butterfly Technology Co., Ltd. (Beijing, China) exemplifies how externally introduced scientific research outputs can be rapidly engineered and embedded into product applications. These cases demonstrate that in contexts characterized by high task independence and strong demands for rapid application within the innovation process, the knowledge-sharing stage may be shortened or even embedded into the application workflow—for instance, by directly uploading analytical results or code into a project repository for reuse—thus reducing the need for extensive communication and sharing.

Path 3 is a knowledge sharing-oriented dual-driven innovation. The path emphasizes the combination of knowledge sharing, knowledge application, and ambidextrous learning, while high knowledge acquisition is not required. For firms with abundant internal knowledge stocks or relatively stable channels for acquiring external knowledge, efficient digital knowledge sharing facilitates cross-departmental and cross-individual communication, collision, and integration. Through shared platforms, employees can rapidly access tacit and explicit knowledge and engage in exchanges with team members; through ambidextrous learning they actively advance the application and recombination of knowledge. For example, some well-established Beijing technology firms such as Lenovo and Yonyou have long accumulated extensive internal knowledge repositories and cross-departmental collaboration mechanisms. In routine innovation activities, employees primarily rely on the organization’s internal knowledge deposits and digital sharing platforms for discussion and application, whereas external knowledge is often introduced automatically via stable corporate partnerships or subscription databases, making its acquisition relatively automated. On an efficient digital knowledge-management and sharing platform, knowledge sharing equates to knowledge acquisition, and subsequent application becomes more effective. Compared with Path 2, Path 3 emphasizes the importance of knowledge sharing in mature technology firms. In this pathway, digital knowledge management primarily aims to construct a vibrant internal knowledge market and an efficient channel for application, thereby empowering employee’s innovative behavior. The joint cultivation and realization of innovation outcomes underscore the empowering role of an internal knowledge ecosystem and ambidextrous balance for innovation.

Table 9 presents seven configurational paths for low employees’ innovative behavior. The overall solution consistency is 0.834, indicating good consistency; the overall solution coverage is 0.699, meaning these paths explain 69.9% of the reasons for non-high innovative behavior, thus showing good coverage. These paths suggest that lacking dimensions of digital knowledge management and organizational learning cannot effectively promote employee innovation. Even when single conditions such as knowledge acquisition, knowledge sharing, or knowledge application exist as core conditions, without the synergistic effect of other variables, employees are unable to exhibit high innovative behavior.

5.4. The Robustness Test

Given that fsQCA is a set-theoretic method, this study conducted robustness testing using set-theoretic specific procedures by adjusting consistency and frequency thresholds. If new configurations identified after adjustments are subsets of previous configurations, the results are considered robust. By raising the PRI consistency threshold from 0.7 to 0.8 and applying a stricter threshold to analyze the truth table, the new configurations for high employees’ innovative behavior remained largely consistent with the previous ones. Similarly, adjusting the frequency threshold to 3 and reanalyzing produced configurations largely consistent with earlier results, demonstrating that the configuration outcomes possess satisfactory robustness.

6. Conclusions and Implications

6.1. Research Conclusions

Multilevel regression analysis reveals that the three dimensions of digital knowledge management—knowledge acquisition, knowledge sharing, and knowledge application—each exert a significant positive effect on employees’ innovative behavior. Both exploitative learning and exploratory learning mediate the relationship between digital knowledge management and employees’ innovative behavior, with the mediating effect of exploitative learning being stronger than that of exploratory learning. Drawing on conservation of resources theory, this study finds that under conditions of resource scarcity, challenging technological demands can produce a “resource predation effect” that diminishes the positive influence of organizational learning on innovative behavior, thereby rendering the moderating effect negative.

Through fsQCA configurational analysis, three distinct pathways to enhancing employees’ high innovative behavior were identified: exploration-driven innovation characterized by full-chain synergistic digital knowledge management; knowledge transformation-oriented dual-driven innovation; and knowledge sharing-oriented dual-driven innovation. High knowledge application appears in all three pathways, strongly indicating that regardless of how knowledge is acquired or shared, and regardless of the emphasis on learning styles, the ultimate transformation of knowledge into practical action and application is the quintessential core driver of employee innovation. High exploratory learning is also present in all three pathways, highlighting that actively seeking and experimenting with new knowledge, technologies, and methods is a common and critical source of motivation in achieving high innovation. The pathways analysis further suggests a certain functional substitutability or complementarity between knowledge acquisition and knowledge sharing. Exploitative learning appears within the knowledge transformation -oriented and knowledge sharing-oriented dual-driven innovation paths, demonstrating that when exploratory learning is already present, its combination with exploitative learning can generate a powerful dual-driven innovation effect. The path characterized by full-chain synergistic exploration-driven innovation shows that even in the absence of prominent exploitative learning, high innovation can be driven through knowledge acquisition, sharing, and application combined with exploratory learning.

Digital knowledge management plays a crucial enabling role. As a core condition in the pathways leading to high employees’ innovative behavior, knowledge acquisition, sharing, application, exploratory learning, and exploitative learning all heavily depend on and can be significantly enhanced by digital tools, indicating that digital knowledge management is a key enabling factor embedded within the operational mechanisms of these core conditions. In the context of the rapid advancement of artificial intelligence, classical theoretical frameworks must be re-evaluated within new situational environments. The knowledge-based view emphasizes that knowledge is the organization’s most important strategic resource, and digital technologies—exemplified by artificial intelligence—substantially enhance the efficiency of knowledge acquisition and recombination, thereby directly accelerating the adoption and deepening of digital knowledge management within organizations.

The principal contributions of this study can be delineated as follows: Firstly, situated within the context of China’s transitional economy, this research systematically constructs the pathways and elucidates the impact mechanisms through which digital knowledge management influences employees’ innovative behavior. This enriches the extant knowledge management literature by incorporating a cross-level transmission chain analysis, thereby offering a new perspective to deepen the understanding of digital empowerment in fostering innovation. Secondly, by employing a methodological triangulation that integrates traditional linear analysis with fuzzy-set qualitative comparative analysis (fsQCA), this study uncovers multiple equivalent pathways that drive high employees’ innovative behavior. This methodological approach advances theoretical insights into the complex causal configurations underpinning the relationship between knowledge management and employee innovation. Thirdly, the present study contributes to the nuanced understanding of technostress within the knowledge innovation landscape, highlighting its differentiated moderating effects in the practice of digital knowledge management.

6.2. Managerial Implications

Based on the above findings, this study provides the following managerial recommendations to assist enterprises in better implementing digital knowledge management during digital transformation, enhancing employee innovation performance, and ultimately achieve the sustainable development of enterprises.

Firstly, Accelerate the advancement of digital knowledge management to promote employees’ innovative behavior. Enterprises should actively introduce digital technologies to optimize knowledge acquisition, sharing, and application processes, thereby improving the efficiency and effectiveness of knowledge management. Specifically, enterprises can focus on building unified digital knowledge management platforms or actively adopting digital tools that facilitate knowledge application, such as intelligent workflows, decision support systems, and low-code platforms. Such platforms or tools should ideally support convenient access to external information, facilitate internal communication and experience sharing, embed smart prompts and best practices within workflows to enable knowledge application and utilization, and support deep mining and optimization of existing knowledge while exploring new knowledge. Enterprises should establish effective knowledge sharing mechanisms to promote collaboration and exchange among employees. Through online collaboration platforms, social media, and other digital tools, enterprises can break down departmental barriers to facilitate cross-departmental and cross-team knowledge sharing, stimulating innovative thinking. It is important to set up incentive and evaluation mechanisms for knowledge application. Using big data analytics on knowledge access, downloads, citations, comments, and sharing behavior, enterprises can identify outdated, inefficient, or missing knowledge, and dynamically update, optimize, or retire knowledge base content.

Secondly, enterprises should select or concurrently develop appropriate innovation pathways aligned with their own resources, structure, and strategy. For instance, startups may rely more on Path 1—full-chain synergistic exploration-driven innovation—to quickly acquire new external knowledge; mature firms with abundant internal knowledge may emphasize Path 3—knowledge sharing-oriented dual-driven innovation. The pathway analysis indicates a certain substitutability or complementarity between knowledge acquisition and sharing. Enterprises need to evaluate their strengths and weaknesses in knowledge acquisition and sharing, and allocate resources accordingly to support robust external knowledge acquisition and internal knowledge circulation and sharing.

Thirdly, while enhancing employees’ digital literacy, enterprises should also improve their stress resilience and establish a sustainable organizational culture. Digital literacy is the cornerstone for employees to effectively operate digital knowledge management systems. In addition to mastering fundamental operational skills, employees in innovation contexts can actively use digital tools to retrieve, evaluate, and integrate information, enabling efficient knowledge flow and transformation into practical application. At the same time, it is essential to actively enhance employees’ stress resilience. The inherent high-risk, fast-paced digital collaboration environment associated with exploratory learning brings pressures such as techno-overload, techno-invasion, and rapid technological iteration, which can easily lead to employee burnout and a decline in innovation willingness. Therefore, building employees’ psychological resilience and improving their capacity to cope with stress is critical. This requires enterprises to establish clear fault-tolerance mechanisms and cultivate a sustainable and high psychological safety culture that encourages curiosity, experimentation, and tolerance for failure. Providing training on stress management and emotional regulation helps employees cope with setbacks and failures. Additionally, systematically alleviating innovation stressors through measures such as setting reasonable goals, granting work autonomy, and ensuring work–life balance is necessary. It is worth noting that improving digital literacy itself can relieve some stress, while robust psychological resilience motivates employees to proactively learn new technologies and bravely try exploratory applications.

6.3. Limitations and Future Directions

This study explores the mechanism by which digital knowledge management influences employee’s innovative behavior based on cross-sectional survey data, reflecting the static effect relationships among variables. This approach has certain limitations in revealing the underlying patterns of digital knowledge management. This study primarily focuses on technology-based enterprises located in Beijing, which introduce certain limitations in terms of industry and geographical scope, thereby constraining the generalizability of the findings. Moreover, although the sample size meets the basic requirements for statistical analysis, the overall scale remains relatively limited, which may to some extent affect the robustness of the results. Future research could expand the sample across broader regions and diverse industries to further enhance the external validity and generalizability of the conclusions.

Under the context of paradigm reconstruction in digital knowledge management, the specific new mechanisms of knowledge flow exhibit high complexity. Therefore, future research could combine both cross-sectional and longitudinal approaches, collecting multi-wave data from broader samples across different regions and types of enterprises to uncover the dynamic causal effects among variables. Qualitative methods such as employee interviews or case studies may be employed to probe the subjective perception mechanisms of technological pressure, thereby yielding a richer understanding of its impacts. Moreover, in advancing the study of new paradigms of digital knowledge management, researchers should further examine how AI-driven modes of human–machine interaction reshape processes of knowledge absorption, integration, and recombination.