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Article

Unpacking the Path from Knowledge Heterogeneity to Team Creativity: A Knowledge-Based View and Dynamic Capability Theory Perspective

by
Hongyi Cai
1,
Heng Zhao
2,* and
Yong Huang
3,*
1
Business School, University of Shanghai for Science and Technology, Shanghai 200093, China
2
School of Economics and Management, Beijing Jiaotong University, Beijing 100044, China
3
School of Public Administration, China University of Geosciences, Wuhan 430079, China
*
Authors to whom correspondence should be addressed.
Adm. Sci. 2025, 15(11), 408; https://doi.org/10.3390/admsci15110408
Submission received: 5 September 2025 / Revised: 8 October 2025 / Accepted: 20 October 2025 / Published: 22 October 2025
(This article belongs to the Special Issue Digital Capabilities in Innovation and Strategy amid Technological Development and Economic Uncertainty)
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Abstract

This study examines how knowledge heterogeneity affects team creativity in the context of China’s digital intelligence transformation and analyzes the mediating effects of digital intelligence enablement and technology management capability. Grounded in the knowledge-based view and dynamic capability theory, a sequential mediation model of the effect of knowledge heterogeneity on team creativity is developed and tested using both hierarchical regression and structural equation modeling. We conducted a two-wave anonymous questionnaire survey in a knowledge-intensive enterprise in Shanghai and obtained 203 valid responses. The results indicate that we draw three main conclusions. First, knowledge heterogeneity has a significant positive effect on team creativity. Second, technology management capability and digital intelligence enablement act as mediators in this relationship. Technology management capability improves the efficiency of transforming heterogeneous knowledge, while digital intelligence enablement facilitates the integration and application of such knowledge. Finally, technology management capability and digital intelligence enablement together form a sequential mediation pathway, where heterogeneous knowledge first enhances technology management capability and then promotes digital intelligence enablement, ultimately fostering team creativity. This study deepens the understanding of how knowledge heterogeneity promotes team creativity and provides implications for advancing digital intelligence transformation and industry competitiveness.

1. Introduction

Digital intelligence transformation has been integrated across industries, driving firms to pursue digital intelligence upgrades and innovation. Technologies such as artificial intelligence, 5G, and big data are widely deployed, and the development of digital intelligence capabilities has accelerated (Hanelt et al., 2021; Plekhanov et al., 2023; Verhoef et al., 2021). Firms are recognized as the primary site of technological innovation, and breakthroughs in key technologies together with the development of major new products are viewed as crucial to industrial upgrades. In many traditional enterprises, however, the transformation of digital intelligence is impeded by limited expertise, insufficient experience and sensitivity, and uncertainty in the external environment. Thus, while some initiatives succeed, many face substantial risk (Oludapo et al., 2024). Within organizations, corporate visions and strategic objectives are realized by teams and employees. Contemporary organizations are typically composed of multiple teams, and organizational performance and goal attainment are shaped by the effectiveness with which specific tasks are carried out (Leblanc et al., 2024; Mathieu et al., 2017). Digital intelligence increases the granularity of the division of labor, and teams increasingly function as basic work units (Hundschell et al., 2022; Mathieu et al., 2017; Salas et al., 2018). Team attributes, such as knowledge breadth and depth and the capacity to sense the external environment, contribute to whether superior decisions are made in dynamic and volatile contexts (J. Wang et al., 2019). In this context, strengthening team creativity to adapt to ongoing transformation is treated as a central concern for both scholars and practitioners.
Two main streams are recognized in research on team creativity. The first stream is concerned with how organizational leadership shapes employee behavior, identifies the mechanisms through which team creativity forms, and considers how leaders cultivate a supportive climate, establish team values, and guide members to leverage collective strengths (Lee et al., 2018). In studies on executive teams, a shift is observed from readily observable attributes such as gender, age, and tenure to latent attributes such as values, personality, and leadership style (Pham & Lo, 2023; Wei et al., 2021). The second stream concerns the effects of team member characteristics on performance. Variation in values, knowledge, and innovation experience is argued to affect team creativity (Hundschell et al., 2022; J. Wang et al., 2019). Overall, individual differences are understood to shape the cognitive foundations and capability of teams. A broader set of solutions for firm growth is provided by greater team heterogeneity, which in turn improves the quality of organizational decision making (Hoever et al., 2012).
Although heterogeneity is recognized as a core issue in team research, attention is primarily paid to the effects of demographic attributes of top management teams such as gender, age, and tenure on organizational performance (Ma et al., 2023; Pham & Lo, 2023). By contrast, knowledge heterogeneity and its influence on team creativity remain underexamined. From a knowledge-based view (KBV), knowledge is regarded as the most critical and valuable organizational and innovation resource. Employees can enhance innovation capability and secure sustained competitive advantage by continuously accumulating and recombining knowledge. Creativity is treated as a central driver of organizational development and is conceptualized as a process in which knowledge is connected and recombined, and doing so leads to the formation of creative ideas and problem-solving solutions. Organizations in China are presented with opportunities generated by a new wave of industrial transformation (Anderson et al., 2014). The advantages of digital intelligence technologies are increasingly evident. Remote collaboration removes location constraints, health care services are digitally networked, and electronic commerce continues to lead to innovations, which provides new avenues for organizational innovation and expands depth and scope (Hanelt et al., 2021; Verhoef et al., 2021). From the perspective of dynamic capability theory, organizations must develop the ability to integrate, build, and reconfigure internal and external resources to adapt to rapidly changing environments, and digital intelligence technologies provide new possibilities for cultivating and enhancing such capabilities. Therefore, while the KBV emphasizes the foundational role of knowledge resources, dynamic capability theory explains how organizations can transform these resources into competitive advantage through specific processes and capabilities. In the context of digital intelligence enablement, it is thus both necessary and urgent to examine how knowledge heterogeneity promotes digital intelligence enablement and enhances team competitiveness.
To fill the above research gap, in this study, a sequential mediation model grounded in KBV and dynamic capability theory is constructed, positing that technology management capability and digital intelligence enablement are key pathways linking knowledge heterogeneity to team creativity. The objectives of this study include three main aspects: first, to test the direct effect of knowledge heterogeneity on team creativity; second, to examine the mediating roles of technology management capability and digital intelligence enablement; and third, to explore whether these mediators jointly form a sequential mechanism, whereby knowledge heterogeneity strengthens technology management capability, which subsequently facilitates digital intelligence enablement and ultimately enhances team creativity.
The contributions of this study are threefold. First, in building on the KBV, the focus of research on team creativity has shifted from demographic differences toward knowledge-based team heterogeneity, which enriches research into deep level heterogeneity.
Second, this study extends the investigation of knowledge heterogeneity to the domain of team creativity and conducts a systematic examination in a digital intelligence context. In doing so, it combines the KBV with dynamic capability theory to reveal the mediating role of technology management capability and digital intelligence enablement. It is thus clarified how knowledge heterogeneity, through dynamic capabilities, strengthens digital transformation and promotes team creativity. Unlike prior work that treats team collaboration, task performance, or financial indicators as outcomes, this study centers on creativity as a key driver of sustained competitive advantage.
Third, this study is focused on the context of China, which is of practical importance. At the national level, digital and intelligent transformation is promoted through continued expansion of digital infrastructure and the industrial internet. At the firm level, wide variation in digital readiness, complex supply chain coordination, and stringent data governance requirements is observed. Under this context, this study investigates how knowledge heterogeneity is transformed into team creativity, highlighting the pathways of knowledge configuration and capability building at the team level when large-scale technology investment and organizational change proceed in parallel. Analysis provides evidence grounded in local practice and offers targeted managerial implications for Chinese firms pursuing digital transformation. The theoretical model of this study is shown in Figure 1.

2. Theoretical Framework and Hypotheses

2.1. Knowledge-Based View and Dynamic Capability Theory

The knowledge-based view (KBV) conceives a firm as an organization that integrates and creates knowledge as its core element. This perspective argues that the knowledge possessed by firms, especially tacit, context-dependent, and difficult-to-transfer proprietary knowledge, constitutes the most scarce and strategically valuable resource and serves as the fundamental basis for achieving and sustaining long-term competitive advantage (Grant, 1993). Differences in knowledge reserves and structures across organizations explain their disparities in capabilities and performance (Argote & Ingram, 2000). Compared with tangible resources, knowledge is socially embedded and causally ambiguous, making it difficult to obtain through markets or imitate by competitors (Barney, 1991; Eisenhardt & Martin, 2000). Thus, in managerial practice, knowledge is not only a production factor but also a key force that builds entry barriers and lasting advantages (Kogut & Zander, 1992). From this perspective, the core task of the firm is to establish mechanisms for effective knowledge acquisition, flow, and integration, and to continually renew capabilities through knowledge recombination and innovation in order to maintain competitive advantage in dynamic environments.
Dynamic capability theory was developed from the resource-based view and focuses on how organizations acquire, integrate, and reconfigure resources in changing environments rather than on the mere possession of existing resources (Eisenhardt & Martin, 2000; Teece, 2014; Teece et al., 1997). Prior research indicates that dynamic capabilities are structural and hierarchical. They comprise higher-order managerial capabilities oriented toward change and renewal and execution capabilities that support daily operations. The former diagnose and correct course to help organizations adapt and upgrade, and the latter ensure the effective functioning of business processes (Adner & Helfat, 2003). Dynamic capabilities change, adjust, and extend static resources so that new value is released in new contexts. Under ongoing advances in information technology, they coordinate and orchestrate diverse resources, overcome static constraints, and build sustainable competitive advantage, with an emphasis on the dynamic nature of research and development, processes, and managerial activities (Eisenhardt & Martin, 2000; Teece, 2007). In the digital and intelligent era, dynamic capabilities enable information sensing, resource integration, and structural reconfiguration under high uncertainty, which mitigates technological shocks, enables innovation, and creates new business value (Vial, 2019). Evidence also shows that as technological iteration accelerates, the theoretical and empirical boundaries of dynamic capabilities expand, and the theory serves as a key foundation for firms seeking competitive advantage through digital intelligence. At the level of micro-foundations, dynamic capabilities are rooted in diverse knowledge structures, efficient operating routines, and rigorous decision making (Adner & Helfat, 2003).
Accordingly, this study considers teams in a digital intelligence context. The KBV emphasizes that knowledge does not automatically translate into competitive advantage; organizations must rely on specific capabilities and mechanisms to achieve systematic integration of knowledge and unlock its potential value. Within this logic, knowledge heterogeneity provides a diversified foundation for the processes of resource acquisition, integration, and reconfiguration highlighted by dynamic capability theory. Furthermore, dynamic capability theory suggests that organizations can absorb, transform, and redeploy dispersed knowledge by continuously sensing the external environment, coordinating resources, and adjusting structures, thereby creating new value under dynamic conditions (Warner & Wäger, 2019). Hence, the integration of the KBV and dynamic capability theory offers a solid theoretical foundation for examining how knowledge heterogeneity is transformed into team creativity.

2.2. Knowledge Heterogeneity and Team Creativity

Knowledge heterogeneity refers to differences among team members in both observable and unobservable attributes. Observable or explicit knowledge heterogeneity includes gender, age, educational background, discipline, and tenure, whereas unobservable or implicit knowledge heterogeneity involves values, cognitive style, personality, and attitudes (Bell et al., 2011; Harrison et al., 2017). Knowledge heterogeneity constitutes the information and knowledge base of the team (Bunderson & Sutcliffe, 2017; van Knippenberg et al., 2004). It increases the amount and variety of information and knowledge available to the team (Dahlin et al., 2005). The KBV emphasizes that knowledge is the most critical strategic resource of organizations, and such heterogeneous knowledge structures help explain differences in capabilities and performance (Grant, 1993; M. C. Wang et al., 2018). When knowledge heterogeneity creates differentials among members, knowledge transfer is facilitated. Members exchange information and express views, which broadens perspectives and supports decisions that advance organizational development (Hoever et al., 2012; Kearney & Gebert, 2009; J. Wang et al., 2019).
Accordingly, a positive association between knowledge heterogeneity and team creativity is proposed. Heterogeneity operates through resource complementarity and cognitive diversity. Varied technologies, knowledge, and experiences expand the range of problem considerations. Interaction among members sparks new insights, promotes the recombination and integration of knowledge, and can generate novel ideas and methods, thereby improving team creativity.
Based on the foregoing analysis, the following hypothesis is proposed.
Hypothesis 1. 
Knowledge heterogeneity positively influences team creativity.

2.3. Mediating Role of Technology Management Capability

Technology management is conceptualized as a composite organizational capability that includes the ability to recognize the importance of business and technology, to formulate business and technology plans, and to execute those plans (Gregory, 1995; Teece, 2007). Technology management capability is understood as the system-level capacity that enables effective planning, development, and implementation of knowledge management, supports value creation processes, and promotes improvements in firm performance (Alavi & Leidner, 2001; Chen & Huang, 2009). This capability ensures the effective implementation of technological innovation. Through robust processes and methods, progress is achieved in knowledge acquisition, technology application, technology forecasting, and resource integration, which enhances the efficiency and effectiveness of knowledge accumulation and technology development and stimulates a steady flow of innovation outcomes (Eisenhardt & Martin, 2000; Zahra & George, 2002).
High knowledge heterogeneity provides complementary advantages across technology scanning, solution evaluation, supplier assessment, system integration, and data governance (Cooper, 2008). Clear process rules can then be established, knowledge codification and reuse are facilitated, and technology management capability is strengthened (Argote & Ingram, 2000). With stronger technology management capability, resources and tasks are orchestrated with greater precision, parallel experimentation and rapid iteration are organized effectively, and cross-system collaboration is coordinated efficiently. The full process from problem decomposition to solution integration becomes more efficient, which raises operational efficiency, supports rigorous allocation of resources, and ultimately improves team creativity (Amabile et al., 1996; Anderson et al., 2014; Eisenhardt & Martin, 2000).
Based on this reasoning, the following hypothesis is proposed.
Hypothesis 2. 
Technology management capability mediates the relationship between knowledge heterogeneity and team creativity.

2.4. Mediating Role of Digital Intelligence Enablement

Digital intelligence enablement is defined as the use of big data, cloud computing, and artificial intelligence to build digital infrastructure and to position data as a key factor in decision making and coordination (Verhoef et al., 2021; Vial, 2019). Its core is summarized in three dimensions: resource datification, process digitization, and organizational intelligence. Through resource datification, capabilities for information acquisition, storage, and analysis are strengthened (McAfee & Brynjolfsson, 2012; Singh & Del Giudice, 2019). Multidisciplinary backgrounds help define critical data objects, design appropriate indicators, expand collection coverage, and improve data quality and usability (Cohen & Levinthal, 1990). Through process digitization, workflows and collaboration patterns are reshaped (Bharadwaj et al., 2013; Sambamurthy et al., 2003). Complementary skills and experience facilitate the mapping of business activities to data flows and task flows, the selection of suitable automation, and the shortening of information transmission and feedback cycles, which increases the efficiency of idea validation (Okhuysen & Bechky, 2009; Sambamurthy et al., 2003). Through organizational intelligence, the timeliness and accuracy of communication, decision making, and resource allocation are enhanced (Leonardi, 2014; Treem & Leonardi, 2013), and professional experiences improve the sensing of new opportunities across the organization (Leonardi, 2014; Vial, 2019). As digital intelligence enablement advances, the efficiency and quality of problem identification, information integration, and solution generation are improved, and team creativity increases accordingly (Nambisan et al., 2017; Sun et al., 2020). It is argued that knowledge heterogeneity strengthens digital intelligence enablement, which provides high-quality inputs and rapid iteration channels for the generation, screening, and testing of ideas and thereby improves team creativity. Based on this reasoning, the following hypothesis is proposed.
Hypothesis 3. 
Digital intelligence enablement mediates the relationship between knowledge heterogeneity and team creativity.

2.5. Chain Mediating Role of Technology Management Capability and Digital Intelligence Enablement

With knowledge heterogeneity, multidisciplinary backgrounds and rich experience are brought together, and stable and reproducible managerial routines gradually form in technology selection, system integration, and data and process governance. Technology management capability is strengthened as a result (Bunderson & Sutcliffe, 2017; Eisenhardt & Martin, 2000; Zollo & Winter, 2002). The KBV emphasizes that heterogeneous knowledge structures provide the conditions for capability development and knowledge utilization, requiring organizations to absorb and integrate dispersed knowledge through managerial processes (Barney, 1991; Eisenhardt & Martin, 2000). Dynamic capability theory further indicates that diverse knowledge provides the micro-foundations for sensing and seizing, which improves the efficiency of opportunity recognition, resource orchestration, and process reconfiguration (Teece, 2007, 2014). The order and coherence generated by managerial capability create the conditions for deeper digital intelligence enablement. Data assets become more usable, business processes become more digitally connected, and collaborative decision making becomes more timely and effective (Leonardi, 2014; Sambamurthy et al., 2003). The cycle from idea generation to validation is compressed, trial and error costs decline, and the frequency and quality of knowledge recombination and solution iteration increase, which enhances team creativity. Based on this reasoning, the following hypothesis is proposed.
Hypothesis 4. 
Technology management capability and digital intelligence enablement jointly mediate in sequence the relationship between knowledge heterogeneity and team creativity.

3. Methods

3.1. Procedure and Sample

Participants were employees whose primary mode of work was organized in teams, and were drawn from a high-tech firm located in the Zhangjiang Hi-Tech Park, Pudong New Area, Shanghai, China. The park is a national high-tech industrial development zone and the core area of the Zhangjiang Comprehensive National Science Center. It has formed a highly concentrated innovation ecosystem in biomedicine, integrated circuits, and artificial intelligence, and is often referred to as the “Silicon Valley of China.” Therefore, high-tech firms in the park are representative in terms of knowledge-intensive production, digital and intelligent technology application, and organizational innovation. We used ArcGIS Pro 3.8 to create the geographical location map of the study area, as shown in Figure 2.
This study used a structured questionnaire to collect data, collecting a series of demographic information with Likert-type scales measuring knowledge heterogeneity, technology management capability, digital intelligence enablement, and team creativity. With assistance from the human resources department, employees were invited to participate on a voluntary basis, and response confidentiality was emphasized. In total, 203 employees participated in the study.
A two-wave design was used with a two-week interval. At Time 1 (T1), questionnaires measuring knowledge heterogeneity, technology management capability, and demographic information were distributed. A total of 350 questionnaires were distributed, and 286 valid questionnaires were returned, yielding a valid response rate of 81.714%. At Time 2 (T2), questionnaires measuring digital intelligence enablement and team creativity were distributed to the 286 respondents who provided valid responses in T1. After excluding questionnaires with patterned responding and unmatched cases, 203 valid paired questionnaires were obtained, yielding a paired valid response rate of 70.979%.
Regarding the sample profile, 55.67% of respondents were men and 44.33% were women, indicating a slightly higher proportion of men but an overall balanced gender composition. In terms of age, 23.65% were 30 years or younger, 63.05% were 31 to 40 years, and 13.30% were 40 years or older. With respect to education, 95.57% held an associate degree or higher. In job positions, 43.35% were non-managerial employees and 37.93% were first-line managers. Descriptive statistics are shown in Table 1.

3.2. Measures

To ensure reliability and validity, established scales are adopted for knowledge heterogeneity, technology management capability, digital intelligence enablement, and team creativity, in addition to control variables. All instruments were adapted into Chinese using a translation and back translation procedure. Items followed a five-point Likert scale, where 1 indicates strongly disagree and 5 indicates strongly agree. The measurement items are presented in Appendix A.
Knowledge heterogeneity was measured with a six-item scale developed by Y. Wu (2014). A representative item is “Our team members differ greatly in their work values.” Cronbach’s α is 0.90.
Technology management capability was assessed with a twenty-item scale adapted from W. W. Wu et al. (2012). A representative item is “Our team mobilizes the funds required for technical activities.” Cronbach’s α is 0.97.
Digital intelligence enablement was measured with a nine-item scale from Liang et al. (2022) that covers three dimensions with three items each: resource datafication, process digitalization, and organizational intelligence. The following are representative items: For resource datafication, “Our team strengthens the management of organizational resources through the introduction of digital technologies.” For process digitalization, “Our team introduces digital technologies into production, sales, or service processes.” For organizational intelligence, “Our team uses digital technologies to make communication across departments more timely and efficient.” Cronbach’s α is 0.92.
Team creativity was assessed with a three-item scale developed by Li et al. (2018). A representative item is “Our team outputs are creative.” Cronbach’s α is 0.84.
Regarding control variables, respondent characteristics could affect study outcomes (Spector & Brannick, 2010); thus, gender, age, education, and job position were accordingly treated as control variables.
The reliability test results indicated that all variables had Cronbach’s α coefficients above 0.70, suggesting good internal consistency of the scales. To further validate the measurement model, both convergent and discriminant validity were examined. Regarding convergent validity, the average variance extracted (AVE) values for all constructs exceeded the recommended threshold of 0.50, all standardized factor loadings were above 0.60, and the composite reliability (CR) values were greater than 0.70, indicating the satisfactory convergent validity of the measurement model. Detailed results are presented in Appendix B.

4. Results

4.1. Confirmatory Factor Analysis

To assess discriminant validity, confirmatory factor analysis (CFA) was conducted using AMOS 24 to compare alternative model specifications. The four-factor model was set as the baseline. Three competing models were specified: a three-factor model that combines knowledge heterogeneity with technology management capability, a two-factor model that combines knowledge heterogeneity with technology management capability and digital intelligence enablement with team creativity, and a single-factor model that loads all indicators on one factor. The corresponding measurement model is presented in Figure 3. Model fit statistics are summarized in Table 2. The four-factor model shows the best fit, with χ2/df = 1.09, root mean square error of approximation (RMSEA) = 0.02, comparative fit index (CFI) = 0.99, incremental fit index (IFI) = 0.99, and Tucker–Lewis index (TLI) = 0.99, while the goodness of fit index (GFI) is reported in Table 2. These results indicate adequate discriminant validity for the study constructs.

4.2. Descriptive Statistics and Correlations

Table 3 reports the means, standard deviations, and correlations for all study variables. Knowledge heterogeneity is positively correlated with team creativity (r = 0.34 **, p < 0.01), technology management capability (r = 0.38 **, p < 0.01), and digital intelligence enablement (r = 0.38 **, p < 0.01). Technology management capability is positively correlated with team creativity (r = 0.34 **, p < 0.01). Digital intelligence enablement is positively correlated with team creativity (r = 0.36 **, p < 0.01). These results provide preliminary support for the proposed relationships among the focal variables.

4.3. Common Method Bias

To address potential common method bias arising from employee self-reports, the Harman single-factor test was conducted. Factors with eigenvalues greater than one emerged, and the first factor accounts for 39.391 percent of the variance, which is below the 40 percent criterion. This result indicates that common method bias is not severe. In addition, a five-factor model that adds an unmeasured latent method factor to the hypothesized four-factor model was estimated and compared with the four-factor model. The five-factor specification does not meaningfully improve model fit, and the changes in fit indices are small (ΔCFI = 0.002, ΔTLI = 0.006, ΔRMSEA = 0.003). Therefore, common method bias is unlikely to discredit the findings. These results indicate that knowledge heterogeneity, technology management capability, digital intelligence enablement, and team creativity represent four distinct constructs and support subsequent analyses.

4.4. Hypothesis Testing

Direct effects are presented in Table 4. Using SPSS 26, hierarchical regression analysis was conducted. After controlling for gender, age, education, and job position, knowledge heterogeneity is significantly positively related to team creativity (B = 0.36 **, p < 0.01), and H1 is supported. When technology management capability and digital intelligence enablement are both included in Model 4, technology management capability has a significant positive effect on team creativity (B = 0.20 *, p < 0.05), and digital intelligence enablement has a significant positive effect on team creativity (B = 0.26 **, p < 0.01). Although the coefficient of knowledge heterogeneity on team creativity decreases (B = 0.20 **, p < 0.01), it remains significant. These findings provide preliminary support for H2 and H3.
A structural equation model was estimated using AMOS 24 to further examine the indirect effects. The model is presented in Figure 4, and the bootstrapping results of direct and indirect effects are summarized in Table 5. The analyses indicate that knowledge heterogeneity has a positive indirect effect on team creativity through technology management capability (effect = 0.07, 95% CI [0.01, 0.14]), thereby supporting H2. Similarly, knowledge heterogeneity exerts a positive indirect effect on team creativity via digital intelligence enablement (effect = 0.06, 95% CI [0.02, 0.12]), supporting H3. Moreover, the sequential mediation pathway through technology management capability and digital intelligence enablement is significant (effect = 0.03, 95% CI [0.01, 0.05]), providing support for H4.

5. General Discussion

As global information technology continues to advance, human society has entered the era of the digital and intelligent economy. Digital intelligence transformation has penetrated various industries, driving firms to continuously pursue digital and intelligent upgrading and innovation. To explore the formation path of team creativity from knowledge heterogeneity, this study integrated the KBV and dynamic capability theory and developed and tested a chain mediation model. The empirical results indicate that knowledge heterogeneity significantly enhances team creativity, while technology management capability and digital intelligence enablement each play mediating roles and together form a sequential mediation mechanism. These findings not only provide a new perspective for understanding how team level knowledge can be transformed into value through organizational capabilities but also offer theoretical reference for managers on how to effectively manage team knowledge. On this basis, the following discussion follows three aspects: theoretical contributions, practical implications, and research limitations.

5.1. Theoretical Contributions

This work provides several theoretical insights that might inform future studies. First, attention is centered on knowledge heterogeneity, which addresses a gap in the literature, where knowledge-based differences receive limited emphasis. Prior research on team heterogeneity tends to focus on demographic variables and seldom highlights knowledge heterogeneity, even though knowledge is shown to be essential to creativity. Drawing on the KBV, this study conceptualizes knowledge heterogeneity as differences among team members in knowledge reserves, experiential backgrounds, and professional skills. Such differences enhance organizational flexibility in acquiring, integrating, and applying knowledge, thereby providing a stronger theoretical basis for explaining the formation of creativity (Guillaume et al., 2017; J. Wang et al., 2019). Recent reviews indicate that the antecedents of creativity and performance involve not only demographic diversity but also task-related deep-level differences, such as the quality of information integration and degree of team reflexivity, which are more closely linked to creative output (Leblanc et al., 2024; Patrício & Franco, 2022). By emphasizing knowledge heterogeneity, this study extends the explanation of team creativity beyond demographic differences to knowledge mechanisms, thereby responding to recent scholarly calls (Hundschell et al., 2022).
Second, this study extends the investigation of knowledge heterogeneity to the field of team creativity. Unlike prior work that mainly tested its effects on team collaboration, task performance, or financial outcomes, creativity is placed at the center and highlighted as the key to achieving sustainable results and long-term competitive advantage for organizations (Anderson et al., 2014). This study focuses on how knowledge heterogeneity, through resource complementarity and cognitive differences, drives the generation of creativity, thereby enriching the theoretical explanation of how team creativity emerges (Ellström et al., 2021).
Finally, this study integrates the KBV with dynamic capability theory to examine the roles of technology management capability and digital intelligence enablement in the relationship between knowledge heterogeneity and team creativity. Previous studies have often relied on direct effects or a single mediator to explain links between diversity and creativity or performance, and the association between demographic differences and outcomes is frequently unstable (Leblanc et al., 2024; Qu et al., 2024). In contrast, this study treats technology management capability and digital intelligence enablement as two sequential mediators and validates a process in which knowledge heterogeneity enhances managerial capability, which subsequently deepens digital intelligence enablement (Ellström et al., 2021; Mikalef et al., 2020). Through these two stages, a more systematic theoretical framework is provided for understanding the formation of team creativity.

5.2. Practical Implications

First, team heterogeneity equips teams with rich capabilities relevant to digital intelligence transformation. Diversity in team composition should be emphasized, especially variation in industry experience and professional skills. Talent profiles can be developed to identify suitable candidates during recruitment. Training and structured communication can be used to strengthen members’ understanding of and commitment to team creativity so that enthusiasm for creative work is sustained.
Second, the digital intelligence era increases environmental uncertainty. The continual emergence of new technologies and products, changes in marketing strategies, and volatility in consumer preferences require constant vigilance and reflection on strategic choices. Team members are encouraged to track current information related to organizational development, learn digital intelligence technologies, and strengthen technology management capability, so that timely responses are made before large environmental shifts and the risk of failure is reduced.
Finally, team knowledge heterogeneity is a valuable resource and should be translated into team creativity that yields competitive advantage. Performance evaluation should move beyond short-term task completion and financial indicators and include process-oriented measures such as opportunity identification, solution iteration, and cross-boundary collaboration, which encourage sensing, seizing, and transformation under uncertainty. Positive feedback for these antecedent behaviors and intermediate outputs should be provided to build sustainable innovation momentum at the organizational level and to convert knowledge heterogeneity into advantage over longer horizons. Exploration resources should be allocated in moderation, an error-tolerant and learning-oriented mechanism should be established, cross-team knowledge sharing and collaboration channels should be strengthened, incentives for knowledge reuse and recombination should be enhanced, and rolling reviews and learning metrics that span the full technology development process should be introduced, so that ideas progress from generation to a stable and efficient cycle.

5.3. Limitations and Future Research

First, although a multi-wave survey design is adopted and the reliability and validity of the key constructs are assessed, the data primarily came from employees’ self-reports of knowledge heterogeneity, technology management capability, digital intelligence enablement, and team creativity. Such self-reported data may be influenced by individual cognitive bias and social desirability. In addition, the sample size of this study is relatively limited and has a certain degree of industry and regional concentration, which may to some extent affect the external generalizability of the findings. Future research could expand the sample across different industries, regions, and organizational types to further examine the applicability and robustness of the proposed model. It is also important to validate the findings with multiple-source data, for example, supervisor ratings and objective quantitative indicators such as the number and quality of patents or the number of successfully implemented ideas, in order to minimize potential bias from single-respondent reports and enhance the credibility of the results.
Second, this study focuses on employees and examines how knowledge heterogeneity affects team creativity through the chain mediation of technology management capability and digital intelligence enablement. In organizational practice, these capabilities rarely emerge in isolation and are shaped by multilevel factors such as the external environment, organizational support climate, leadership behavior, and organizational culture. Employee perceptions of challenge and opportunity from digitalization may vary with industry technological change, the team climate for adopting new technologies, and the attitudes of supervisors and coworkers. Future research can incorporate multilevel or team context variables and test boundary conditions such as external uncertainty, developmental feedback from supervisors, and a shared learning climate to improve the applicability of the conclusions in complex organizational settings.
Finally, beyond technology management capability and digital intelligence enablement, team creativity is likely to be influenced by individual differences, institutional support, and the stage of organizational development. Future work can include self-efficacy, digital literacy, startup versus mature stages, and different industry contexts, and can test whether the effects of knowledge heterogeneity on team creativity vary across these conditions, consistent with guidance on research design and common method variance provided by prior work (Podsakoff et al., 2003).

6. Conclusions

In the context of digital intelligence transformation, unique creativity has become a core element for enterprises to maintain their leading position through waves of innovation and entrepreneurship. The findings of this study reveal that diverse knowledge heterogeneity provides fundamental conditions for teams to generate creative outcomes. Organizations need to utilize technology management capabilities to promote the digitalization of resources, the digitization of processes, and organizational intelligence, thereby transforming knowledge into creativity and ultimately establishing a future-oriented competitive advantage.

Author Contributions

Conceptualization, H.C. and H.Z.; methodology, H.C.; software, H.Z.; validation, H.C.; formal analysis, Y.H.; investigation, H.C.; resources, H.C.; data curation, H.Z.; writing—original draft preparation, H.C.; writing—review and editing, H.Z. and Y.H.; funding acquisition, H.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Fundamental Research Funds for the Central Universities [Grant No. 2025YJS124].

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of the Business School, University of Shanghai for Science and Technology (approval code: USST20240618006; approval date: 18 June 2024).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.

Acknowledgments

We sincerely thank all the participants in this study, including teachers, survey respondents, and University of Shanghai for Science and Technology for providing an outstanding research environment and support.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

The following are the survey items.
Knowledge heterogeneity:
  • Our team members differ greatly in their educational backgrounds.
  • Our team members differ greatly in their academic majors.
  • Our team members differ greatly in their professional skills.
  • Our team members differ greatly in their work values.
  • Our team members differ greatly in their work experience.
  • Our team members differ greatly in their perceptions of how to complete tasks.
Technology management capability:
  • Our team mobilizes the funds required for technical activities.
  • Our team ensures the effective use of funds in technological activities.
  • Our team can effectively manage machinery and equipment.
  • Our team has formulated a development strategy for technical talent.
  • Our team has dedicated personnel for technology management.
  • Our team can effectively manage technical employees.
  • Our team emphasizes team building among technical employees.
  • Our team can establish effective communication channels among technical employees.
  • Our team systematically collects technological intelligence.
  • Our team maintains comprehensive technical information archives.
  • Our team promptly evaluates achieved technological outcomes.
  • Our team proactively applies for technology patents.
  • Our team fosters a culture focused on technological innovation.
  • Our team adjusts the organizational structure to meet the requirements of technological activities.
  • Our team establishes effective technological cooperation partnerships.
  • Our team plans for technological activities.
  • Our team can establish a total quality management system.
  • Our team can establish a technology standards system.
  • Our team carries out activities to implement technology standards.
  • Our team can effectively manage technological risks.
Digital intelligence enablement:
  • Our team strengthens the management of organizational resources through the introduction of digital technologies.
  • Our team uses digital technologies to collect, store, and analyze data from the operations and management processes.
  • Our team uses digital technologies to digitize non-data information and materials.
  • Our team introduces digital technologies into production, sales, or service processes.
  • Our team introduces digital technologies into business processes related to procurement and supplier relationships.
  • Our team introduces digital technologies into business processes related to after sales service and customer relationships.
  • Our team uses digital technologies to make communication across departments more timely and efficient.
  • Our team uses digital technologies to make management decision making and modification more timely and effective.
  • Our team uses digital technologies to make personnel arrangement and allocation more rational and efficient.
Team creativity:
  • Our team outputs are creative.
  • Our team outputs are original and practical.
  • Our team outputs demonstrate the team’s ability to creatively use existing information or resources.

Appendix B. Validity and Reliability of Items

VariablesItemsStandardized Factor LoadingsCronbach’s αCRAVE
Knowledge heterogeneityKH10.7900.9040.9040.612
KH20.759
KH30.776
KH40.754
KH50.802
KH60.811
Technology management capabilityTMC10.7280.9660.9660.585
TMC20.734
TMC30.776
TMC40.754
TMC50.796
TMC60.769
TMC70.775
TMC80.763
TMC90.760
TMC100.767
TMC110.764
TMC120.753
TMC130.725
TMC140.758
TMC150.759
TMC160.813
TMC170.808
TMC180.721
TMC190.787
TMC200.779
Digital intelligence enablementDIE10.7560.9270.9270.585
DIE20.777
DIE30.782
DIE40.708
DIE50.823
DIE60.726
DIE70.78
DIE80.762
DIE90.764
Team creativityTC10.7700.8360.8370.631
TC20.790
TC30.822

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Admsci 15 00408 g001
Figure 1. Theoretical model.
Figure 1. Theoretical model.
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Figure 2. Map of Shanghai Zhangjiang Hi-Tech Park.
Figure 2. Map of Shanghai Zhangjiang Hi-Tech Park.
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Figure 3. Confirmatory factor analysis model diagram.
Figure 3. Confirmatory factor analysis model diagram.
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Figure 4. Structural equation model analysis diagram.
Figure 4. Structural equation model analysis diagram.
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Table 1. Participant profile.
Table 1. Participant profile.
VariablesCategoryN = 203%
GenderMale11355.67
Female9044.33
Age21–30 years4823.65
31–40 years12863.05
Above 40 years2713.3
EducationHigh school or technical secondary school94.43
Bachelor’s or associate degree12762.56
Master’s degree4823.65
Doctoral degree199.36
PositionRegular employee8843.35
Junior manager7737.93
Middle manager2914.29
Senior manager94.43
Table 2. Comparison of alternative models in confirmatory factor analysis (N = 203).
Table 2. Comparison of alternative models in confirmatory factor analysis (N = 203).
Modelχ2/dfRMSEACFIIFITLI
Four-factor model: (KH, TMC, DIE, TC)1.090.020.990.990.99
Three-factor model: (KH + TMC, DIE, TC)1.970.070.870.870.87
Two-factor model: (KH + TMC, DIE + TC)2.270.080.830.840.82
One-factor model: (KH + TMC + DIE + TC)3.570.110.660.660.64
Note: KH = knowledge heterogeneity; TMC = technology management capability; DIE = digital intelligence enablement; TC = team creativity.
Table 3. Means, Standard Deviations, and Correlation Coefficients of Variables (N = 203).
Table 3. Means, Standard Deviations, and Correlation Coefficients of Variables (N = 203).
VariablesMSD12345678
1. Gender1.440.501
2. Age2.900.60−0.011
3. Education3.380.72−0.15 *−0.101
4. Position1.800.850.030.63 **−0.17 *1
5. KH3.270.99−0.01−0.08−0.080.031
6. TMC3.300.90−0.040.01−0.030.050.38 **1
7. DIE3.420.91−0.17 *−0.050.05−0.020.38 **0.40 **1
8. TC3.311.010.020.03−0.040.010.34 **0.34 **0.36 **1
Note: * p < 0.05 ** p < 0.01, KH = knowledge heterogeneity; TMC = technology management capability; DIE = digital intelligence enablement; TC = team creativity.
Table 4. Effect testing (N = 203).
Table 4. Effect testing (N = 203).
VariablesTMCDIETC
M1M2M3M4
Constant2.07 **1.89 **1.77 **0.71
Control Variable
Gender−0.06−0.27 *0.050.14
Age0.04−0.010.160.15
Education0.010.06−0.01−0.03
Position0.02−0.03 −0.07−0.07
Mediating variable
KH0.35 **0.24 **0.36 **0.20 **
TMC 0.30 ** 0.20 *
DIE 0.26 **
R20.150.250.120.22
Adjusted R20.130.220.100.19
F6.9610.715.597.75
Note: * p < 0.05 ** p < 0.01, KH = knowledge heterogeneity; TMC = technology management capability; DIE = digital intelligence enablement; TC = team creativity.
Table 5. Results of indirect effects of the sequential mediation model (N = 203).
Table 5. Results of indirect effects of the sequential mediation model (N = 203).
PathwaysEffectBoot SE95% CI
BootLLBootUL
Total Effect0.360.070.220.49
Direct Effect
KH → TC0.200.070.050.34
Indirect Effect
Total Indirect Effect0.160.040.090.24
KH → TMC → TC0.070.030.010.14
KH → DIE → TC0.060.030.020.12
KH → TMC → DIE → TC0.030.010.010.05
Note: KH = knowledge heterogeneity; TMC = technology management capability; DIE = digital intelligence enablement; TC = team creativity.
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Cai, H.; Zhao, H.; Huang, Y. Unpacking the Path from Knowledge Heterogeneity to Team Creativity: A Knowledge-Based View and Dynamic Capability Theory Perspective. Adm. Sci. 2025, 15, 408. https://doi.org/10.3390/admsci15110408

AMA Style

Cai H, Zhao H, Huang Y. Unpacking the Path from Knowledge Heterogeneity to Team Creativity: A Knowledge-Based View and Dynamic Capability Theory Perspective. Administrative Sciences. 2025; 15(11):408. https://doi.org/10.3390/admsci15110408

Chicago/Turabian Style

Cai, Hongyi, Heng Zhao, and Yong Huang. 2025. "Unpacking the Path from Knowledge Heterogeneity to Team Creativity: A Knowledge-Based View and Dynamic Capability Theory Perspective" Administrative Sciences 15, no. 11: 408. https://doi.org/10.3390/admsci15110408

APA Style

Cai, H., Zhao, H., & Huang, Y. (2025). Unpacking the Path from Knowledge Heterogeneity to Team Creativity: A Knowledge-Based View and Dynamic Capability Theory Perspective. Administrative Sciences, 15(11), 408. https://doi.org/10.3390/admsci15110408

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