Dynamic capability focuses on “doing the right things”(Teece, 2015), aiming to enable organizations to renew their capabilities to adapt to changing environment (e.g., exploration) (Dixon et al., 2014). As one of the dominant theories in the field strategic management in recent years (Di Stefano et al., 2014; Forkmann et al., 2018), dynamic capability theory is applied to organizations to explain how the frequency of effectively reconfiguring resources contributes to achieving competitive advantage (Braganza et al., 2017).

In various studies, the definition of dynamic capabilities differs depending on the purpose (Barreto, 2010). However, a widely accepted composite definition by key contributors to the dynamic capabilities literature is that dynamic capabilities are the abilities of an organization to purposefully create, extend, or modify its resource base (Helfat et al., 2009). Here, the “resource base” refers to the organization’s resources, including tangible and intangible assets, human capital, and capabilities. This definition is currently regarded by key contributors as the most comprehensive reflection of the dynamic nature of dynamic capability (Munoz-Penas et al., 2024). In stable environments (Helfat and Winter, 2011), dynamic capability research claims to explain continuous, routine-based organizational change (Wenzel et al., 2021). Specifically, dynamic capability is considered to be a potential for systematically solve problems, which benefits from its tendency to sense opportunities and threats, this enables organizations to make timely decisions and effectively implement strategic decisions and changes, and thereby ensuring the right direction (Ferreira et al., 2020).

In contemporary organizational and managerial processes undergoing change, the primary task is to identify the need for or opportunity of transformation (Helfat et al., 2009). Accordingly, dynamic capabilities are viewed as “a combination of broad organizational capabilities and specific actions that work together to achieve organizational transformation” (Yeow et al., 2018), as well as “a subset of organizational and individual capabilities specifically dedicated to strategic change” (Helfat and Raubitschek, 2018). Moreover, since dynamic capabilities often rely on three general learning processes—experience accumulation, knowledge articulation, and knowledge codification (Zollo and Winter, 2002)—to enable capability evolution within organizations, it can be argued that knowledge constitutes the cognitive foundation of dynamic capabilities.

As in most of literature on focuses on the ability of an organization to sense, seize, and transform its ecosystem (Teece, 2007; Teece, 2014). This three-dimensional categorization enhances the conceptual consistency and clarity of dynamic capabilities (Eriksson, 2014). Dynamic capabilities, as higher-order capabilities (Cepeda and Vera, 2007; Collis, 1994; Teece, 2023; Winter, 2003), support the internal development of necessary competences (Gulbrandsen et al., 2017), thereby enabling unconventional managerial decision-making to play a greater role. In the higher education sector, institutions have been increasingly challenged for their capacity to respond to technological advancements, rapid commercialization, and social change (Bejinaru, 2017). The application of dynamic capabilities to higher education institutions is thus primarily reflected in their responsibility to function as innovative ecosystems that create value for society (Faccin et al., 2022). Dynamic capabilities are closely linked to innovation capacities in higher education (Akram and Hilman, 2017), and this theoretical lens offers a structural approach for strategic management within such institutions (Teece, 2018). It also assists in analyzing institutional activities in complex contexts (Heaton et al., 2019), with the aim of maximizing internal knowledge flow (Heaton et al., 2019; Kelleher and Ulrichsen, 2023).

In knowledge-intensive domains, dynamic capabilities are essentially the development of knowledge-based dynamic capabilities (Kaur, 2019). Logically, dynamic capabilities are founded on all human-related factors, including skills, shared practical knowledge, and knowledge that facilitates purposeful action, indicating that knowledge creation serves as the operational pathway through which dynamic capabilities are actualized (Eisenhardt and Martin, 2000). Through knowledge creation, teams are able to establish new routines (Dong et al., 2004; Li and Chen, 2024). However, current research has primarily emphasized the “adaptive” dimension of dynamic capabilities—namely, the rapid reconfiguration of existing knowledge to respond to environmental changes. Much of this scholarship focuses on how teams develop dynamic capabilities to enhance firm performance, such as team learning capabilities (Harvey et al., 2018) and team R&D capabilities (Wang and Luo, 2013), or capabilities related to resource absorption, integration, decision optimization, and trust-based communication (Zhang et al., 2017). In contrast, relatively limited attention has been given to the “creative” dimension of dynamic capabilities—namely, the capacity to generate new knowledge and respond to complex environments.

High-quality collaboration for problem-solving requires the sharing of resources and skills among team members (Mola et al., 2020), enabling problem resolution through processes that foster knowledge renewal and collaboration. Ambrosini and Bowman (2009) argued that dynamic capabilities emerge from the co-evolution of tacit experiential accumulation and the articulation and codification of explicit knowledge activities. From the perspective of internal organizational concretization processes (Eisenhardt and Martin, 2000), creative dynamic capabilities are rooted in meso-level team activities within organizations—that is, in the interactions among team members (Nonaka et al., 2016). Nonaka et al. (2016) proposed a complementary approach integrating the SECI model (socialization, externalization, combination, internalization) with the dynamic capabilities framework, emphasizing that the knowledge creation process serves as the operational pathway of team dynamic capabilities. In other words, the knowledge creation process forms the foundation of team dynamic capabilities. Given the significant role of knowledge sharing in enhancing team effectiveness (Alsharo et al., 2017; Kim et al., 2023; Lin and Huang, 2020), dynamic knowledge creation is likely to constitute a form of team dynamic capability. That is, when team members possess complementary expertise and have mutual awareness of one another’s knowledge and tasks, teams are more capable of dynamic functioning (Salvato and Vassolo, 2018), thereby enabling the realization of the true value of team collaboration (Thayer et al., 2018).

Previous literature has largely acknowledged that the knowledge evolution phase of dynamic capabilities emphasizes both cognitive and behavioral foundations simultaneously (Dong et al., 2004; Nonaka et al., 2000). The evolutionary process of knowledge-based dynamic capabilities is characterized by a cyclical progression through four stages—variation, internal selection, retention, and propagation—indicating that cognitive efforts and behavioral efforts occur concurrently (Dong et al., 2004). Leadership, as a behavioral foundation of dynamic capabilities (Gheitarani et al., 2023), was integrated early on into the dynamic knowledge creation process through the unified model of SECI, Ba, and leadership (Nonaka et al., 2000). Similarly, the integrated model of knowledge-based dynamic capabilities (Nonaka et al., 2016) presents a unified process encompassing both cognitive and behavioral bases.

However, the value and impact of dynamic capabilities at the team level remain ambiguous and contested (Schilke, 2014). Wilden et al. (2016) argued that the research on dynamic capabilities should shift from the question of whether they influence performance to how they do so. Existing literature confirms that the mechanism through which dynamic capabilities lead to performance outcomes remains an unresolved issue in empirical research (Eriksson, 2014; Zhou et al., 2019). In other words, the “how” question has yet to be fully addressed (Wenzel et al., 2021). In light of this, recent research has begun to explore the indirect effects of dynamic capabilities (Baía and Ferreira, 2019; Laaksonen and Peltoniemi, 2018).

As previously noted, the dynamic knowledge creation process (SECI) inherently encompasses both cognitive and behavioral effort components. However, as Nonaka and Takeuchi (2021) emphasized, while knowledge creation enables teams to acquire new insights, it does not necessarily endow them with the wisdom to apply that knowledge effectively. The true value of knowledge must be realized through “knowledge practice.” According to Duchek (2020), resilience depends on the interplay between cognitive and behavioral capabilities and concrete actions. Building on the Knowledge-to-Action (KTA) Framework proposed by Graham et al. (2006), it is argued that following knowledge creation, teams members must apply, utilize, and translate knowledge into action in order to achieve resilience.

Teams are essentially goal-oriented entities, and team resilience is recognized as the team’s ability to handle problems and overcome obstacles (Alliger et al., 2015; Lengnick-Hall et al., 2011). This capability enables teams to proactively adjust their course of action to successfully execute specific tasks, thereby enhancing reliability, sustainability, and overall performance (Hartwig et al., 2020; Marchese et al., 2018; Specking et al., 2019). Theoretically, resilient teams are capable of rapidly identifying, designing, and implementing change while avoiding new obstacles that may impair team effectiveness beyond their current capabilities (Hoffman and Hancock, 2017). Given that interpersonal relationships form the foundation of resilience (Luthar, 2015), team resilience has been described as an emergent state arising from shared beliefs among team members (Kennedy et al., 2016), a collective outcome (Morgan et al., 2017), and a team goal cultivated through a shared vision, mutually agreed-upon “team charter,” and regular reinforcement (Morgan et al., 2019). On this basis, scholars have called for exploring team resilience through a consensus-based perspective that emphasizes emergence from team member interactions (Chapman et al., 2020; Raetze et al., 2021). This interpretation aligns with prior research that uses individual-level data to study collective phenomena when aggregation is not feasible.

Given its inherently social nature, a major research direction in team resilience focuses on team resources and interaction processes. For instance, team efficacy and collaboration (Strode et al., 2022), team personality (Mitchell et al., 2025), emotional and behavioral integration (Chen and Zhang, 2021; Hartmann et al., 2021), and the dynamic management of knowledge differences (Liu et al., 2023) have all been identified as factors that can foster team resilience (Liu and Harms, 2025). These research trends align with the recommendation in the dynamic capabilities literature to integrate organizational theories (e.g., resilience) and systems thinking to explore the development and functioning of dynamic capabilities in broader contexts (Arndt, 2019). From a knowledge management perspective, previous studies have demonstrated a significant positive correlation between knowledge management and resilience (Ibrahim Ismael et al., 2021; Singh et al., 2025; Yao et al., 2025). Particularly in certain professional environments, maintaining resilience at one level may require transformative change at another. In this sense, resilience is largely about organizational members learning how to change in order not to be changed (Walker, 2020), especially given the critical importance of knowledge integration within higher education institutions (Zahra et al., 2020).

Communication encompasses all processes by which one person’s perspective influences another’s; it is a social behavior used to share attitudes, viewpoints, information, knowledge, and opinions (Shannon and Weaver, 1949). In organizational management, communication refers to the comprehensive process of sending, receiving, and exchanging multi-level and multi-content information among organizational members. It serves as the foundation for information transmission between individuals, institutions, and organizations, reflecting the processes of organizational design, management, and implementation. Therefore, it is critical to planning and decision-making (Khan et al., 2017). Particularly in the context of strategic change, communication plays a key role in how organizations deliberately use it to fulfill their overall mission (Frandsen and Johansen, 2017; Hallahan et al., 2007).

In knowledge management, the acquisition, transfer, and utilization of knowledge constitute the systemic flow of knowledge resources (Drucker, 1988). Communication is therefore regarded as one of the integral components of the dynamic knowledge evolution process, as exemplified in the SECI model (Nonaka et al., 1996). In this process, communication primarily functions to establish new routines for knowledge through widespread dissemination within the organization. Based on team collaboration (Gervits et al., 2016), communicative emphasizes collaboration among team members grounded in communication to facilitate knowledge creation. For instance, task-oriented, process-oriented, and socio-emotional-oriented communication allow team members to alternately focus on task demands, coordination issues, and interpersonal relationships, respectively (Stempfle and Badke-Schaub, 2002). Although these three types of communication differ significantly in their effectiveness for problem solving (Stempfle and Badke-Schaub, 2002), open communication channels within teams can still foster shared understanding through dialogic interaction (Clark and Schaefer, 1989).

However, as new knowledge accumulates into knowledge assets, its practical application often encounters common obstacles. Team members’ limited understanding of newly generated knowledge may hinder its enactment. In other words, the transition from knowledge creation to implementation is inherently a socialized process dependent on relational interactions; thus, dissemination and communication are essential for translating knowledge into action (Gagnon, 2011). According to sensemaking theory (Weick, 1995), sensemaking is viewed as a set of mechanisms that organizations typically use to define norms and rules for perceiving, interpreting, trusting, and acting (Sackmann, 1991). Communicative functions as one such behavioral mechanism, aiming to facilitate strategic information exchange among team members and serve as a foundation for decision-making processes.

Leadership is characterized as an influential relationship among [people] aimed at achieving genuine transformation that reflects shared goals (Rost, 1993). It is regarded as a collective phenomenon shared among employees (Denis et al., 2012; Lord et al., 2017; Will, 2016; Yammarino et al., 2012) and a social phenomenon (Mumford et al., 2012), indicating that the focus of leadership development has shifted from individuals to the cultivation of relationships, teams, networks, and organizations (Day, 2000; Stogdill and Shartle, 1948). In other words, leadership is founded on social capital, transitioning from leader competencies to dialogic relationships (Eva et al., 2021). Simply put, leadership is not about a single individual or a position; rather, it represents a complex moral relationship between individuals, founded on trust, obligation, commitment, emotions, and a shared noble vision (Ciulla, 2004). It is an emergent attribute of interactions aimed at collective outcomes (Denis et al., 2012).

Nonaka and Toyama (2002) emphasize that the integrative function of leadership lies in “creating a knowledge vision, managing the knowledge-creation space, maintaining creative routines, and building an incentive system.” In other words, effective knowledge creation requires a pragmatic and dialogical form of leadership (Nonaka and Toyama, 2007). Leadership research has linked specific management styles to successful knowledge management (Lakshman, 2009). For example, empowering leadership is positively associated with knowledge management practices (Singh, 2008; Türkmendağ and Tuna, 2022), and transformational, transactional, knowledge-oriented, and strategic leadership styles have all been found to exert sustained and positive influences on knowledge management processes (Al Amiri et al., 2020). At the team level, distributed leadership is also recognized as a vital source of dynamic creativity (Nonaka et al., 2016). Some studies specifically highlight the critical role of leadership in knowledge management activities and processes—for instance, Von Krogh et al. (2012) integrated concepts such as Ba (knowledge-creating context), the SECI model, knowledge assets, and leadership behaviors to develop a knowledge-creating situational leadership framework (Nonaka and Konno, 1998; Nonaka et al., 1996), as well as the unified model of dynamic knowledge creation proposed by Nonaka et al. (2000).

In certain transformational environments, teams are primarily oriented toward problem-solving capabilities. Sternberg et al. (2021) suggest that employing the “meta-intelligence” model enables flexibility in generating problem-solving strategies. For instance, leaders may first use their creative skills to generate novel and high-quality solutions; second, they apply analytical skills to evaluate the logical coherence and feasibility of the proposed solutions; and third, they utilize wisdom-based skills to ensure that solutions contribute to the common good (Sternberg, 1998; Sternberg, 2003a; Sternberg, 2019; Sternberg, 2020). Therefore, leadership should influence the generation, sharing, and application of knowledge (Mac Gillivray, 2018). From a knowledge-practice perspective, effective leadership plays a critical role in decision-making by developing and integrating three core skills—wisdom, intelligence, and creativity (WICS) (Sternberg, 2003b). For example, knowledge-based leadership has been shown to encourage the development and application of knowledge practices (Donate and de Pablo, 2015). Given the practical challenges in knowledge management (Ding et al., 2019) and the value of dynamic capabilities as a basis for innovation in strategic planning (Nonaka et al., 2016), wise leadership consistently aims to guide team members toward decisions that benefit the collective (Nonaka and Takeuchi, 2011). Leaders who possess practical wisdom can optimistically enhance a team’s dynamic capabilities (Nonaka et al., 2016). Therefore, wise leadership teams may actively contribute to fostering environments conducive to knowledge sharing, encouraging member participation, and enhancing team spirit, thereby improving the efficiency of knowledge practices (Ding et al., 2019).

The response of a team to problems is partly based on how these issues are understood and interpreted (Ashmos et al., 1998; Burke et al., 2018; Dutton and Jackson, 1987; Gagné, 2018; Hitt and Tyler, 1991; Schneider and De Meyer, 1991; Thomas et al., 1993; Thomas and McDaniel, 1990; Von Krogh, 2018). Interpretation is an expression of collective thought (Weick and Roberts, 1993), the team needs to develop a shared processing mechanism to interpret events (Walsh and Ungson, 1991).

In strategic management, information is utilized to interpret environmental changes, generate new knowledge, and inform decision-making on courses of action. These processes involve interwoven behaviors that contribute to the organization’s deep interpretation of information (Choo, 1996). Prior studies have examined how decision-makers interpret strategic events through the lens of strategic issue diagnosis (SID) (Dutton et al., 1983). The SID framework emphasizes the need to construct understanding and assign meaning to an issue before linking it with potential solutions, thereby helping decision-makers avoid the “blindness” effect in strategic sensemaking (Dutton et al., 1983). Generally, interpretation is defined as an organization’s ability to make sense of its environment in multidimensional ways, especially under conditions of strategic complexity (Neill et al., 2007). This involves constructing cognitive frames to encode environmental cues and assign meaning to them for situational understanding. In a team context, interpretation focuses on how team members use the information and knowledge they possess to influence others (Thomas and McDaniel, 1990) and shape decision rationales. When new knowledge encounters barriers, teams may need to engage in reconstructive interpretation (Franke, 2023) and adopt an interpretive turn (Nicklin, 2012) to explore dynamic and decentralized frameworks for knowledge practice. This requires attention to how purposeful interpretive spaces are created (Liedtka, 2000), and how such interpretations either facilitate or inhibit collective team action within the broader knowledge system.

Analytical is defined as “judgment based on psychological reasoning” (Allinson and Hayes, 1996) and refers to a rational, thoughtful, and conscious judgment (Epstein et al., 1996). It represents the extent to which data, facts, and logical reasoning are relied upon when addressing problems, wise decision making, or conducting evaluations (Wang et al., 2017). In the context of knowledge creation, team members primarily integrate diverse forms of both tacit and explicit knowledge to generate innovative ideas (Olaisen and Revang, 2018; Von Krogh et al., 2000). The analytical mode of knowledge creation refers to the understanding and interpretation of characteristics of the (natural) world (Moodysson et al., 2008). Previous research has shown that predictive analytics can influence knowledge creation in marketing (Hair, 2007). The decision-making process itself enhances the understanding of both the issue at hand and the processes involved. In other words, decision-making and knowledge creation are interdependent processes.

However, team members primarily apply new knowledge for problem-solving purposes. Previous research suggests that many problem-solving approaches are analytically driven and supported (Hoerl and Vining, 2021). Analytical capability, as a core component of the metacognitive approach to problem-solving (Sternberg et al., 2021), can be examined through the WICS framework (wisdom-intelligence-creativity-synthesized) (Sternberg, 2003a; Sternberg, 2019), which emphasizes the interaction among creative, analytical, and practical mechanisms in shaping a team’s ability to solve problems effectively. In particular, multi-perspective analysis reflects the team’s sensemaking capacity, requiring the ability to integrate diverse viewpoints, perform cognitive blending in decision-making processes, and transform problem identification into deep understanding (Neill et al., 2007). From this perspective, broad and diverse new knowledge indeed supports sustainable action processes and capacity building within teams (Caniglia et al., 2021).

As noted earlier, the dynamic knowledge creation process (SECI) already involves both cognitive and behavioral efforts. However, following Wilden et al. (2016), we argue that the research focus on dynamic capability literature should shift from whether it affects performance to how it does so. Our position is that existing literature has not fully answered the “how” question (Wenzel et al., 2021). Although prior research has addressed the “adaptive” dimension of dynamic capability, few have examined its “creative” dimension and subsequent knowledge enactment. As noted by Nonaka and Takeuchi (2021), knowledge only realizes its true value when it is enacted through purposeful practice. Based on the above-identified research gaps, the novelty of this study lies in drawing on the concept of Knowledge-to-Action (KTA) to explain how team dynamic capability contributes to team resilience.

In this paper, the theoretical foundation of the mediating mechanisms is rooted in sensemaking theory, particularly the three core dimensions widely recognized in organizational literature—communicative, interpretation, and analytical (Weick, 1995; Neill et al., 2007). These constructs have often been studied as dimensions of sensemaking capability. Building on this foundation, we intentionally disaggregate these three dimensions to examine their individual mediating roles. Moreover, we introduce wise leadership (Nonaka et al., 2016)—a construct closely linked to knowledge creation—as an additional and interrelated mediator. This combination forms a set of related yet distinct mediating mechanisms, which reflects a new theoretical perspective: it explains how team dynamic capability contributes to team resilience through the functional integration of sensemaking and knowledge activation processes. However, these four constructs are not dimensions of team dynamic capability, Instead, they are functional enabling mechanisms that emerge following the team’s dynamic knowledge creation process. In other words, the newly created knowledge enabled by dynamic capability requires knowledge enactment—through communicative, leadership, interpretation, and analytical—in order to generate value and inform resilient decision-making.

To Exploratory factor analysis of the scale variables revealed 8 factors with eigenvalues greater than1. The variance explained by the first unrotated factor was 32.815%, which is below the 40% threshold, indicating that there is no severe common method bias in the data of this study. After adding the common method latent factor, CMIN (x²) decreased by 44.092, degrees of freedom (df) decreased by 28, and CMIN/df (x²/df) decreased by 0.033. The RMSEA and SRMR values decreased by 0.002 and 0.003, respectively, whereas the CFI and TLI values increased by 0.002 and 0.001, respectively. The model fit did not change significantly, indicating that the CMV in this study’s data is not severe.

A six-factor model was constructed to analyze the overall construct validity of the different variables. The structural equation paths are shown in Figure 2.

Among the results of the path model fit indices, the RMSEA value was 0.031 and the SRMR value was 0.057, both of which are below 0.08. For the parsimonious fit index, the CMIN/DF value was 1.590, within the acceptable range of 1 to 3. With respect to the incremental fit indices, the IFI, TLI, and CFI values were all above 0.9. All fit indices were within acceptable ranges, indicating that the path model fit was acceptable.

The results of the path effects are shown in Table 4. Specifically, the independent variable team dynamic capability (TDC) had affected on communicative (CM) (β = 0.620, p < 0.001), therefore, Hypothesis H1 is supported. Team dynamic capability also affects leadership (LD) (β = 0.575, p < 0.001), therefore, hypothesis H2 is supported. Similarly, team dynamic capability (TDC) affects interpretation (IN) (β = 0.582, p < 0.001), hypothesis H3 is supported, and team dynamic capability (TDC) affects analytical (AN) (β = 0.489, p < 0.001), hypothesis H4 is supported.

Furthermore, communicative (CM) affected on team resilience (TR) (β = 0.133, p = 0.003), and hypothesis H5 was supported. Leadership (LD) affected team resilience (TR) (β = 0.191, p < 0.001), and hypothesis H6 is supported. Interpretation (IN) affected team resilience (TR) (β = 0.188, p < 0.001), hypothesis H7 was supported, and analytical (AN) affected team resilience (TR) (β = 0.344, p < 0.001); thus, hypothesis H8 was supported.

According to the path results, the independent variable of team dynamic capability (TDC) has a significant positive effect on the dependent variable of team resilience (TR) (β = 0.183, p = 0.009). To further investigate whether the four variables communicative (CM), leadership (LD), interpretation (IN), and analytical (AN) play indirect roles in the relationship between team dynamic capability and team resilience, each of these 4 variables was tested as a mediator to examine the mediating effects as shown in Table 5.

For the results of the mediating effect of communicative, the indirect effect value was 0.082, the significance level Sig (p) was 0.020, which is less than 0.05, and the Z score from the Sobel test was 2.343, which is greater than 1.96. These results indicate that the mediating effect of communicative is significant. Thus, hypothesis H9 is supported. For the results of the mediating effect of leadership, the indirect effect value was 0.110, the significance level Sig (p) was less than 0.001, and the Z score from the Sobel test was 3.793, which is greater than 1.96. These results indicate that the mediating effect of leadership is significant. Thus, hypothesis H10 is supported. For the results of the mediating effect of interpretation, the indirect effect value was 0.109, the significance level Sig (p) was 0.001, which is less than 0.05, and the Z score from the Sobel test was 3.633, which is greater than 1.96. These results indicate that the mediating effect of interpretation is significant. Thus, hypothesis H11is supported. For the results of the mediating effect analytical, the indirect effect value was 0.168, the significance level Sig (p) was less than 0.001, and the Z score from the Sobel test was 6.720, which is greater than 1.96. These results indicate that the mediating effect of the analytical is significant. Thus, hypothesis H12 is supported.

First, by focusing on the dynamic processes of teams, this study deepens the understanding of team resilience in the context of organizational change and clarifies theoretical boundaries by proposing a shift from a “resource-preservation” perspective to a “functional-evolution” perspective. As noted by Galy et al. (2023), Brykman and King (2021), since 2011, much of the literature has conceptualized resilience based on the Conservation of Resources (COR) theory (Hartmann et al., 2020; Hartwig et al., 2020; Hobfoll, 2011; Hobfoll et al., 2018; Stoverink et al., 2020; Westman, 2001), portraying resilience as a static attribute and a key mechanism for linking and protecting various resources through intersecting processes. This view primarily supports the restorative function of resilience. However, in relatively stable yet complex transformation contexts—often described as brittle, anxious, non-linear, and incomprehensible (BANI) systems (Cascio, 2020)—resilience increasingly entails the accumulation of resources, such as knowledge (Cascio, 2020), and is better understood as a dynamic function that supports sustainability and adaptive development in response to multifaceted challenges. Against this backdrop, the present study adopts a dynamic capability perspective, emphasizing how teams engage in sensing, seizing, and reconfiguring to proactively adapt to change. This reconceptualizes resilience as an evolutionary outcome rather than merely a restorative attribute, offering a novel theoretical lens for understanding team resilience.

Second, this study explores how resilience emerges through the interplay of multiple causal mechanisms by investigating the mediating effects among key behavioral constructs. As previously noted, although one of the foundations of resilience lies in the accumulation of knowledge resources, Nonaka and Takeuchi (2021) emphasize that while knowledge creation enables teams to acquire new insights, it alone does not confer the wisdom necessary for applying knowledge effectively. The true value of knowledge is realized through “knowledge practice,” that is, through the application, utilization, dissemination, and transformation of knowledge into action. Building on this premise, this study identifies communicative, leadership, interpretation, and analytical capacities as critical behavioral capabilities that collectively function as mediating mechanisms in the transformation of dynamic capabilities into resilience. This mechanistic perspective not only deepens the understanding of how dynamic capabilities contribute to resilience but also echoes Duchek (2020) view that resilience depends on the interaction between cognitive and behavioral capacities and organizational action. Furthermore, this behavioral pathway aligns with Zollo and Winter (2002) theory of organizational capability evolution, which emphasizes the gradual development of capabilities through mechanisms such as experiential learning, knowledge articulation, and codification. By uncovering the mediating behavioral pathways, this study provides a more actionable theoretical foundation for understanding the mechanisms that enhance team resilience.

First, it is essential to encourage knowledge creation processes within teams. As knowledge-based institutions, universities rely heavily on productivity and the effective generation of knowledge (Nkambule et al., 2024). In the context of innovation and change, high-quality development in universities involves the activation, reorganization, and systematization of knowledge. The dynamic capability of knowledge creation enables teams to aggregate differentiated knowledge, allowing members to efficiently and innovatively accumulate knowledge within a short time frame, even as their individual domains of expertise become more specialized. Team members can respond to complex environments by developing diverse skills, behaviors, and attitudes that collectively enhance the team’s capacity to address environmental challenges and produce valuable outcomes. Moreover, for universities and their working teams, managers should place team collaboration at the core of knowledge creation, emphasizing the participatory design of team members. Collective creation should be leveraged as a means of knowledge mobilization—enabling the sharing, activation, and utilization of available knowledge (Langley et al., 2018). This approach reflects the team’s capacity to sense its environment, capture advantageous information, and reconstruct it meaningfully. For example, institutions may establish interdisciplinary workshops or innovation labs, and develop dedicated knowledge integration platforms for teaching and research teams, or collaborative platforms for administrators. These platforms can systematically document team members’ expertise, interests, progress, and resource needs, thereby enhancing the efficiency of knowledge sharing and mobilization.

Second, it is crucial to prioritize the development of team dynamic capabilities within universities. In the practice of knowledge management in academic teams, it is important to recognize that the process of developing dynamic capabilities is influenced by collective functional mechanisms. University team management should acknowledge that dynamic capabilities are shaped and sustained by structured collective processes. Research teams engaged in organized scientific activities are expected to demonstrate collective intelligence in their outputs. Accordingly, team members must possess lifelong learning abilities, effective interpersonal communication skills (Aziz et al., 2024), critical thinking, and strong knowledge management competencies (Ding et al., 2019).

Third, as team resilience is considered a form of shared consensus outcome (Chapman et al., 2020; Raetze et al., 2021), strengthening team resilience can enhance problem-solving and decision-making quality within university management teams, as well as foster innovation in management processes. Given the current strategic importance of digital transformation (DT), resilience-building plays a pivotal role in guiding and enabling such transitions (Benavides et al., 2020; Rof et al., 2020). Emphasizing contingency planning and reflective mechanisms within teams requires members to document daily management processes, encourages continuous learning, and fosters anticipation of change, thereby enabling team members to become more adaptable, stable, and self-reliant (Shaya et al., 2023).

Although this paper provides constructive insights into team resilience, several limitations should be acknowledged. The research model is grounded in the context of local public universities, which restricts the generalizability of the findings due to the singular organizational type and cultural background. It does not account for the hierarchical structure of Chinese higher education institutions (e.g., national vs. local universities), nor does it test the applicability of the model across industries (e.g., business, nonprofit sectors) or cultural settings (e.g., high vs. low power distance societies). These contextual differences are increasingly recognized as both limitations and opportunities for future cross-cultural research. Future studies should consider expanding the sample scope to include a broader range of institutional types and geographic regions. This would allow for an exploration of how dynamic capability configurations function differently across various sectors and cultural environments, thereby contributing to the development of a more universally applicable theoretical framework of team resilience.

This paper employed a serial mediation model to represent the dynamic functions of teams, which remains exploratory in nature. The proposed sequence is not intended to be definitive or exhaustive; rather, it reflects a theoretically grounded configuration informed by the literature on knowledge creation, sensemaking, and team learning (e.g., Nonaka et al., 2016; Weick, 1995; Edmondson, 1999). Future research could test alternative models, such as adopting the Knowledge-to-Action Framework (KTA) (Graham et al., 2006) as a comprehensive theoretical foundation. Additionally, incorporating contextual moderators may further refine the mechanisms through which team dynamic capabilities influence resilience, thereby contributing to the development of a more robust theoretical model.

In line with Farnese et al. (2019), the cross-sectional design precludes the ability to make statements of causal relationships on organizational performance indicators, and future studies utilizing time-lagged or multisource data are recommended. The findings of this study reflect individual-level perceptions rather than objectively constructed team-level phenomena. Future research should adopt multilevel designs with appropriate aggregation indices to more accurately capture team-level constructs. Additionally, the integration of team dynamic capability with other variables in a combined model was intended to assess discriminant validity. Although hierarchical modeling might be more conceptually appropriate, technical constraints in addressing discriminant validity at the single-dimension level limited this approach. Future research may explore the use of hierarchical modeling followed by integration into a comprehensive structural model.