Temporal Coordination Mechanisms and Team Resilience: An Event System Perspective on Leaders’ Pacing Styles

Abstract

Modern organizations operate in dynamic environments where temporal alignment is critical for adaptive capacity and team resilience. Grounded in Event System Theory (EST) and Temporal Coordination Theory (TCT), this study examines how leaders’ pacing styles function as critical temporal regulation mechanisms that influence team resilience via shared temporal cognition. Using multisource data from 82 team leaders and 384 members in Chinese technology enterprises listed on the STAR Market, we find that steady pacing, characterized by a balanced and predictable temporal rhythm, enhances team resilience through the emergent property of shared temporal cognition. However, the positive effect of steady pacing on shared temporal cognition weakens when teams perceive high crisis event strength, suggesting that external temporal shocks critically attenuate the efficacy of routine temporal regulation. The study extends EST and TCT by revealing steady pacing as a temporal buffer strategy that fosters resilience against external shocks, and highlights the need for Temporal Calibration practices when event intensity is high. Practical implications for managing team rhythms under varying crisis intensities are discussed.

1. Introduction

In today’s rapidly evolving business landscape, where multitasking and tight deadlines have become prevailing trends, imposing high-pressure work styles with time constraints, work teams inevitably face a variety of crises and must navigate an environment characterized by volatility and uncertainty [1,2]. These crises, ranging from chronic stressors to acute shocks [3], are particularly salient for Chinese technology innovation enterprises, especially those in the artificial intelligence sector, where rapid iteration cycles, high time sensitivity, and intense competitive pressure amplify the likelihood of team dysfunction and failure [4]. Consequently, team resilience, defined as the collective capacity to adapt, recover, and grow from adversity, has become a central determinant of organizational survival and innovation [5,6,7].

Despite the growing interest in team resilience, the existing literature has predominantly approached the construct through static or structural lenses. Previous research on team resilience has made significant strides in identifying its antecedents [8], which can be synthesized into two primary streams. The first stream focuses on structural composition and stable resources, suggesting that resilience stems from inherent team characteristics such as diversity, size, and member tenure [9], or from accumulated resource stocks like collective efficacy and trust [10,11]. The second stream emphasizes general relational processes, positing that high-quality connections and behavioral integration create a psychological safety net that buffers teams against stress [6,12]. While valuable, these perspectives tend to treat resilience as a pre-existing stock of capabilities. They largely overlook the temporal agility required for resilience: specifically, the challenge of how teams must actively regulate and re-synchronize their cognitive and behavioral paces in the face of disruptive external events [3].

Furthermore, the role of leadership in fostering this temporal adaptability remains underexplored. While studies have confirmed that positive leadership styles, such as transformational leadership, generally support team resilience [13,14], they typically view leadership influence through a motivational or relational lens. Thus, studying pacing style is not merely filling a gap, but an urgent theoretical necessity to reveal the temporal scaffolding that holds a team’s cognitive functions together during disruption. Moreover, compared to individual-level and organization-level resilience research, team-level resilience research is relatively scarce [3,8].

The pacing style (deadline-oriented, steady, and U-shaped), which describes how leaders distribute their effort over time when working towards deadlines, is posited to play a pivotal role in team performance [15]. Prior research has predominantly focused on the economic and strategic aspects of time management, often overlooking the psychological and cognitive dimensions that are particularly salient in high-growth firms [16]. To address this, we specifically examine steady pacing, a style characterized by balanced and predictable effort distribution, as a mechanism that regulates the team’s internal clock. For instance, leaders transition to this style by moving away from chaotic, ad hoc meetings and macro-level deadlines, instead instituting structural predictability through fixed rhythmic check-ins (e.g., daily stand-ups) and decomposing large tasks into granular milestones with uniform intervals. We propose that leaders’ steady pacing enhances resilience by cultivating shared temporal cognition, defined as a team’s collective understanding of time-related aspects such as deadlines and milestones. This internal temporal synchronization is crucial because it ensures consistent expectations and minimal coordination friction when the team needs to quickly adapt [17].

To theoretically ground these dynamics, we integrate Temporal Coordination Theory (TCT) and Event System Theory (EST). TCT provides the process lens, explaining how leaders’ pacing styles achieve internal temporal synchronization necessary for effective teamwork [18]. Concurrently, EST offers the crucial contextual framework by conceptualizing crises as events with specific attributes, namely crisis event strength, that disrupt organizational functioning [19]. We argue that resilience is achieved not through static structures, but through dynamic temporal calibration, a continuous process of aligning internal rhythms to the intensity of external shocks. This framework allows us to explore a crucial boundary condition: whether the beneficial effects of routine temporal regulation (steady pacing) break down when the intensity of the external shock is overwhelming.

The academic value of this study lies in three aspects. Firstly, we advance EST and TCT by proposing a dynamic framework for resilience. This framework is the first to integrate these two theories, effectively shifting the paradigm from studying resilience as a static capacity [20] to understanding it as a process of dynamic temporal regulation against external shocks. Secondly, we contribute to TCT by specifying that shared temporal cognition serves as the key cognitive microfoundation linking temporal leadership to team resilience. This moves the theoretical focus beyond structural alignment [18,21] to the essential role of cognitive synchronization in complex, event-driven environments. Thirdly, we provide crucial contextual extensions to temporal leadership literature. By empirically challenging the pervasive “faster is better” assumption [22] in high-velocity sectors, we establish steady pacing as a superior adaptive strategy for cultivating collective cognitive resources, thereby enriching the theory’s boundary conditions and practical relevance.

2. Theory and Hypotheses Development

2.1. Event System Theory

Modern high-technology enterprises, particularly those operating in dynamic and volatile markets, function as complex adaptive systems where stability is constantly challenged by rapid external changes, technical shocks, and crisis events [23,24]. In such environments, the core question is not just if teams possess resources, but how they dynamically deploy and coordinate these resources during moments of extreme volatility to maintain team resilience—the ability to adapt and bounce back from adversity [25]. Traditional theories, which often focus on stable traits or cumulative processes, struggle to capture this dynamic, non-linear relationship between acute shocks and team response. Therefore, to provide a theoretically rigorous lens for our model, we adopt EST as our overarching framework [19].

EST offers a critical shift in perspective by postulating that organizational and team outcomes are dramatically shaped by the triggering of specific, critical events [19]. Events are defined as dynamic triggers within organizational systems that are bounded in time and space, capable of activating system responses at cognitive, emotional, and behavioral levels [26]. EST is uniquely suited to our study for two key reasons. First, it anchors our crucial contextual variable: EST explicitly defines and operationalizes crisis event strength, which is the primary source of disruption we investigate in the technology sector [27]. Second, it guides our theoretical reasoning by asserting that the effects of internal processes (such as leadership pacing) are fundamentally context-dependent, intensifying or weakening based on the characteristics of the external event.

EST defines event strength by three interacting dimensions—novelty, disruption, and criticality—that jointly determine the degree to which an event captures attention and elicits system-level responses [28]. Novelty refers to the extent to which an event deviates from prior knowledge or routines. Disruption reflects the degree to which an event interferes with ongoing processes, roles, or structures, leading to the temporary breakdown of established collaboration routines [27]. Criticality denotes the importance of an event for organizational goals, survival, or strategic outcomes, signifying the event’s potential consequences for performance and viability [29].

However, EST, while robust in characterizing the shock, is silent on the specific temporal mechanisms teams use to buffer these shocks. To fully unpack the black box of team coordination during a crisis, we integrate TCT as a complementary process lens. While EST explains the destabilizing nature of the external shock, TCT explains the internal mechanism of time alignment, allowing us to examine how leaders’ steady pacing builds the crucial resource of shared temporal cognition against the external turbulence defined by EST.

2.2. Temporal Coordination Theory: Pacing Style and Team Resilience

Building on the foundation of EST, we utilize TCT to understand how leadership behavior functions as a critical internal mechanism. TCT, rooted in early work on group development cycles [30] and organizational time consciousness [16], systematically addresses the essential organizational function of managing temporal alignment, pace, and sequencing [18]. This theory posits that effective teamwork hinges not only on what is done, but critically on when and how fast collective actions are executed [31].

This theoretical emphasis on collective temporal management has evolved from earlier research on individual time behaviors. Theoretical research on time in organizational settings initially began with managerial trait theories [32], which later broadened to include the construct of temporal perceptions and team time alignment [33]. As the field matured under the TCT framework, the focus shifted from individual characteristics to collective, behavioral processes, gradually coalescing into the construct of temporal leadership [34]. Within this stream, pacing style emerges as a distinct and crucial behavioral aspect of temporal leadership [35]. Pacing style reflects how leaders distribute their effort over time when working towards deadlines, which significantly impacts team coordination, conflict management, and shared mental models [36]. Due to inherent cognitive differences among members, misalignments in time allocation inevitably arise, which can cause tension and interfere with strategy implementation [37]. Leaders’ temporal division is therefore essential in mitigating these disagreements and setting the motivational force of goal-setting strategies [35].

Pacing style encompasses three primary styles: deadline-oriented, steady, and U-shaped. Deadline-oriented pacing is characterized by leaders who tend to concentrate most of their efforts on tasks just before the due date, often leveraging urgency to boost efficiency during critical periods [36]. U-shaped pacing combines intense effort at the start and finish of a task with a period of lower activity in between, risking inefficiencies and potential loss of momentum during the mid-phase [36]. We focus on the steady pacing style, which involves spreading task activities evenly over time, allowing leaders to maintain a balanced workload and ensuring work progresses smoothly and predictably [1,38].

Team leaders play an important role in shaping team resilience, and this role is even more valuable in times of crisis [10,13]. Team resilience is broadly explored from three perspectives (capability, process, and consensus) [6,9,39]. For this research, we adopt the process perspective, which views team resilience as a dynamic psychosocial process that protects members from negative impacts through behavioral–attitudinal interactions [40]. We argue that the steady pacing style provides the optimal temporal structure to support this resilient process.

Leaders with a steady pacing style allocate time resources efficiently and improve the team’s ability to adapt through structured time management practices [34]. Steady-paced leaders ensure smooth progress toward completion by breaking down tasks into milestone sub-goals, mitigating risks associated with last-minute rushes near deadlines [1,41]. Compared to deadline-oriented pacing, steady pacing offers a more balanced workload distribution, preventing burnout and promoting consistent performance over time, which is essential for sustained adaptability [36,38].

From the TCT perspective, this consistency is crucial because it enhances resilience through two specific mechanisms. First, it leads to reduced temporal uncertainty [42]. By establishing predictable milestones, steady pacing creates a reliable “temporal anchor” and time structure for the team [43], minimizing the cognitive resources team members must expend monitoring the timeline. This frees up crucial cognitive capacity to cope with unexpected stressors and avoids the detrimental effects of constantly rushing [44]. Second, steady pacing encourages the implicit creation of temporal slack or redundancy. By preventing the team from being constantly exhausted, it ensures that a reserve of energy and time is available for rapid adaptive coordination and recovery when a crisis inevitably hits, making the team less susceptible to falling into discrepant temporal structures [45,46].

Furthermore, the steady pacing style supports a resilient team culture by providing a stable framework that enables open communication and proactive problem-solving [9,40]. By providing this structural stability and time reserve, steady pacing directly enhances the team’s ability to maintain or quickly restore its operational capacity under external shock. Therefore, we hypothesize the following:

H1. 

Team leaders’ steady pacing style has a positive effect on team resilience.

2.3. The Mediating Role of Shared Temporal Cognition

While steady pacing provides a structural foundation for time management, shared temporal cognition is the key emergent cognitive mechanism through which pacing translates into team resilience. Shared temporal cognition refers to the extent to which team members hold consistent and aligned understandings regarding the team’s overall time flow, temporal demands, and pace of work [47]. As a critical component of TCT, shared temporal cognition functions as a dynamic collective resource that is vital for minimal and adaptive coordination in fast-paced environments [48], reducing friction and promoting seamless interaction during task execution [49].

Firstly, we posit that steady pacing facilitates the formation of shared temporal cognition. Leader pacing serves as a consistent, observable input that shapes the team’s temporal environment. A steady pacing style involves frequent, predictable communication of milestones and uniform effort distribution. This reliable temporal signaling provides team members with a continuous, low-variability stream of time-related information. Through iterative interactions and shared observation of the leader’s consistent rhythm, members are continuously reinforced with the same temporal expectations, leading to the convergence of individual time perceptions into an accurate, shared mental model of the team’s temporal environment [50].

Secondly, we argue that shared temporal cognition is a powerful predictor of team resilience, and crucially, that it serves as a unique mediator. We posit that shared temporal cognition is a time-specific coordination resource that is uniquely suited to facilitate rapid adaptation and recovery during an event-driven crisis, unlike broader, less time-sensitive constructs such as team efficacy or trust [49]. In the face of a crisis event, high shared temporal cognition ensures that all members instinctively agree on the temporal urgency and required speed of response [51]. This temporal alignment enables low-communication coordination: team members can adjust their timelines and task priorities with minimal explicit discussion, achieving a high-speed “resynchronization” crucial for the initial recovery phase of resilience [48,50]. This collective alignment enhances the team’s ability to withstand and recover from setbacks, contributing significantly to its resilience [52,53,54]. Furthermore, shared temporal cognition emerges as a collective cognitive alignment within the team, reflecting the synchronized effort necessary to manage temporal demands [34]. Based on the strong theoretical arguments for the positive effect of steady pacing on shared temporal cognition, and the subsequent positive influence of shared temporal cognition on team resilience, we hypothesize the full mediating role:

H2. 

Shared temporal cognition mediates the positive relationship between team leaders’ steady pacing style and team resilience.

2.4. The Moderating Role of Crisis Event Strength

While steady pacing is effective in promoting shared temporal cognition, EST suggests that the positive influence of an internal mechanism is fundamentally context-dependent, susceptible to the intensity of external events [12]. We propose that crisis event strength—our key EST construct—attenuates the positive relationship between a leader’s steady pacing style and shared temporal cognition. When event strength is high, the stability provided by the leader’s rhythm is overwhelmed, and the coordination mechanism fails.

This weakening effect stems directly from the three core dimensions of high event strength (Novelty, Disruption, and Criticality). Firstly, high novelty and high disruption severely undercut the utility of a predictable, steady pace. Shared temporal cognition is formed because the leader’s rhythm provides a consistent time signal; however, a high-novelty crisis introduces massive temporal uncertainty—the team’s existing temporal map becomes obsolete as unseen technological challenges demand immediate interpretation and temporal estimation [55]. Similarly, high disruption breaks established workflows and creates chaotic, non-linear time demands. In this context, the leader’s steady pace (a signal of past consistency) is insufficient to help members navigate the current extreme unpredictability, preventing the convergence of individual temporal perceptions into shared temporal cognition. The external event signal is simply louder and more salient than the internal pace signal.

Secondly, high Criticality forces a detrimental shift in team behavior, moving the focus from proactive coordination to reactive crisis management. A highly critical event elevates stress and urgency. Team members may become preoccupied with immediate concerns, which alters their perception of time and priorities [56]. The psychological pressure placed on team members can lead to increased stress levels and a tendency to concentrate on short-term solutions [25]. This pressure forces a transition from structural pacing to immediate, short-term triage [57]. This high-stakes environment leads to temporal differentiation: team members abandon the long-term, structural rhythm of the steady pace in favor of decentralized, localized action to address the most pressing, immediate threats [34]. When team members operate in disparate temporal frames, prioritizing localized, immediate deadlines, they cannot share a consistent view of the overall timeline or pace. The collective energy is focused on “fighting fires” rather than on “building shared cognitive resources”, leading to the fragmentation of temporal perceptions and the failure to establish shared temporal cognition. Therefore, high event strength degrades the capacity of a steady pace to foster shared temporal cognition.

Finally, synthesizing EST and TCT, we propose a moderated mediation model. The ability of a steady pace to build the resilient resource is weakened by the strength of the external shock. Thus, the indirect path from steady pacing to team resilience via shared temporal cognition is dependent on the external environment defined by EST. Therefore, we hypothesize:

H3. 

Crisis event strength weakens the positive relationship between leaders’ steady pacing style and shared temporal cognition, such that leaders’ steady pacing style is less positively related to shared temporal cognition when crisis event strength is high.

H4. 

Crisis event strength weakens the positive indirect relationship between leaders’ steady pacing style and team resilience via shared temporal cognition, such that the indirect relationship is weaker when crisis event strength is high.

3. Method

3.1. Procedure and Sample

We conducted an empirical study to validate our hypotheses by engaging core business teams from ten Chinese technology innovation enterprises listed on the STAR Market, including strategic, investment, and R&D teams, all operating within the artificial intelligence sector. Among these companies, five have demonstrated consistent operational stability, while the other five have encountered periods of sudden operational difficulties, facing instability crises. Prior to collecting team questionnaires, we conducted on-site visits to these enterprises and held interviews with team leaders and members to gain deeper insights into their practices and challenges. The selected firms were established more than three years ago and operate in an environment characterized by rapid technological iteration, high time sensitivity, and intense pressure, often facing tight deadlines (DDLs). The inclusion of both resilient and crisis-affected firms allows us to explore how steady leadership pacing and effective time management practices can impact team resilience under varying degrees of pressure and adversity.

To address potential common method biases effectively, we collected data from team members and their respective leaders independently [58]. The formal survey was conducted in two stages [59]. Initially, we invited 117 project teams to participate in our study. In total, we reached out to about 580 team members and 117 leaders. In Stage 1, we aimed to collect data on the independent and moderating variables. To achieve this, we distributed questionnaires to all team members regarding ‘Crisis Event Strength,’ while team leaders were asked to fill out a separate questionnaire focusing on their ‘Pacing Style’. Out of the approximately 580 invited members, 423 completed the initial questionnaires, resulting in a response rate of around 73%. Among the 117 invited team leaders, 96 responded with their pacing style assessments, achieving a response rate of approximately 82%. Stage 2 aimed to collect data on the mediating and outcome variables. Therefore, only the participants who provided valid responses at Stage 1 were surveyed at Stage 2. The interval between Stages 1 and 2 was three months, and we reminded participants to complete the surveys within the specified time frame online. In Stage 2, team members were invited to complete questionnaires regarding ‘Shared Temporal Cognition.’ Meanwhile, team leaders were invited to complete a follow-up questionnaire on ‘Team Resilience.’ Of the leaders who initially participated, 82 completed the follow-up questionnaire, maintaining a participation rate of about 85.4%. Similarly, among the members who completed Stage 1, 384 completed the Stage 2 questionnaires, resulting in a response rate of approximately 90.8%. To ensure data consistency, we retained only those teams where both leaders and members had responded to both phases of the survey. This resulted in a final analytical sample of 82 teams, with 384 members (excluding team leaders) completing all relevant questionnaires. The team size ranged from 3 to 8 members (exclusive of the team leaders), with an average size of 5.02 members per team (SD = 1.21). Among the team members, 58.59% were male, with an average age of 31.55 years (SD = 7.55). For the team leaders, 71.95% were male, with an average age of 42.56 years (SD = 5.13).

3.2. Measures

In our study, we employed a five-point Likert scale (1 = strongly disagree, 5 = strongly agree) to gauge participants’ responses. The scales utilized in our research are derived from well-established instruments that have been widely recognized and employed in the premier international literature. To ensure their applicability within the context of Chinese technology innovation enterprises, we followed a rigorous translation and back-translation procedure [60]. The English scales were first translated into Chinese and then back-translated into English by independent bilingual scholars with expertise in the field. Minor linguistic adjustments were made to ensure conceptual equivalence and contextual accuracy, while preserving the original factor structure. Consequently, we computed and reported the reliability, specifically Cronbach’s alpha, for each scale. Please refer to the Supplementary materials for the complete scales of all key variables.

3.2.1. Steady Pacing Style

To assess team leaders’ steady pacing style, we utilized a 3-item scale adapted from Gevers et al. (2013) [36]. This scale measures the extent to which leaders encourage a steady distribution of workload (α = 0.87). A representative item from this scale includes the following: ‘When managing the team, I work steadily on tasks, spreading the workload evenly over time (e.g., 3 h per week until the deadline)’. These scales were administered to the leaders of the teams.

3.2.2. Team Resilience

Team resilience was measured using a 6-item scale adapted from Carmeli et al. (2013) [6]. This scale captures the team’s capacity to adapt, recover from adversity, and improve continuously (α = 0.83). A sample item from this scale is “When faced with difficulties, the team finds creative ways to change the situation.” The scale was administered to the leaders of the teams.

3.2.3. Shared Temporal Cognition

Shared temporal cognition was assessed using a 4-item scale adapted from Gevers et al. (2006) [15], measuring the degree to which team members hold common perceptions regarding time management and deadlines (α = 0.80). A sample item from this scale is “In my team, we have the same opinions about meeting deadlines.” These items were administered to the team members.

3.2.4. Crisis Event Strength

Crisis event strength was measured using an 11-item scale adapted from Morgeson and DeRue (2006) [61]. This scale evaluates the novelty, disruption, and criticality of crisis events (α = 0.89). Before responding to the items, participants were asked to select the type of crisis their team frequently encounters: internal crisis or external crisis. Definitions and examples for each type of crisis were provided. A sample item from this scale is “We cannot rely on established procedures and practices in responding to the crisis events.” These items were administered to the team members.

3.2.5. Control Variables

In our analysis, we have meticulously identified key control variables to ensure the robustness of our findings. Given that our data were sourced from ten distinct companies, we accounted for potential systematic effects by devising nine corresponding dummy variables. This approach allows us to isolate company-specific influences and enhances the robustness of our analytical model. Secondly, given the variability in team size and its potential influence on resilience and temporal cognition, we control for the team size within each team [62]. Thirdly, recognizing the impact of team diversity on collective dynamics, we account for age diversity and gender diversity within the teams [63,64,65]. The diversity in age was measured using the coefficient of variation, which is a reliable method for capturing the extent of variation within a team. Additionally, gender diversity was assessed using Blau’s (1977) index of heterogeneity, a widely accepted measure in the field of organizational studies [66]. This index is calculated as 1 − ${\sum }_{i=1}^n{p}_i^2$, where $p_i^2$ is the proportion of a team’s members in the $i^{th}$ gender category. Fourthly, recognizing the potential impact of the type of crisis experienced by employees on their resilience and temporal cognition, we control for the type of crisis faced by the employees within each team (internal crisis = 0, external crisis = 1). This distinction is crucial as it allows us to differentiate the effects of internal team dynamics from those stemming from external factors, which may have different implications for team resilience [67]. Fifthly, we control for potential confounding factors introduced by the other two pacing styles—deadline pacing style and the U-shaped pacing style. Both of these two variables were measured using a five-point Likert scale. These scales were administered to the leaders of the teams. Furthermore, given the established relationship between an employee’s tenure and their level of commitment and familiarity with the team’s culture and processes [68], we also control for the length of time an employee has been with the company, referred to as post-tenure (1 = 1 year or less, 2 = 2–3 years, 3 = 4–6 years, 4 = 7–9 years, 5 = 10 years or above). The demographic information required for these calculations was collected through self-reports provided by the team members. We conducted all the analyses with and without these control variables for a robustness check and obtained consistent results, suggesting that our findings are robust across different methodological approaches.

3.3. Analytical Strategies

We tested our hypothetical model with a three-step procedure. First, we conducted confirmatory factor analyses (CFAs) using AMOS 20.0 to evaluate the factor structure of our four key variables: steady pacing style, team resilience, shared temporal cognition, and crisis event strength.

Second, we calculated within-group interrater reliability (Rwg; ref. [69]), intraclass correlations (ICC [1]), and reliability of means (ICC [2]; refs. [70,71]) to justify aggregating the data from the individual level to the team level. In the present study, the moderating and mediating variable are considered at the team level, necessitating the aggregation of individual employees’ responses to the team level. These statistical assessments are crucial to determine the appropriateness of data aggregation [72].

Third, to better account for the covariance among the moderators and mediator, we ran an overall path analysis to test the hypotheses. We estimated the 95% confidence intervals (Cls) for the indirect and conditional indirect relationships with 20,000 bootstrapping iterations [73]. We then performed path analyses with R version 4.3.3 [74]. To enhance the interpretability of the interactions within our model, we mean-centered the variables used to create the interaction terms before using them for analysis [75].

To reduce common method bias, several steps were taken to ensure the robustness of our data collection and analysis. First, the independent and dependent variables in this study were collected at different time points, which to some extent reduced common method bias. Second, the Harman’s single-factor test was used to examine common method bias, and the results showed that the cumulative explained variance of the variables was 34.09%, which is less than 40%. This indicates that there is no single factor explaining all the variables, suggesting the absence of common method bias. Meanwhile, the fit indices of the single-factor model in the confirmatory factor analysis presented in

Table 1. The results of CFA.

Model	Factor	χ2/df	RMESA	CFI	TLI
Four-factor model	SPS; TR; STC; CES	1.38	0.03	0.99	0.99
Three-factor model	SPS + STC; TR; CES	7.63	0.13	0.64	0.60
Two-factor model	SPS + CES; STC + TR	11.25	0.16	0.43	0.37
One-factor model	SPS + TR + STC + CES	16.56	0.20	0.50	0.40

Notes: SPS denotes Steady pacing style, TR denotes team resilience, STC denotes shared temporal cognition and CES denotes crisis event strength.

were not ideal (χ²/df = 16.58, RMSEA = 0.20, CFI = 0.50, TLI = 0.40).

4. Results

4.1. Confirmatory Factor Analyses

The results of the confirmatory factor analysis (see Table 1) showed that the four-factor model had the best fit indices: χ²/df = 1.38, RMSEA = 0.03, CFI = 0.99, TLI = 0.99, suggesting good discriminant validity among the variables. To further substantiate the discriminant validity, particularly between the core temporal constructs, and to address potential temporal clustering effects in our time-lagged design, we compared the proposed model against theoretically plausible alternative models [76].

4.2. Justification for Data Aggregation

As noted, we calculated Rwg, ICC [1], and ICC [2] to see whether we could use group means of variables reported by team members in the team-level analyses [70]. The results revealed a substantial degree of agreement among members from the same teams, as reflected in their ratings of shared temporal cognition (Rwg_mean = 0.91, Rwg_median = 0.91, ICC [1] = 0.59, ICC [2] = 0.87) and crisis event strength (Rwg_mean = 0.92, Rwg_median = 0.95, ICC [1] = 0.55, ICC [2] = 0.85). The ICC [1] values of 0.55 and 0.59 are substantial, exceeding the median value of 0.12 typically observed in organizational research [70]. Theoretically, this high level of non-random variance is expected and justified for both constructs. Consistent with TCT, the high consistency in shared temporal cognition is highly anticipated because it represents a team’s shared understanding regarding the pacing and timing of activities [15]. Specifically, STC is an emergent state cultivated by the consistent, structured temporal signal of the leader’s steady pacing [47]. This predictable external anchor standardizes team members’ perceptions of task duration and workflow rhythm, thereby leading to a predictable convergence in their temporal mental models. Similarly, the high consistency in crisis event strength is justified: consistent with EST [19], the novelty, disruption, and criticality inherent in the measured crisis event create highly salient and objective environmental conditions. These shared, intense conditions foster a strong convergence of perceptions among team members within the same unit, thereby legitimizing the aggregation of these constructs to the team level.

4.3. Descriptive Statistics

Table 2. Means, standard deviations, and correlations among variables.

Variable	Mean	SD	1	2	3	4	5	6	7	8	9	10
1. Team size	5.02	1.21
2. Post Tenure	3.05	1.56	0.07
3. Age diversity	0.46	0.24	0.07	−0.14 **
4. Gender diversity	0.41	0.12	0.23 **	0.04	0.05
5. Crisis type	0.64	0.48	−0.06	0.04	−0.08	−0.02
6. Deadline pacing	3.82	0.77	−0.17 **	0.09	−0.07	−0.05	0.06
7. U−shape pacing	4.36	0.69	−0.14 **	0.02	−0.01	0.10	0.05	0.18 **
8. Steady pacing	3.77	0.84	0.02	−0.09	−0.00	−0.04	−0.05	−0.43 **	0.02
9. Shared	3.70	0.73	0.18	−0.05	0.10	0.06	−0.01	−0.28 **	0.08	0.11 *
10. Crisis	3.19	0.80	0.00	−0.09	0.11 *	−0.05	−0.02	−0.23 **	−0.09	0.36 **	0.24 **
11. Team resilience	3.75	0.70	0.22 **	0.03	−0.04	0.09	−0.05	−0.17 **	0.15 **	0.17 **	0.06 **	−0.04

Note: Gender was coded as 1 = male, 0 = female. Age was coded as 1 = 22–24 years, 2 = 25–29 years, 3 = 30–35 years, 4 = 36–40 years, 5 = 40–45 years, 6 = 45 years or above. Post tenure was coded as 1 = 1 year or below, 2 = 2–3 years, 3 = 4–6 years, 4 = 7–9 years, 5 = 10 years or above. Crisis type was coded as 0 = internal crisis, 1 = external crisis. * p < 0.05, ** p < 0.01. Internal consistency reliability is shown in parentheses.

displays the means, standard deviations and correlations among our study’s variables. In line with our theoretical arguments, team resilience was positively related to steady pacing style and shared temporal cognition and negatively related to crisis event strength. These results provided preliminary evidence for our predicted relationships, which we tested in the following subsections.

4.4. Main Effect

Figure 1 presents the findings from the hierarchical regression analyses, revealing significant relationships between predictors and the criterion variable of team resilience, which presents the results obtained after incorporating all control variables.

The results indicate that leaders’ steady pacing style had a significant positive effect on team resilience after adding controls (b = 0.16, p < 0.001) (see Figure 1), supporting H1. Notably, in the same model, neither U-shaped nor deadline pacing styles had a significant effect on team resilience.

4.5. Mediation Effect of Shared Temporal Cognition

Figure 1 shows that steady pacing style had a significant positive effect on shared temporal cognition (b = 0.10, p < 0.001), and shared temporal cognition had a significant positive effect on team resilience (b = 0.59, p < 0.001). As

Table 3. The results of mediating effects.

Path	Coefficient	SE	LLCI (95%)	ULCI (95%)
Total effect: SPS → TR	0.14	0.04	0.06	0.22
Direct effect	0.08	0.03	0.02	0.15
M: SPS → STC → TR	0.06	0.03	0.01	0.11

Notes: N = 384. SPS denotes Steady pacing style, TR denotes team resilience, STC denotes shared temporal cognition and CES denotes crisis event strength; → represent the direction of path.

shown, the 20,000 iterations of bootstrapping indicated that the indirect relationship between steady pacing style and team resilience via shared temporal cognition was positive and had a 95% confidence interval (CI) that excluded zero (indirect effect = 0.06, 95% CI [0.01, 0.11]). Thus, Hypothesis 2 was supported.

4.6. Moderation Effect of Crisis Event Strength

Hypothesis 3 predicted that crisis event strength weakens the positive relationship between steady pacing style and shared temporal cognition. As shown in Figure 1, the interaction effect of steady pacing style and crisis event strength on shared temporal cognition was negative and significant (b = −0.30, p < 0.001). Following Cohen et al. (2003) [75] in Figure 2, we display the interaction effect graphically at two levels (i.e., ±1 SD) of crisis event strength. The simple slope tests indicated that steady pacing style was positively related to shared temporal cognition at lower levels of crisis event strength (β = 0.47, p < 0.001), but was not significantly related to it at higher levels of crisis event strength (β = 0.09, p = 0.19). Therefore, Hypothesis 3 was supported.

Hypothesis 4 proposed that crisis event strength weakens the indirect relationship between steady pacing style and team resilience via shared temporal cognition.

Table 4. Analysis of the moderated mediating effects.

Moderating Role	Path: Steady Pacing Style → Shared Temporal Cognition → Team Resilience
	Indirect Effect	LLCI (95%)	ULCI (95%)
Low crisis event strength	0.13	0.06	0.20
High crisis event strength	0.05	−0.00	0.11
Moderated mediation effect	−0.18	−0.24	−0.11

Notes: N = 384; → represent the direction of path.

shows that results from the 20,000 iterations of bootstrapping indicated that there was a significant conditional indirect relationship between steady pacing style and team resilience via shared temporal cognition, such that the indirect relationship was significant when crisis event strength was lower (indirect effect = 0.13, 95% Cl [0.06, 0.20]) but not when it was higher (indirect effect = 0.05, 95% CI [−0.00, 0.11]). These results supported Hypothesis 4.

5. Discussion

This study, framed by the integration of EST and TCT, investigates how leader pacing styles shape team resilience. Our findings underscore the critical role of the steady pacing style as a temporal coordination mechanism in complex, dynamic environments. The mechanism functions because steady pacing provides the stable, predictable rhythm necessary to cultivate shared temporal cognition, which serves as the essential internal resource for navigating external shocks. This insight is critically reinforced by our empirical finding that the Deadline-oriented and U-shaped pacing styles yielded no significant effects. We interpret this as a contextualized insight into TCT: Deadline-oriented pacing acts as the organizational baseline norm under endemic pressure [77], offering no distinctive advantage, while U-shaped pacing introduces temporal inconsistency that fundamentally undermines the formation of stable shared temporal cognition. Thus, the effectiveness of temporal leadership depends on the quality of the rhythm in fostering cognitive predictability. From the perspective of EST, we establish that the protective function of the shared temporal cognition mechanism is significantly attenuated when the external crisis intensity is extremely high, highlighting the dynamic fragility of temporal alignment [19,78].

5.1. Theoretical Contributions

The theoretical contributions of this research encompass three significant aspects. First, we advance the literature on team resilience by proposing and empirically validating a dynamic framework that integrates EST with TCT. In contrast to prior resilience research, which often adopts a static view of team capacity [20] or studies EST and TCT frameworks in isolation, our study provides a cohesive dynamic model. We utilize EST to rigorously define and operationalize the external “event” (Crisis Event Strength) and TCT to explain the internal “response” (temporal coordination). This theoretical synthesis demonstrates that resilience emerges from the dynamic interplay where the effectiveness of internal temporal coordination is contingent upon the intensity of external events [79]. This integration offers a crucial progression from fragmented explanations, clarifying how teams internally organize to withstand externally defined threats.

Second, we deepen TCT by identifying shared temporal cognition as the specific cognitive micro-foundation through which leadership pacing fosters resilience, while also delineating its clear boundary condition. While prior TCT research has focused heavily on structural coordination tools (e.g., deadlines and schedules) [18,21], our study specifies that steady pacing style is the key behavioral antecedent for cultivating shared temporal cognition. This finding shifts the focus of TCT from purely structural conformity to cognitive synchronization, revealing that shared temporal cognition functions as the “cognitive infrastructure” and “social glue” that allows teams to better integrate their activities, minimize temporal conflicts [80], and sustain functionality when facing disruption. We further contribute a key boundary condition to TCT: the efficacy of the steady pacing mechanism is significantly weakened by high Crisis Event Strength [81].

Third, we extend team resilience literature by challenging the pervasive “faster is better” assumption in high-velocity environments, and by establishing shared temporal cognition as a novel resource base. Conventional wisdom and some literature suggest that urgency and aggressive rhythms are necessary for success in high-tech, fast-paced contexts [22]. Our finding that steady pacing is the superior style for building resilience in Chinese technology enterprises counters this assumption. We argue that in contexts requiring complex interdependence and innovation, the temporal stability provided by a steady pace is paradoxically more adaptive than aggressive rhythms because it permits the crucial cognitive resource of shared temporal cognition to be built and maintained [82]. This work enriches resilience theory by defining shared temporal cognition as a vital collective cognitive resource for adaptive capacity.

5.2. Managerial Implications

Our findings offer actionable strategies for leaders in technology firms to transition from general time management to the deliberate design of temporal architectures. First, leaders should implement steady pacing through structural and ritualized predictability to regulate workload and facilitate resource allocation. Consistent with prior temporal leadership research [16,42], steady pacing provides a stable rhythm that helps teams anticipate challenges and allocate resources efficiently. This requires decomposing macro-deadlines into granular milestones with uniform intervals, utilizing visual timeline tools such as Kanban boards for transparency, and establishing rhythmic check-ins, such as fixed daily stand-ups, rather than relying on ad hoc emergency meetings.

Second, leaders must actively build shared temporal cognition through temporal calibration. This involves fostering collective understanding through explicit communication practices, including pre-project alignment sessions to define urgency thresholds before commencement. Additionally, conducting synchronized temporal checks during a crisis ensures the collective mental model remains consistent as the situation evolves.

Third, leaders must be prepared to deploy adaptive countermeasures when routine pacing fails. Our findings indicate that under high crisis event strength, steady pacing alone may be insufficient. Therefore, we suggest a specific contingency strategy termed the code red temporal protocol. When a crisis hits a critical threshold of disruption, leaders should temporarily suspend the standard steady rhythm and switch to a high-frequency pulse mode. This approach is characterized by short, intense, hourly alignment sessions to rapidly synchronize information, acting as an emergency countermeasure to prevent the fragmentation of shared temporal cognition before stabilizing back to a steady pace once the shock subsides.

5.3. Limitations and Future Directions

Despite the study’s contributions, limitations exist that provide avenues for future research. Firstly, the current study primarily uncovered the positive impact of steady pacing style on team resilience, while the effects of deadline-oriented and U-shaped pacing styles were not as pronounced. This disparity may be due to that steady pacing might inherently foster a more consistent work environment, which is conducive to building resilience over time. In contrast, the other two could introduce variability that complicates the measurement of their impacts. Future research should delve deeper into these alternative pacing styles, exploring if they exhibit curvilinear effects or are more effective in tasks requiring low interdependence, potentially utilizing the aforementioned longitudinal designs.

Secondly, while the study utilized EST to analyze crisis events, it focused mainly on strength of the event (novelty, disruption, and criticality). EST also considers the temporal and spatial dimensions of events, which were not extensively measured in this research. As revealed in the latest research by Pérez-Nordtvedt & Harrison (2025) [83], temporal resilience exists, and it may vary with the passage of time. Future studies might employ longitudinal designs to capture the evolving nature of crises and their impact over time. In addition, given that recovery from setbacks often involves complex cognitive processing such as reflection rather than just tolerance [84], future research could extend this work by employing system dynamics modeling or agent-based simulations to capture the nonlinear feedback processes underlying temporal coordination and resilience [85]. Such computational approaches would further elucidate how variations in pacing rhythm propagate through collective cognitive networks to affect emergent system stability.

Thirdly, the study’s focus on Chinese companies may limit the generalizability of its findings. Cultural factors significantly influence leadership practices and team behavior, and the specific context of tech firms in China may not be directly applicable to other settings. Future research might compare pacing leadership in Western versus Eastern cultures, or examine its efficacy in traditional industries compared to innovative sectors (e.g., ref. [86]).

Fourthly, we acknowledge a measurement limitation concerning the leader pacing style variable. Although leader self-report is theoretically justifiable for capturing strategic intent, this approach is susceptible to social desirability bias [58]. While our multi-source design mitigates overall common method bias, the potential for leaders to over-report socially desirable behaviors (like steady pacing) remains a concern. Future research should consider employing team member ratings of leader pacing style or utilizing objective behavioral measures to provide a more comprehensive and triangulated assessment of temporal management behaviors.

6. Conclusions

In conclusion, this study offers a compelling explanation for team resilience by integrating EST and TCT. We demonstrate that steady pacing style serves as a vital regulatory mechanism, cultivating the collective temporal resource of shared temporal cognition to effectively buffer the team against external shocks. Crucially, this mechanism is sensitive to crisis event strength. From a systems perspective, these findings collectively portray team resilience as an emergent property arising from continuous alignment, feedback, and adaptation among system components. For organizations navigating volatility, resilience is achieved not through simple velocity, but by establishing a predictable, stable temporal rhythm that aligns the team’s internal cognition with external task demands, allowing the collective system to absorb and persist [56]. Thus, temporal regulation is underscored as a key design principle for resilient teams.