Linking Frontline Employee Self-Efficacy to Customers Service Performance in Healthcare Industry: A Dynamic Capability Perspective

Introduction

Medical services, as personalized professional offerings, play a crucial role for frontline employees (FLEs) in delivering high-quality service,¹ meeting customer demands,^2,3 and shaping customer experiences.⁴ In the increasingly competitive healthcare market environment, management recognizes that investing in the knowledge, skills, and capabilities of FLEs through effective human resource practices is one of the key factors for organizational success and survival.⁵ It has been reported that organizations have spent millions of dollars on training and developing the capabilities of FLEs, yet there still exist performance disparities,⁶ and research on the reasons behind these disparities has been found to be limited.⁷

Self-efficacy is considered a key predictive indicator of job performance.^8,9 The positive correlation between self-efficacy and performance has been supported in numerous studies. However, some research suggests that the relationship between self-efficacy and performance is not always positive; instead, it can be neutral or even negative, depending on how self-efficacy exerts its influence.¹⁰ A meta-analysis by Sitzmann and Yeo indicated that self-efficacy is related to performance indirectly through factors such as planning, attention, goal setting, resource allocation, and other performance-influencing factors.¹¹ Given that job performance is an important concept in fields like organizational psychology and organizational behavior,¹² understanding potential mechanisms that may explain the self-efficacy-performance relationship is of paramount importance.

While there has been productive research on the mechanisms through which self-efficacy influences performance,^13–15 there are still some key areas that remain unexplored or have received limited attention. Against the backdrop of today’s rapidly changing external environment, employees need to dynamically perceive changes in the external environment and develop the ability to adapt to new challenging conditions. These abilities are considered employees’ dynamic capabilities,⁷ which contribute to facilitating outstanding performance among employees. Furthermore, self-efficacy is a particular cognitive resource characterized by its relative plasticity and openness to change and development,¹⁶ which aids in the development of dynamic capabilities for perceiving environmental changes and addressing current issues.

However, to the best of our knowledge, no prior research has identified dynamic capability as the primary mediator of the impact of self-efficacy on service performance. Furthermore, research on this topic is relatively scarce, despite dynamic capability being a critical skill for employees to adapt to environmental changes and gain a competitive edge. Given these arguments, it is necessary to adopt a dynamic capability perspective when exploring the relationship between self-efficacy and service performance, focusing on the contribution process of cognitive resources. Therefore, this study, from a dynamic capability perspective, examines how FLEs’ self-efficacy influences their sensing capability and reconfiguring capability to enhance service performance, delving deeper into the process of self-efficacy-dynamic capability-service performance.

The empirical background is medical aesthetic hospitals. First, the market for medical aesthetic services is characterized by continuous product and technological innovations, accompanied by intense competition.¹⁷ Second, the advent of the internet and social media has further contributed to a highly competitive market environment in the medical aesthetic industry.¹⁸ Third, in medical aesthetic services, customers prioritize functional quality, such as empathy and care, over technical quality associated with surgical procedures or clinical treatments.¹⁹ Therefore, the medical aesthetic hospital, as the research setting, offers a rich and unique backdrop for investigating the key constructs of this study.

This study makes significant contributions to the existing literature. First, with the ongoing discussions on the relationship between self-efficacy and performance as a backdrop, this study endeavors to capture the internal processes engaged in addressing the gaps identified in the existing literature. Second, there is limited research on dynamic capability as an individual capability, especially in the healthcare industry.^20,21 This study investigates the dynamic capabilities of medical aesthetic doctors, addressing the gap in the literature regarding the lack of attention to employee roles in dynamic capability research.^20,22 Lastly, the study employs customer evaluations as a measure of service performance, where employee service performance reflects behaviors that assist and serve customers.²³ This utilization of customer evaluations helps track the quality of services provided by FLEs and aligns with the growing demand to expand the measurement of service performance from a producer perspective to an external perspective.^24,25

Literature Review and Hypotheses Development

Dynamic Capabilities Theory

Dynamic capabilities refer to the capacity of an organization to sense, integrate, and reconfigure its internal and external resources in order to effectively respond to a rapidly changing environment.²⁶ The dynamic capability view highlights two aspects of achieving competitive advantage: dynamic and capability. Dynamic refers to the strategic response required to adapt to environmental changes, while capability is the ability to effectively utilize knowledge, capabilities, and resources to accomplish a specific objective.²⁷ Dynamic capabilities enable companies to adapt and reallocate resources to capitalize on opportunities for change, serving as the bedrock for achieving competitive advantage.²⁸ FLEs, who directly engage with customers and possess specialized practical knowledge regarding complex service procedures and evolving customer needs, represent a significant source of emerging opportunities and threats.²⁹ Therefore, the ability of organizations to dynamically match their environments is reflected in the competence of the FLEs who work closely with customers. The dynamic capabilities of employees have emerged as crucial for establishing a sustainable competitive advantage in intricate and volatile environments.^28,30 In healthcare settings, providers significantly shape patients’ perceptions of quality and value.¹ With the healthcare environment growing increasingly dynamic, a critical link exists between the dynamic capabilities of healthcare providers and their service performance.^21,31

Based on the studies conducted by Faccin et al and Wilden and Gudergan, this study examines two interconnected capabilities within the framework of dynamic capabilities: sensing capability and reconfiguring capability.^32,33 Sensing capability aids FLEs in scanning external threats and opportunities as well as identifying strengths and weaknesses within the internal knowledge base to comprehend customers, competitors, and the market environment.^33,34 Reconfiguring capability refers to the capacity to integrate and apply knowledge from internal or external sources, enabling the effective expansion and updating of one’s own capabilities in response to market and technological changes.^35,36 Numerous studies have demonstrated the interrelationship between sensing and reconfiguring capabilities, with the benefits derived from sensing capabilities being realized through reconfiguring activities.^33,37,38

Self-Efficacy and Dynamic Capabilities

Self-efficacy generates an individual’s general ability to explain various intentional behaviors.³⁹ Employees’ self-efficacy empowers them to face challenges and try out new ideas, thereby promoting workflow efficiency⁴⁰ According to Parker et al, individuals need to develop a sense of self-efficacy in order to embrace proactive behaviors within organizational contexts.⁴¹ Self-efficacy not only directly affects behavior, but also influences behavior through other contributing factors.⁴² For instance, self-efficacy shapes individuals’ perception of the structural elements of the environment, including the barriers and opportunities it presents, thereby influencing employees’ behavioral performance. Individuals with low self-efficacy tend to perceive their efforts as futile when faced with environmental barriers, whereas those with high self-efficacy will actively seek ways to surmount them. Consiglio et al argued that self-efficacy positively influences individuals’ perception of the environment.¹³ Employees with a high level of self-efficacy actively seek favorable opportunities provided by the social environment through self-regulated social activities. Accordingly, the following hypothesis is developed.

H1: FLEs’ self-efficacy is positively related to their sensing capability.

As employees’ self-efficacy increases, they gain the confidence necessary to accomplish their work goals. Additionally, employees with high self-efficacy strive to enhance service delivery, thus making contributions to the organization.⁴³ Enhancing services necessitates the continuous adaptation or customization of resources to meet customer needs,⁴⁴ therefore, employees with high self-efficacy are more likely to integrate resources effectively. In a globalized and dynamic business environment, highly self-motivated employees are more likely to proactively seek resources and seize opportunities to engage in specific action processes.⁴⁵ Employees who possess confidence in their own abilities exhibit more effective behaviors and demonstrate superior resource integration compared to those lacking such confidence. Accordingly, it is hypothesized as follows.

H2: FLEs’ self-efficacy is positively related to their reconfiguring capability.

Dynamic Capabilities of FLEs

Sensing capability captures environmental changes that can be perceived as opportunities or threats, providing input for resource reconfiguration.⁴⁶ Innovation that creates new products and services to meet customer needs is the core activity of reconfiguration, requiring new technical knowledge and new knowledge about customers, competitors, and the market environment. Sensing capability plays a vital role in facilitating this process.⁴⁷ Reconfiguring capability relies on the foundation of sensing capability, which offers valuable insights into the environment, enabling precise identification of user needs, comprehension of competitors, and informed decision-making regarding timing and methods for adjustments.³⁸ Simultaneously, employees’ ongoing awareness of environmental changes allows them to grasp the diverse characteristics of the environment, including the opportunities and threats it presents. This awareness motivates them to effectively integrate and modify existing resources to bolster competitive advantage.³³ Accordingly, the following hypothesis is prescribed.

H3: FLEs’ sensing capability is positively related to their reconfiguring capability.

Dynamic Capabilities and Service Performance

In turbulent market environments, FLEs, who engage in extensive interactions with customers, are more prone to acquiring distinctive information and comprehending changes in environmental characteristics (eg, changes in customer preferences, technological advances, etc.), along with the associated opportunities and threats.³³ Employees with strong sensing capabilities can enhance their comprehension of market knowledge and environmental characteristics, resulting in a more holistic understanding of customer needs, improved service performance, and ultimately, an elevated level of customer experience.^48,49 Accordingly, the following hypotheses are formulated.

H4: FLEs’ sensing capability is positively related to customer service performance.

Reconfiguring capabilities enable individuals or groups to collect previously implemented solutions for specific problems and utilize this knowledge to create new products, processes, systems, and services through learning and application, suitable for implementation in new environments.³⁸ Reconfiguring capabilities establishes and safeguards intangible assets, such as knowledge, that underpin superior performance.^35,38 By engaging in resource reconfiguration, employees can generate enhanced solutions, adapt service behaviors, and elevate service delivery. Delivering high-quality services assists in establishing and sustaining long-term customer relationships.^50,51 Accordingly, the following hypothesis is developed.

H5: FLEs’ reconfiguring capability is positively related to customer service performance.

With these hypotheses, our conceptual model is shown in Figure 1.

Figure 1 Research Model.

Materials and Methods

Sample and Procedure

This study selected two medical aesthetic hospitals in Zhejiang Province as research samples. The main reasons are as follows: first, the medical aesthetics market, as a branch of the healthcare market, has experienced remarkable growth in recent years, accompanied by escalating competition within a highly competitive market environment. Second, the hospitals are located in Zhejiang Province, the region with the highest per capita disposable income in mainland China, which provide sufficient economic support for the expansion of non-basic medical aesthetics services, but also face more competitive market environment. Finally, the two medical aesthetic hospitals affiliated with a private chain group are representative in terms of internal advantages, developmental opportunities, and capital operations.

We investigated the perspectives of both healthcare providers and customers regarding healthcare provider services. The reasons are as follows: first, service delivery is a dynamic process involving interactive engagement between providers and customers, warranting the inclusion of both perspectives in the discussion.⁵² Second, prior research underscores the need to expand the focus of performance measurement in service contexts beyond the producer’s viewpoint to encompass an external perspective on service performance.^24,25,53 Therefore, this study asked healthcare providers to complete questionnaires measuring self-efficacy and dynamic capabilities and customers to complete a questionnaire on their perceptions of provider service performance.

The study primarily employed field research to collect questionnaires from doctors at the medical aesthetic hospital and patients who had visited or consulted at the hospital. Data collection took place between December 10, 2020, and December 26, 2020. Prize incentives were employed as a measure to mitigate response bias and the incentivize participation of doctors and customers from two medical aesthetic hospitals in the questionnaire survey. Concurrently with the administration of the doctor’s questionnaire, a customer questionnaire was randomly distributed among the doctor’s clients visiting the clinic to assess the doctor’s service performance from the customers’ perspective. The ratio of doctor questionnaires to customer questionnaires was initially set at 1:5. A total of 133 doctors’ questionnaires were completed, accounting for 90.1% of the entire staff, of which 123 were deemed valid. 791 customer questionnaires were collected, out of which 762 were considered valid. The ratio of doctor questionnaires to customer questionnaires was 1:6.2, with one doctor questionnaire corresponding to a maximum of 20 customer questionnaires and a minimum of 3 customer questionnaires. Table 1 and Table 2 present the demographic and sociological characteristics of the respondents.

Table 1 Characteristics of the Doctors Sample

Table 2 Characteristics of the Consumers Sample

Measures

In order to ensure validity, our measurement items were derived from the existing literature and adapted to the medical aesthetic scenario. Our questionnaire’s items were all developed in English initially, then we translated them into Chinese using the back-translation procedure. The items were evaluated on 7-point Likert scales, with 1 indicating complete disagreement and 7 indicating complete agreement.

Self-Efficacy Scale (SE)

FLEs’ self-efficacy was measured using a five-item scale adapted from Riggs et al.⁵⁴ Doctors were asked to rate each item. Sample item included ‘My idea of medical service is very original’ (α=0.895).

Dynamic Capabilities Scale (DC)

We adopted eleven-item scale from Wilden and Gudergan to measure dynamic capabilities.³³ Doctors were asked to rate each item. This scale has two subscales that measure sensing capability and reconfiguring capability separately. Sample item for sensing capability was ‘I am always on the lookout for the latest information on medical aesthetic products/technologies/services’. One example for reconfiguring capability was ‘I can integrate the skills I have learned with other technologies’. Cronbach’s alpha was 0.853 for sensing capability, and 0.899 for reconfiguring capability.

Service Performance Scale (SP)

Service performance was measured by seven items adopted from Liao and Chuang.²³ Customers were asked to rate each item. A sample item was ‘My doctor is friendly and helpful’ (α=0.919).

Control Variables

Because previous research has revealed that age, gender, and years of work may be related to service performance,^23,55 we controlled for these factors in further analyses. Since the data was collected from two hospitals, we controlled for dummy variables to mitigate the possibility of a correlated effect between the hospitals.

Data Analysis

This study employed partial least squares structural equation modeling (PLS-SEM) as the primary statistical technique. PLS-SEM, characterized by its exploratory nature and enhanced predictability and flexibility, optimizes the explained variance of latent variables to elucidate the model.⁵⁶ PLS-SEM exhibits low sample size requirements, typically 10 times the number of paths directed at the latent variable, and is suitable for non-normal distribution data. Given the small sample size and the preliminary stage of theoretical development of the model, PLS-SEM was selected to validate the proposed research model. All statistical analyses in this study were conducted using SPSS 22.0 and Smart PLS 3.0 software.

Results

Nonresponse Bias and Common Method Bias Check

The sample of medical aesthetic doctors in this study accounted for 90.1% of the entire employee population, suggesting the absence of nonresponse bias among the doctors. Customer sample data were collected from two medical aesthetic hospitals situated in distinct regions using convenience sampling. To examine nonresponse bias, this study employed chi-square tests to test the survey samples from the two hospitals against the total population (ie, all customers who visited the hospitals in 2020) separately. The results of the analyses indicated no significant difference between the samples and the overall population (p > 0.05). Additionally, before conducting empirical analyses, this study examined the demographic and sociological characteristics of the two samples from the aesthetic hospitals. The results indicated no significant difference between the two samples regarding demographic characteristics, including gender, age, and education (p > 0.05), suggesting the absence of significant nonresponse bias.

This study employed Harman’s single-factor test to detect potential common method bias.⁵⁷ The results revealed that the first factor explained 46.609% of the total variance in the explanatory variables, which falls below the 50% threshold.⁵⁸ Thus, there was no significant common method bias in this study.

Reliability and Validity Testing

To the best of our knowledge, this study represents the first examination of the scale in the context of a medical aesthetic hospital. Therefore, the reliability test was performed independently using exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Initially, exploratory factor analysis was conducted using SPSS 22.0 in this study. Considering the KMO value of 0.920 and the statistically significant result of Bartlett’s spherical test, factor analysis was conducted in this study. The questionnaire consisted of a total of 23 items; however, due to factor loadings below 0.5 or factor cross-loadings,⁵⁸ 2 items (SS5 and RC6) were excluded, resulting in a final set of 21 items. Employee self-efficacy, sensing capability, reconfiguring capability, and service performance were identified as factors with eigenvalues greater than one, collectively explaining 67.995% of the variance. As a result, the indicators in this study had good structural validity.

As the constructs in this study were reflective, the measurement models were evaluated by assessing the reliability, convergent validity, and discriminant validity. Reliability testing assesses the consistency, stability, and reliability of the data to measure its reliability. The SmartPLS validation factor analysis, presented in Table 3, revealed that all variables exhibited Cronbach’s α values exceeding 0.8, and the composite reliability (CR) values exceeded 0.8, satisfying the reliability criteria. These findings indicate a high level of consistency and reliability within the measurement model.⁵⁹

Table 3 Properties of Measurement Model

Convergent validity assesses the theoretical relevance of the question items.⁶⁰ All factor loadings were higher than the critical value of 0.7,⁵⁷ except for RC4, which is also acceptable at 0.677. Additionally, the average variance extracted (AVE) for all variables was higher than 0.5.⁶⁰ Hence, the model passed the convergent validity test. Table 3 shows all the details.

Discriminant validity examines the extent to which a measure is unrelated to other constructs that should differ. Table 4 presents the results indicating that the square roots of the AVEs are all greater than the bifactor correlation coefficients, aligning with the Fornell and Larcker criteria.⁶⁰ As shown in Table 5, Heterotrait Monotrait (HTMT) values are all greater than the lowest threshold value of 0.85.⁶¹ These confirm discriminant validity.

Table 4 Discriminant Validity Test (Fornell & Larcker Criteria)

Table 5 Discriminant Validity Test (HTMT)

Path Analysis

We conducted a bootstrapping procedure to test our hypotheses (5000 samples). Figure 2 presents the results of the structural model. The predictive relevance of the model was examined by assessing the effect size and explained the variance of the endogenous constructs. This model explains 25.5% of the variance in sensing capability, 54.4% of the variance in reconfiguring capability, and 58.7% of the variance in service performance. Two endogenous constructs had R² values above 0.33, indicating moderate explanatory strength.⁶² In addition, the effect sizes (f²) for the supported hypotheses range from 0.039 to 0.482. These effect sizes correspond to small, medium, and large effects, respectively, according to the criteria of 0.02, 0.15, and 0.35.⁵⁶ Hence, the model’s predictive relevance was supported.

Figure 2 Structural model results. Abbreviation: NS, Not significant. Notes: **p<0.01;***p<0.001.

The structural model estimation results supported a positive effect for the path from self-efficacy to sensing capability (β = 0.442; p < 0.001; f² = 0.343) and reconfiguring capability (β = 0.355; p < 0.001; f² = 0.210), in support of H1 and H2, respectively. The path from sensing capability to reconfiguring capability (β = 0.433; p < 0.001; f² = 0.390) was significant in support of H3. Surprisingly, the path between sensing capability → service performance was not significant and failed to support H4 (β = 0.197; p > 0.05; f² = 0.039). However, the missing element appeared to be the effect of reconfiguring capability. Support was found for H5’s reconfiguring capability → service performance path (β = 0.554; p < 0.001; f² = 0.482). Table 6 summarizes the results of the hypothesis testing.

Table 6 Structural Relationship

Post Hoc Analyses of the Mediating Effects

This study examined the mediating effect of sensing and reconfiguring capabilities through post hoc analyses.⁵⁶ The results revealed that the mediating path from SE to SP via SC comes out to be 0.087, not significant. There is no evidence of a mediating effect of sensing capability in the relationship between SE and SP. In addition to this, all indirect effects were statistically significant (p < 0.01) and the confidence interval of the indirect effect excluded 0, confirming the mediating effect. The assessment of direct effects indicated that reconfiguring capability partially mediated the relationship between SE and SP; and fully mediated the relationship between SC and SP. Additionally, sensing capability and reconfiguring capability partially mediated the relationship between SC and SP. In conclusion, the results suggest that dynamic capabilities play a significant mediating role in the influence of self-efficacy on service performance. Table 7 presents the detailed results.

Table 7 Results of Mediation Testing

Discussion

The results of the empirical research, carried out with doctors of medical aesthetic hospitals, showed that dynamic capabilities were positively related to service performance, as shown by the proposed model. The other factor, self-efficacy, had its effects by dynamic capabilities. This study makes several theoretical contributions to the field in the following ways.

This study assessed the relationship between dynamic capabilities and service performance. The findings indicated that reconfiguring capability can enhance service performance, whereas sensing capability does not directly improve service performance but indirectly influences it through reconfiguring capability. A possible explanation for these findings is that the core dimensions of dynamic capabilities — structure and frequency — are loosely coupled and can change independently.⁶³ Hence, the sense of environmental change and the active acquisition of knowledge from external sources may not directly translate into a competitive advantage. For example, as competitive conditions change, opportunities may have a lower value than initially assessed. Given that various types of services necessitate adaptive and creative approaches to support clients,³⁷ this finding helps to gain a more detailed understanding of the impact of different dimensions of dynamic capabilities. Prior research has explored the positive relationship between employees’ dynamic capabilities and job performance.^64,65 Our study builds upon this by further confirming that dynamic capabilities constitute a multidimensional concept, enhancing our understanding of this domain. Developing dynamic capabilities into a multilevel structure has the potential to yield broader and more impactful implications in the field of service management. Moreover, it may facilitate additional connections with the macro- or organizational-levels of dynamic capability frameworks found in earlier literature.⁶⁶

The findings of this study also highlighted the significant role of self-efficacy as a key antecedent of dynamic capabilities. Furthermore, self-efficacy positively influences both sensing and reconfiguring capabilities. Consistent with previous research,⁶⁷ this study confirmed that high self-efficacy fosters proactive and conscious engagement in activities that demand exploratory effort and an effective response to the environment. In addition to predicting task-related effort and performance, employee self-efficacy also facilitates the promotion of organizational citizenship behavior.⁶⁸ This study demonstrated how FLEs’ self-efficacy enables them to go beyond their prescribed roles, enhancing their dynamic capabilities. In contrast to prior research that has primarily focused on the role of manager cognition in dynamic capabilities,^69,70 our study provides a different perspective, showing the influence of FLEs’ internal perceptions on dynamic capabilities.

Another important finding was that self-efficacy affects service performance not only directly but also through dynamic capabilities. In competitive work environments, employees with high self-efficacy are more likely to engage in learning new things, volunteering for new projects, and taking on additional tasks.⁷¹ These behaviors contribute to the development of their dynamic capabilities and positively impact service performance. This hypothesis is supported by previous research showing that self-efficacy positively affects performance when it enhances the allocation of cognitive resources to tasks.^72,73 Previous research has emphasized the importance of cognitive resources such as optimism, creativity, and job crafting in strengthening the positive relationship between self-efficacy and work outcomes.^14,15,43 Our research findings indicate that dynamic capabilities, functioning as higher-order abilities, can mobilize and leverage cognitive resources. This underscores the merit of exploring the role of dynamic capabilities in the relationship between self-efficacy and service performance, a pathway not previously validated by researchers. This study investigates the role of dynamic capabilities in the healthcare sector, providing novel insights into a domain that has received limited attention from researchers in the past.

Managerial Implications

Employee service performance plays a critical role for both managers and organizations.⁵⁵ This study provides valuable insights into management practices in healthcare organizations by suggesting strategies for improving employee service performance. First, self-efficacy positively influences service performance through dynamic capabilities. Understanding the antecedents of service performance can help to manage the self-efficacy of FLEs and cultivate their dynamic capabilities to enhance service performance. Efficacy beliefs can be developed through targeted strategies, therefore, training for employees should emphasize key sources of self-efficacy such as mastery experience, social modeling, social persuasion, and efficacy beliefs.⁴² Targeted interventions should be implemented to develop a highly motivated workforce to achieve a competitive advantage. Second, during the recruitment and selection process, it is essential to prioritize the assessment of employees’ self-efficacy and dynamic capabilities as they profoundly influence the sustainability of individuals and the organization as a whole.^7,65,74

Third, the development of adaptive capabilities is crucial for enhancing service performance, especially in the face of a rapidly changing environment.⁷⁵ Sensing capability enables employees to scan and gather knowledge and technology. Reconfiguring capability enables them to adjust their knowledge, resources, and capabilities. These behaviors and capabilities are increasingly crucial for the future success of organizations.⁷ Therefore, effectively managing the dynamic capabilities of FLEs is a valuable approach to maintaining sustainable competitive advantages. Managers who invest in improving their employees’ dynamic capabilities will benefit from the relationship between self-efficacy and service performance. Organizations should leverage the autonomy and motivation of FLEs, encourage them to come up with new ideas, adopt and implement innovative methods or products, and foster an environment conducive to developing dynamic capabilities.

Limitations and Further Research

This study is not free from all the limitations. First, the participants in this study were recruited from two medical aesthetic hospitals in China. Future research should aim to enhance the external validity of the current findings by recruiting diverse samples from the medical field, including different regions and countries. Second, despite utilization of multi-source data from customers and employees in this study, the cross-sectional design hampers the efficiency of summarizing structures and approximating causal relationships among them. Therefore, future research should consider using longitudinal data to establish accurate causal relationships.^76,77 Additionally, a longitudinal study design can help alleviate potential biases; for example, self-efficacy and dynamic capabilities may change over time,⁷⁸ and longitudinal studies can help identify variable patterns of cognitive resources and personal capabilities over time. Finally, this study explored the relationship between self-efficacy, dynamic capabilities, and service performance. However, the proposed theoretical model only explained 58.7% of the variance. Future research should propose a comprehensive theoretical framework that integrates cognitive, motivational, and emotional processes to thoroughly explore the underlying mechanisms through which self-efficacy influences service performance.⁴² Moreover, given the high level of environmental turbulence, dynamic capabilities will become increasingly significant.³³ Future research could provide further insights by exploring the potential moderating role of contextual factors, such as environmental factors.