2.1 Social CRM capability from a dynamic capabilities theory

The digital transformation of Customer Relationship Management (CRM) marks a fundamental shift in how organizations interact with customers. Traditional CRM systems relied primarily on structured data collection through conventional channels. However, the proliferation of social media platforms has revolutionized customer engagement paradigms (Trainor et al., 2014; Wang & Kim, 2017). Contemporary social media platforms enable real-time data acquisition, allowing customers to share product reviews and disseminate product information instantaneously within their networks. This evolution provides organizations with immediate, valuable insights into customer preferences and behaviors, gathered through social interactions (Dissanayake et al., 2018). In contrast to the delayed feedback mechanisms of traditional CRM, Social CRM offers a dynamic framework that enhances customer engagement and allows organizations to rapidly adapt to evolving customer needs (Lamrhari et al., 2022).

Scholarly literature defines social CRM capability as an organizational competency that integrates social media technologies, processes, and customer engagement strategies to deliver superior value propositions (Diffley et al., 2018; Keramati et al., 2010). The theoretical foundation for analyzing this capability is rooted in Dynamic Capabilities Theory, which provides a robust framework for understanding how organizations develop and leverage social CRM capabilities to address environmental dynamics. This perspective underscores the importance of continuously reconfiguring organizational resources and capabilities to respond to changing market conditions (Pedron et al., 2018; Teece et al., 1997).

Dynamic Capabilities Theory identifies three interconnected dimensions that are particularly relevant in the social CRM context (Chatterjee et al., 2021; Wang & Kim, 2017). First, sensing capability refers to an organization's proficiency in monitoring and interpreting social media signals to detect market trends and customer needs. For example, firms can utilize advanced social listening tools to identify customer sentiment and emerging product trends. Second, seizing capability highlights the ability to proactively respond to identified opportunities by engaging customers through social platforms. Organizations often deploy targeted marketing campaigns or develop real-time customer support initiatives to seize these opportunities. Third, reconfiguring capability addresses how organizations adapt and transform their internal processes and structures based on social media insights. For instance, firms may restructure workflows to integrate social media data into decision-making processes.

These dimensions collectively enable organizations to effectively capture and utilize social media-based customer insights for enhanced relationship management. The integration of these capabilities allows firms to achieve superior market responsiveness, accelerate product innovation, and improve competitive positioning in the digital marketplace (Pedron et al., 2018). Notably, sensing, seizing, and reconfiguring capabilities do not operate independently but instead function as an interconnected system. For example, insights derived from sensing capability inform seizing strategies, while effective reconfiguration ensures that the organization can sustain these processes over time.

Thus, Dynamic Capabilities Theory provides a valuable framework for understanding how social CRM capabilities can be leveraged to generate continuous streams of customer insights and adapt to the rapidly evolving digital landscape. This study explores how these capabilities enhance firms' operational processes and overall performance, as discussed in the following section.

2.2 The effect of social CRM capability on social enterprise performance

The effectiveness of social CRM capabilities in improving firm performance is particularly relevant in today's digital business environment (Gamage et al., 2023; Hakimi & Mehdi, 2020). Organizations must continuously adapt to changing customer expectations and market conditions to maintain their competitive position. Social CRM capabilities provide the technological and organizational infrastructure necessary for managing these complex market dynamics effectively (Foltean et al., 2019). From a dynamic capability’s perspective, firm performance represents an organization's ability to achieve its operational and financial objectives through effective resource deployment and market adaptation (Pedron et al., 2018). In this study, firm performance is assessed using key financial indicators such as sales growth rate, return on investment (ROI), profitability increase, and sales profit margin (Cao & Zhang, 2011). Understanding how social CRM capabilities influence these performance metrics is crucial for organizations seeking to enhance their competitive position in the digital age.

Empirical evidence substantiates the critical role of social CRM capabilities in enhancing organizational performance. Diffley et al. (2018) demonstrated how organizations leveraging social CRM capabilities achieve superior performance by generating enhanced customer insights. Wang and Kim (2017) discovered that social CRM capabilities significantly improved firms' customer relationship performance by enabling real-time responses to customer needs.

Foltean et al. (2019) found that organizations effectively utilizing social CRM capabilities exhibit superior market responsiveness and improved profitability. These capabilities allowed organizations to streamline customer service processes, reduce operational costs, and increase revenues through more effective customer relationship management. Chatterjee et al. (2021) showed that the effectiveness of social CRM capabilities could vary depending on institutional contexts and pressures. Medjani and Barnes (2021) verified that organizations with robust social CRM capabilities demonstrated improved customer retention rates and higher sales growth compared to their competitors. These capabilities enabled organizations to develop more targeted marketing strategies and provide personalized customer experiences, subsequently increasing customer satisfaction and loyalty.

In recent research, Chatterjee et al. (2023) reported that social CRM capabilities enable organizations to develop and maintain effective stakeholder relationships through enhanced communication and engagement mechanisms. According to Malki et al. (2024), systematic data collection and analysis across social media platforms allow organizations to better understand customer preferences, predict market trends, and rapidly respond to changing business environments. This enhanced responsiveness enabled by social CRM capabilities helps organizations optimize operational processes and improve market positioning. Therefore, based on both theoretical arguments and empirical evidence, we propose that:

H1: Social CRM capabilities will increase social enterprise performance.

Despite these valuable insights, several research gaps remain. First, while existing studies have examined social CRM capability's direct effects on performance outcomes, the moderating role of institutional pressures remains underexplored (Wang & Kim, 2017). Second, most research has focused on large corporations, leaving questions about how social enterprises, which operate under unique institutional pressures, can develop and leverage this capability (Diffley et al., 2018). Third, the interaction between different types of institutional pressures in shaping social CRM capability's effectiveness requires further investigation, particularly in the context of social enterprises where multiple institutional logics coexist. Therefore, this study discusses the impact of institutional pressures on the relationship between social CRM capability and firm performance of social enterprises in the following section.

2.3 Institutional pressures on social CRM capabilities and social enterprise performance

The development and deployment of social CRM capability does not occur in isolation but is significantly influenced by institutional pressures within the organization's environment (Dimaggio & Powell, 2004). Institutional Theory suggests that organizations' adoption and implementation of technologies and practices are shaped by coercive, mimetic, and normative pressures (Scott, 2008). In the context of social CRM, these pressures manifest through regulatory requirements for data protection (coercive), successful competitors' social CRM practices (mimetic), and industry standards for customer engagement (normative) (Dewnarain et al., 2021; Luo et al., 2024).

According to Institutional Theory, organizations do not only adopt practices for technical efficiency but also for social legitimacy. Organizations are compelled to align with the rules, norms, and beliefs of the institutional environment to ensure survival and success, which leads to isomorphism within the organizational field (DiMaggio & Powell, 1983, 2004; Meyer & Rowan, 1977). Given that social enterprises simultaneously pursue social and economic value, institutional pressures are likely to play a more significant role in shaping how these enterprises develop and implement social CRM capabilities.

Moreover, social CRM, unlike traditional CRM, has innovative characteristics, which may lead to varied ways in which institutional pressures are absorbed. These different forms of absorption will have significant implications for the influence of social CRM on organizational performance. Thus, institutional pressures not only shape the development of social CRM but also moderate the relationship between social CRM capability and the performance of social enterprises.

This study aims to address these gaps by examining how institutional pressures moderate the relationship between social CRM capability and firm performance in social enterprises. By integrating Dynamic Capabilities Theory with Institutional Theory, this study will provide a more comprehensive understanding of how social enterprises can effectively develop and deploy social CRM capabilities while navigating their unique institutional environment. Therefore, based on both theoretical arguments and empirical evidence, we propose the following hypothesis and establish sub-hypotheses for the three pressures:

H2: Institutional pressure will be moderate on the social CRM capability and social enterprise performance.

Coercive pressure refers to the formal and informal pressures exerted on an organization by other organizations it depends on and by the cultural expectations of the society in which it operates, as conceptualized in Weber’s legitimate authority framework (Dimaggio & Powell, 2004). These pressures can be both formal, such as laws, regulations, and government policies, and informal, including societal expectations or stakeholder demands. In the context of social CRM, coercive pressures are evident in the form of compliance requirements for data protection laws or customer information security regulations. For social enterprises, coercive pressures are often influenced by government regulations, legal obligations, and the expectations of funders and stakeholders (Battilana & Lee, 2014).

Based on the scholarly literature, Foltean et al. (2019) investigated the impact of customer coercive pressure on social media technology (SMT) usage, confirming that institutional factors play a crucial role in promoting SMT adoption, enhancing CRM capabilities, and improving organizational performance. Al-Omoush et al. (2021) similarly discovered that external pressures significantly influence social CRM adoption. Latif et al. (2020) further verified that coercive pressures arise from powerful stakeholders such as government organizations, NGOs, customers, and suppliers, demonstrating that firms adopt innovative technologies under the influence of government bodies, regulatory authorities, parent companies, competitors, and resource-dominant organizations. These coercive forces shape how organizations develop and deploy social CRM capabilities, potentially affecting their operational performance and their ability to comply with the evolving demands of both regulatory authorities and stakeholders. Therefore, we propose the following sub-hypothesis:

H2a: Coercive pressure will positively moderate the effect of social CRM capability on social enterprise performance.

Normative pressure stems from Parsons' theory of socialization and studies on professionalization (Parsons, 1968). The source of normative pressure includes professional networks, educational institutions, and professional associations, which establish standards and norms that organizations are expected to adhere to. In the modern context, this pressure is manifested through industry standards, professional certifications, and best practice guidelines. The spread of norms through specialization and networks plays a significant role in influencing organizational behavior. Mizruchi and Fein (1999) analyzed how expert networks and education influence the adoption of organizational practices. In the context of social enterprises, Besharov et al. (2019) studied the impact of professional associations, educational programs, and industry standards on the operational practices of social enterprises. Specifically, in social enterprises, normative pressure from such networks can guide the design and deployment of social CRM strategies, shaping how these organizations adapt to industry standards (Mohammed et al., 2024). Therefore, based on both theoretical arguments and empirical evidence, we propose the following sub-hypothesis:

H2b: Normative pressure will positively moderate the effect of social CRM capability on social enterprise performance.

Finally, mimetic pressure develops from March and Simon's research on strategies for coping with uncertainty (Simon, 1958). When faced with uncertainty, organizations tend to imitate the behavior of successful peers within the same industry. This tendency for imitation can manifest in various ways, including benchmarking, mimicking best practices, and adopting risk-avoidance strategies. In the modern context, this often involves strategies such as digital transformation, the introduction of innovative technologies, and business model innovation. Liang et al. (2007) analyzed the phenomenon of organizations benchmarking successful cases from competitors during the process of adopting information systems. In the context of social enterprises, Nicholls and Huybrechts (2016) explored mimetic behavior as a strategy for organizations to gain legitimacy. Specifically, in the context of social CRM, this mimetic pressure can significantly affect how social enterprises decide to implement social CRM capabilities, especially when these enterprises observe and seek to emulate the practices of industry leaders (Foltean et al., 2019; Guha, 2021; Marolt et al., 2022). Therefore, we propose the following sub-hypothesis:

H2c: Mimetic pressure will positively moderate the effect of social CRM capability on social enterprise performance.

Figure 1 shows a research model.

Figure 1

Research Framework

3.1 Data collection procedures

To test the hypotheses of this study, a survey was conducted targeting enterprises registered with the Korea Social Enterprise Promotion Agency. The survey was carried out over approximately two months, utilizing both telephone interviews and online questionnaires to collect responses. From a pool of approximately 3,700 registered enterprises, a random selection of about 600 firms was made for a preliminary telephone survey to assess their suitability for this study. Two selection criteria were applied to ensure appropriate responses: (1) the respondent had to hold a managerial position, and (2) they needed to possess sufficient knowledge of the overall operations of the social enterprise. Once suitable respondents were identified, the study's purpose was explained, participation was requested, and online questionnaires were distributed. Ultimately, after undergoing a rigorous four-step data cleaning process—which included identifying missing values, detecting outliers, verifying data consistency, and reviewing patterns based on prior knowledge and logical reasoning—the final sample consisted of 161 valid responses. The large initial sample was reduced due to non-responses, incomplete data, and data inconsistencies. For survey development, measurement items from previous studies were adopted. The initial questionnaire was drafted in English based on prior research and then translated into Korean. To ensure accuracy and reliability, two bilingual experts proficient in both Korean and English conducted a final review of the translated items. Additionally, feedback was gathered from social enterprise managers in Korea to assess the relevance and clarity of the Korean version of the questionnaire. Based on all received feedback, necessary revisions were made before distributing the final version of the survey. Ethical considerations were addressed by informing respondents about the study's purpose, obtaining their consent, and ensuring the confidentiality of responses. Table 1 presents the demographic characteristics of the sample.

Table 1

The Sample Demographics (N=161).

3.2 Measurement items

In this study, the independent variable, social CRM capability, was measured using five items from Luo et al. (2024). The moderating variable, coercive pressure, was measured using five items, while normative pressure and mimetic pressure were each assessed using three items, all adapted from the study by Gupta et al. (2020). Lastly, the dependent variable, social enterprise performance, was evaluated using four items derived from Wang et al. (2023), Armstrong and Overton (1977). All measurement items were assessed using a seven-point Likert scale.

To enhance the accuracy and validity of the research model, four control variables were incorporated to account for external influences on firm performance: the type of major client groups, revenue, financial support, and non-financial support. First, the type of major client groups was included to examine how different customer segments—such as individual consumers (B to C), businesses and organizations (B to B), and public institutions (B to G)—affect social enterprise performance. Understanding these major client groups is crucial because varying customer bases can result in different resource allocation strategies and priorities, which in turn can influence the outcomes of a firm. Second, revenue was controlled to reflect the financial scale of the organization, as this directly impacts the firm's ability to invest in resources and capabilities that can drive performance. Larger revenues typically enable firms to adopt advanced strategies, technologies, and innovations, which can lead to better overall performance. Third, financial support from external sources, such as grants or subsidies provided by the government or local communities, was considered to assess its role in enhancing the resources and capabilities of social enterprises. This type of support is crucial for social enterprises as it can significantly affect their ability to achieve objectives and improve performance outcomes. Finally, non-financial support was incorporated to examine the impact of external guidance, partnerships, and networks, such as advisory services. This type of support plays a vital role in shaping strategies, improving operational capacity, and ultimately contributing to the enhanced performance of social enterprises.

3.3 Common method bias

To address potential non-response bias, early and late respondents were compared based on both firm-level demographic variables [e.g., type of social enterprises, major client groups, number of employees, revenue, and return on sales (ROS)] and respondent-level characteristics (e.g., years of work experience, job position), following the approach of Armstrong and Overton (1977). The t-test analysis showed no significant differences across most variables, except for financial support and ROA, which were not statistically significant. These findings suggest that non-response bias is unlikely to pose a significant threat to the validity of this study. Furthermore, to mitigate common method bias (CMB), a common latent factor was introduced. The model fit indices of the original model (χ²/df = 1.500, RMSEA = 0.056, CFI = 0.980, and IFI = 0.980) and the extended model with the common latent factor (χ²/df = 1.506, RMSEA = 0.056, CFI = 0.980, and IFI = 0.980) were highly similar. This result indicates that common method bias is unlikely to be a significant issue in this study (Chang et al., 2020).

3.4 Validity and reliability testing

Prior to hypothesis testing, it was essential to confirm the validity of the measurement items and ensure data consistency. To assess validity, confirmatory factor analysis (CFA) was performed using AMOS 22.0. The model fit indices indicated an acceptable overall fit: χ² = 238.519; df = 159; χ²/df = 1.500; CFI = 0.980; TLI = 0.976; IFI = 0.980; and RMSEA = 0.056. These indices confirm the unidimensionality of the measurement items. Next, convergent validity was assessed based on confirmatory factor analysis (CFA) results by calculating standardized factor loadings (SFL), composite reliability (CR), and average variance extracted (AVE). The values were as follows: SFL ranged from 0.786 to 0.978, CR ranged from 0.928 to 0.970, and AVE ranged from 0.765 to 0.911. As all values exceeded the recommended thresholds, convergent validity was confirmed. The final list of five constructs, along with standardized factor loadings, is presented in Table 2. Lastly, discriminant validity was evaluated by comparing the square root of the AVE for each construct with the absolute correlation coefficients between that construct and all other constructs. The results indicated that the smallest AVE value was greater than the highest squared correlation coefficient, providing evidence that each construct is distinct from others and does not suffer from excessive overlap (Fornell, 1981). This confirms that the measurement items effectively capture the intended constructs rather than unrelated factors. Additionally, further verification was conducted by comparing the maximum shared squared variance (MSV) and maximum reliability [MaxR(H)] for each construct. Since the AVE values exceeded the MSV values, it indicates that the variance captured by each construct is greater than the variance shared with other constructs (Hair et al., 2010). This provides additional support for discriminant validity, ensuring that each construct maintains its conceptual distinctiveness. To assess reliability, Cronbach’s α was calculated for each construct. All five constructs had Cronbach’s α values exceeding 0.70, demonstrating that the measurement items were both validated and reliable (Bonett & Wright, 2015). Table 3 presents the correlations, composite reliability (CR), average variance extracted (AVE), maximum shared variance (MSV), maximum reliability [MaxR(H)], and Cronbach’s α values.

Table 2

Measurement Items

Table 3

Correlation Analysis and Discriminant Validity

5.1 Theoretical discussions

From the perspective of the dynamic capabilities view, this study predicted that social CRM capabilities serve as a critical mechanism that enables social enterprises to identify opportunities, strengthen stakeholder relationships, and adapt to evolving market demands. Since social enterprises operate in dynamic environments, their ability to reconfigure resources and integrate social CRM capabilities effectively is essential for enhancing overall performance. Our findings support the argument that social CRM capabilities function as a strategic asset, allowing social enterprises to improve performance by continuously responding to environmental changes. This aligns with the dynamic capabilities perspective, which emphasizes that firms vary in their ability to acquire and deploy resources, leading to differences in performance over time (Eisenhardt & Martin, 2000; Teece et al., 1997). This perspective is particularly relevant for social enterprises, as they must continuously adapt to changing market conditions by integrating and transforming available resources in innovative ways (Morgan et al., 2009). Our attempt to conceptualize social CRM capabilities as dynamic capabilities is consistent with previous research. For instance, Mention et al. (2019) identified social media as a source of dynamic capability for innovation. Similarly, Trainor et al. (2014) argued that firms achieve performance improvements when they develop specific capabilities—such as social CRM capabilities—and integrate them with other complementary resources. Furthermore, Wang and Kim (2017) emphasized that social media usage fosters the development of social CRM capabilities, which, in turn, enhance customer engagement and firm performance. These findings collectively suggest that social CRM capabilities improve internal adaptability by enabling firms to continuously reconfigure and optimize resources, reinforcing the principles of the Dynamic Capabilities View.

In addition to the dynamic capabilities view, we employed institutional theory to provide a complementary perspective on how external pressures shape the extent to which social enterprises can effectively leverage social CRM capabilities. Institutional theory posits that organizations’ adoption and implementation of technologies and practices are influenced by coercive, mimetic, and normative pressures (Scott, 2008). This framework serves as a powerful explanatory lens, allowing us to examine the impact of external institutional forces on organizational decision-making and performance (Liang et al., 2007). By incorporating the three distinct isomorphic pressures into our conceptual model, we contribute to the institutional theory literature by demonstrating how these external forces shape the effectiveness of social CRM capabilities in the social enterprise context. Addressing this issue is particularly critical for social enterprises, as they must align with institutional rules, norms, and societal expectations to ensure legitimacy and long-term survival. Failure to conform to these institutional forces may hinder their ability to thrive within their respective ecosystems. Our findings align with existing studies that emphasize the interplay between internal capabilities and external institutional pressures. For instance, Liang et al. (2007) found that institutional forces must be integrated into models predicting the influence of top management on enterprise system assimilation. Further evidence suggests that institutional pressures enhance firms' ability to adopt climate change initiatives, ultimately leading to improved performance in business-to-business markets (Bag et al., 2023). These findings reinforce our argument that institutional forces should not be overlooked when assessing the strategic role of social CRM capabilities in social enterprises. By bridging dynamic capabilities theory and institutional theory, our study offers a more comprehensive framework for understanding how both internal competencies and external institutional pressures jointly shape social enterprise performance. This dual-theoretical approach underscores the necessity of integrating both organizational adaptability and institutional legitimacy to develop a holistic understanding of social CRM capabilities in the social enterprise sector.

Finally, our empirical results reveal that among the three distinct institutional forces, mimetic pressures serve as an enabler that strengthens the positive impact of social CRM capabilities on social enterprise performance, whereas coercive pressures and normative pressures do not. This finding aligns with broader discussions in the literature, where prior research has produced mixed results regarding the role of institutional forces across different contexts. Some studies suggest that institutional forces can act as enablers. For example, regulatory pressures and mimetic pressures have been found to positively influence firms’ green technology innovation by encouraging firms to adopt sustainable practices and improve competitiveness (Qi et al., 2021). Liang et al. (2007) also highlighted in the context of enterprise resource planning (ERP) system adoption that both mimetic pressures and coercive pressures promote top management participation, which, in turn, facilitates the ERP assimilation process. However, normative pressures did not demonstrate a significant impact. Conversely, other studies have shown that institutional pressures can impose constraints. For instance, Borsatto and Bazani (2021) argued in the context of environmental management that although environmental regulations can drive manufacturers toward green innovation, excessive regulatory pressure may lead to higher compliance costs in the short term, thereby reducing firms’ capacity to invest in innovation. Additionally, Zhong and Um (2024) discovered that regulatory pressure negatively affects the relationship between customer integration and green innovation, as stringent regulations shift firms' focus toward compliance and risk aversion, limiting their ability to leverage customer insights for innovation.

These findings collectively indicate that institutional forces can function as either enablers or constraints, depending on the context, and that their effects are not necessarily uniform. Based on our research findings, we extend this discussion by demonstrating that in our study context, mimetic pressures act as an enabler, whereas coercive and normative pressures do not. Notably, the negative sign of the Social CRM capability × Normative Pressure interaction coefficient (β = −0.182), although statistically non-significant, warrants theoretical attention. This directional pattern suggests that normative pressures—rooted in professional norms and industry standards—may not simply be neutral but could potentially constrain the performance benefits of social CRM capabilities under certain conditions. One plausible explanation is that in the social enterprise context, normative pressures tend to emphasize conformity to externally defined professional standards rather than encouraging the adaptive or innovative use of CRM capabilities. When social enterprises prioritize meeting such externally defined standards, their attention and resources may be diverted from the flexible, stakeholder-responsive utilization of social CRM that drives performance improvement. While this interpretation requires caution given the lack of statistical significance, the negative directionality aligns with prior findings suggesting that external conformity pressures can shift organizational focus toward compliance and risk aversion rather than innovation (Zhong & Um, 2024). Future research with larger samples should empirically examine whether this suppressive tendency of normative pressures reaches statistical significance under different institutional conditions or cultural contexts.

This study provides the following interpretations regarding our findings. First, mimetic pressures enhance the impact of social CRM capabilities on social enterprise performance because social enterprises tend to adopt best practices and strategies from successful peers, which accelerates the implementation and optimization of social CRM capabilities. This imitation process helps social enterprises reduce uncertainty and gain legitimacy in competitive environments. Second, coercive pressures do not function as enablers because external regulatory demands often create rigid compliance requirements, diverting resources away from the effective utilization of social CRM capabilities. Instead of fostering innovation and adaptability, coercive pressures may lead social enterprises to focus on short-term regulatory adherence rather than long-term performance improvements. Finally, normative pressures fail to enhance the impact of social CRM capabilities because professional norms and industry standards, while important, may not provide direct incentives for social enterprises to integrate and optimize their social CRM capabilities. Unlike mimetic pressures, which drive competitive alignment, normative pressures often emphasize conformity over innovation, thereby limiting their ability to serve as performance-enhancing forces. By clarifying the distinct roles of these institutional forces, our study contributes to a more nuanced understanding of how external pressures shape the effectiveness of social CRM capabilities in social enterprises.

5.2 Practical discussions

The findings of this study offer valuable insights for social enterprise practitioners seeking to leverage social CRM capabilities to enhance performance. Given that social CRM capabilities play a crucial role in strengthening stakeholder engagement and optimizing resource utilization, social enterprises should prioritize investments in these capabilities to build stronger relationships with beneficiaries, donors, and other key stakeholders. By doing so, they can achieve their social and financial objectives financial and social performance, enhance trust among various stakeholders, and drive long-term sustainability. From a quality management perspective, social CRM capabilities can also be understood as a mechanism for continuous customer value improvement: by systematically collecting stakeholder feedback through social media (sensing), translating insights into service adjustments (seizing), and restructuring operational processes accordingly (reconfiguring), social enterprises can enhance both service quality and customer satisfaction— core objectives shared with quality management principles.

Our results highlight the enabling role of mimetic pressures in facilitating the effective implementation of social CRM capabilities. However, the practical implication extends beyond simply observing competitors. Social enterprise managers should establish structured peer-learning mechanisms—such as inter-organizational CRM practice consortia within the social enterprise ecosystem—to systematically absorb and adapt proven CRM strategies. Concretely, this means: (1) actively participating in government-supported social enterprise networks (e.g., Korea Social Enterprise Promotion Agency’s knowledge-sharing programs) to identify and adopt CRM practices demonstrated by high-performing peer organizations; (2) implementing periodic ‘CRM quality audits’ that benchmark their social media engagement processes against standards demonstrated by successful comparable organizations; and (3) using mimetic learning not as mere imitation, but as a catalyst for adapting CRM processes to the organization’s unique stakeholder base—carefully distinguishing the differing quality expectations of B2C, B2B, and B2G customer segments. By taking this structured approach to mimetic learning, social enterprises can reduce implementation uncertainty, accelerate capability development, and enhance their ability to engage stakeholders more effectively.

In addition, while coercive pressures do not enhance the impact of social CRM capabilities, social enterprises must navigate regulatory requirements without allowing compliance burdens to overshadow their strategic goals. Instead of viewing regulations solely as constraints, practitioners should seek ways to align compliance efforts with their broader mission, ensuring that legal adherence does not hinder innovation in stakeholder engagement and resource optimization.

Finally, the lack of significant influence from normative pressures suggests that industry norms and professional standards alone are insufficient in driving the effective use of social CRM capabilities. While adhering to ethical and professional guidelines remains essential, social enterprises should go beyond standard practices by proactively experimenting with new CRM strategies tailored to their specific needs. Encouraging a culture of continuous learning and innovation will enable social enterprises to maximize the benefits of social CRM capabilities rather than relying solely on established norms.

Overall, this study underscores the need for social enterprises to take a strategic and adaptive approach when leveraging social CRM capabilities. By understanding how external institutional forces shape their effectiveness, practitioners can make more informed decisions about technology adoption, stakeholder engagement, and long-term performance improvement.

5.3 Limitations and future study directions

Despite the innovative insights this study provides into the construction mechanism of social customer relationship management (CRM) capabilities in social enterprises, several methodological and theoretical limitations remain. These limitations establish a clear framework for future research directions: First, at the methodological level, three major constraints exist: (1) In terms of research design, although the structural equation model based on cross sectional data can verify associations between variables, it is inherently limited by the nature of cross-sectional data, making it difficult to establish rigorous causal inferences. Future research is recommended to adopt a longitudinal tracking design, collecting multi-wave data to construct a dynamic panel model that can reveal the lagged effects and evolutionary pathways of social CRM capabilities on the performance of social enterprises. (2) Regarding sample selection, while the empirical foundation focusing on South Korean social enterprises is representative, it may be constrained by the country's unique institutional environment and cultural characteristics. Future studies could employ cross-cultural comparative research methods, selecting cases from different institutional contexts—such as developing economies like mainland China—to conduct multi-case comparative analyses and examine the external validity of the theoretical model. (3) In terms of measurement tools, although the inclusion of a common method factor test helps mitigate common method bias, self-reported scales still suffer from subjective evaluation limitations. Future research could adopt a mixed-methods approach by integrating objective indicators such as corporate annual reports and social media interaction data to establish a triangulation framework based on multi-source heterogeneous data.

Second, at the theoretical level, two areas for expansion emerge. First, the selection of institutional pressure dimensions exhibits selection bias. The current study focuses on the three dimensions of coercive, mimetic, and normative pressures proposed by DiMaggio and Powell, but does not incorporate key elements such as cognitive legitimacy from other institutional theories. Future research could introduce more complex institutional frameworks to examine how factors such as industry regulatory intensity and stakeholder cognitive schemas moderate the impact of social CRM capabilities. Second, the moderating variable system requires further refinement. The current model only considers the moderating effect of institutional pressure, while contextual factors such as the maturity of digital transformation and organizational ambidexterity may create multi-level interaction effects. Future studies could incorporate macro- or micro-level moderating variables, such as organizational dynamic capabilities, to deepen the understanding of the "context-capability-performance" chain relationship. Finally, at the practical application level, a deeper exploration of the underlying mechanisms is needed. This study identifies the transmission pathway through which institutional pressure influences the performance of social enterprises via social CRM capabilities but does not fully deconstruct its micro-level operational mechanisms. Future research could employ additional methodological approaches, such as embedded case studies, to examine CRM practices in social enterprises and uncover the organizational learning mechanisms and knowledge transformation processes through which institutional pressure translates into concrete managerial practices (e.g., through social network analysis). By addressing these limitations, future studies can further advance the theoretical and practical understanding of social CRM capabilities and explore their impact on the performance of social enterprises in different institutional environments.