Small and medium-sized enterprises (SMEs) are widely recognized as key drivers of economic progress (Gunjati & Adake, 2020; Yasiukovich & Haddara, 2021). They play a vital role in the economic and social development of the markets in which they operate, accounting for over 90% of all businesses in both developed and emerging economies (Mhlongo & Daya, 2023). In emerging markets, SMEs represent the majority of businesses, contribute up to 50% of gross domestic product (GDP), and employ approximately 70% to 85% of the workforce (Madison et al., 2022).

In South Africa, SMEs are considered the backbone of the economy, comprising more than 98% of all businesses and employing over 60% of the national workforce (Rajagopaul et al., 2020). According to Botha et al. (2021), SMEs have been championed as a key solution to the country’s sluggish economic growth and persistently high unemployment rate.

Despite their pivotal role in the South African economy, SMEs continue to face numerous challenges that contribute to a high failure rate—estimated at between 60% and 80% within the first two years of operation (Mhlongo & Daya, 2023). In light of this vulnerability, further research is needed to identify strategies that can enhance SME resilience and sustainability. The present study argues that relationship management offers a valuable means for SMEs to strengthen operations and reduce the risk of failure. By investing in strong, trust-based relationships, SMEs may improve customer retention, differentiate themselves in the market, and build long-term viability.

Customer relationship management (CRM) refers to the set of managerial processes that organizations apply to develop and retain customer relationships in business-to-consumer markets in order to achieve sustainable competitive advantage (Alghamdi, 2023; Hassan et al., 2019). CRM provides a framework for maintaining customer relationships (Arellano-Rodríguez, 2024) and encompasses the practices, strategies, tools, and technologies used by firms to analyze and manage customer interactions (Garg & Madan, 2024; Roberts-Lombard & Tait, 2018). CRM enables businesses to respond to the evolving preferences of their customers and to influence customer behavior accordingly (Garg & Madan, 2024). It focuses on increasing customer lifetime value through client retention, ultimately improving customer equity (Matsuoka, 2022). The benefits of CRM include improved customer retention, increased customer satisfaction, higher sales and profits, and enhanced customer loyalty through repeat business (Farmania et al., 2021; Hamida et al., 2023; Marozau et al., 2024).

Two main schools of thought can be identified in the literature on CRM (Nguyen & Waring, 2013; Ullah et al., 2020). The first conceptualizes CRM as a set of technology-driven solutions for sales, marketing, and customer management (Bekata & Kero, 2024; Kim, 2012). This perspective emphasizes the efficient flow of information between parties and focuses on cost reduction and operational efficiency (Nguyen & Waring, 2013). The second school of thought views CRM as a strategic activity through which organizations cultivate and sustain customer relationships (Gilboa et al., 2019; Sedalo et al., 2022). This view emphasizes customer attraction and retention (Nguyen & Waring, 2013). These two differing perspectives reflect the contested nature of CRM as a concept, with no universally accepted definition.

Aligned with these two schools of thought, researchers have explored CRM in SMEs across various contexts. Within the technology-driven perspective, some studies have focused on CRM systems and technological adoption (Chatterjee et al., 2022; Lecerf & Omrani, 2020; Marozau et al., 2024), while others have examined CRM’s role in enhancing small business performance (Dalla Pozza et al., 2018; Nurfarida et al., 2023). From the relational perspective, researchers have studied CRM as a source of competitive advantage (Alghamdi, 2023; Bhat & Darzi, 2018). For instance, Al Karim et al. (2024) investigated the link between CRM and competitive advantage and found that customer loyalty mediates this relationship. Additional studies have focused on the use of social media in fostering CRM (Cao & Weerawardena, 2023; Hassan et al., 2019; Sedalo et al., 2022), while others have examined relational dimensions such as customer engagement and relationship management (Gilboa et al., 2019; Zegullaj et al., 2023).

The present study adopts the second school of thought, emphasizing the relational aspects of CRM. This approach centers on building and sustaining long-term customer relationships as a means of achieving competitive advantage in the SME context.

An analysis of existing research on customer relationship management (CRM) reveals that the concept has been interpreted and operationalized in varying ways by different scholars. For example, Binsaeed et al. (2023) identified innovation performance, customer engagement, and CRM capabilities as core components of CRM. Similarly, Ali et al. (2020) considered entrepreneurial orientation, market orientation, and total quality management to be integral elements of CRM. In another context, Giannakis-Bompolis and Boutsouki (2014), in their study of CRM in the Greek retail banking sector, found that customer overall satisfaction, customer engagement, affective commitment, and organizational social media presence were key components.

As summarized in Table 1 below, recent literature suggests that the components of CRM vary depending on each organization’s goals, strategic priorities, and operational context. This variation reflects the flexibility of CRM as a framework that can be tailored to different industries, organizational sizes, and market environments.

A review of the studies presented in Table 1 indicates that CRM research in SMEs has been diverse, focusing on several key areas. Dominant themes include CRM adoption, customer satisfaction, and performance improvement. The findings also reveal that CRM has been conceptualized differently across studies, with each scholar adapting the definition to suit the specific organizational or industry context under investigation. In line with this, the current study adopts owner/manager-perceived value, business systems, and stakeholder relationships as the core components of CRM.

Relationship marketing is a CRM strategy that encompasses all activities aimed at establishing, maintaining, and developing successful relational exchanges (Salam & Hoque, 2019). Through ongoing communication and interaction, relationship marketing facilitates the creation of collaborative relationships between an organization and its stakeholders (Herman et al., 2021). The benefits of relationship marketing include lower marketing costs, increased customer loyalty, improved profitability, and greater organizational stability (Sedalo et al., 2022).

According to Salam and Hoque (2019), relationship marketing is particularly important for SMEs, as it can support both customer acquisition and retention. The structural characteristics of SMEs often make them especially well suited to relationship marketing. For example, SMEs typically operate with less organizational bureaucracy than larger firms, allowing them to respond more quickly to customer needs and thereby strengthen customer relationships (Heiens et al., 2019). CRM enables the implementation of relationship marketing strategies within the organization and helps deliver the long-term benefits of relational approaches.

This study is grounded in the Resource-Based View (RBV), which posits that firms achieve sustainable competitive advantage through internal resources—physical, financial, and human—that are valuable, rare, inimitable, and non-substitutable (Ferreira & Ferreira, 2025). The RBV emphasizes the importance of organizational resources and competencies as critical drivers of success and competitive positioning (Chiarelli, 2021; Malhotra et al., 2025; Perdana & Prasasti, 2023).

Building on this theoretical foundation, the current study proposes that the perceived value of relationship marketing, business systems, and stakeholder relationships are strategic intangible assets that support effective customer relationship management (CRM). Specifically, it suggests that the owner/manager’s perception of the value of relationship marketing, the systems required to support those relationships, and the quality of stakeholder relationships together constitute the key components of CRM in SMEs operating in the retail sector of an emerging market.

The study further argues that the successful implementation of CRM depends on the alignment of people, processes, and technology (Wynn et al., 2016). In particular, it emphasizes the central role of top management’s perceptions in shaping an organization’s strategic orientation toward CRM and determining the extent and effectiveness of its implementation.

This study employed a survey approach to gather quantitative data. The survey was conducted among a representative sample of SME owners and managers operating in the retail sector in South Africa. A structured questionnaire was distributed in person by field workers to 300 potential respondents. The target population consisted of SME owners/managers, with individual managers serving as the units of analysis. Cross-sectional data were collected for the purposes of this study.

To qualify for participation, respondents were required to meet two criteria: (1) they had to be between the ages of 18 and 65, and (2) they had to be either the owner or manager of the retail SME from which the data were being collected. Owners and managers were selected as the target population because the study focused on managerial perceptions, and it was assumed that these individuals possessed sufficient experience and knowledge to provide meaningful and accurate responses.

Before beginning the questionnaire, each potential respondent was asked to read the consent form printed on the front page and indicate their willingness to participate in the study. After data collection, all responses were screened for errors and missing values. A total of 267 valid questionnaires were retained for analysis, representing a 92% response rate.

The final sample included 91 males (33%) and 176 females (66%). In terms of age, the largest group of respondents (n = 80; 29%) were between 25 and 29 years old, followed by those aged 30–34 (n = 71; 25.7%), 35–39 (n = 44; 15.9%), 40 and above (n = 43; 15.6%), and 18–24 (n = 37; 13.4%). The relatively low number of participants in the youngest age group was expected, given the study’s focus on managerial roles.

The sample was ethnically diverse, comprising 163 African respondents (69.1%), 50 mixed race (18.1%), 32 Indian (11.6%), 23 White (8.3%), and 6 Asian (2.2%). Regarding respondents' positions within their businesses, 124 identified as owners (44.9%), 87 as managers (31.5%), and 57 as both owners and managers (20.7%).

With respect to tenure, most participants reported having been in their current role for 1–5 years (n = 122; 44.2%), followed by 6–10 years (n = 109; 39.5%), 11–25 years (n = 32; 11.6%), and over 25 years (n = 6; 2.2%). These demographic characteristics indicate that the respondents possess substantial experience within the SME sector and are well positioned to provide reliable insights for the purposes of this study.

The questionnaire used in this study was custom-designed to suit the specific characteristics of the target population. As no existing scales were found that adequately captured the variables of interest within the SME context, the researchers developed the items based on insights drawn from relevant literature (AlQershi et al., 2020; Plakoyiannaki, 2005; Pohludka & Štverková, 2019; Zaby & Wilde, 2018). In particular, items were formulated to assess owners’ and managers’ perceptions of the value of customer relationship management (CRM) within their organizations.

Given the exploratory nature of the study, a broad range of variables was included in the questionnaire to ensure that the constructs under investigation were comprehensively measured. All items were assessed using a five-point Likert scale, where 1 indicated strongly disagree and 5 indicated strongly agree.

CRM was measured using 15 items and was treated as a reflective construct, with indicators representing various manifestations of the customer orientation of owners and managers.

Perceived value was measured using 7 items, also as a reflective construct, representing the owners’/managers’ perceptions of the value of customer relationships.

Business systems were measured using 10 items and treated as a formative construct, composed of attributes such as customer tracking systems, pricing systems, and system accessibility.

Stakeholder relationships were measured using 11 items and treated as a formative construct, composed of attributes including customer feedback mechanisms, satisfaction monitoring, and customer-focused business practices.

To assess the reliability of the measurement model, Cronbach’s alpha (CA) and composite reliability (CR) were used. Cronbach’s alpha values for all constructs ranged from 0.656 (perceived value) to 0.860 (stakeholder relationships). Except for perceived value, all alpha values exceeded the commonly accepted threshold of 0.70 for construct reliability (Hair et al., 2019), thereby confirming the reliability of most constructs.

Composite reliability, which also measures internal consistency, was examined as an additional indicator. Composite reliability values ranged from 0.810 (perceived value) to 0.880 (stakeholder relationships), all exceeding the recommended minimum threshold of 0.70 (Hair et al., 2019). These values confirm that the constructs are internally consistent and reliable.

Discriminant validity was evaluated using the average variance extracted (AVE). An AVE of at least 0.50 is typically recommended; however, according to Fornell and Larcker (1981), an AVE above 0.40 may still be acceptable if the composite reliability exceeds 0.60. The AVE values for this study were 0.462 for CRM, 0.589 for perceived value, 0.483 for business systems, and 0.484 for stakeholder relationships. Although three of the AVE values were below 0.50, they remain acceptable under the criteria outlined by Fornell and Larcker (1981) and supported by Suprapto et al. (2020), given that all corresponding CR values exceed 0.60. These results therefore confirm the model’s convergent validity.

Factor loadings were also examined. Given the exploratory nature of the study and the fact that the items were newly developed by the researchers, a minimum threshold of 0.50 was deemed acceptable (Arellano-Rodríguez, 2024). Factor loadings for all constructs ranged from 0.519 to 0.838. Items with loadings below 0.50 were removed from the final model. All remaining factor loadings had p-values less than 0.05, indicating statistical significance.

Table 3 presents the factor loadings, Cronbach’s alpha, composite reliability, and AVE values for each construct.

Having confirmed the reliability and convergent validity of the measurement model, the Fornell and Larcker (1981) criterion was applied to assess discriminant validity. Evaluating discriminant validity is essential in research involving latent variables, as it helps prevent issues of multicollinearity. According to this criterion, discriminant validity is established when the square root of the average variance extracted (AVE) for a given construct is greater than its correlations with all other constructs (Maduku, 2024). The results of the discriminant validity analysis, presented in Table 4, indicate that for each construct, the square root of the AVE exceeds its correlations with the other constructs. This confirms the discriminant validity of the measurement model.

In addition to the Fornell–Larcker criterion, the Heterotrait–Monotrait Ratio (HTMT) was used to further assess discriminant validity. This method is particularly appropriate when partial least squares structural equation modeling is employed, as it addresses some of the limitations of the Fornell–Larcker approach (Henseler et al., 2015). HTMT values closer to 1 indicate a lack of discriminant validity, whereas values below the threshold of 0.85 are considered acceptable (Henseler et al., 2016; Roemer et al., 2021). A visual inspection of the results presented in Table 5 shows that none of the HTMT values approached 1, thereby confirming the discriminant validity of the constructs.

The hypothesized relationships presented in Figure 1 were tested using structural equation modeling. To assess the structural model, path coefficients, t-values, and the coefficient of determination (R²) were examined. Figure 2 presents the results of the structural model, which tested the relationships among the study’s latent constructs.

Path coefficients were evaluated as indicators of the strength and direction of the relationships between independent (predictor) and dependent (outcome) variables. Specifically, they quantify the extent to which one variable affects another within the model.

An examination of the path coefficients revealed that all three hypothesized relationships were statistically significant. The results provided support for Hypotheses 1, 2, and 3. Specifically, perceived value had a significant positive effect on CRM (H1: β = 0.212, t = 4.755, p = .000), as did business systems (H2: β = 0.147, t = 3.445, p = .001) and stakeholder relationships (H3: β = 0.366, t = 8.212, p = .000). These findings indicate that perceived value, business systems, and stakeholder relationships each have a statistically significant effect on CRM. The path coefficients are presented in Table 6 below.

In addition to the path coefficients, the coefficient of determination (R²) was assessed to evaluate the explanatory power of the model. R² indicates the proportion of variance in the dependent variable that is explained by the independent variables, with values ranging from 0 to 1. In this model, the R² value for CRM was 0.250, suggesting that 25% of the variance in CRM is explained by perceived value, business systems, and stakeholder relationships. This relatively modest explanatory power indicates that additional variables relevant to CRM were not included in the model. Future research should consider incorporating other constructs to enhance the model's robustness and explanatory capacity.