“Customer loyalty and trust in South African retail banking”

Customer loyalty and trust are key elements for the success of retail banking. For this reason, it is crucial to investigate the predictors of these elements. This study aims to model service quality, customer satisfaction, and commitment influencing customer loyalty and trust in South African retail banking. The target population is a growing banking customer segment – Generation Y. A sample of 271 Generation Y customers participated in the survey. Their responses were analyzed using AMOS, whereby a structural equation model was developed. Although the structural model suggests that service quality (β = 0.097; p = 0.175) is an insignificant predictor of customer loyalty in retail banking, the influence remains positive. Moreover, the model infers that customer satisfaction (β = 0.793; p = 0.001) predicts customer loyalty in retail banking and that customer satisfaction (β = 0.715; p = 0.001) and commitment (β = 0.257; p = 0.001) influence trust in retail banking. All the model fit indices (NFI = 0.95; RFI = 0.92; IFI = 0.97; TLI = 0.96; CFI = 0.97; RMSEA = 0.06; SRMR = 0.03) infer that the model is reliable, valid, and ultimately good fitting measurement tool of customer loyalty and trust in retail banking. The results provide insights into the most critical factors in building customer loyalty and trust among Generation Y customers in South African retail banking. Moreover, they can help to develop marketing and customer service strategies to improve these outcomes.


INTRODUCTION
Customer loyalty and trust are critical factors in the retail banking industry. These factors significantly impact brand reputation, customer satisfaction, profitability, and overall success of the banking institutions. Loyal and trusting customers are more likely to engage in repeat business and refer new customers to a bank. In contrast, dissatisfied and distrustful customers are likely to switch to competitors or voice negative opinions, resulting in a loss of revenue and a damaged reputation for a bank.

LITERATURE REVIEW
The interplay between customer loyalty and trust is crucial for banks. Trust lays the foundation for loyalty, and loyal customers, in turn, reinforce trust through their continued engagement and advocacy. Banks must prioritize trust-building initiatives, deliver exceptional customer experiences, and continually invest in security and ethical practices to foster customer loyalty in the long run.
According to Veloutsou et al. (2004), customer loyalty and trust are positively associated with customer satisfaction in the banking industry. Satisfied customers tend to stay loyal and recommend the bank to others. Additionally, loyal customers are more likely to trust the bank and engage in positive word-of-mouth communication. Similarly, Lewis and Soureli (2006) found that customer trust and loyalty were crucial determinants of bank performance. The study showed loyal customers were more profitable and less likely to switch to competitors.
Customer trust is positively associated with customer loyalty, indicating that trust is critical in building and maintaining customer relationships. Staying within the retail banking context, Leninkumar (2017) investigated the relationship between customer loyalty, trust, and customer satisfaction. They found that customer trust was positively associated with customer satisfaction and loyalty, indicating that building trust is critical in retaining customers and increasing profitability. Boonlertvanich (2019) explored the impact of perceived service quality, customer satisfac-tion, and trust on customer loyalty among retail banking customers. The results showed that all three factors positively associated with customer loyalty, highlighting the importance of providing high-quality services and building customer trust. Finally, Omoregie et al. (2019) investigated the impact of customer trust and satisfaction on retail bank loyalty. Thus, customer trust is an important predictor of customer loyalty, indicating that building trust is crucial in retaining customers and promoting positive word-of-mouth communication.
Customer loyalty is essential to any business, as it can significantly impact a company's profitability and growth. The concept of customer loyalty has been studied extensively in the literature, focusing on the influencing factors. Two critical factors that have been found to influence customer loyalty are service quality and customer satisfaction.
Service quality is a crucial factor that influences customer loyalty. It is defined as the extent to which a service meets or exceeds customer expectations. This multi-dimensional concept includes various dimensions such as reliability, responsiveness, empathy, tangibles, and assurance (Parasuraman et al., 1988). Caruana (2002), Cronin and Taylor (1992), and Parasuraman et al. (1988) found that service quality has a significant impact on customer loyalty. S. Dam and T. Dam (2021), Fida et al. (2020), and Supriyanto et al. (2021) showed that service quality still has a significant impact on customer loyalty.
Customer satisfaction is another critical factor that impacts customer loyalty (Hayati et al., 2020;Nguyen et al., 2020;Supriyanto et al., 2021). Customer satisfaction is the extent to which a customer's expectations are met or exceeded by a product or service (Oliver, 1980). Satisfied customers are more likely to remain loyal to a company, increasing customer retention and profitability (Nguyen et al., 2020). Moreover, satisfied customers tend to spread positive word-of-mouth and refer others to the company, enhancing customer acquisition and retention. In various contexts, Anderson and Fornell (1994) and Oliver (1997) found that customer satisfaction significantly influences customer loyalty. In addition, Jahan and Shahria (2022), Kibret and Dinber (2016), and Puriwat and Tripopsakul (2017) showed that customer satisfaction remains important in upholding customer loyalty.
From a retail banking perspective, evidence in the literature suggests that service quality and customer satisfaction are essential predictors of customer loyalty. For example, Fida et al. (2020) found that service quality significantly influences customer loyalty in the banking industry. Similarly, Omoregie et al. (2019) and Sasono et al. (2021) noted that customer satisfaction significantly predicts customer loyalty in the retail banking industry. Earlier results reported by Jamal and Naser (2002) are consistent with these findings.
Customer satisfaction and commitment are important to building trust between customers and businesses. Ampornklinkaew (2023) found that high levels of customer satisfaction and commitment, which refers to the level of dedication and loyalty that a customer has toward a particular brand or company, can lead to increased levels of trust in a business, which can in turn lead to increased customer loyalty and repeat business. Ashraf et al. (2017) claimed that customer satisfaction and commitment are positively related to trust in a business. This relationship is more substantial in industries with high competition and information asymmetry between businesses and customers, such as retail banks. Similarly, Toqeer et al. (2021) found a positive relationship between customer satisfaction and trust in banking. According to Schirmer et al. (2018), customer commitment mediates the relationship between customer satisfaction and trust, and this relationship is more robust when customers have a high level of trust in a business. Similarly, Cui et al. (2020) determined that customer satisfaction and commitment are positively related to trust in a business. Moreover, trust mediates the relationship between customer satisfaction and customer loyalty (Osman & Sentosa, 2013). This suggests that trust is an important factor in building customer loyalty.
Other studies have shown a more complex relationship between customer satisfaction, commitment, and trust. For example, Singh and Jasial (2021) noted that while customer satisfaction positively affects trust. Mofokeng (2021) discovered that this relationship is moderated by the level of prior experience with the service provider. The study showed that customers with higher levels of prior experience were less influenced by customer satisfaction in their trust in the service provider. Ikramuddin and Mariyudi (2021) found that the perceived quality of the product or service mediates the relationship between customer satisfaction and trust. The study showed that customer satisfaction leads to a perception of high-quality products or services and greater trust in the company. Shin et al. (2019) demonstrated that the company's perceived reputation moderates the relationship between customer commitment and trust. Customers who perceive the company to have a positive reputation are more likely to trust the company, regardless of their level of commitment. According to Huang et al. (2021), the relationship between customer commitment and trust is mediated by perceived value. Thus, customer commitment leads to a perception of higher value and greater trust in the company.
Overall, all the findings highlight that service quality, customer satisfaction, and commitment are key factors in building a loyal customer base and trust. By fostering these factors, retail banks can increase customer trust and loyalty among all customer segments, including the Generation Y banking segment, leading to long-term relationships and tremendous success.
Generation Y, also known as millennials, is an essential demographic for retail banks due to their significant purchasing power and potential for long-term customer loyalty. Recent studies have emphasized the importance of this generation for retail banks. For example, Shams et al. (2020) not-ed that Generation Y customers trust banks more than other generations. These customers are more likely to use digital banking services, making them a critical demographic for the future of the retail banking industry. Similarly, Windasari et al. (2022) discovered that Generation Y tend to employ online banking services and is more receptive to personalized financial advice from banks. The study also found that digital channels positively influence the customer experience for Generation Y customers. Moreover, Dietz et al. (2020) determined that Generation Y customers significantly impact the financial performance of retail banks. Thus, banks that effectively cater to the needs and preferences of Generation Y customers are more likely to achieve higher levels of customer loyalty, leading to better financial performance.
The literature review supports the importance of customer loyalty and trust in the retail banking industry. Banks that prioritize building stable relationships with their customers, particularly Generation Y banking customers, by providing high-quality services, fostering trust, and promoting customer loyalty are more likely to succeed in a highly competitive retail banking market.
This study aims to develop a structural model that predicts South African Generation Y customers' retail banking loyalty and trust. In particular, the model tests whether service quality and customer satisfaction are predictors of Generation Y banking customers' bank loyalty and whether customer satisfaction and commitment influence their trust in their retail bank.

METHOD
This study intends to describe the attitudes, perceptions, and behaviors (descriptive approach) of a specific population at a specific time (single-cross-sectional approach). The specified population of interest is Generation Y (age: 18-24) retail banking customers. To collect data, a non-probability convenience sample of 400 participants was used. Fieldworkers approached the individuals in a public place and asked them to participate in the survey. This method has limitations in terms of sample representativeness and potential biases. As an ethical requirement in research, study participation was voluntary.
The data were collected by asking participants to complete the questionnaire themselves. To maintain good ethical practice, a questionnaire cover letter stated the study's objective and that the information provided was confidential. The questionnaire is two-fold. Section A captured participants' demographics. Section B included established scales from previous studies to measure various factors. These factors consisted of customer loyalty, service quality, customer commitment, and trust, which were measured using the scales validated by Lewis and Soureli (2006). In addition, customer satisfaction was evaluated using a scale validated by Veloutsou et al. (2004). The survey used a six-point Likert scale to measure each factor, with three items per factor. The participants rated their level of agreement or disagreement with each statement, with one indicating "strongly disagree" and six indicating "strongly agree." The data analysis for this study involved two IBM Statistical Packages: SPSS and AMOS, Version 27. Descriptive statistics were used to summarize the data, while reliability and validity statistics were used to assess the quality of the measures used in the study. Correlation statistics were also used to examine the relationships between variables. In addition, diagnostics for collinearity were conducted to identify any issues with multicollinearity among the variables. Confirmatory factor analysis (CFA) using the maximum likelihood approach was conducted to test the validity of the measurement model. Finally, structural equation modeling was performed.

RESULTS
To ensure the accuracy and validity of the data, a data cleaning process was implemented to eliminate any questionnaires that did not meet the specified target population criteria for the study. As a result, 271 questionnaires were deemed suitable for further analysis, reflecting a study response rate of almost 70%.
The sample for this study was composed of individuals between 18 and 24 years old, following the specified target population criteria. The sample included a slightly higher number of male participants than females representing all language groups, five race designations, and nine South African provinces. Table 1 tables the sample statistics.
After profiling the sample in demographics, the study conducted a Principal Components Analysis (PCA) using the Varimax rotation method. The purpose of the PCA was to identify any cross-loading of component items and to ensure that none of the items loaded on a component that does not align with existing literature. To verify that the data set was suitable for PCA, the study conducted two statistical tests: the Kaiser-Meyer-Olkin (KMO) test, with a value of 0.909, and Bartlett's Test of Sphericity, which yielded a significant chi-square (χ2) value of 2895.723, df 105, p ≤ 0.001 (Pallant, 2020). The results of the PCA, including the rotated components, communalities, eigenvalues, and percentage of variance extracted, are summarized in Table 2. Table 2 shows that the five extracted components accounted for approximately 81% of the total variance, and component items were not cross-loaded. The items loaded as expected according to the existing literature. Additionally, each communality had a value above 0.40, indicating that the items within each component were adequately related to one another (Costello & Osborne, 2005). Furthermore, all component loadings were above 0.50, indicating both statistical and practical significance (Hair et al., 2019). These findings support the conclusion that the factor structure of the five components aligns with the existing literature. Next, the paper proceeded to perform a maximum likelihood CFA in AMOS. This analysis included several measures, such as internal consistency (α) and composite reliability (CR), as well as convergent, discriminant, and construct validity assessments. Additionally, the study evaluated the model fit indices to ensure the validity and reliability of the results. After establishing the reliability and validity of the measurement model, the next step was to evaluate its standardized loading estimates/regression weights, error variance, and model fit. To ensure convergent validity, the standardized regression weights needed to exceed 0.50 (Fornell & Larker, 1981). Additionally, the model fit was evaluated using several criteria. An excellent model fit was indicated by meeting the following criteria: CMIN/DF between one and three, comparative-fit index (CFI) greater than 0.95, standardized root mean square residual (SRMR) less than 0.08, root mean square error of approximation (RMSEA) less than 0.06, and PClose greater than 0.05 (CFA, 2023). To further assess the model fit, the normed-fit index (NFI), incremental-fit index (IFI), and Tucker-Lewis index (TLI) were also considered. An acceptable model fit requirement for these indices was a value above 0.90. Table 4 reports on the findings. Table 4 shows that all standardized regression weights exceeded 0.50 and that the measurement model met all the specified model fit criteria. These findings indicate that the measurement model may be subjected to path analysis. Table 5 outlines the summary statistics, including the mean (X ) and standard deviation (σ), as well as a t-test (one-sample), correlations (r) (Pearson's product-moment), and multicollinearity meas- ures. These statistics were calculated prior to conducting path analysis. The one-sample t-test was used to determine the statistical significance of the latent factors, while the correlation coefficients were computed to assess the relationships between them. Measures of multicollinearity, such as tolerance (TV) and variance inflation factor (VIF) values, were also calculated to test for multicollinearity between the factors.
The t-test results indicated that all latent factors were statistically significant (p ≤ 0.1), exceeding the expected X of 3.5, with t-statistics in the range of 48.058 and 101.158 (p = 0.000). The statistical significance of the means was further supported by the lower and upper confidence interval values, which did not include zero (Lane, n.d.). Additionally, all latent factors were considered practically significant, with Cohen's d-values ranging from 0.790 to 1.573 (large effect size) (Cohen, 1992).
Pearson's r values are shown in Table 5, demonstrating significant positive correlations (p ≤ 0.01) among all latent factors. This confirms nomological validity (Malhotra, 2020) and indicates no multicollinearity issues between the factors. None of the coefficients were above 0.90, which is the recommended threshold to identify multicollinearity (Pallant, 2020). Collinearity diagnostics were performed on the independent factors (subject number = dependent variable) to further address potential multicollinearity issues. As shown in Table 5, the TVs surpass 0.10, and the average VIF of 2.179 is well below 10, indicating the absence of serious multicollinearity between the factors (Hair et al., 2019).
Next, path analysis commenced. Table 6 shows the results of the structural model paths, including the unstandardized and standardized regression estimates (β) and the corresponding standard errors (SE) and p-values generated by AMOS. Note: LOY -customer loyalty; QUAL -service quality; COM -customer commitment; TRU -trust; SAT -customer satisfaction.  Table 6 indicates that service quality positively predicts Generation Y banking customers' loyalty, but the influence is statistically insignificant. On the other hand, the influence of customer satisfaction on loyalty is statistically significant and positive. Moreover, customer satisfaction and commitment have a statistically significant positive effect on customer trust in retail banking. The squared multiple correlations (SMCs) for the structural model are 0.731 and 0.789 for loyalty and trust, respectively, indicating that the model explains 73% and 79% of the variance in these variables for Generation Y banking customers. Figure 1 visually represents the structural model.
The structural model's fit criteria were met, indicating that it is an excellent model for predicting customer loyalty and trust in retail banking. The fit criteria are summarized in Table 7.

DISCUSSION
The study suggests that service quality has a positive relationship with customer loyalty among Generation Y banking customers. However, this influence is statistically insignificant. This finding implies that while service quality may impact loyalty, it may not be a significant driver compared to other factors. Shankar and Jebarajakirthy (2019) and Supriyanto et al. (2021) found results alike, explaining that service quality has no effect on customer loyalty but through customer satisfaction as an intermediary.
The study also found that customer satisfaction has a statistically significant and positive influence on customer loyalty. This finding implies that when Generation Y customers are satisfied with their banking experience, they are more likely to exhibit loyalty toward the bank. The findings of various   The study found that the structural model predicting customer loyalty and trust in retail banking is an excellent fitting, valid, and reliable model. Given this result, retail banks can improve customer loyalty and trust in retail banking by focusing on service quality, customer satisfaction, and commitment. Improving customer loyalty in retail banking can be achieved by focusing on service quality and customer satisfaction. One way to enhance service quality is by training employees to be friendly, knowledgeable, and responsive to customer needs. Banks should also work to reduce wait times, streamline processes, and minimize errors. Measuring customer satisfaction regularly and using feedback to identify areas for improvement is critical in improving overall customer satisfaction. By creating personalized offerings based on customer data and preferences, banks can offer tailored products and services that meet the unique needs of their customers. Providing value-added services, such as financial planning resources and exclusive events, can also help differentiate banks from competitors and build loyalty.
Leveraging technology, such as mobile apps and online banking, can provide convenience and per-sonalized experiences to customers. Finally, fostering a culture of customer-centricity can help build a positive and memorable experience for customers, leading to increased loyalty over time. By prioritizing customer needs and aligning all employees with this goal, banks can build longterm loyalty and set themselves apart in a competitive marketplace.
Building trust in retail banking is essential to creating long-term customer relationships. To achieve this, banks need to focus on customer satisfaction and commitment. Clear and transparent communication is a crucial element in building trust. Banks should ensure that customers understand the terms and conditions of their products and services and proactively communicate any changes. Prioritizing customer satisfaction is another critical factor in building trust. Banks should regularly measure customer satisfaction and use feedback to improve their products and services. Also, fostering a customer service culture and ensuring employees prioritize customer needs can build trust. Offering loyalty programs, personalized services, and rewards for customer loyalty can boost customer commitment, which is also essential in building trust. Ensuring the security and privacy of customer information is another critical element of building trust. Finally, providing financial education can help customers make informed decisions about their finances, ultimately leading to increased trust in retail banking.

CONCLUSION
This study investigated the predictors of customer loyalty and trust in a South African retail banking context, concentrating on Generation Y customers. The study found that service quality was a positive yet insignificant predictor of customer loyalty, whereas customer satisfaction was a key predictor of customer loyalty. In addition, the results show that customer satisfaction and commitment were essential factors in building trust in retail banks. The study also found that the model used to analyze the data was a reliable and valid measurement tool of customer loyalty and trust in retail banks.
The study's findings suggest that retail banks can improve customer loyalty and trust by prioritizing service quality and customer satisfaction and fostering customer commitment. Overall, the study's results provide valuable insights for marketing and customer service strategies to improve customer loyalty and trust among Generation Y customers in South African retail banking. These insights can guide retail banks to strengthen customer relationships and improve overall customer experience.