Abstract
Orientation: Revisiting the satisfaction–loyalty link is critical to better understand the driving factors of satisfaction and loyalty. This is especially true considering the increasing competitive nature of South Africa’s banking industry.
Research purpose: The study revisits the satisfaction–loyalty link and the mediating effect of affective and calculative commitment on the satisfaction–loyalty link.
Motivation for the study: Numerous scholars have explored and proposed different pathways to enhance the satisfaction–loyalty link in multiple contexts and industries. To date, there is, however, no clear pathway in a business-to-consumer (B2C) context and further research is required on how to strengthen this link.
Research approach/design and method: A quantitative descripto-explanatory study that is cross-sectional in nature was used to collect empirical data from 871 banking customers in South Africa purposively selected using interviewer-administered questionnaires. SmartPLS 3.2.7 was used to test the direct effect (H1–H4) and Hayes Process Macro for SPSS (Model 4) was used to test the indirect effects (H5 and H6).
Main findings: Satisfaction has a significant and positive relationship with affective and calculative commitment, and calculative commitment in turn has a significant and positive relationship with loyalty. Both affective and calculative commitments partially mediate the relationship between satisfaction and loyalty.
Practical/managerial implications: The importance of affective commitment in the relationship between satisfaction and loyalty among customers of South African retail banks is highlighted.
Contribution/value-add: This study provides an enhanced understanding of the importance of affective and calculative commitment in strengthening the satisfaction–loyalty link.
Keywords: banking; banking customers; satisfaction; affective commitment; calculative commitment; loyalty.
Introduction
Within the context of published marketing literature, there is a lack of concurrence on the pathways towards strengthening the satisfaction–loyalty link (Chatzi, Peitzika & Konsolaki 2024; Mntande, Stiehler–Mulder & Roberts-Lombard 2023). This is apparent considering that since the start of a new era multiple scholars have explored and proposed pathways to enhance the satisfaction–loyalty link in multiple contexts and industries. For example, scholars such as Lee, Lee and Feick (2001) and Nguyen et al. (2020) argued over the past two decades that the satisfaction–loyalty link is brittle and a deeper understanding of the inherent service and economic needs of business-to-consumer (B2C) customers is required when deciding to continue to be loyal to a business brand. Chatzi et al. (2024) and Walsh, Evanschitzky and Wunderlich (2008) concurred, arguing that a deeper comprehension of the satisfaction–loyalty link requires an understanding that loyalty is multidimensional and impacted by customers’ perceptions of economic benefits and service perceptions when engaging with providers. Interestingly, Soliha et al. (2021) identified a different pathway to strengthen the satisfaction–loyalty link, validating that consumers’ intentions to remain loyal to a business are guided by their willingness to trust the provider, rather than the economic benefits obtained from the relationship. Consequently, it becomes evident that there is no clear pathway defining the nature of the satisfaction–loyalty link in a B2C context and that further research is required on how to strengthen the link in multiple settings and contexts (Chatzi et al. 2024; Lee et al. 2001; Nguyen et al. 2020; Soliha et al. 2021; Walsh et al. 2008). This is especially important to note considering that a new school of thought has evolved which debates the tenuous nature of the satisfaction–loyalty link, indicating further research is needed to validate that multiple pathways can be considered to enhance the satisfaction–loyalty relationship beyond satisfaction alone (Smith 2020; Windasari & Albashrawi 2021).
Numerous scholars (Evanschitzky, Stan & Nagengast 2022; Menidjel et al. 2020) have confirmed that a focus on satisfaction alone is no longer sufficient in securing the future loyalty intention of customers in a B2C context. They argue that service providers need to also focus on affective and calculative commitment as drivers of customers’ loyalty intentions (Abid et al. 2023; Sanaullah et al. 2022). Through a deeper understanding of customers’ emotional and economic needs, service providers will be enabled to deliver more successfully on customers’ expectations, thereby strengthening their future loyalty intentions (Calvo-Porral & Lévy-Mangin 2020). As such, this study argues that in a B2C relationship building context, service providers should develop an enhanced understanding of the importance of affective and calculative commitment in strengthening the satisfaction–loyalty link. The reason being that customers who are emotionally attached to a brand tend to be more loyal, even when they experience a decrease in satisfaction (Batool et al. 2023). Contrastingly, customers who stay with a brand because of calculative commitment illustrate a greater intention to change their provider brand if they perceive that the benefits of switching outweigh the costs (Ganaie & Bhat 2020). In marketing literature, scholars have predominantly explored direct relationships between satisfaction, commitment (affective and calculative), and loyalty (Lee et al. 2023; Putri & Junaidi 2022). Yet, the mediating role of affective and calculative commitment on the satisfaction–loyalty link remains unexplored (Shafiq et al. 2023). Investigating these relationships is imperative as customers’ loyalty intentions imply a willingness to buy back a particular product or service as part of their forthcoming purchase intention (Ru & Jantan 2023). This is an important outcome for service businesses, such as banks, that operate in a highly competitive environment and where banking customers’ loyalty becomes increasingly uncertain.
Considering the discussion presented earlier, this study focuses on the identified gaps by exploring the relationships between satisfaction, commitment (affective and calculative), and loyalty. In addition, the study investigates the mediating effect of affective and calculative commitment on the satisfaction–loyalty link. The focus of the study is the South African banking industry (inclusive of banks with Islamic banking windows). The industry is highly competitive and characterised by high levels of customer churn. Furthermore, banking customers in South Africa are provided generic products and services, making it easier for customers to switch to a competitor banking brand (BusinessTech 25 November 2019). Consequently, banks need to develop an enhanced understanding of their customers’ changing service and product needs to stimulate their future commitment, and ultimately their loyalty to the bank (Bleach 2023; Mthelebofu 2022; PwC 2023). Thus, the authors opine that there is a need in South Africa for banks to secure enhanced knowledge of how banking customers’ satisfaction, affective commitment, and calculative commitment levels impact their future loyalty intentions towards their bank.
The study makes numerous contributions to theory, adding value to its theoretical debate. Firstly, it establishes the importance of affective and calculative commitment as postcedents to satisfaction. Secondly, it determines the significance of commitment (affective and calculative commitment) on banking customers’ loyalty behaviour. Finally, it proposes a new pathway to strengthen the satisfaction–loyalty link through affective and calculative commitment as mediating variables as empirically validated by the study (refer to Table 7 to Table 9 for the empirical confirmation and the discussion section). In terms of the industry value add of the study, a contribution is made to the banking industry by informing banks on how satisfaction, affective commitment, and calculative commitment can strengthen loyalty (see Table 7 to Table 10, the discussion of the competing models, and the theoretical contribution section).
Theoretical framework
Overview of the South African banking industry
The South African banking industry is characterised by 81 institutions, consisting of local and foreign-owned banking brands. The largest competitors in the retail banking industry are South African (Absa, Nedbank, First National Bank, Standard Bank, Investec Bank, and Capitec Bank), reflecting a combined asset value worth of US$474 283 million (Ford 2022). Because of the South African banking industry’s competitive nature, a deeper understanding of customers’ product and service needs is becoming increasingly important to secure differentiation in the marketplace and reduce customer churn (Schoeman n.d.). This is especially true considering that new innovative banking brands, such as TymeBank, Discovery Bank, Bank Zero, and HBZ Bank, have entered the South African banking industry offering customer-focused, affordable, and innovative banking products and services that are aligned to South Africans’ convenience, religious, and digital needs (Bleach 2023; The Banking Association South Africa 2021). Considering the increased competitive nature of South Africa’s banking industry, banks also need to develop increased knowledge of what drives customer commitment and ultimately the future loyalty intentions of their customer base (Consultancy-me.com 2023; Moosa & Kashiramka 2023; The Banking Association South Africa 2021).
Relationship marketing theory
The study draws on principles of relationship marketing theory, as the key premise of this theory is to nurture long-term relationships between partners that are founded on mutual benefit to strengthen future loyalty intention (Deepika & Singh 2023; Lee et al. 2023). Furthermore, the relationship marketing theory purports that through the development of a relational approach founded on satisfaction and commitment, future loyalty intention can be strengthened (Mujahidin et al. 2022; Oluwajana, Adeshola & Olowu 2022; Sleiman et al. 2021). Marketing scholars, such as Ananda, Kumar and Singh (2023) and Mock et al. (2023), concur stating that satisfaction and commitment are critical antecedents to secure the future loyalty of customers in multiple settings. This notion is supported by the empirical findings of Farooq and Moon (2020) and Roberts-Lombard et al. (2022) stating that when banking customers feel more attached to their bank, which enhances their overall level of satisfaction, they become more loyal towards their bank. As such, the relationship marketing theory is therefore founded on a customer-orientated approach that stimulates loyalty intention, which could enhance the profitability of the business in the long-term (Omoregie et al. 2019; Svotwa et al. 2023).
Satisfaction
Marketing scholars such as Ahmed et al. (2023) refer to satisfaction as an emotional assessment when purchasing and consuming a product or service over a specified period. In the marketing literature, satisfaction is based on a consumer’s independent assessment of an experience. Such an experience is founded on a person’s individual perceptions which can lead to a positive outcome. (Al-Slehat 2021). The assessment of satisfaction in this study focused on the customer’s satisfaction with the banking experience, the overall satisfaction with the bank, and the extent to which expectations of the customer have been met (Giovanis, Athanasopoulou & Tsoukatos 2015).
Affective commitment
Affective commitment is described as the emotive connection that a customer has with its service provider (Iglesias, Markovic & Rialp 2019). The evaluation of affective commitment focused on aspects including emotional association with the service provider, overall feelings of happiness and feelings of belonging and association with the service provider (Giovanis et al. 2015).
Calculative commitment
Calculative commitment implies a customer’s rational decision to remain committed to a service provider when the benefits of association outweigh the cost of switching to a competitor (Junaidi, Wicaksono & Hamka 2022). The study measured calculative commitment by focusing on aspects like benefits received and costs incurred through association with the service provider, service convenience, service engagement, and switching opportunities available (Giovanis et al. 2015).
Loyalty (attitudinal and behavioural)
Marketing scholars have argued that loyalty is founded on multiple dimensions, but that attitudinal and behavioural loyalty are mostly used in marketing to measure future loyalty intentions (Boonlertvanich 2019). Attitudinal loyalty implies customers’ intentions to repurchase from a supplier based on customers’ emotive attachment to the provider (Tegambwage & Kasoga 2022). Contrastingly, behavioural loyalty encompasses customers’ desire to buy from a provider based on overall contentment with the supplier’s products and services (Suhartanto 2019). Both these dimensions of loyalty were applied to this study, as the assessment of loyalty from attitudinal and behavioural standpoints improves the overall projective ability of loyalty (Lin & Wang 2006). Consequently, the attitudinal and behavioural loyalty of customers in this study were assessed by ascertaining customers’ feelings as well as repurchase intentions towards the service provider (Le et al. 2023; Mandhachitara & Poolthong 2011).
Conceptual model development
Interrelationships between constructs (direct effects)
In relationship marketing literature, satisfaction is validated as an important factor that influences customers’ future affective commitment (Rather et al. 2019). The reinforcement of affective commitment can be secured by providing customer experiences that enhance customers’ feeling of attachment and belonging to a service provider (e.g., a bank) (Roy, Gruner & Guo 2022). Consequently, when customers feel they can identify with the business values and practices of their bank, they are more inclined to be satisfied with the service and product deliverables received (Moosa & Kashiramka 2023). Therefore, it is purported that satisfaction is positively related to affective commitment. Consequently, it is hypothesised that:
H1: Satisfaction has a significant and positive relationship with affective commitment.
Satisfaction implies a customer’s contentment with the capability of a service provider to provide on their product and service expectations (Basir et al. 2022). Therefore, if customers are of the opinion that the benefits secured from their bank are in excess of the value add offered by other banks, that the overall experience with their bank is positive, and that it would be too expensive for them to change to a different bank, they will remain committed to their bank (Temerak & El-Manstrly 2019). Such commitment is founded on customers’ satisfaction experience with their bank, which influences their future commitment intentions directly (Supriyanto, Wiyono & Burhanuddin 2021; Van Tonder & De Beer 2018). Subsequently, it is hypothesised that:
H2: Satisfaction has a significant and positive relationship with calculative commitment.
When customers’ commitment is guided by their affinity and attachment to a service provider, their intention to remain loyal is strengthened (Levy 2022). Therefore, if customers have a kinship with their bank because of its transparent, ethical, and engaging business approach, there will be a greater intent to remain loyal to the service provider (Prabhu & Aithal 2023). Suhartanto et al. (2020) concurred, stating that customers’ future loyalty will be defined by service providers’ ability to develop inclusive relationships with their customers. This will require service providers (e.g., banks) to secure a more in-depth awareness of customer emotions and customer psychology in the future attitudinal loyalty formation process. As a result, it is hypothesised that:
H3: Affective commitment has a significant and positive relationship with customer loyalty.
Multiple scholars have confirmed that the faithfulness of customers is guided by the perception of benefits secured versus costs incurred. Consequently, customers’ future loyalty intentions are positively stimulated if the benefit–cost ratio is in favour of the benefits secured from remaining in a relationship with the provider of the service (such as a bank) (Khoa 2021; Ngugi et al. 2020). Nataraj and Rajendran (2018) agreed, stating that customers’ attitude towards a service provider (e.g., a bank) is influenced by their level of calculative commitment. As such, customers who reflect high levels of calculative commitment will reflect high levels of affinity with their service provider and not easily turn to a competitor (Boateng 2020; Junaidi et al. 2022). Hence, it is hypothesised that:
H4: Calculative commitment has a significant and positive relationship with customer loyalty.
Indirect effects
Considering hypotheses H1 and H3, there is a possibility that affective commitment serves as a mediator between satisfaction and loyalty. Customers that are satisfied with a service provider’s product or service experience reflect a greater emotional attachment to the brand, which strengthens their future loyalty intentions (Baidoun & Anderson 2023). Karim et al. (2023) professed that in the banking industry, satisfaction has a positive relationship with affective commitment, while the latter positively impacts customers’ loyalty intentions. Consequently, it is hypothesised that:
H5: Affective commitment mediates the relationship between satisfaction and loyalty.
Bearing in mind H2 and H4, there is a likelihood that calculative commitment serves as a mediator between satisfaction and loyalty. As such, scholars like Putri and Junaidi (2022) have argued that when customers perceive the benefits received from their bank to be value-adding and the overall cost of switching their bank to be high, their calculative commitment is strengthened, which enhances future loyalty intentions. In addition, Farooq and Moon (2020) and Ganaie and Bhat (2020) established that in the banking industries of Pakistan and India, a positive relationship exists between customers’ overall level of satisfaction and their calculative commitment, with the latter positively impacting loyalty intention. As such, it is hypothesised that:
H6: Calculative commitment mediates the relationship between satisfaction and loyalty.
Bearing in mind the discussion above, Figure 1 is proposed, reflecting on the different relationship and hypotheses suggested for the study.
Research methodology
Research design, population, and sample
The study’s philosophical underpinning is positivist. The researchers undertook a quantitative cross-sectional descripto-explanatory study to collect empirical data from respondents (Saunders, Lewis & Thornhill 2016). The study targeted retail banking customers in South Africa, specifically those residing in the Gauteng province and who were 18 years or older. Purposive sampling was used to select the study’s respondents, which is a less time-consuming sampling method that allows for the collection of valid and reliable responses from respondents (Hair, Page & Brunsveld 2019). Data obtained from 871 questionnaires were analysed.
Questionnaire and data collection
The data were collected using interviewer-administered questionnaires comprising closed-ended responses. This approach was followed to collect data directly from respondents through interviewers (Shaw et al. 2002). Each questionnaire consisted of a preface explaining the study and assisted in obtaining informed consent from respondents. This was followed with sections gathering data concerning the respondents’ profile and their patronage of a bank. Subsequently, the study’s constructs (affective and calculative commitment, satisfaction, and loyalty) were measured using a Likert-type scale with seven points, where one signified ‘strongly disagree’ and seven signified ‘strongly agree’. Scholars (e.g., Improta et al. 2019) argue that Likert scales are widely used in social sciences to collect data. Table 1 outlines the items measured. The questionnaires were fielded by fieldworkers from a field services company.
| TABLE 1: Items that measure the study’s constructs. |
Analysing the data
After the data were edited, cleaned, and entered into Excel, researchers proceeded with the analysis. A descriptive analysis of the data was undertaken to describe the profile and bank patronage habits of the respondents and to calculate the standard deviations and means for the items measuring the study’s constructs. With the aid of Mplus (version 7.4), the direct effects (H1–H4) were tested. To determine the appropriate estimator for estimating the models proposed for this study, the data obtained were appraised for univariate normality. When univariate normality is not present, the MLM estimator is recommended, and when univariate normality is present, the ML estimator is recommended (Muthén & Muthén 1998–2017). In this instance, the Shapiro–Wilk as well as the Kolmogorov–Smirnov tests were used to appraise univariate normality. As the data were not normally distributed, the MLM estimator was used. A confirmatory factor analysis was then carried out to evaluate the psychometric properties of the measurement model. Composite reliability (CR) in addition to internal consistency (Cronbach’s alpha values) were assessed. Discriminant validity and convergent validity were also assessed, followed by an evaluation of the model fit indices of the measurement model. Table 2 provides insight into the recommended cut-off values for the validity and reliability criteria. Thereafter, the model fit indices of the structural model were assessed, and the direct effects evaluated (H1–H4). Table 3 provides the model fit indices as well as the recommended cut-off values. The indirect effects (H5–H6) were tested utilising Hayes Process Macro for SPSS (Model 4). Bootstrapping estimation (5000 resamples) was used and generated the 95% bias-corrected confidence intervals (BCCIs). The direct and indirect effects were then evaluated for the lack of a zero between the lower-level and upper-level confidence intervals (LLCIs and ULCIs). Guidelines proposed by Zhao, Lynch and Chen (2010) assisted in identifying the nature of the mediation. Finally, competing models were considered and the Bayesian information criterion (BIC) values and the Akaike information criterion (AIC) were evaluated to uncover the model with the best possible balance between intricacy and fit, exhibiting lower AIC and BIC values when compared to other models (Posada & Buckley 2004; Van de Schoot, Lugtig & Hox 2012).
| TABLE 2: Recommended cut-off values for reliability and validity criteria. |
| TABLE 3: Model fit indices and recommended cut-off values. |
Ethical considerations
Ethical clearance to conduct this study was obtained from the Henley Business School Ethics Committee (No. SREC-HBS-20220214-DAPE4141) and the University of Johannesburg’s College of Business and Economics Research Ethics Committee (CBEREC) (No. 2021SCiiS006). Human participants provided oral consent. No names or contact details of participants were recorded. The hard copies of the interviewer-administered questionnaires and data file are kept in safe storage by the researchers. Data will be retained for five years.
Results
Profile and patronage habits of respondents
Table 4 provides the respondents’ demographic profile and bank patronage habits. The sample’s gender was balanced, with 52.3% females and 47.6% males. Just over a third of respondents were aged 18–30. Most respondents had a post-school qualification (45%), 39.7% were full-time employed, and 23% were self-employed. A significant portion (37.8%) of respondents had an Islamic bank account. Nearly half of respondents (49.5%) spent R100 or less on bank charges and more than two thirds (69.2%) had a savings account.
| TABLE 4: The respondents’ demographic profile and bank patronage habits (N = 871). |
Means and standard deviations for constructs and items
Table 1 provides insight into the item and construct means and standard deviations. The construct means ranged between 4.96 for affective commitment and 5.68 for satisfaction. The standard deviations for the constructs ranged from 0.956 for satisfaction to 1.260 for affective commitment.
Measurement model assessment
From Table 5, there is proof of convergent validity and reliability when the results are compared to the recommended cut-off values for the reliability and validity criteria presented in Table 2. The factor loadings exceeded 0.7 and were statistically significant at p < 0.01, two-tailed. The AVE extracted for every construct was greater than 0.5 and the CR as well as Cronbach’s alpha values exceeded 0.7. It was not necessary to remove any items.
| TABLE 5: Reliabilities and convergent validity assessment. |
Table 6 proves there is discriminant validity, as the square root of the AVE values all surpass the correlation with the other constructs, as indicated in Table 2 (Fornell & Larcker 1981).
| TABLE 6: Discriminant validity assessment. |
When comparing the model fit statistics with recommended cut-off values for the model fit indices, it was evident that the measurement model matches the data quite well. The Satorra–Bentler χ2/degrees of freedom (df) ratio was smaller than 3 (2.98), the root mean square error of approximation (RMSEA) was less than 0.08 (0.067), the comparative fit index (CFI) exceeded 0.9 (0.948), the Tucker–Lewis index (TLI) exceeded 0.9 (0.935), and the standardised root mean square residual (SRMR) was less than 0.08 (0.064). Based on these findings, the researchers proceeded with the structural model assessment.
Structural model assessment
When comparing the model fit statistics with recommended cut-off values for the model fit indices, it can be seen that the structural model fitted the data adequately, except for the Satorra–Bentler χ2/df ratio, which was marginally above the cut-off value of 3 at 3.19. The RMSEA was less than 0.08 (0.075), the CFI was higher than 0.9 (0.943), the TLI was greater than 0.9 (0.949), and the SRMR was smaller than 0.08 (0.067). Therefore, the researchers proceeded with inspecting the structural paths. Table 7 shows that satisfaction has a significant and positive relationship with affective as well as calculative commitment, and that calculative commitment has a significant and positive relationship with loyalty. Satisfaction had a stronger relationship with calculative commitment (β = 0.919; p-value < 0.01) than with affective commitment (β = 0.707; p-value < 0.01) and between calculative commitment and loyalty (β = 0.772; p-value < 0.01). Affective commitment did not have a significant and positive relationship with loyalty. In the end, satisfaction and calculative commitment were key drivers of loyalty. With respect to the direct effects, H1, H2, and H4 were supported. H3 was not supported.
| TABLE 7: Standardised estimates in the structural model. |
Mediation analysis
Table 8 shows the results for the indirect effects (H5 and H6) of the study. Affective commitment partially mediated the relationship between satisfaction and loyalty (0.114; 95% BCCI [0.073; 0.157]) and calculative commitment partially mediated the relationship between satisfaction and loyalty (0.208; 95% BCCI [0.151; 0.267]). Consequently, H5 and H6 were supported.
Summary of findings with respect to the direct and indirect effects
Table 9 summarises the findings in relation to the hypotheses formulated for the study.
Assessment of competing models
The model hypothesised and tested in this study was compared to four competing models, as affective and calculative commitment partially mediated the relationship between satisfaction and loyalty. Table 10 provides the results for the four competing models and the model of this study (see Figure 1). Competing model 1 proposes paths in sequence from satisfaction to affective commitment to calculative commitment and to loyalty. Competing model 2 suggests paths in sequence from satisfaction to calculative commitment to affective commitment and to loyalty. Competing model 3 advocates direct paths from satisfaction to affective commitment, calculative commitment, and loyalty, with direct paths from affective and calculative commitment to loyalty. Competing model 4 recommends direct paths from satisfaction, affective commitment, and calculative commitment to loyalty. When considering the AIC and BIC values for the different models, it is evident from Table 10 that the hypothesised and tested model for this study exhibits the lowest AIC and BIC values when compared to the four competing models. Therefore, this indicates that this is the model with the best possible balance between intricacy and fit (Posada & Buckley 2004; Van de Schoot et al. 2012).
| TABLE 10: Assessment of competing models proposed for the study’s model. |
Discussion
The satisfaction–loyalty link has been widely explored. Multiple scholars have validated the fact that the strengthening of the satisfaction–loyalty link cannot be secured through the proposition of set relationships, but that continuous exploration is required considering situational differences (Kim, Steinhoff & Palmatier 2021; Menidjel et al. 2020). Consequently, this study intended to investigate the satisfaction–loyalty link through affective and calculative commitment as interlocking and intervening variables. The study established that satisfaction is an important antecedent to the affective and calculative commitment of customers in the banking industry of an emergent market. This finding aligns with the findings of Fatima et al. (2020) and Mbango (2018) in multiple service settings (banking and hospitality), stating that customers’ affective and calculative commitment are guided by the overall level of satisfaction with a service provider. Furthermore, this study concludes that the relationship between calculative commitment and loyalty is significant and positive, but that banking customers’ affective commitment does not significantly affect loyalty. Batool et al. (2023) and Karim et al. (2023) concurred that in a banking context, calculative commitment is positively related to customers’ future loyalty intentions. Yet, Farooq and Moon (2020) argued that in a banking context, affective commitment plays a crucial role in strengthening customers’ future loyalty intentions. Conclusively, both affective and calculative commitments infer a partial mediation role between the satisfaction and future loyalty intentions of banking customers in South Africa, as an emergent African market. Panda, Jain and Nambudiri (2022) concur, indicating the importance of affective commitment and calculative commitment as mediators in a B2C relational context.
Customers want their bank to provide experiences characterised by professional, friendly, and interactive engagement, service convenience, and ethical behaviour (Al Khoury et al. 2023). Moreover, these customers want their banks to deliver on their expectations in a reliable, trustworthy, and honest manner (Anitha & Hemanathan 2022). Through such an approach, banking customers will be better able to identify with their bank and feel a sense of belonging when engaging with their banking brand. Moreover, these customers want to experience convenience through all platforms of engagement with their bank, and to be provided with added value that will strengthen their willingness to remain loyal to their bank.
Theoretical implications
The relevance of affective and calculative commitment in the satisfaction–loyalty relationship is reaffirmed by the outcome of the study. The study establishes that satisfaction has positive and significant relationships with affective and calculative commitment. Calculative commitment also has a positive relationship with customer loyalty and both affective commitment and calculative commitment partially mediate to the satisfaction–loyalty relationship in a B2C context.
Understanding satisfaction as an important antecedent to commitment as a multidimensional construct and loyalty
The findings that flow from the study reaffirm the interrelationships between satisfaction, affective commitment, calculative commitment, and loyalty (refer to Table 7 and Table 9). A deeper understanding of these relationships is vital to the relationship marketing domain. Commitment is an important outcome of satisfaction, where banking customers’ overall satisfaction does not influence their general commitment to a banking brand, but their affective commitment and economic commitment separately (Rather et al. 2019). Consequently, should banking customers have a positive and interactive engagement with their bank that is guided by the personalisation of services, the delivery of services that are friendly, engaging, and reflects integrity, the customers reflect a stronger intent to be associated with the bank (Ali & Naeem 2019). In addition, when customers experience convenience, accessibility to their banking services through multiple platforms, and the belief that the banking brand invests financially in communities through social upliftment programmes, there is a greater willingness to stay in a relationship with the brand (Safari 2019). Although satisfaction alone is still a crucial antecedent to loyalty (Asnawi, Sukoco & Fanani 2020), the study’s results confirm the importance of satisfaction as a critical antecedent to affective and calculative commitment in the satisfaction–loyalty relationship.
Insight on affective commitment as a mediator in strengthening the satisfaction–loyalty link
The outcome of the study validates the mediation function of affective commitment on the satisfaction–loyalty link (see Table 8 and Table 9). Scholars (e.g., Farooq & Moon 2020) have argued that by creating feelings of belonging to a brand, customers become increasingly emotionally attached to the brand, which stimulates contentment and future loyalty. Therefore, by acknowledging customers as individuals through interaction and communications, understanding their individual expectations and exceeding the delivery of such expectations, engaging with customers through personalised service deliverables, and responding to customer enquiries efficiently and reliably, customers develop feelings of association to the brand, which strengthens their future loyalty intentions. Hence, businesses need to become increasingly aware that satisfaction alone does not guarantee customers’ future loyalty anymore, but that strengthening their affective commitment can stimulate a willingness to stay faithful to the business brand in the future (Khraiwish et al. 2022). New studies could also expand the results from this study through an exploration of the mediating role of affective commitment on the satisfaction–loyalty relationship.
Knowledge on calculative commitment as an important component in the development of the satisfaction–loyalty relationship
The study’s findings validate the direct relationship between calculative commitment and loyalty, as well as the mediating role of calculative commitment on the satisfaction–loyalty link (see Table 7, Table 8, and Table 9). Therefore, it seems that the preference for a service provider and the intent to remain in a relationship with the provider are impacted by the overall perception of benefits received (Junaidi et al. 2022). Consequently, when customers are of the opinion that the relationship with a service provider is to their benefit regarding aspects such as convenience, association, and service deliverables, there is a greater intent to remain committed and loyal to the relationship. Authors agree with this outcome, stating that when it becomes too costly for customers to switch service providers or when the expectation is that a competitor could not provide a similar level of service engagement, the customers’ calculative commitment is strengthened. Previous research also states that customers’ calculative commitment can contribute to a future intention to remain loyal to a service provider (Farooq & Moon 2022; Roberts-Lombard et al. 2022). Hence, although customers are satisfied with a business’s products and services, in-depth knowledge of what defines their calculative commitment is crucial to strengthen their future willingness to remain loyal to the business brand (Karim et al. 2023).
Finally, the model proposed in this study is the model with the best possible balance between intricacy and fit where the interrelationships between the four constructs are concerned.
Practical implications
From a practical viewpoint, it is evident that banks need to strengthen the satisfaction–loyalty link by considering workable action plans that will embrace affective and calculative commitment. For example, modern banking customers make decisions on where and how to do their personal banking much faster. This implies that banks need to become more engaged with their customer base through online platforms (websites and apps), social media platforms (Instagram, Facebook, Twitter or YouTube), or person-to-person interaction (in-branch interactions). This implies interaction with customers where they are, securing two-way communication that strengthens a feeling of belonging and association with the brand, and delivering services characterised by convenience, integrity, reliability, and professionalism to banking customers. Furthermore, banks should develop increased knowledge of their customer base’s financial needs. This can be done through research with their customer base, informing them of their changing expectations, which could inform future product and service strategy development. For example, in terms of product innovation, banks can develop applications that secure convenience through artificial intelligence (AI) engagement, incorporate AI service tools like chatbots that speak the customer’s language and assisting banking customers 24/7 with frequently asked questions, and secure an omni-channel service to customers that is founded on accessibility and convenience and is experience-driven. In addition, the human factor cannot be neglected, thereby securing consistent employee training on aspects, such as conflict management, voice tone usage when interacting with customers, honesty when communicating, and the development of understanding through training on human psychology and behaviour.
Furthermore, banks should emphasise professional banking practices through visual cues online and offline. This needs to be creatively communicated to customers through pop-ups, chatbots, television advertisements, or poster notifications in branches. Moreover, banking customers want to experience multiple benefits when pursuing banking products as a financial option. Therefore, banks need to enable customers to secure positive experiences through banking services received and securing regular online and offline engagement on how the banks practise ethical, moral, and transparent banking practices. This can be done through regular webinars offered to customers, in banking information sessions, access to annual banking reports, and other financial records deemed important to validate the banks’ ethical banking practices. Through continuous research on the evolving needs of customer segments, retail banks can also develop and refine banking products that are aligned to the financial needs of their customers. Conclusively, through consistent training and development, and the implementation of AI tools that are accessible to customers, banks can secure a consumer-friendly banking environment characterised by inclusion, professionalism, efficiency, convenience, and sympathy.
Conclusion
The study determined that in an emerging market context, the satisfaction–loyalty link can be strengthened by considering affective and calculative commitment. The application of commitment, through both direct and indirect effects, could be secured as a path in a services context to strengthen the satisfaction–loyalty link. In the banking environment of an emerging market, overall service experience is a critical factor that impacts banking customers’ emotional and economic attachment. Yet, if these customers are overall satisfied, both affective and calculative commitment can assist in strengthening their future loyalty intentions. Therefore, banks in emerging markets need to be innovative in the development of their products and services to address the evolutionary needs of customers in different product segments, such as general banking and Islamic banking.
Research limitations
The exploration of the satisfaction-loyalty link was secured through a dual path of affective and calculative commitment. In addition, this path was explored in a single service setting in a cross-sectional research context in an emergent African market. Yet, the study validated that the strengthening of the satisfaction–loyalty link can be secured through multiple paths in the banking context of South Africa. Such an understanding should guide the banking industry in South Africa, as an emergent market, on how to strengthen the future loyalty intentions of their customer base. Furthermore, the study focused on a single emerging market, reflecting the opinions of respondents in just one province therein. Additionally, the study followed a cross-sectional approach, which does not secure a longitudinal perspective of participants’ views. Conclusively, a more demographic-specific representation of the study’s population could have been secured.
Areas for future research
Potential research in the future could consider a comparative analysis of the proposed model between developed and emerging markets globally, or between emerging markets in Africa. Moreover, the application of the model to a different service setting, such as tourism, cellular services, air travel, or insurance, could be considered. The model could also be tested in a business-to-business context, which is different from research conducted in a traditional B2C approach, where the customer is an individual and not a business. Furthermore, new variables, such as customer experience, customer delight, or customer engagement, could be applied as moderators or mediators to explore the satisfaction–loyalty link. The study could also establish the different opinions of respondents residing in various geographical parts of an emerging market to determine differences and similarities. Finally, a qualitative methodological approach could be considered in the future to establish deeper insights into respondents’ satisfaction–loyalty perceptions.
Acknowledgements
Competing interests
The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.
Authors’ contributions
D.J.P. and M.R.-L. contributed equally to the conceptualisation, design, and execution of the research study as well as the production of this manuscript emanating from the research study.
Funding information
Henley Business School Africa provided funding or support for the work presented in this submission.
Data availability
The anonymised data set is available from the authors for the perusal of the reviewers.
Disclaimer
The views and opinions expressed in this article are those of the authors and are the product of professional research. It does not necessarily reflect the official policy or position of any affiliated institution, funder, agency, or that of the publisher. The authors are responsible for this article’s results, findings, and content.
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