Perceived value refers to the value exchange perception of the customer in terms of the benefits received versus the costs incurred to obtain the product or service (Pandey et al. 2020). Zietsman, Mostert and Svensson (2020) argued that perceived value is two-dimensional in nature. The first dimension encompasses the extrinsic nature (value is based on the product or service offering itself) of value. The second dimension considers value from an intrinsic (value is guided by the usage experience of the product or service) perspective. For the purpose of this study, both dimensions of perceived value (namely extrinsic and intrinsic) were measured (Nyadzayo 2010).

Numerous scholars in the marketing field have described expertise as the knowledge ability of employees to manage the needs and expectations of customers (Vy & Tam 2021). For the purpose of this study, expertise encompasses customer opinion regarding employee knowledge and standing to secure future engagement and commitment to a supplier (Lombardi, Sassetti & Cavaliere 2019). In a South African context, expertise is viewed as a critical element in securing long-term relationship building in a competitive market context (Taoana, Quaye & Abratt 2021.)

In a business context, a customer-orientated approach encompasses a deep and centred attitude towards understanding and addressing customer needs (Tseng 2019). Customer orientation encompasses a professional orientation towards service delivery that is guided by an in-depth understanding of the expectations of customers to strengthen long-term customer satisfaction (Mistrean 2021).

The customers of a service provider (such as a bank) perceive service fairness to be aligned with the level of service quality received (Seiders & Berry 1998). Consequently, the service experience of a customer can be assessed as either fair or unfair in the context of the service delivery process (Bhatt 2020). Considering this, service fairness for the purpose of this study is described as the customers’ assessment of the level of justice received through a service engagement with a provider. Customers expect a service provider to satisfy them through an experience that is perceived as reasonable and that secures the satisfaction of their needs in an equitable manner (Hwang et al. 2019).

In the marketing literature, affective commitment is used to measure the emotional attachment of a customer with a service provider, such as a bank (Van Tonder & Petzer 2021). Affective commitment is guided by consumer emotion, which influences their future intention to commitment to a service provider (Suhartanto et al. 2020). As a result, factors such as feelings of happiness, joy, belonging and association guide a customer’s affective commitment to their service provider in this study (Fatima & Mascio 2020).

Calculative commitment is founded on relationships that are more economical in nature (Gezhi, Jingyan & Xiang 2020). This form of commitment is based on a customer’s evaluation of the benefits received from the relationship versus the cost of remaining in the relationship (Anouze, Alamro & Awwad 2019). Consequently, the calculative commitment of a customer to a service provider in this study is guided by a rational assessment of the benefit–cost ratio as well as the availability of competitors at a given time (Ryu & Park 2020; Zhang & Zhang 2021).

Loyalty has been extensively validated in the marketing literature as an important element in the relationship-building process. Numerous scholars have confirmed the significance of measuring loyalty from a multidimensional perspective (namely, attitudinal and behavioural loyalty) (Rasool, Shah & Tanveer 2021). In a service context, the perception of the customer towards the level of service quality and overall value received, as well as the level of satisfaction attained, influences both attitudinal and behavioural loyalty (Christian, Anning-Dorson & Tackie 2021; El-Manstrly et al. 2011). Therefore, to strengthen the foretelling ability of loyalty, both attitudinal and behavioural loyalties were applied to the study (Pritchard & Howard 1997).

Hansopaheluwakan et al. (2020) defined perceived value as the total performance measurement that consumers hold over a product or service, determining their future perceptions of this product or service. The move from traditional marketing to relationship marketing has led to brands focusing more on the consumers’ affective commitment to better understand them and provide the best-quality products and services (Maheshwari, Lodorfos & Jacobsen 2014). Affective commitment, which is the customers’ emotional attachment to the brands, is influenced by the value the customers perceive. The level of perceived value of a service impacts the customers’ emotional bond with the brands. Therefore, it is hypothesised that:

According to Ali et al. (2018), customers who get quality services view the service providers positively and are more committed, less complaining, more agreeable and prepared to pay premium prices, which can lead to a more noteworthy profit. A brand’s ability to meet and exceed the customers’ perceived economic expectations and to keep its promises leads to calculative commitment, as customers’ perceived switching costs are higher compared to the benefits of committing to a brand (Kim & Kim 2020). Kungumapriya and Malarmathi (2018) concurred, stating that the calculative commitment of a customer will be high if the rewards of remaining associated with the provider are more than the costs to remain in the relationship, as well as if the cost to switch to a competitor is high. This implies that customers perceive the value to remain in the relationship positively, strengthening the overall calculative commitment (Petzer & Van Tonder 2019). Against this discussion, the following hypothesis is formulated:

Customer orientation embraces a service-engaging approach that is founded on the identification and management of customer needs and expectations in a professional manner. This approach intends to strengthen the long-term satisfaction and ultimately the potential loyalty of customers (Alavi et al. 2018). Alteren and Tudoran (2016) proposed that customer orientation is vital in strengthening affective commitment. When customers have identified a positive customer focus in their engagement with a service provider, they give positive feedback by feeling attached to the provider (e.g. a bank) and want to maintain the relationship (Van Tonder & Petzer 2021). Wang (2016) concurred with this finding, arguing that emotional attachment to a particular brand will arise once a service provider goes all out to seek customers’ opinions and track their feelings. Consequently, it is hypothesised that:

From a service provider’s perspective, customer orientation can be described as the engagement between the service and the customer that leads to the customer’s positive behaviour towards a service provider (Jung & Yoo 2019). This is especially true from an economical perspective, as a customer anticipates the economic benefits from the relationship with the provider (e.g. a bank) to be more than the costs incurred when continuing with the relationship (Vera & Trujillo 2013). As a result, the financial benefits accrued from the relationship should exceed the costs of remaining in the relationship to sustain the customer’s willingness to support the service provider (Petersen et al. 2018). Hence, it is hypothesised that:

Expertise is the capability that the staff members of an organisation possess to assist customers to fulfil their needs based on the customers’ judgement (Dickson, McVittie & Kapilashrami 2018). Petzer and Roberts-Lombard (2021) argued that expertise encompasses having favourable skills and knowledge to execute the service rendered. Employee expertise strengthens the overall brand quality perception of organisations and positively affects customer commitment, creating a competitive advantage for the service provider (Makanyeza & Mumiriki 2016). Expertise directly influences customers’ emotional well-being through the perception developed of the service experience attained (Lombardi et al. 2019). Accordingly, customers feel emotionally attached to service providers if they perceive employees to be well-trained, knowledgeable and professional in their ability to service their needs and expectations (Bahadur, Aziz & Zulfiqar 2018). Thus, it is hypothesised that:

Employee expertise offers customers the reassurance that employees possess the right skills and knowledge (Lombardi et al. 2019). Scholars (e.g. Saxena & Singh 2015; Suhartanto et al. 2020) argue that customers measure the benefit of remaining with the service provider against their level of engagement with employees, determining whether the latter provides a service experience that validates the service fees or costs paid for the service. Customers want to feel that the cost of remaining in the relationship with the bank is less than the value received from the expertise of the bank employees (Ali et al. 2018). According to Saxena and Singh (2015) and Yuan, Lai and Chu (2019), employee expertise makes it economically worthwhile to remain in the relationship, as its value-add exceeds the financial cost of leaving the bank. As a result, it is hypothesised that:

Service fairness is the assessment made by customers relating to the level of justice which businesses present through their behaviour (Crisafulli & Singh 2016; Gokmenoglu & Amir 2021). Customers determine service fairness through their engagement with the providers when purchasing products and services (Bhatt 2020; Namkung & Jang 2010). Customers evaluate their encounters directly with individuals (e.g. employees) and perceive the outcome as just or unjust, which can affect their emotional state, opinions and behaviour (Sofiana & Prihandono 2019). Affective commitment remains strong when customers perceive that it is emotionally beneficial to be committed to the relationship in the long term (Farooq & Moon 2020). Therefore, it is hypothesised that:

Service fairness influences customers’ calculative commitment with service providers, such as banks (Kim & Kim 2020). Calculative commitment is formed when customers choose to develop a sustainable relationship with service providers in the future (Dalziel, Harris & Laing 2021). However, this will depend on the customers having to remain in the relationship in future. This need will be guided by the overall cost of switching to a competitor, amongst other factors. The perceived termination of switching costs associated with leaving for an alternative service provider is also considered (Shukla, Banerjee & Singh 2016). Kim, Shin and Koo (2018a) contended that when customers recognise positive levels of fairness in the service provided, it positively affects their calculative commitment, as the customers’ perceived service fairness of the service providers can influence the economic benefit or cost of being associated with the organisations (Evanschitzky et al. 2017). Consequently, it is hypothesised that:

Affective commitment involves a customer’s emotional response concerning a service provider, determining the level of attachment to the brand (Iglesias, Markovic & Rialp 2019). According to Sari and Wijaya (2019), customers’ affective commitment is guided by their emotional attachment with the service business, such as a bank. The customer’s affective commitment is built on aspects such as attachment with the brand, feeling of belonging to the brand and overall level of attachment with the service provider (Shukla et al. 2016). When customers feel an emotional bond with the brands, it positively influences their calculative commitment, because the customers’ emotional connection to the brands can influence the economic benefit or cost of being associated with them (Kim & Kim 2020). In addition, the emotional connection of customers with service providers influences their affective commitment to the supplier brands (Khan et al. 2020). Therefore, if customers feel emotionally committed to a brand, it influences their economical perception of remaining in the relationship. Hence, affective commitment influences calculative commitment (Poushneh & Vasquez-Parraga 2019). Considering this, it can be hypothesised that:

Mikulić, Šerić and Milković (2017) explained that the level of affective commitment is significant in influencing customer loyalty towards a service business. Customers will show a greater intention to establish long-term relationships with service providers when there is a favourable emotional attachment to the services rendered (Al Samman & Mohammed 2021). Scholars like Chuang et al. (2016) and Lacroix, Rajaobelina and St-Onge (2020) agreed that when a customer feels emotionally attached to a service provider, an emotional bond is developed, stimulating a future intent to become loyal to the service brand. Hence, if customers are emotionally attached to the service providers through feelings of belonging and identification with the brands, their affective commitment is strengthened and thereby their loyalty (Junaidi, Wicaksono & Hamka 2021). From the above, the following hypothesis is formulated:

Calculative commitment is a customer’s feeling of responsibility towards a service business when less attractive alternatives can be considered or when there are no other providers to be considered (Shukla et al. 2016). Dessart, Aldás-Manzano and Veloutsou (2019) argued that a customer’s calculative commitment towards a provider will only be established if the overall benefits of remaining in the relationship exceed the costs of remaining in the relationship. Consequently, calculative commitment will positively influence the customer’s future loyalty. This is specifically relevant when the benefit–cost ratio for the customer contains more benefits than costs (Carvalho & Fernandes 2018). Ojeme, Robson and Coates (2018) stated that a customer’s calculative commitment to a provider will remain strong when the economic benefits of such a relationship outweigh the cost of remaining in the relationship. However, when the cost of switching providers is very high, the customer may remain in the relationship for economic reasons, thus strengthening positive behavioural intentions (Richard & Zhang 2012). Consequently, the customer’s loyalty to a service provider will remain strong when the economic cost of remaining in the relationship is lower than the value obtained to continue with the relationship in future (Izogo & Jayawardhena 2019). Thus, it is hypothesised that:

Considering the discussion above, a research model is proposed in Figure 1.

An exploratory factor analysis (EFA) was conducted to perform a first-order factor analysis. Williams, Onsman and Brown (2010) stated that EFA can be perceived as a method of measure applied to establish how variables in a data set are testing formulated hypotheses. The application of an EFA is secured when constructs are used in their original form or when the inclusion or exclusion of constructs is secured for further data analysis. Furthermore, the measure of sampling adequacy for the anti-image correlation secured from SPSS indicated that the communality values for all the items of the seven constructs were above the minimum value limit of 0.377 (Pallant 2016; Zikmund et al. 2017). Therefore, the constructs used in the study (inclusive of their items) were perceived as satisfactory to secure further data analysis.

The application of Cronbach’s alpha values was secured to establish internal consistency. Cronbach’s alpha implies value as a measure of scale reliability, where an acceptable value is 0.7, with a preferred value being 0.8 or higher. Saunders, Lewis and Thornhill (2016) stated that values below 0.7 do not reflect reliability. Considering this, the Cronbach’s alpha values obtained for this study reflected reliability, as illustrated by Table 2. As the Cronbach’s alpha values for all the constructs in the study exceeded the 0.7 cut-off, internal consistency reliability was achieved.

The various scale items used in the questionnaire and applied to the study were assessed by the researchers. Moreover, a pilot study was conducted to determine the validity of the questionnaire as the research instrument applied to the study. The pilot study intended to determine the validity of the questionnaire before it was formally fielded.

Regarding the different constructs in the study, different statistics such as the mean (X¯) and standard deviation (σ) were calculated. Table 3 illustrates the Pearson’s product-moment correlation matrix, and the different mean and standard deviation values for each of the constructs applied to the study.

To establish whether the correlations between the constructs are significant, the R-value should be between 1 and –1, where 1 suggests a perfect relationship and a value below –1 suggests a negative relationship between the constructs or variables. From the data in Table 3, it is clear that the relationships between the constructs are positive, as all the Pearson coefficient values are greater than 0.05 (R > 0.05). This also confirms that none of the constructs have a negative relationship, as they are all positive values. In conclusion, the relationship between the constructs can be accepted as positive, with the highest relationship being between customer orientation and expertise, with a Pearson correlation value of 0.890.

All respondents participating in the study had their own bank accounts at any one of the selected major banking brands applied to the study. On average, the respondents were 35 years old and, considering that the study required gender quotas to be filled, the sample was nearly equally split between male and female respondents. Most respondents spoke English as their first language (35.6%), followed by Sepedi (22.7%). Furthermore, the largest number of respondents (39.9%) stated that they were full-time employees of an organisation, had bank accounts with Capitec Bank (31.3%) and FNB (24.8%), had a savings account (74.8%) or a chequing account (38.5%) at their bank and spent on average R500 a month on banking charges (77.3%). Finally, most respondents held a post-matric qualification (65.8%), followed by those with a Grade 12 certificate (29.9%). The respondents had also been with their bank for approximately four or more years.

Before a regression analysis was conducted, the assumptions of multiple regression analysis were confirmed. The sample size had to exceed 82 when considering the formula N > 50 + 8m (m = number of independent variables). In this study, the sample size consisted of 400 respondents (Tabachnick & Fidell 2007), exceeding the minimum required number of sample elements. In terms of multicollinearity, high correlation between the variables is only present if the Pearson’s correlation coefficient (r) is equal to or higher than 0.9. In this study, all the relationships tested showed correlation, with no relational correlation being above 0.9 (see Table 4 and Table 5). As a result, multicollinearity was not perceived as a risk.

Furthermore, normality in the study was established by considering histograms where normal distribution is clear when the largest number of scores are concentrated around the central point. The histograms applied to the study illustrated normality for all formulated hypotheses. Finally, in terms of linearity, residuals should secure a linear relationship with the predicted dependent variable scores (Pallant 2010). Scatter plots were used to test linearity and all linear relationships were present across all hypotheses. Considering this, all assumptions were secured to conduct a multiple regression analysis.

The application of multiple regression analysis was secured to determine the inter-relationship between selected variables in the study. This discussion follows below.

The study findings conclude that customer orientation, perceived value and expertise have a significant and positive influence on affective and calculative commitment. Furthermore, affective and calculative commitments have a positive and significant influence on customer loyalty. Previous research (Herjanto & Amin 2020; Komulainen & Saraniemi 2019; Nora 2019) has confirmed that the more knowledgeable, service-orientated and understanding banks are of their customers’ service and product expectations, the more committed, and ultimately loyal, their customer base will become to the banking brands. Moreover, scholars (e.g. Ganaie & Bhat 2020; Schirmer et al. 2018) have confirmed that enhanced levels of affective and calculative commitment strengthen customer loyalty.

The results of this study also validate that the scales used to measure perceived value, expertise, customer orientation, service fairness, affective commitment, calculative commitment and customer loyalty regarding banking services in South Africa are reliable and valid. The proposed hypothesised model has been validated, endorsing the relationship between affective and calculative commitments and their stimuli, and the influence on the loyalty intention of banking customers in South Africa.

It is notable that retail banks should make the commitment–loyalty link stronger by applying strategies that will improve customers’ affective and calculative commitment. For example, banks should ensure that their customers develop a feeling of belonging that is based on the principles of inclusion, respect and identification. Retail bank customers want to be engaged with their banks through multiple platforms that reflect both offline and online channels. They want bank products to be developed with them, not for them, and want service delivery to encompass their service needs expectations. Consequently, banks should secure an enjoyable customer experience that is characteristic of service qualities such as friendliness, knowledgeable staff, engagement through open communication channels and convenience.

Banks should also develop extensive knowledge of the financial product needs of their customers. Through active research engagement (both qualitative and quantitative research techniques can be applied), banks can regularly determine the financial product expectations of their customer segments, whether such expectations are changing and how new benefits can be developed to address customers’ evolving needs. Furthermore, banks can provide customers with new, innovative products that are deconstructed. This implies that customers only pay for services they use, thereby lowering their service fees and banking costs.

Furthermore, retail banks should focus on building an end-to-end customer experience that is able to combine customer data as well as operations in all applications and call centres. This will make it difficult for customers to switch to competitor products and services. Banks can also reward customers who continually show repeat actions with the brand, and these actions include repeat purchases. Loyalty programmes in the form of coupons, discounts and vouchers are a great way of encouraging repeat purchases for customers. Rewarding customer actions is important to secure future customer commitment. Customers want to feel part of a company when their actions are rewarded through appreciation strategies. Hence, retail banks can reward customers holding any account at the bank for three years and more with a R400 voucher when they reach three years with proven loyalty and reward a customer of five years’ standing with free 300 shopping points for four months. Moreover, service providers can send personalised appreciation messages to their customers every quarter of the financial year and ask for feedback on service delivery through a short survey.

Finally, banks need to become more focused on the provision of a value-driven service through an omnichannel approach. This will ensure that customers are enabled to use various channels to enhance their overall bank experience. Through this approach, convenience is strengthened, and opportunity costs are reduced. Banks can also support their customers through continuous 24/7 service support in the form of live chats and chatbot engagements. This technological support can secure service provision to customers continuously, rather than within the framework of limited business hours.