Determinants of Customer Switching Behaviour in the Zimbabwean Banking Industry

Determinants of Customer Switching Behaviour in the Zimbabwean Banking Industry

Takaivona Vhimisai, Thomas Bhiri and Tendekayivanhu Mutambanadzo

National University of Science and Technology, Zimbabwe

DOI: https://dx.doi.org/10.47772/IJRISS.2024.8120367

Received: 16 December 2024; Accepted: 20 December 2024; Published: 24 January 2025

ABSTRACT

The world over, retail banks are grappling with the problem of excessive customer defections which negatively impacts their cost structures, revenues and profitability. This study examined the determinants of a surge in customer defections witnessed in the Zimbabwean retail banking industry. An explanatory design was adopted to gather categorical data from 5 Customer Relationship Managers and 402 bank customers, based in 13 cities across the country, through telephone interviews and self-administered questionnaires respectively. Sampling was done in multiple stages, starting with the clustering of potential participants around their cities of residence, followed by their stratification into customers of 5 retail banks which have branches in each of the 13 cities, leading to the random selection of an equal number of participants from each stratum. Using a generalised ordinal logistic regression model, the study revealed that transaction costs, bank reputation, distance from the bank, attractiveness of competing banks’ promotional efforts and level of education are significant positive predictors whereas service quality is a significant negative predictor of customers’ likelihood of switching banks. However, factors such as a customer’s age, gender and switching experience are insignificant considerations in customer switching decisions. The study concluded that, in Zimbabwe, a customer’s bank switching decision is driven by a combination of situational, influential and reactional factors. To tame the scourge of excessive customer defections, it is imperative for retail banks to enhance customer retention by conducting frequent customer needs reviews, regular service quality checks and refresher courses for staff, improving their customer relationship management functions by fully embracing the “Know Your Customer” (KYC) principle as well as establishing secure, affordable and user-friendly electronic banking delivery channels at their customers’ doorsteps.

Key words: Customer, Rational Choice, Retail Bank, Service quality, Switching behaviour

INTRODUCTION

The problem of excessive bank customer switching behavior has bothered academics and practitioners since time immemorial. Switching behavior is defined as defection or customer exit (Hirschman, 1970), customer’s decision to stop patronizing the service firm completely (Bolton and Bronkhurst, 1995) or, from a banking industry perspective, the shifting of customers from one bank to another (Garland, 2002). Customer defection is costly for banks as it not only weighs down heavily on revenue generation but also minimizes the bank’s ability to cut operating costs. Thus, Clemes et al (2007) lament banks to treat customer loyalty and customer retention as integral components of their survival strategies. In their view, maintaining an existing customer is way cheaper than attracting a new one as the advertising, sales, and set-up costs can be amortized over a longer customer lifetime. Similarly, the seminal work of Reichheld and Sasser (1990) has shown the massive potential of customer loyalty in contributing to revenue growth, reduction in customer defections and growth of a firm’s customer base through positive word-of-mouth advertising. The deregulation of the financial services industry, coupled with increasing globalization of the financial markets and the emergence of new financial technology firms have intensified competition among financial services organisations, leading to serious behavioral changes in bank customers. Chakravarty et al. (2004) asserts that the prevailing competitive banking environment as well as the availability of relatively homogeneous financial products from diverse service providers make banking particularly more susceptible to customer switching behavior. The problem is particularly worrying in the Zimbabwean banking industry as the rate of customer defections is not only high but also keeps increasing. As aptly demonstrated by the Banking Survey (2020), most retail banks in Zimbabwe have, on average, lost 3% of their market shares since dollarisation of the economy in 2009.

The study sought to evaluate the determinants of customer switching behavior in the Zimbabwean retail banking sector. This was achieved through an evaluation of the influence of factors such as customer demographics, switching experience, poor service quality, bank reputation, transaction costs, competing bank promotional efforts and distance from the bank on customers’ likelihood of switching bank service providers.

LITERATURE REVIEW

The shifting of a customer from one commercial bank in favour of the services offered by another bank is a phenomenon which can be explained in terms of several theories. These include the Transaction Cost theory (Coase, 1937 and Williamson, 1985), the Rational Choice theory (Scott, 2001), the Social Exchange theory and the Theory of Planned Behaviour (Fishbein and Ajzen, 1975), among others. This study is informed by the Rational Choice theory which was introduced by Adam Smith in 1776 to explain the hidden forces in the market which influenced people’s choices. The theory postulates that an individual uses rational choices to make decisions and take actions whose outcomes are in their own self-interest. Such actions maximise benefits and minimise costs to the concerned individual. Thus, the theory suggests that customers explore a broad spectrum of factors and make bank switching decisions based on a cost benefit analysis of associating with alternative banks.

The study of customers’ switching behaviour has garnered significant attention in the field of banking and consumer behaviour. Empirical research has produced divergent findings showing that the matter is far from being settled. Perhaps the most valuable insights into the phenomenon are provided in the seminal work of Keaveney (1995) which groups the factors that influence customer switching decisions into 3 categories namely situational, influential, and reactional triggers.

Roos and Gustafsson (2011) describe situational triggers as changes in customers’ conditions that cause them to re-evaluate their existing relationships. Such triggers include demographic changes, changes in the customer’s work situation, and changes in the customer’s life situation. In this regard, individual differences, in bank customers, such as past switching behaviour, length of service experience, expenditure, product involvement and knowledge as well as demographic characteristics are good indicators of the customer’s likelihood to switch service providers. According to Farah (2017), customer switching intensions are primarily influenced by attitudes towards a particular bank’s services. Bugyei (2019) and Tram et al. (2021) found similar evidence in Ghana and Hanoi respectively. In these studies, customer trust and attractiveness of alternatives emerge as crucial triggers of customer switching decisions. Investigating customer switching intentions in Indonesian Islamic banks, Riptiono et al (2020) discovered that customer attitude towards Islamic banks was largely influenced by customer trust, customer awareness and religiosity. Apart from that, Hati et al (2020) found that past switching experience was positively correlated with the customer’s likelihood of switching again. Kim and Jang (2015) posit that there is an inverse relationship between age and the customer’s probability of switching. This finding indicates that senior consumers have better emotional self-regulation skills and thus recover from negative emotions more quickly, which leads to higher post-recovery satisfaction than younger consumers. Van der Cruijsen and Diepstraten (2017) have shown that highly educated people are more sensitive to competitors’ offers and know when and how to switch than less-educated customers. In terms of gender, Boo et al. (2013) and Teeroovengadum (2020) argue that males are more assertive and instrumental and therefore are more likely to defect to other banks than females.

Esteves (2014) defines influential triggers as market and competition-triggered switching factors, which use marketing mix elements to attract customers to a service provider. One influential factor that has received overwhelming attention in academic circles is service quality. Generally, there is consensus among scholars that service quality promotes customer loyalty and, therefore, is inversely related with desire to switch banks. Thus, Jayanthi and Anupama (2020), Justin et al. (2016), Pick (2014) and Wirtz et al. (2014) have demonstrated that a bank which offers better quality products, promotional effort, distribution and reasonable prices will most likely snatch customers from its competitors. In a study of the Indian banking sector, Agarwal (2019) concluded that service quality is one of the most significant factors in the switching decisions of customers. Studies by Thaichon et al. (2017), Zulfitri and Rohman (2019), Clemes et al (2010) and Nzowa (2022) lend empirical support to this finding. Thaichon et al. (2017), attribute customer switching in the Australian banking industry to service quality, among other quality related factors such as responsiveness to service failure, convenience and perceived customer value. This was corroborated by Zulfitri and Rohman (2019) in Indonesia, Clemes et al (2010) in New Zealand and Nzowa (2022) in Tanzania. However, Tanzanian bank customers were found not to value a bank’s responsiveness to service failure that much. Extant literature also shows that the importance of service quality transcends the borders of banking model that is predominant in a given setting. This is evidenced by Muhdar et al. (2023) who investigated the determinants of customer switching from conventional banks to Islamic banks in Indonesia and concluded that without being mediated by service quality matters, customer religiosity has no significant influence on switching decisions. Other influential triggers include perceived benefits of switching as well as competitors’ price. For example, a customer may be tempted to switch by relative advantage (Adapa and Roy, 2017) and affordable prices (Vyas et al, 2018) offered by other banks. In a competitive environment, pricing is one of few marketing mix elements which can differentiate service providers and hence can influence both the incidence and direction of switching.

Reactional triggers are a service’s procedural and relationship-related factors influencing consumer switching behaviour (Roos and Gustafsson, 2011) as well as employees’ displayed behavioural controls for routine service delivery (Victorino et al, 2012). Such triggers include poor service, service failure, failed service encounters, service inconvenience, the service involvement level, the customer’s ability to make accurate behaviour predictions and other critical incidents during interactions between customers and service providers, resulting in customer dissatisfaction. For example, Yu (2014) acknowledged that an employee’s bad attitude could trigger switching. Similarly, Hauff (2019) and Vyas and Raitani (2014) confirm that employees’ responses to core service failure is the most important determinant of consumer switching. Besides core service failure, Nyarko (2015) identifies pricing problems and denied services as the main causes of switching behaviour in the service industry. Since bank services are delivered in the exact location in which they are produced, Boo et al. (2013) assert that the presence of fellow customers in the service delivery and service failure could also influence switching behaviour. In their study, Tooba et al. (2016), found complaint handling, benefit-loss costs, dependence, and calculative commitment as elements of a reactional trigger. Nyarko (2015) identified high transaction fees, the attractiveness of alternatives, the inconvenience of bank location, and the bank’s inability to respond quickly to system failure as significant factors in customer switching behaviour prediction. Rosh and Offermann (2013) defined control as policies and procedures designed by an organization to guide the activities of the employees in the discharge of their duties. Paul et al. (2015) demonstrated that organisational control measures (formal or informal) strongly influence customers’ service performance perceptions and their subsequent switching decisions. According to Mosavi et al. (2018), bank customer switching intentions could be prevented by doing everything it takes to improve their satisfaction with bank services. This view is echoed by Kim and Jindabot (2021) whose study in Cambodia revealed that switching behaviour was largely a by-product of customer dissatisfaction.

Although bank customer switching has been studied in many countries around the world, the problem has largely been ignored by Zimbabwean researchers. It has mainly received a cursory address either as part and parcel of the broader nexus between digital transformation and customer relationship management (Kaondera et al, 2023) or as part of general customer loyalty (Makudza, 2020). Only a few studies, for example, Nyakuwanikwa et al (2024), have attempted to directly fill this gap. While Nyakuwanikwa et al (2024) used descriptive statistics pertaining to the views of bank employees to reach their conclusions, the present study used a generalised ordinal logistic model to draw conclusions from Customer Relationship Managers’ and bank customers’ views.

METHODOLOGY

In keeping with the pragmatism research paradigm, the study adopted an explanatory design as it sought not only to observe the switching behaviour of customers but also to investigate the manner and extent to which identified factors were driving these customers’ likelihood of switching retail banks in Zimbabwe. According to Statistica Market Insights, as at June 2024, there were 6.5 million bank deposit accounts in Zimbabwe which translates to 3.7 million geographically dispersed account holders. Since Zimbabwean financial institutions are mainly concentrated in cities and towns, the study targeted retail bank customers from 13 cities spread across the whole country. Sampling was done in multiple stages, starting with the clustering of potential participants around each of the 13 cities, followed by stratification of the population into 5 subgroups, with each subgroup representing customers from a distinct retail bank which has a branch in each of the 13 cities. In the end, 46 bank customers, equally spread across the 5 chosen retail banks, were randomly selected from each city, yielding a sample of 598 bank account holders. The sample was made up of participants from diverse geographical locations, age groups, gender, educational levels and professions thereby guaranteeing generalisability of research findings to all bank customers in Zimbabwe. Since prior switching experience, switching intentions and switching behaviour as a whole are concepts which are best understood from the consumer’s perspective, data was mainly collected directly from the sampled bank customers using a self-administered questionnaire. Serve for the demographic section, the design of the rest of the questionnaire followed a 5 point Likert scale with the possible responses ranging from “Strongly Disagree” to “Strongly Agree”. To complement this self-reported data, telephone interviews were conducted with a Customer Relationship Manager at each of the 5 qualifying retail banks. Since the dependent variable (likelihood of switching) is categorical in nature, a generalised ordinal logistic regression model was used to analyse data. As formulated by Fu (1998) and Williams (2006), the model is expressed as follows:

Where ϒ is the ordered categorical variable, α_j is the intercept, β_1j, β_2j…β_pj are the logit coefficients and X₁, X₂,…,X_p are the predictor variables. Based on literature, the empirical model measuring the log odds of a customer falling in a higher category of likelihood to switch from one bank to another is specified as follows:

Where the dependent variable, customer’s likelihood of switching banks (LoS) is an ordered categorical variable ranging from “Highly Unlikely” to “Highly Likely”. It was measured as the mean response category of a respondent’s answers to questions pertaining to the explanatory variables in the model. Service quality (SQ), transaction cost (TC), bank reputation (BRep), competing bank advertisements (CBAD) and distance from the bank (DfB) are categorical predictor variables measured on a 5-point Likert scale ranging from 1 “Strongly Disagree” to 5 “Strongly Agree”. Respondents were asked to indicate their level of agreement with propositions pertaining to their likelihood of switching banks if there was a noticeable change in each of these factors. Switching Experience (SE) denotes the number of times a respondent has previously switched banks. Age is a categorical variable with 1=18-35 years; 2=36-64 years and 3=65+ years. Gender (Gen) is a dummy variable taking values 1 if male and 2 if female. Level of Education (LoEd) is a categorical variable with 1=Secondary; 2=Diploma; 3=Bachelor’s degree and 4=Postgraduate degree. Four diagnostic tests informed the choice of a generalised ordinal logistic model. Since the sample of bank customers used in this study is greater than 100 (N=402), the Kolmogorov-Smirnov normality test informed the choice of a non-parametric approach to data analysis whereas the Deviance and Pearson Chi-square statistics tested the model’s fit. The Omnibus test helped in checking if the overall model had upheld the proportional odds assumption.

FINDINGS AND DISCUSSION

Out of the 598 questionnaires which were distributed to bank customers, 402 (67%) were completed and returned in usable form. Participants from diverse age groups, gender, educational and income levels were fairly represented in the study, making the findings generalisable to a wide spectrum of bank customers in Zimbabwe. Male participants constituted 54.7% with the remainder (45.3%) being female. Mature adults in the 36 to 64-year age group (59%) dominated the survey, followed by youthful adults in the 18 to 35-year age group (35%) and retirees (6%) who had the least representation. The participants were fairly spread across all educational levels with Secondary school, Diploma, Bachelor’s degree and Postgraduate degree holders having 41.5%, 19%, 18.9% and 20.6% representation respectively. In terms of monthly income, those in the US$301 and above category (29.9%) were dominant, with the rest falling in the US$251 to US$300 (24.4%), US$201 to US$250 (17.9%), US$151 to US$200 (14.4%) and US$100 to US$150 (13.4%) brackets. The majority of respondents (73.4%) confirmed that they had prior bank switching experience. A few new and young account holders (26.6%) had never switched banks before. Of those who had prior defection experience, 45% indicated that they had switched banks for 1 to 3 times, while 15.9% and 8.7% had done so for 4 to 6 times and 7 to 9 times respectively. Only 4.2% had ever switched for 10 or more times. Respondents mainly attributed their previous switching decisions to poor service quality (17.2%), high service charges (13.9%), long queues (6.7%), bank’s failure to meet their cash withdrawal needs (6.2%), dysfunctional ATMs (6.2%), unavailability of loans (5.2%) and salary delays (5%) among others. Factors such as lower cash withdrawal limits (2.7%), higher minimum balance (2.2%) and rumours of their current bank’s failure (1.5%) were given as peripheral drivers of previous switching intentions and decisions.

Normality Test

Since the sample of bank customers used in this study is greater than 100 (N=402), the test for normality was done using the Kolmogorov-Smirnov approach. As shown on Table 1 below, the variables are statistically significant (p=0.000), hence the dataset is not normally distributed. The same result is obtained when the dataset is transformed to its logarithmic form. Thus, a non-parametric test in the form of ordinal logistic regression was appropriate for data analysis.

Table 1: Normality Test Results

Results from the Ordinal Logistic Regression Model

All conventional measures show that the ordinal logistic model used in this study fits the data, hence the model truly addresses the determinants of customer switching behaviour in Zimbabwe’s retail banking sector. Both the Deviance (p=0.537) and the Pearson Chi-Square (p=2.171) are statistically insignificant, hence the model has a good fit. Thus, the predictors included in the model contribute to the prediction of bank customer switching behaviour in Zimbabwe.

Table 2: Goodness of Fit

Likewise, the Omnibus test results are statistically significant (p=0.000) showing that the predictors included in this study significantly improve the model’s fit compared to the null model. More importantly, this result indicates that the proportional odds assumption is untenable, which paved way for the use of a generalised ordinal logistic regression model.

Table 3: Omnibus Test Results

Parameter Estimates

Table 4 shows the parameter estimates from the generalised ordinal logistic regression model which was used to measure the impact of the identified triggers on customers’ likelihood of switching banks in Zimbabwe. Level of education (β=0.222; p=0.002) is the only demographic factor which significantly predicts the switching behaviour of bank customers in Zimbabwe. The odds ratio (Exp(β)=1.249), shows that as a customer takes a step higher with their level of education, there is a predicted increase of 1.249 in the odds of being in a higher category of likelihood to switch banks. Thus, as suggested by Van der Cruijsen and Diepstraten (2017), in Zimbabwe, the more educated a bank customer is, the higher their probability of defecting to another bank. Contrary to assertions by Kim and Jang (2015), and Teeroovengadum (2020), gender (β=0.334; p=0.146) and age (β=0.140; p=0.064) are insignificant triggers of customers’ likelihood of switching banks in Zimbabwe.

Table 4: Parameter Estimates

Contrary to a priori expectations, a customer’s switching experience has been found to be an insignificant negative predictor of customer switching behaviour (β=-0.042; p=0.645). The odds ratio (Exp(β)=0.958) implies that the odds of a customer being on higher level of likelihood to switch banks decreases by a factor of 0.958 for every one-unit increase in the number of times they have switched banks before. Thus, contrary to Hati et al (2020), as switching experience increases, there is a decreasing probability that the customer’s likelihood of switching banks will increase to a higher level. Perhaps, this is a manifestation of customers’ disillusionment with their previous switching experiences as high service charges, long queues and failure by banks to meet the withdrawal needs of customers are vices which are common to all Zimbabwean commercial banks. Thus, customers with prior switching experience would already have realised the folly of switching banks for such generic problems.

Service quality is a significant negative driver of customers’ likelihood of switching (β=-0.533; p=0.001). The odds ratio (Exp(β)=0.587) indicates that the odds of being in a higher category of likelihood to switch banks decreases by a factor of 0.587 for a marginal improvement in the quality of a bank’s services, holding other predictor variables constant. Thus, the probability of switching banks decreases as service quality improves. This corroborates Kim and Jindabot (2021) who observed that Cambodian customers mainly switch banks in protest over poor service quality and depressed satisfaction levels.

Transaction costs (β=0.604; p=0.001), competing bank adverts (β=0.818; p=0.000) and distance from the bank (β=0.418; p=0.001) are all significant positive drivers of a bank customer’s likelihood of switching. They have odds ratios of 1.830, 2.267 and 1.519 respectively. Thus, if transaction costs, competing bank adverts and distance from the bank increase marginally, the probability that a typical Zimbabwean bank customer becomes more likely to switch banks increases by a wider margin. This confirms findings by Nyarko (2015), who largely attributed Ghanaian customer switching decisions to reactional triggers such as exorbitant prices, the attractiveness of alternatives and inconvenient bank locations.

Interestingly, the study reveals that bank reputation is a significant positive predictor of customer defections (β=0.614; p=0.000). The odds ratio (Exp(β)=1.847) shows that a marginal improvement in the service provider’s reputation increases the odds of the customer’s likelihood of switching banks to a higher level. This finding contradicts scholars such as Hauff (2019), who have concluded that there is an inverse relationship between a bank’s perceived integrity and financial stability, on the one hand, and customer switching behaviour, on the other. This contradiction probably emanates from the uniqueness of Zimbabwean banking practices, where reputable banks are well-known for their adherence to strict and conservative lending policies, charging usurious rates of interest on advances as well as levying extortionate service charges which drive customers to less reputable banks.

CONCLUSIONS

Like any other banking system around the world, the Zimbabwean retail banking sector is grappling with the problem of excessive customer switching. In line with the Rational Choice theory, customer switching in the Zimbabwean retail banking industry is driven by a combination of situational, influential as well as reactional triggers. Firstly, the higher the customer’s level of education, the greater their propensity to switch banks. Factors such as poor service quality, negative bank reputation, high service fees, inconvenient bank location and attractiveness of competing banks’ products are serious considerations that stimulate tendencies of customer defection in the Zimbabwean banking sector. From a counterfactual perspective, quality-driven retail banks which offer competitively priced services through appropriate and convenient distribution channels are the major switching destinations of defecting customers. Contrary to the switching behaviour of bank customers in other jurisdictions, bank reputation positively drives Zimbabwean bank customers’ switching decisions. However, the influence of customer demographics such as gender and age as well as switching experience is ambiguous. Thus, bank customer defection knows no gender nor age group. Lastly, in Zimbabwe, repeat bank defectors are not so keen to keep switching banks.

RECOMMENDATIONS

Given the dynamic nature of the Zimbabwean financial services landscape, it is necessary for retail banks to continuously seek customer post-purchase feedback and encourage customers to seize the opportunity to give honest evaluations of their bank’s service offerings. This can be achieved by periodically administering short hard copy questionnaires for customers who rely on physical branches for banking services and an online version of the same for those customers who utilise online banking platforms. For the banks to build a solid base of loyal customers, it would also be prudent for them to frequently conduct customer needs assessments. To this end, a quarterly review of each customer’s financial services needs should be instituted through both the “brick and motor” as well as online channels. Apart from that, there is need for retail banks to put service quality back to the centre of their business models with a view to improving their appeal to existing as well as potential customers. This requires retail banks to resuscitate dedicated staff training programmes which are designed to inculcate a service quality culture within their internal customers. Service quality refresher training courses as well as annual prizes for the best service quality oriented staff members could also help to achieve customer retention.  Retail banks must also fully embrace the “Know Your Customer” (KYC) principle as a way of enhancing customer retention through improved customer relationship management. The ensuing customer retention has potential to improve the bank’s cost efficiency which, ultimately, leads to a possible reduction in bank account transaction costs. To counter alternative bank promotional efforts, it is recommended that retail banks should leverage social media platforms, payslips and fintechs to implement affordable and robust promotional campaigns of their own. It is also important for retail banks in Zimbabwe to bring financial services to their customers’ doorsteps by establishing electronic banking delivery channels such as ATMs at business centres in all residential suburbs. Since this study was grounded in the Rational Choice theory, it is recommended that future studies should examine the impact of emotional and psychological triggers such as trust, loyalty and dissatisfaction on customer switching behaviour in the Zimbabwean retail banking industry.

REFERENCES

1. Adapa, S. and Roy, S. K. (2017), “Consumers’ post-adoption behaviour towards Internet banking: empirical evidence from Australia”, Behaviour and Information Technology, vol. 36, number 9, doi: 10.1080/0144929X.2017.1319498.
2. Agarwal, K. P. (2019), “Drivers of bank/branch switching international behaviour in retail banking: Evidence from the Indian banking customers”, Journal of Marketing and Consumer Research, vol. 54, pp. 1-12.
3. Bolton, R. N., and Bronkhurst, T. M. (1995), “The relationship between customer complaints to the firm and subsequent exist behaviour”, Advances in Consumer Research, vol. 22, pp. 92-100.
4. Boo, H. C., Mattila, A. S., and Tan, C. Y. (2013), ‘Effectiveness of recovery actions on deviant customer behaviour – the moderating role of gender’, International Journal of Hospitality Management, vol. 35, pp. 180–192.
5. Bugyei, G. (2019), “Customers’ switching behaviour in the banking industry in Cape Coast Metropolis, Ghana”. Journal of Marketing and Consumer Research, vol. 61, no. 1, pp. 1-8.
6. Chakravarty, S., Feinberg, R. and Rhee, E. (2004), “Relationships and individuals’ bank switching behaviour”, Journal of Economic Psychology, vol. 25, pp. 501-27
7. Clemes, M., D., Gan, C., and Zheng, L, Y. (2007), “Customer Switching Behaviour in the New Zealand Banking Industry”, Banks and Bank Systems, vol. 2, no. 4.
8. Clemes, M.D., Gan, C. and Zheng, L.Y. (2010), “Customer switching behavior in the New Zealand banking industry”, Banks and Bank Systems, vol. 2, no. 4, pp. 50-62.
9. Coase, R. H. (1937). “The nature of the firm”. Economica, vol. 4, no. 16, pp. 386-405.
10. Esteves, R. B. (2014), “Behaviour-based price discrimination with retention offers”. Information Economics and Policy, vol. 27, pp. 39–51.
11. Farah, M. F. (2017), ‘Application of the theory of planned behaviour to customer switching intentions in the context of bank consolidations’, International Journal of Bank Marketing, vol. 35, no. 1, pp. 147–172.
12. Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, Intention, and Behaviour: An Introduction to Theory and Research, Boston, Addison-Wesley Reading, MA.
13. Fu, V. (1998), “Estimating generalised ordered logit models”. Stata Technical Bulletin, vol. 44, pp. 27-30
14. Garland, R. (2002), “Estimating customer defection in personal retail banking”, The International Journal of Bank Marketing, vol. 20, no. 7, pp. 317-25.
15. Gerrard, P. and Cunningham, J. B. (2004), “Consumer switching behaviour in the Asia banking market”, The Journal of Service Marketing, vol. 18, no. 3, pp. 215-225.
16. Hati, S. R., Gayatri, G. and Indraswari, K. D. (2020), “Migration (Hijra) to Islamic bank based on push– pull–mooring theory: a services marketing mix perspective”, Journal of Islamic Marketing, vol. 12, no. 8, pp. 162-179.
17. Hauff, J.C. (2019), “Reasons to switch: empowered vs less powerful bank customers”, International Journal of Bank Marketing, Vol. 37 No. 6, doi: 10.1108/IJBM-08-2018-0231.
18. Hirschman, A.O. (1970), Exit, Voice and Loyalty, Harvard University Press, Cambridge, MA
19. Jayanthi, V. and Anupama, R. (2020), ‘The impact of customer knowledge management and customer satisfaction on customer trust and loyalty in banking sector’, Our Heritage, vol. 68, no. 10, pp. 50–56.
20. Justin, P., Arun, M, and Garima, S., (2016), ‘Impact of service quality on customer satisfaction in private and public sector banks’, International Journal of Bank Marketing, vol. 34, no. 5, pp. 606–622.
21. Kaondera, P. R., Chikazhe, L., Munyimi, T. F. and Nyagadza, B. (2023), Buttressing customer relationship management through digital transformation: perspectives from Zimbabwe’s commercial banks, Cogent Social Sciences, (2023), 9: 2191432
22. Keaveney, S.M. (1995), “Customer switching behavior in service industries: an exploratory study”, Journal of Marketing, vol. 59, no. 2, pp. 71-82.
23. Kim, D. H. and Jang, S. (2015), ‘Cognitive decline and emotional regulation of senior consumers’, International Journal of Hospitality Management, vol. 44, pp. 111–119.
24. Kim, L. and Jindabot, T. (2021), “A Structural Framework for Switching Intention in the Cambodian Banking Industry”, The Retail and Marketing Review, vol. 17, issue 2 (2021) pp. 66-83
25. Makudza, F. (2023), Augmenting customer loyalty through customer experience management in the banking industry, Journal of Asian Business and Economic Studies, vol. 28, no. 3, pp. 191-203
26. Mosavi, S.M., Sangari, M.S. and Keramati, A. (2018), “An integrative framework for customer switching behavior”, Service Industries Journal, vol. 38, no. 15-16
27. Muhdar, H. M., Mardiana, D. A., Muin, R. and Darwis, R. (2023), “From conventional to Islamic banks: Factors affecting customer interests and decision making in selecting banks”. Banks and Bank Systems, vol. 18, no. 3, pp. 61-73.
28. Nyakuwanikwa, P., Chisaka, B. M., Madzivire, B., and Goshomi, E. (2024), “Customer Bank Switching Behaviour in Zimbabwe’s Retail Banking Sector of Zimbabwe”. African Journal of Commercial Studies, vol. 5, no. 3, pp. 143-156.
29. Nyarko, I.K. (2015), “Evaluation of factors influencing switching behaviour by Ghana commercial bank customers”, British Journal of Marketing Studies, vol. 3, no. 8, pp. 35-46.
30. Nzowa, S. (2022), “Customer switching intentions in commercial banks: A case of selected commercial banks in Dar es Salaam”, Consumer Behaviour Review, vol. 5, no. 3, pp. 21-35.
31. Paul, M., Hennig-Thurau, T. and Groth, M., (2015), “Tightening or loosening the “iron cage”? The impact of formal and informal display controls on service customers. Journal of Business Research, vol. 68, no. 5, pp.1062-1073.
32. Pick, D. (2014), ‘Switching is easy”: service firm communications to encourage customer switching’, Journal of Retailing and Consumer Services, vol. 21, pp. 502–509.
33. Reichheld, F.F. and Sasser, E. (1990), “Zero Defections: Quality Comes to Services”. Harvard Business Review, vol. 68, pp. 105-111.
34. Riptiono, S., Suroso, A., and Anggraeni, A. I. (2020), ‘Examining the determinant factors on consumer switching intention toward Islamic bank in Central Java, Indonesia’, Humanities & Social Sciences Reviews, vol. 8, no. 2, pp. 364–372.
35. Roos, I. and Gustafsson, A. (2011), ‘The influence of active and passive customer behaviour on switching in customer relationships’, Managing Service Quality: An International Journal, vol. 21, no. 5, pp. 448–464.
36. Rosh, L. and Offermann, L. (2013), ‘Be yourself, but carefully’, Harvard Business Review, vol. 91, no. 10, pp. 135–139.
37. Scott, J. (2001). Rational choice theory. In N. J. Smelser & P. B. Baltes (Eds.), International Encyclopedia of the Social & Behavioral Sciences (pp. 12862-12865). Elsevier.
38. Statistica Market Insights (June 2024)
39. Teeroovengadum, V. (2020), ‘Service quality dimensions as predictors of customer satisfaction and loyalty in the banking industry: moderating effects of gender’, European Business Review [Online]. Available at doi: 10.1108/EBR-10-2019-0270
40. Thaichon, P., Quach, S., Bavalur, A. S., and Nair, M. (2017), “Managing Customer Switching Behavior in the Banking Industry”, Services Marketing Quarterly, vol. 38, no. 3, pp. 142-154.
41. Tooba, A., Rubab, Z., and Umar, D. (2016), ‘Impact of customer satisfaction on image, trust, loyalty and the customer switching behaviour in conventional and Islamic banking: evidence from Pakistan’, American Journal of Business and Society, vol. 1, no. 3, pp. 154–165.
42. Tram, N.T. A., Van, P. T T., and Ha, L. T. H. (2021), “Determinants Influencing Customer’s Switching Behavior towards Banking Service Providers in Hanoi”, Journal of Economics, Finance and Management Studies, vol. 4, no. 6, pp. 794-800.
43. Van der Cruijsen, C. and Diepstraten, M. (2017), ‘Banking products: you can take them with you, so why don’t you?’ Journal of Financial Services Research, vol. 52, no. 2, pp. 123–154.
44. Victorino, L., Verma, R., Bonner, B. L., and Wardell, D. G. (2012), ‘Can customers detect script usage in service encounters? An experimental video analysis’, Journal of Service Research, vol. 15, no. 4, pp. 390–400.
45. Vyas, V. and Raitani, S. (2014), “Drivers of customers’ switching behaviour in Indian banking industry”, International Journal of Bank Marketing, vol. 32, no. 4, pp. 321-342.
46. Vyas, V., Roy, A. and Raitani, S. (2018), “Do the competitors affect cross-buying decisions?”, International Journal of Bank Marketing, Vol. 36 No. 1, doi: 10.1108/IJBM-06-2016-0085.
47. Williamson O. E. (1985). The economic institutions of capitalism: Firms, markets, relational contracting. Free Press.
48. Williams, R. (2006), “Generalised ordered logit/partial proportional odds models for ordinal dependent variables”, The Stata Journal, vol. 6, no.1, pp. 58-82
49. Wirtz, J., Xiao, P., Chiang, J., and Malhotra, N. (2014), ‘Contrasting the drivers of switching intent and switching behaviour in contractual service settings’, Journal of Retailing, vol. 90, pp. 463–480.
50. Yu, C. (2014), “Consumer switching behaviour from online banking to mobile banking”, International Journal of Cyber Society and Education, vol. 7, no. 1, pp. 1–28.
51. Zulfitri, S., and Rohman, F. (2019), “The influence of price perception, service quality and variation behavior on electronic banking”, International Journal of Economics & Business Administration, vol. 7, no. 1, pp. 183–200.