The effect of customer experience, customer satisfaction and word of mouth intention on customer loyalty: The moderating role of consumer demographics

Abstract

Customer experience, satisfaction and word-of-mouth intention contribute a crucial part in enhancing customer loyalty in the banking sector. This study examines the moderators of the effect of customer experience, satisfaction and word-of-mouth intentions on customer loyalty. Data were collected from bank customers (n = 650) using a structured questionnaire through a cross-sectional survey in Harare, Zimbabwe. Customer experience, satisfaction and word-of-mouth intention were found to have a direct positive effect on loyalty. Age was found to moderate the effect of customer satisfaction on loyalty. However, gender, education and income did not moderate the effect of customer satisfaction on loyalty. These results contribute to the existing body of services marketing literature. Banks are advised to consider customer experience, satisfaction, word-of-mouth intention and age when designing strategies to improve customer loyalty.

Keywords:

• banking
• customer experience
• customer loyalty
• customer satisfaction
• word of mouth intention

PUBLIC INTEREST STATEMENT

Customer experience, satisfaction and word-of-mouth intention contribute a crucial role in the success of banks. This study examines the moderators of the effect of customer experience, satisfaction and word-of-mouth intention on customer loyalty within the banking sector in Zimbabwe. The study specifically tests the moderating effect of consumer demographics (gender, age, education and income) on the relationship between customer experience, satisfaction and word-of-mouth intention on customer loyalty. The study reveal that satisfied customers are loyal to the banks. The study also reveals that customer experience and word-of-mouth intention influence customer loyalty. Age was found to be moderating the effect of customer satisfaction on loyalty. When age of respondents is high the relationship between customer satisfaction and loyalty becomes stronger and vice versa. Subsequently, banks are encouraged to consider customer experience, satisfaction, word-of-mouth intention and consumer demographics when seeking to improve customer loyalty.

1. Introduction

The business world globally has been transformed by the emergency of new technology and the banking sector is not spared (Kumar et al., 2020). Digital technology has substituted physical documents in an attempt to eradicate errors, delays, risk and most importantly the cost of banking (Kumar et al., 2020). Along with the new wave of technology is increased customer expectations from the banks since bank customers are becoming more educated and informed; thus, customers expect a better experience from the banks (Kumar & Anbazhagan, 2020; Kumar et al., 2020). Competition has also affected the banking industry as each bank seeks to gain market share through customer loyalty (Kumar & Anbazhagan, 2020).

The new technology has led to the adoption of e-banking and automation thereby creating more competition among the banks (Kumar & Anbazhagan, 2020). In the same manner, customers are becoming more service and price-conscious concerning their financial transactions (Becker & Jaakkola, 2020). The banking sector has now been faced with a wave of multiple accounts and customer switching hence banks are alarmed to create customer loyalty (Kumar & Anbazhagan, 2020). This presents a daunting challenge for banks to lock-in customers so as to enhance customer loyalty (Makudza, 2020). Banks are now focusing on identifying factors influencing customer loyalty so that they can put the effort in turning ordinary customers into loyal customers (Chikazhe et al., 2021).

The banking sector in Zimbabwe has been characterised by crisis especially from 2007 and bank confidence has been eroded and the banking public no longer have trust in the banks. The current banking environment is harsh and the confidence of the banking public has been very low due to the dent in the banking system failures (Makanyeza & Chikazhe, 2017). The bank failures in Zimbabwe affected the bank customers who lost their investments and deposits and this has dented the confidence of the banking public (Makanyeza & Chikazhe, 2017). This has led to negative perceptions by the general banking public due to the past failures in the economy at large (Makudza, 2020). It is against this background that banks in Zimbabwe seek to improve loyalty through customer experience, satisfaction and word-of-mouth intention (Makanyeza & Chikazhe, 2017; Makudza, 2020; Shambira, 2020).

Customer loyalty is regarded as a popular construct in marketing and its importance in creating a sustainable advantage for banks cannot be overemphasized (Mukerjee, 2018; Tabrani et al., 2018). Several scholars and practitioners believe that loyalty has a powerful consequence on company performance such as nourishing and increasing sales, profitability and lessen the marketing and operational costs (Mukerjee, 2018; Valipour et al., 2018). Loyal customers are unwilling to switch brands or service providers and it would be expensive and difficult for the competitors to disrupt that loyalty (Situmorang et al., 2017). Customers end up being loyal due to the experience they get during interactions with the service providers (Becker & Jaakkola, 2020).

Banks that fail to offer memorable customer experiences face threats from competitors because customer experience affect consumer behaviour issues such as loyalty and satisfaction (Chen et al., 2015). Customers always seek to be satisfied by the firm’s offering, hence banks must priories customer satisfaction as a key element to attain loyalty (Husnain & Akhtar, 2015). word-of-mouth intention is a crucial determinant of loyalty (Chen et al., 2015). Consumer behaviour is also influenced by demographic factors (Pitchayadejanant & Nakpathom, 2016). Demographic factors impact customer perceptions in a different manner in the banking sector (Akbar, 2013).

There are many studies carried out in the banking industry on customer experience, satisfaction, word-of-mouth intention and customer loyalty (Abdul, 2019; Abuzid & Abbas, 2017; J. Bhatt & Patel, 2020; Fernandes & Pinto, 2019; Homburg et al., 2017; Kumar & Anbazhagan, 2020; Makanyeza & Chikazhe, 2017; Makudza, 2020; Mbama & Ezepue, 2018; Mulari & Komulainen, 2019; Suvarchala & Narasimha, 2018; Tabrani et al., 2018). Nevertheless, it is crucial to note that there are rather scant studies that have examined moderators of customer experience, satisfaction and word-of-mouth intention on customer loyalty. Furthermore, research is unconvincing on the role played by consumer demographics on the effect of customer experience, satisfaction and word-of-mouth intention on customer loyalty. Hence, this research attempts to narrow this knowledge gap by investigating the moderating effect of consumer demographics on the effect of customer experience, satisfaction and word-of-mouth intention on customer loyalty. The specific research objectives were to determine the effects of customer experience, satisfaction and word-of-mouth intention on customer loyalty; and to test the moderating role of gender, age, education and income on the effects of customer experience, satisfaction and word-of-mouth intention on customer loyalty.

The rest of the article includes literature review that covers the theoretical framework on customer experience, satisfaction, word-of-mouth intention and customer loyalty. Subsequently, the article discusses the research hypotheses development, research model, methodology, results and discussion, followed by the implications and conclusion.

2. Theoretical framework

2.1. Customer experience

Customer experience refers to the inner and personal response that customers have to all direct or indirect interactions with a firm (Kavitha & Haritha, 2018). Customer experience concentrates on the perceptions of interaction between the firm and the customer that provokes a reaction (J. Bhatt & Patel, 2020). Customer experience emanate from a positive post-experience evaluation of customer experience against customer’s pre-experience expectations (Gahler et al., 2019). This study understands customer experience as naturally related to the necessity to provide superb services to customers during interactions.

The notion of customer experience has captured the minds of marketers and has arisen as an important dimension in contemporary marketing due to its capability to accomplish competitive advantage, differentiation and success for the firm (Borishade et al., 2019; Kim & Chen, 2019; Lemon & Verhoef, 2016; Rather, 2020; Terblanche, 2018; Trivedi, 2019). Worlu et al. (2016) posit that customer experience is now a critical management tool because it affects consumer actions. Customer experience is regarded as a strategy to foster loyalty, satisfaction, positive word-of-mouth referrals, reduced complaints and improved retention (Fernandes & Pinto, 2019; Wijaya et al., 2019). Therefore, banks must create a wow factor so that customers defend, recommend and refer the bank to others.

2.2. Customer satisfaction

Customer satisfaction emanates from the assessment of anticipated performance against the real perceived performance and the paid price (Abedi & Jahed, 2020). Customer satisfaction is a typical performance measure and is a result of the superiority of the customer experience and aspects around the gap between customer expectations and the actual experience (Ban & Jun, 2019; Milner & Furnham, 2017). This study comprehends satisfaction as an occurrence where offerings offered by specific banks meet or surpass client requirements. Satisfaction contributes to repeat purchases, favourable word-of-mouth intention, loyalty and eventually higher profit and reduction of costs (Chiguvi & Guruwo, 2017; Ngo & Nguyen, 2016; Shourov et al., 2018; Tao & Kim, 2019; Tripathi, 2017). Banks must ensure that their customers are satisfied if they want to be competitive, profitable and retain loyal customers.

2.3. word-of-mouth intention

An unending longing among customers to participate in informal communication and discussion concerning ownership, features of brands, representatives of firms and/or the firm itself is referred to as word-of-mouth intention (Fazal-e-Hasan et al., 2017; Jham, 2018). In the banking industry context, word-of-mouth intention refers to an intent to participate in spreading a positive word about a bank after successfully enjoying its services (Iqbal & Hassan, 2018). word-of-mouth intention messages directly impact consumer purchasing decisions (Chen et al., 2015; Jham, 2018; Jung & Seock, 2017). Iqbal and Hassan (2018) postulate that word-of-mouth information aids consumers to appreciate and visualize expectations about the firm’s offerings before its consumption. Customers rely on informal communication in order to lessen perceived risk prior to purchase of a product (Iqbal & Hassan, 2018). Customers treated with excellent service get involved in positive word-of-mouth (Iqbal & Hassan, 2018).

2.4. Customer loyalty

Loyalty is a profoundly held promise to buy again or revisit a chosen brand unswervingly in the future (Saleem et al., 2018). Customer loyalty is the longing of customers to continue being faithful to an organization and continuing patronage over time (Setiawan & Sayuti, 2017). This study comprehends loyalty as the commitment to rebuy and repatronise products frequently in the future notwithstanding marketing efforts and situational influences (Mukerjee, 2018). Loyalty results in reduced operational expenses, improved revenue and profitability and lower marketing expenses (Janahi & Al Mubarak, 2017).

Customer loyalty is influenced by factors like customer experience, satisfaction and word-of-mouth intention (Husnain & Akhtar, 2015; Pattanayak et al., 2017). Valipour et al. (2018) posit that loyal customers have a tendency to purchase more bank products, continue with the same bank, eager to attempt new products, cheaper to assist and recommend others to bank with the same bank. Likewise, Kumar and Anbazhagan (2020) argued that loyal customers are unlikely be expected to switch to rivals due to variation in prices or incentives.

2.5. Consumer demographics

A. Bhatt and Bhatt (2016) define consumer demographics as the consumer characteristics like age, income, gender, literacy and education. Consumer demographics are crucial in services marketing as they influence decision-making and choices (Chauhan et al., 2016; Kamboj & Singh, 2018; Olasina, 2015). Chawla and Joshi (2017) argued that the respondents’ demographics like age, income, gender and education impact buying decision-making process within the banking sector. Consumer demographics were found to impact satisfaction and loyalty within the banking setup (Shaikh & Karjaluoto, 2015). Similarly, demographic and psychographic factors such as income, age, education and gender influence consumer decisions in the banking industry (Olasina, 2015; Shaikh & Karjaluoto, 2015). Furthermore, demographic aspects like age, income, residential area, gender, employment status and marital status impact on bank customers’ behavioural intentions (Lee et al., 2015).

3. Development of research hypotheses and research model

Customer experience and loyalty relationship have been confirmed to be positive within the banking sector (Alnawas & Hemsley-Brown, 2019; Kandampully et al., 2018). A study by Mbama and Ezepue (2018) confirmed that customer experience positively influence loyalty. Various studies reported the positive effect of customer experience on loyalty (Borishade et al., 2019; Foroudi et al., 2016; Hwang & Seo, 2016; Mulari & Komulainen, 2019; Srivastava & Kaul, 2016). Thus, it can be hypothesised that:

H1: Customer experience has a positive effect on customer loyalty.

The association between satisfaction and loyalty has been recognized as vital within the banking sector (Borishade et al., 2019; Kasiri et al., 2017). Research by Saleem et al. (2016) concluded that satisfaction has an influence on loyalty. Similarly, a study by Desai (2019) confirmed a significant association between overall satisfaction and loyalty. A general consensus exists among scholars that customer satisfaction influences loyalty (Mbama & Ezepue, 2018; Ozatac et al., 2016). Thus, it can be hypothesised that:

H2: Customer satisfaction has a positive effect on customer loyalty

word-of-mouth intention has been found to positively influence loyalty within the banking sector (Jung & Seock, 2017; Saleem et al., 2018). A study by Iqbal and Hassan (2018) found that word-of-mouth intention directly impacts loyalty behaviour. The effect of word-of-mouth intention on loyalty was established as positive (Ban & Jun, 2019; Erjavec et al., 2016; Kandampully et al., 2015; Tao & Kim, 2019; Tripathi, 2017). Therefore, it can be hypothesised that:

H3. word-of-mouth intention has a positive effect on customer loyalty.

Gender has been found to influence loyalty and females were found to be more loyal than man (Dimitriades, 2006; Gonçalves et al., 2012; Lee et al., 2015; Ndubisi, 2006). Therefore, the customer experience–loyalty relationship is stronger in females than males. Thus, it can be hypothesised that:

H4a: The effect of customer experience on loyalty is stronger in female than male consumers

Age has been found to influence consumer decisions and choices (A. Bhatt & Bhatt, 2016). The older and younger consumers have different purchase behaviour, young consumers consider different brands whereas older consumers are more loyalty to more established brands (Chikazhe et al., 2021). Thus, customer experience relationship is stronger on older consumers than young ones. Hence it can be proposed that:

H4b: The effect of customer experience on loyalty is stronger in older than younger consumers

Education influence consumer buying decisions and choices (Chauhan et al., 2016; Kamboj & Singh, 2018; Olasina, 2015). Consumers with higher education can easily conduct and enjoy searching for new information concerning goods and services more than those with lower education do. (Chikazhe et al., 2021). Therefore, the connection between customer experience and loyalty is stronger on less educated than the more educated. Thus, it can be posited that:

H4c: The effect of customer experience on loyalty is stronger in less educated than more educated consumers

Income has been found to influence consumer purchase decisions (Chikazhe et al., 2021). Higher income consumers can easily shift from one particular brand to the other, unlike lower income consumers who tend to be stuck with certain brands because they cannot afford other brands (Chikazhe et al., 2021). The effect of customer experience on loyalty is stronger in consumers that have lower incomes than those with higher incomes (Gonçalves et al., 2012). Based on this argument, it is prudent to accept that the customer experience–loyalty relationship is stronger among consumers with lower than higher income. Thus, it is hypothesised that:

H4d: The effect of customer experience on loyalty is stronger in lower income than higher income consumers

A study by Ndubisi (2006) concluded that females are more loyal compared to males within the banking sector. Similarly, a study by Dimitriades (2006) in Greece’s financial, retailing, entertainment and transportation services found that women were more loyal than men. Women and men differ in terms of buying behaviours because women have a tendency to be more involved in buying activities than men (Chikazhe et al., 2021). As a result, women develop stronger connections with brands than men do (Chikazhe et al., 2021). This makes women more loyal to particular brands than men (Chikazhe et al., 2021). Likewise, Gonçalves et al. (2012) observed that the customer satisfaction-loyalty link is stronger in women than men. Based on these remarks, it is rational to assume that the customer satisfaction–loyalty relationship will be stronger in females than males. Therefore, it is proposed that:

H5a: The effect of customer satisfaction on loyalty is stronger in female than male consumers

Age influence positively the connection amid customer satisfaction and loyalty (A. Bhatt & Bhatt, 2016). Chawla and Joshi (2017) confirm that age influence buying decision-making process within the banking sector. Existing literature confirm distinguished variances in consumer buying behaviour between adult and young consumers (Chikazhe et al., 2021). Younger consumers are more energetic and consider more brands when making purchase decisions than older consumers consider (Chikazhe et al., 2021). Older consumers usually choose well-established brands, avoiding newer brands because the ability to process purchasing information decreases as consumers grow older (Chikazhe et al., 2021). As such, older consumers are more loyal to particular brands than younger consumers are (Chikazhe et al., 2021). Similarly, the connection between customer satisfaction and loyalty is robust in older than young consumers (Gonçalves et al., 2012). Therefore, it is hypothesised that:

H5b: The effect of customer satisfaction on loyalty is stronger in older than younger consumers

Education has been found to influence consumer buying decisions (Chauhan et al., 2016; Kamboj & Singh, 2018; Olasina, 2015). Consumers with higher education can easily conduct and enjoy searching for new information concerning products more than those with lower education do. (Chikazhe et al., 2021). Therefore, the connection amid satisfaction and loyalty is stronger on less educated than the more educated. Thus, it can be posited that:

H5c: The effect of customer satisfaction on loyalty is stronger in less educated than more educated consumers

The effect of income on consumer buying decisions is noteworthy (Chikazhe et al., 2021). Thus, higher income consumers have less barriers when choosing brands (Chikazhe et al., 2021). They can easily try new things (Chikazhe et al., 2021). Hence, they are less loyal than lower income consumers are. Satisfaction effect on loyalty is stronger in consumers that has lower income than those with high income (Gonçalves et al., 2012). Based on this argument, it makes sense to accept that the customer satisfaction–loyalty relationship is stronger in consumers with lower than higher income. Thus, it is hypothesised that:

H5d: The effect of customer satisfaction on loyalty is stronger in lower income than higher income consumers

Gender has been found to influence consumer purchase decisions (Lee et al., 2015). Females were more loyal compared to males in the banking sector (Ndubisi, 2006). Also, Dimitriades (2006) in Greece’s financial, retailing, entertainment and transportation services concluded that women were more loyal than men. Women develop stronger connections with brands than men do (Chikazhe et al., 2021). Likewise, Gonçalves et al. (2012) observed that the word-of-mouth intention-customer loyalty link is stronger in women than men. Based on these remarks, it is rational to assume that the word-of-mouth intention-customer loyalty relationship will be stronger in females than males. Therefore, it is posited that:

H6a: The effect of word-of-mouth intention on customer loyalty is stronger in female than male consumers

Age has been found to impact on consumer decisions within the banking sector (Chawla & Joshi, 2017). Consumer demographics such as age were found to influence customer experience and loyalty in the banking sector (Shaikh & Karjaluoto, 2015). Younger consumers tend to shift from one brand to the other, whereas older consumers tend to stick to their chosen brands and become loyal over time (Chikazhe et al., 2021). Correspondingly, the connection between word-of-mouth intention and loyalty is stronger in older than younger consumers (Gonçalves et al., 2012). Therefore, it is postulated that:

H6b: The effect of word-of-mouth intention on customer loyalty is stronger in older than younger consumers

Education has been found to influence consumer purchase decisions in services industry and banking sector as well (Chauhan et al., 2016; Kamboj & Singh, 2018; Lee et al., 2015; Olasina, 2015). Consumers with higher education can easily look for new brands than those consumers with lower education, hence they are less loyal and may switch easily to new brands (Chikazhe et al., 2021). Therefore, the connection between word-of-mouth intention and customer loyalty is stronger on less educated than the more educated. Thus, it can be suggested that:

H6c: The effect of word-of-mouth intention on customer loyalty is stronger in less educated than more educated consumers

Income has been confirmed to influence consumer decisions within the banking sector (Chawla & Joshi, 2017; Lee et al., 2015). Higher income consumers can easily shift from one brand to the other contrasting to lower income consumers who tend to be loyal to certain brands because they may not afford other brands (Chikazhe et al., 2021). This makes lower income consumers more loyal to brands than higher income consumers (Chikazhe et al., 2021). As such, the effect of word-of-mouth intention on loyalty tends to be stronger among lower income than higher income consumers (Gonçalves et al., 2012). Based on these submissions, it can be suggested that word-of-mouth intention-customer-loyalty relationship is stronger in consumers with lower income than consumers with higher income. Thus, it is proposed that:

H6d: The effect of word-of-mouth intention on customer loyalty is stronger in lower income than higher income consumers

Based on the foregoing discussion, the research model is proposed in Figure 1.

Figure 1. Research model.

4. Research methodology

This component covers questionnaire measures and design, sampling and data collection methods adopted in this research.

4.1. Questionnaire design and measures

Appendix A shows the measurement scale with items that were used to measure customer experience (CEX), customer satisfaction (CSAT), word-of-mouth intention (WOMI) and customer loyalty (CLOY) based on the Likert scale which ranged from 1 (Strongly disagree) to 5 (Strongly agree). The study borrowed items from previous related studies and modified them to suit the current study (Alavijeh et al., 2018; Chahal et al., 2017; Fernandes & Pinto, 2019; Mbama & Ezepue, 2018; Yasin et al., 2020). The items for all constructs focused on perceptions of bank customers.

4.2. Sampling and data collection

The population comprises 19,320 bank customers in Harare (FBC Securities, 2013). A cross-sectional survey of bank customers was done using interviewer-administered questionnaires where bank customers were randomly selected by intercepting them as they leave the banking halls. The sample size was 650 bank customers and this was within the range of sample sizes adopted by previous studies (Abuzid & Abbas, 2017; Chahal et al., 2017; Chikazhe et al., 2021; Liang et al., 2009; Saleem et al., 2016; Zalloum et al., 2019). Data were collected for a period of six months, that is, from 15 June 2021 to 14 December 2021. All the research participants voluntarily participated in the study. Privacy and confidentiality were observed as data were used for academic purposes only. The researchers sought consent from the participants and all participants were informed that they were free to withdraw from the study at any time. Table 1 indicates the demographic characteristics of bank customers who participated in this study.

Table 1. Demography of bank customers

Characteristic		Frequency	Percent (%)
Gender
	Male	388	59.7
	Female	262	40.3
Age
	18 to 19 years	34	5.2
	20 to 29 years	96	14.8
	30 to 39 years	190	29.2
	40 to 49 years	256	39.4
	50 years and above	74	11.4
Education
	Primary	42	6.5
	Secondary	285	43.8
	Tertiary	323	49.7
Income
	Less than 50,000	251	38.6
	50,000 to 99,999	169	26.0
	100,000 to 149,999	136	20.9
	150,000 to 199,999	67	10.3
	At least 200,000	27	4.2

The basic characteristics of respondents, as depicted in Table 1 show that there were more male (59.7%) than female (40.5%) respondents. This suggests that the banking clientele in Zimbabwe is dominated by males (Shambira, 2020). This is plausible because the working-class population that can afford to open bank accounts consists of more males than females. The majority (94.8%) of the respondents were aged from 20 to 50 years and above. In Zimbabwe, this age group represents the economic active group that can afford to open and maintain bank accounts (Makanyeza & Chikazhe, 2017). The respondents had accomplished the following levels of education: primary level 6.5%, secondary 43.8% and tertiary 49.7%. This could make sense because Zimbabwe is generally, an educated country (Makudza, 2020). The majority (85.5%) of the respondents were earning less than ZWL$150,000 (equivalent to about US$500 at the time of data collection) per month. This suggests that the majority of the participants were earning low salaries.

5. Analysis and results

5.1. Data validation

Data were validated using data normality test, non-response bias test, common method bias (CMB) analysis, discriminant validity and convergent validity.

5.1.1. Data normality test

Normality test was performed to ascertain if data were normally spread (Gupta et al., 2019; Park, 2015). The normality test guides the type of inferential statistics to be performed (Ahad et al., 2011; Mbah & Paothong, 2015). Z-scores were the basis for checking for data normality. The scores were calculated in SPSS version 20. The Z-scores (n = 650) for all the variables and items were within the range −3.97 and +3.97 with a statistical significance of p ≤ 0.001. This suggests that data were approximately normally distributed (Arbuckle, 2009; Pallant, 2010; Razali & Wah, 2011).

5.1.2. Non-response bias

Non-response bias is a type of bias caused by differences between respondents and non-respondents (Berg, 2005; Lahaut et al., 2002). It is significant to conduct a non-response bias test because non-response by other respondents makes the sample size smaller and consequently negatively impact on the drawing of conclusions (Studer et al., 2013). The technique by Armstrong and Overton (1977) was used to assess for non-response bias. Using this method, implies that each of the items of the last half of the responses were compared against those of the first half of the responses. No substantial differences were noted in the means for the two waves of the responses. This shows that the study did not suffer from non-response bias.

5.1.3. CMB

CMB is an outcome of differences in responses that stem from the instrument itself instead of biases of the respondents that the instrument tries to reveal (Siemsen et al., 2010). Also, the CMB expounds the measurement error that is brought in by the friendliness of participants who aim to give positive answers (MacKenzie & Podsakoff, 2012). CMB impacts item validity and reliability and the covariation between hidden constructs (Min et al., 2016). Harman’s single-factor assessment was used in measuring CMB. Also, exploratory factor analysis was performed in SPSS version 20, fixing the number of factors at 1. Prior studies such as Kim et al. (2013) recommend that the existence of a single factor with a variance explained greater than 50% suggests that there is CMB. The solution gave a factor with a variance explained of 35.966%. This implies that CMB did not affect this research.

5.1.4. Convergent validity

Convergent validity was measured by means of measurement model fit indices, Cronbach’s alpha reliability, composite reliability, standardised factor loadings, individual item reliabilities (squared multiple correlations), critical ratios and average variance extracted. Table 2 indicate that least settings for the measurement model fit indices were fulfilled.

Table 2. Measurement model fit indices

Fit indices	Measurement model	Recommended values	Sources
χ2/DF	2.09	≤3.000	Hair et al. (2006), Khajeheian and Ebrahimi (2020), Nayanajith and Damunupola (2019), Sardana and Bajpai (2020)
GFI	.904	>0.900
AGFI	.939	>0.900
NFI	.957	>0.900
TLI	.944	>0.900
CFI	.972	>0.900
RMSEA	.056	<0.080

Results in Table 3 show that least settings for convergent validity were attained since all standardised factor loadings were above the recommended 0.5 cut-off point (Hair et al., 2006; Liang & Chia, 2014; Pallant, 2010; Wixom & Watson, 2001; Yong & Pearce, 2013). Critical ratios were appropriately large and significant at p < 0.001. Individual item reliabilities, composite reliabilities and Cronbach’s alpha values were all acceptable as they were above 0.5 (Hair et al., 2010). Critical ratios were large enough and significant at p < 0.001. All constructs had average variance extracted above 0.5 as indicated in Table 4 (Field et al., 2012; Park, 2015).

Table 3. Constructs, standardized factor loadings (λ), individual item reliabilities (squared multiple correlations), critical ratios (CRs), individual reliability and composite reliability

Constructs	λ	Individual item reliability	CR	Cronbach’s alpha	Composite reliability
Customer experience
CEX1	.746	.557	-	0.874	.911
CEX2	.851	.724	8.660***
CEX3	.845	.714	8.180***
CEX4	.762	.581	6.943***
CEX5	.785	.616	15.220***
CEX6	.771	.594	15.548***
Customer satisfaction
CSAT1	.780	.608	-	0.917	.917
CSAT2	.828	.686	15.013***
CSAT3	.838	.702	10.809***
CSAT4	.809	.654	17.198***
CSAT5	.776	.602	17.339***
CSAT6	.793	.629	15.631***
word-of-mouth intention
WOMI1	.727	.529	-	0.899	.890
WOMI2	.796	.634	12.409***
WOMI3	.818	.669	16.278***
WOMI4	.729	.531	16.982***
WOMI5	.713	.508	17.092***
WOMI6	.765	.585	15.942***
Customer loyalty
CLOY1	.834	.696	-	0.866	.934
CLOY2	.821	.674	13.171***
CLOY3	.753	.567	14.513***
CLOY4	.856	.733	16.184***
CLOY5	.894	.799	17.204***
CLOY6	.865	.748	17.079***

Note:—CR is fixed; *** p < 0.001

Table 4. Average variance extracted (AVEs) and squared inter-construct correlations (SICCs)

Construct	CEX	CSAT	WOMI	CLOY
Customer experience (CEX)	.794
Customer Satisfaction (CSAT)	.395	.804
word-of-mouth intention (WOMI)	.363	.366	.759
Customer loyalty (CLOY)	.401	.297	.429	.838

Note: Diagonal elements in bold represent AVEs

5.2. Discriminant validity

Results in Table 4 show that the condition for discriminant validity was achieved. All AVEs (diagonal elements) were above the squared inter-construct correlations, (Field, 2009; Park, 2015).

5.3. Testing research hypotheses

5.3.1. Structural equation modelling

H1, H2 and H3 were tested using structural equation modelling (SEM) in AMOS version 21. Model fit indices (χ2/ DF = 2.09; GFI = 0.904; AGFI = 0.939; NFI = 0.957; TLI = 0.944; CFI = 0.972; RMSEA = 0.56) were acceptable. Results for H1 show that customer experience has a direct positive effect on customer loyalty (SRW = 0.234, CR = 8.094, p ˂ 0.001). Therefore, H1 was supported. Results for H2 show that customer satisfaction has a direct positive effect on customer loyalty (standardised regression weight [SRW] = 0.337, CR = 12.712, p < 0.001). Therefore, H2 was supported. Results for H3 show that word-of-mouth intention has a direct positive effect on customer loyalty (SRW = 0.339, CR = 6.501, p < 0.001). Therefore, H3 was supported. The results for hypotheses testing for H1, H2 and H3 are shown in Table 5.

Table 5. Results of hypotheses testing (H1, H2 and H3)

Hypotheses	Hypothesised Relationship	SRW	CR	Remark
H1	Customer Experience → Customer Loyalty	0.234	8.094***	Supported
H2	Customer Satisfaction → Customer Loyalty	0.337	12.712***	Supported
H3	word-of-mouth Intention → Customer Loyalty	0.339	6.501***	Supported

Notes: SRW standardized regression weight, CR critical ratio, ** significant at p < 0.05, *** significant at p < 0.001

5.3.2. Moderated regression

In testing H4(a-d), H5(a-d) and H6(a-d) a moderated regression analysis was performed. Results are summarised in Table 6.

Table 6. Coefficients of moderated regression model

Variable	Beta	t-statistic	p value
Customer experience	.011	.279	.780
Customer satisfaction	.112	2.866	.004
word-of-mouth intention	.249	6.562	.000
Gender	.057	1.473	.141
Age	.001	.028	.978
Education	−.009	−.219	.827
Income	.053	1.353	.176
Customer experience*Gender	−.029	−.750	.454
Customer experience*Age	.065	1.635	.103
Customer experience*Education	.058	1.465	.143
Customer experience*Income	.060	1.511	.131
Customer satisfaction*Gender	−.004	−.092	.927
Customer satisfaction*Age	.083	2.137	.033***
Customer satisfaction*Education	−.006	−.158	.874
Customer satisfaction*Income	.034	.875	.382
word-of-mouth intention*Gender	−.074	−1.959	.051
word-of-mouth intention*Age	−.059	−1.552	.121
word-of-mouth intention*Education	−.071	−1.860	.063
word-of-mouth intention*Income	−.038	−.999	.318

Notes: R Square = 0.113, F = 23.564, significant at p < 0.05

Results show that coefficients for the interaction terms (Customer experience*Gender, Customer experience*Age, Customer experience*Education and Customer experience*Income) were insignificant (p > 0.05). This suggests that gender, age, education and income do not moderate the effect of customer experience on customer loyalty. Therefore, H4a, H4b, H4c and H4d were not supported. Furthermore, the interactions (Customer satisfaction*Gender, Customer satisfaction*Education and Customer satisfaction*Income) were insignificant (p > 0.05). Only the interaction between customer satisfaction and age was significant at p < 0.05. Hence H5b was supported. This suggests that age moderate the effect of customer satisfaction on customer loyalty. When age of respondents is high the relationship between customer satisfaction and loyalty becomes stronger and vice versa. As such, older consumers are more loyal than younger consumers are. However, gender, education and income do not moderate the effect of customer satisfaction on customer loyalty. Therefore, H5a, H5c and H5d were not supported. Also, the interaction (word-of-mouth intention*Gender, word-of-mouth intention*Age, word-of-mouth intention*Education and word-of-mouth intention*Income) were insignificant (p > 0.05). This suggests that gender, age, education and income do not moderate the effect of word-of-mouth intention on customer loyalty. Therefore, H6a, H6b, H6c and H6d were not supported.

6. Discussion

In the services marketing literature, in spite of the call for enhancement of customer experience to increase customer loyalty (Ban & Jun, 2019; Shourov et al., 2018; Tao & Kim, 2019; Tweneboah-Koduah & Farley, 2016). There is a necessity to include other variables to further reinforce this relationship. Customer satisfaction and word-of-mouth intention are crucial in services marketing particularly in the banking industry (Chen et al., 2015; Jham, 2018; Jung & Seock, 2017). There is inconclusive empirical literature about the moderating effects of consumer demographics on the relationship among customer experience, satisfaction and loyalty. Accordingly, the current research was done to reduce the current knowledge gap in services marketing literature.

The study shows that and customer experience, satisfaction and word-of-mouth intention are crucial aspects that impact on customer loyalty within the banking setting. As anticipated, the study found that customer experience has a positive effect on customer loyalty and these findings confirm previous studies (Borishade et al., 2019; Foroudi et al., 2016; Hwang & Seo, 2016; Mulari & Komulainen, 2019; Srivastava & Kaul, 2016). Also, the study concluded that customer satisfaction has a direct positive effect on loyalty. These findings are in line with the current findings by previous scholars (Borishade et al., 2019; Kasiri et al., 2017; Mbama & Ezepue, 2018). Furthermore, the study findings reveal that word-of-mouth intention has a positive effect on customer loyalty and these results confirm extant literature (Jung & Seock, 2017; Saleem et al., 2018). This imply that when formulating strategies for improving customer loyalty, banks must take into cognisance customer experience, customer satisfaction and word-of-mouth intention. These findings augment the understanding of the relationship among customer experience, customer satisfaction and word-of-mouth intention on customer loyalty.

The study also ascertains that age moderates the effect of customer satisfaction on loyalty. In a similar study, age has been found to influence positively the connection between customer satisfaction and loyalty (A. Bhatt & Bhatt, 2016). Chawla and Joshi (2017) confirm that age influences buying decision-making process within the banking sector. Existing literature confirms distinguished variances in consumer buying behaviour between adult and young consumers (Chikazhe et al., 2021). Younger consumers are more energetic and consider more brands when making purchase decisions than older consumers consider (Chikazhe et al., 2021). Older consumers usually choose well-established brands, avoiding newer brands because the ability to process purchasing information decreases as consumers grow older (Chikazhe et al., 2021). As such, older consumers are more loyal to particular brands than younger consumers are (Chikazhe et al., 2021). Similarly, the connection between customer satisfaction and loyalty is more robust in older than young consumers (Gonçalves et al., 2012). Nevertheless, age does not moderate the effect of customer experience and word-of-mouth intention on customer loyalty. Also, gender, education and income do not moderate the effect of customer experience and word-of-mouth intention on customer loyalty. Thus, demographic variables such as age play an important role on customer satisfaction–loyalty relationship. Nonetheless, age do not influence the customer experience–loyalty relationship as well as the word-of-mouth intention–loyalty relationship. Furthermore, demographic variables like gender, education and income do not play an important role in the relationship between customer satisfaction and loyalty as well as between word-of-mouth intention on customer loyalty within the banking setting.

7. Implications

For banks to achieve high levels of loyalty among bank customers, banks are advised to look into aspects like customer experience, customer satisfaction and word-of-mouth intention. Thus, banks are advised to contemplate on all these variables as a set predictors of customer loyalty instead of viewing them separately. Foremost, banks are encouraged to improve customer experience by handling customer problems well, prompt customer service, ensure their products are easy to use, meet service needs and requirements and provide error free services. Subsequently, banks ought to improve customer satisfaction by meeting and exceeding customer expectations and improve the relationship between the bank and its customers. Finally, banks are advised to improve on word-of-mouth intention by addressing all customer pains, glitches or difficulties that are inherent in interactions between the customers and the bank. Once this is addressed customers may recommend, speak positively, encourage others and forward the bank’s promotional messages to other people. Also, crucial is to take cognisance of the moderating role of age on the relationship between customer satisfaction and loyalty, thus banks must ensure they create loyalty programmes especially for the younger generations so that they become more loyal like the older generation.

8. Conclusion

The drive of this research was to study the moderators of the effect of customer experience, satisfaction and word-of-mouth intention on loyalty. This study reveals that customer experience, satisfaction and word-of-mouth intention has a positive effect on loyalty. Banks that offer memorable customer experience, satisfy their customers and stimulate word-of-mouth intention by removing customer pains strengthens the connection amongst customer experience, satisfaction and word-of-mouth intention on customer loyalty. Age moderates the effect of satisfaction on loyalty; nevertheless, age do not moderate the effect of customer experience and word-of-mouth intention on customer loyalty. Demographic factors like gender, education and income do not moderate the effect of customer experience, satisfaction and word-of-mouth intention on customer loyalty within the banking sector. The study has limitations that necessitate for further studies to be carried out. For example, the research was carried out in one sector and in one country. This makes it hard to generalise the findings. Thus, it is proposed that more studies be done in other sectors and in other markets to enhance the generalisability of the results.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The authors received no direct funding for this research.

Notes on contributors

Wilbert Manyanga

Wilbert Manyanga is a final-year PhD student at Chinhoyi University of Technology. He is currently a lecturer at the same university in the Department of Marketing. Prior to that he was a banker and a marketing practitioner with experience of working in the Banking industry, FMCG and Aviation industry. His research area of interest is customer experience.

Charles Makanyeza

Charles Makanyeza is a senior academic, researcher and consultant who commands respect among his peers. Among many educational qualifications, he holds a PhD in Marketing from the University of KwaZulu-Natal, South Africa. He is an Associate Professor of Marketing and Strategy at the Namibia Business School, University of Namibia. His research areas of interest include marketing and strategy.

Zororo Muranda

Zororo Muranda is a Professor of Marketing at Chinhoyi University of Technology in the Department of Marketing. His research area of interest is marketing.

Appendix A:

Measurement scale

Table

Rate the following items based Customer experience CEX1: My bank handles customer problems well CEX2: My bank offers prompt customer service CEX3: My bank’s products are ease to use CEX4: My bank always meets my service needs and requirements CEX5: My bank provides me error free services CEX6: My overall experience with my bank is pleasing Customer satisfaction CSAT1: I feel satisfied with the products offered by my bank CSAT2: The service offered by my bank exceed my expectations CSAT3: I am pleased with my bank’s offerings CSAT4: I feel satisfied after each bank transaction with my bank CSAT5: I feel satisfied with the relationship between me and my bank CSAT6: Overall, I am satisfied with the services offered by my bank

word-of-mouth intention WOMI1: I would recommend the bank to any of my friends, family and neighbours WOMI2: I would speak positively about my bank to other people WOMI3: I would recommend my bank to someone who seeks my advice WOM4: I will encourage others to consider banking with my bank WOMI5: I will have conversation with other customers about my bank WOMI6: I will forward my bank’s promotional messages to other people Customer loyalty CLOY1: I have a commitment to stay with my bank CLOY2: I consider my bank as my first choice among other banks in the area CLOY3: My bank comes first when I am deciding on bank services CLOY4: I have intention to continuously use services from my bank in future CLOY5: I will continue to hold an account with my bank CLOY6: I will continue with my bank although other banks launched many promotions