Mobile bank applications: loyalty of young bank customers Mustafa Nourallaha,*, Christer Strandberga, Peter Öhmana aDepartment of Economics, Geography, Law and Tourism, and Centre for Research on Economic Relations, Mid Sweden University, SE-851 70 Sundsvall, Sweden Abstract The purpose of this study is to investigate how young bank customers (YBCs) perceive the rela- tionships between several antecedents (i.e., usability, responsiveness, customer satisfaction, and reli- ability) and loyalty in the context of mobile bank applications (MBAs). An electronic questionnaire was sent to 500 YBCs in Sweden, 146 of whom completed it. Confirmatory factor analysis was used to test the measurement model, and structural equation modeling was used to test the hypotheses. The results indicate that usability is indirectly related to loyalty through responsiveness and customer satisfaction. The study contributes to the literature by developing a usability–loyalty model of YBCs using MBAs. © 2021 Academy of Financial Services. All rights reserved. JEL classification: M Keywords: Usability; Customer satisfaction; Loyalty; Mobile bank application; Young bank customers 1. Introduction Studies in the literature on loyalty in the financial services context have indicated that while bank customers in general are loyal (e.g., Strandberg, Wahlberg, & Öhman, 2015), young bank customers (YBCs) are often not (Nicoletti, 2017). YBCs are twice as likely to change banks as are older bank customers (Accenture, 2015). Although YBCs represent an important customer category for traditional banks (Foscht, Maloles, Schloffer, Chia, & Sinha, 2010), members of this group show a tendency to use financial services provided by FinTech companies. Gomber, Kauffman, Parker, and Weber (2018) state that YBCs seem to *Corresponding author. Tel: +46 10 142 79 75; fax: +46 10 142 85 10. E-mail address: mustafa.nourallah@miun.se (M. Nourallah) 1057-0810/21/$ – see front matter © 2021 Academy of Financial Services. All rights reserved. Financial Services Review 29 (2021) 147–167 prefer financial services provided by Google, Amazon, Apple, or Paypal, that is, FinTech companies, rather than by traditional banks. It has also been emphasized that “FinTech com- panies offer new products and solutions which fulfill customers’ needs that have previously not or not sufficiently been addressed by incumbent financial service providers [e.g., tradi- tional banks].” (Gomber, Koch, & Siering, 2017, p. 540). The ambition of FinTech companies to provide one-fifth of financial services by 2020 (Gimpel, Rau, & Röglinger, 2018) has led to competition between these companies and tra- ditional banks, and YBCs seem to be the target of both these groups. Traditional banks have developed mobile bank applications (MBAs), that is, an advanced type of mobile banking. MBAs allow customers connected to the Internet to conduct various financial tasks, such as checking account balances, transferring money, and paying bills (Malaquias & Hwang, 2019). Simultaneously, the FinTech companies have promoted mobile-only banks (MOBs), a recent innovation that offers financial services to customers connected to the Internet solely via mobile applications (Nourallah, Strandberg, & Öhman, 2019). One place where this rivalry between FinTech companies and banks is found is Sweden. In 2018, the first MOB, N26 launched mobile financial services, and in 2019, another MOB, Lunar Way announced that every month roughly 6,000 new customers subscribed to their services (Lundell, 2019). The literature identifies several research gaps concerning the various antecedents of loyalty applicable to MBAs based on the perceptions of YBCs. In their review of the loyalty literature, Kandampully, Zhang, and Bilgihan (2015) highlight the mobile loyalty of the young generation as a future research area. In another review of the mobile banking literature, Tam and Oliveira (2017, p. 1060) state that “knowing the determinants of the postadoption phase, and keeping customers loyal to m-banking are the emerging issues that should be considered in future research.” Larsson and Viitaoja (2017) emphasize the need to investigate how usability affects loyalty. Another research area was summarized by Chakraborty and Sengupta (2013), who dis- cuss the need to study the relationship between customer satisfaction and loyalty in the MBA context. Iberahim, Taufik, Adzmir, and Saharuddin (2016) investigate reliability and respon- siveness in the automated teller machine (ATM) context, and suggest considering these con- cepts in other contexts as well. Addressing these research gaps, the current study investigates how YBCs perceive the relationships between a number of antecedents (i.e., usability, respon- siveness, customer satisfaction, and reliability) and loyalty in the context of MBAs. The structure of the rest of the article is as follows: the next section presents the frame of reference; section three concerns methodological issues; section four presents the results; and section five concludes the article. 2. Frame of reference and hypothesis development 2.1. The context of the study 2.1.1. Mobile financial services Shaikh and Karjaluoto (2019) argue that mobile financial services can be divided into mo- bile banking, mobile payments, and mobile money. The first two types of mobile financial 148 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 services are found in more inclusive financial systems, for example, in Sweden, and are con- ducted with more inclusive customer segments, that is, customers who have access to bank- ing services. The third service represents a relationship between a mobile money solution, such as M-Pesa, and nonbank customers. This service is common in less inclusive financial systems, for example, in Sub-Saharan Africa (Demirguc-Kunt, Klapper, Singer, Ansar, & Hess, 2018), and in less inclusive customer segments, that is, customers who face difficulties (e.g., long distance) in accessing banking services. Shaikh and Karjaluoto (2019) examine the landscape of mobile financial services, giving insight into the types of relationships between more or less inclusive financial systems and more or less inclusive customer segments. However, they do not differentiate between the types of financial institutions that offer mobile financial services, that is, traditional banks and FinTech companies. Shaikh and Karjaluoto (2019) use mobile banking to refer to vari- ous types of mobile financial services, including mobile banking provided by traditional banks. Because mobile banking does not represent a homogeneous type, it can be divided into services provided by wireless application protocol (WAP), short message service (SMS), and MBAs. It is worth noting that WAP and SMS banking represent earlier versions of mobile bank- ing in which bank customers access their bank accounts via either a mobile Internet browser or SMS. These rudimentary types of mobile banking prompted remarkable customer aver- sion. For example, during the 2003–2006 period, 15 German banks stopped offering such services to customers due to lack of use (Scornavacca & Hoehle, 2007).1 In South Korea, only 4% of online customers adopted these earlier versions of mobile banking in that period (Lee, Park, Chung, and Blakeney, 2012). Moreover, “in 2003 . . . less than 1% of banking transactions in Taiwan were conducted through mobile handsets” (Luarn & Lin, 2005, p. 874). Similar situations existed in Finland (Suoranta & Mattila, 2004), China (Laforet & Li, 2005), and the United States (Mallat, Rossi, & Tuunainen, 2004). Earlier versions of mobile banking were not as widespread as expected (Koenig-Lewis, Palmer, & Moll, 2010; Mohammadi, 2015; Shaikh & Karjaluoto, 2015). Mobile banking system limitations, such as tiny screens and keypads and slower transaction speeds, caused this aversion (Laukkanen, 2007; Lee & Chung, 2009). However, since 2007—after the first iPhone was launched (Shaikh & Karjaluoto, 2019)—the situation changed dramatically and MBAs have become a basic means of conducting daily financial transactions such as check- ing balances, transferring money, and paying bills (Liébana-Cabanillas, Alonso-Dos-Santos, Soto-Fuentes, & Valderrama-Palma, 2017; Tan & Lau, 2016). This change likely emerged due to greater accessibility to the Internet (Lu, Tzeng, Cheng, & Hsu, 2015), advanced gen- erations of smartphones (Shaikh & Karjaluoto, 2015), and the development of application technology (Sun, Wang, & Wang, 2015). 2.1.2. Young bank customers Young customers are more enthusiastic about using their mobile phones than are mem- bers of other age groups (Yeh, Wang, & Yieh, 2016), and they have advanced skills in deal- ing with various technological financial platforms (Killins, 2017). They also spend M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 149 significant amounts of time using these platforms (Kaur & Medury, 2011). It is worth noting that reaching YBCs is a top priority for banks (Tan & Lau, 2016). Recent studies recommend investigating bank customers, such as YBCs, who possess lim- ited financial information (Aydin & Akben Selcuk, 2019). Moreover, YBCs will seek home mortgages and other financial services in the near future, so it is important for banks to secure loyal YBCs, given that FinTech companies will be the main providers of financial services (Gimpel et al., 2018) and that these companies can satisfy customers in other and possibly better ways than can traditional banks (Gomber et al., 2017). YBCs can contribute to increased bank profits in terms of immediate profits, future profitability, market share, and diverse profitable relationships (Foscht et al., 2010). The literature reveals that different terms have been used interchangeably to refer to YBCs: the millennial generation (e.g., Tan & Lau, 2016), the young generation (e.g., Koenig-Lewis et al., 2010), and generation Y (Killins, 2017). Also, previous studies have used different age groups when investigating YBCs. Calisir and Gumussoy (2008) use the 18–26-year age range, Sum Chau and Ngai (2010) 16–29 years, and Akturan and Tezcan (2012) 16–25 years. In this study, YBCs are bank customers aged 18–29 years, that is, the interval from first being considered “adult” in Sweden to the highest year considered in the three studies mentioned above. 2.2. Conceptual framework 2.2.1. Central concepts Electronic financial services refer to accessing a bank account via computers and/or mo- bile financial services (Shaikh & Karjaluoto, 2019). In this context, studies have addressed responsiveness and reliability (Broderick & Vachirapornpuk, 2002), customer satisfaction (Sampaio, Ladeira, & Santini, 2017), and loyalty (Larsson & Viitaoja, 2017). Overall, stud- ies report that customer satisfaction and loyalty are the most important factors delivering a good experience (Berraies, Yahia, & Hannachi, 2017), while reliability is identified as a nec- essary risk-related factor in technology-based financial services (Hanafizadeh, Behboudi, Koshksaray, & Tabar, 2014). In a similar vein, Sindwani and Goel (2015) argue that respon- siveness is an important concept in the electronic financial services context. A number of previous studies have addressed usability-related issues (e.g., Mohammadi, 2015). The International Organization for Standardization (IOS, 1998) defines usability as “the extent to which a product can be used by specified users to achieve specified goals.” Kang, Lee, and Lee (2012) state that MBA usability likely concerns mobile interface and navigation issues. Casaló, Flavian, and Guinalı́u (2007, 2008) and Flavian, Guinalı́u, and Gurrea (2006) find that in the banking industry, the essence of usability is represented by ease of understanding, observed content, simplicity, speed, ease of site navigation, and user control. From an electronic financial services perspective, customer satisfaction is created by meeting customer expectations regarding financial issues (Amin, 2016), while loyalty is seen as a dichotomy between attitude and behavior. Attitudinal loyalty includes “a degree of 150 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 dispositional commitment, in terms of some unique value associated with the brand” (Lin & Wang, 2006, p. 272), and behavioral loyalty refers to a customer’s repurchase behavior, due to their liking for particular financial services (Amin, 2016). It is worth mentioning that most previous studies of service quality have used the SERVQUAL instrument (Parasuraman, Zeithaml, & Berry, 1988), which consists of five dimensions: tangibles, reliability, responsiveness, assurance, and empathy. The current study excludes three of these dimensions: tangibles, assurance, and empathy. Parasuraman et al. (1998, p. 23) state that tangibles are “physical facilities, equipment, and appearance of per- sonnel,” assurance is the “knowledge and courtesy of employees and their ability to inspire trust and confidence,” and empathy is “caring, individualized attention the firm provides its customers.” It can be argued that these dimensions are related to the customer–employee relationship dimension, which is not part of the MBA context. Hence, the current study only uses the responsiveness and reliability dimensions of service quality, since MBAs have evolved in an environment in which the nature of mobile–human interaction differs from personal interaction (Oliveira, Thomas, Baptista, & Campos, 2016), and because YBCs do not prefer personal connections when accessing banking services (Carlander, Gamble, Gärling, Hauff, Johansson, & Holmen, 2018). In this regard, responsiveness is the willing- ness to help consumers and provide prompt service (Parasuraman et al., 1988), and in terms of MBAs, it has two components: service speed and technology (Iberahim et al., 2016). Reliability is defined as “the ability to perform the promised service dependably and accu- rately” (Parasuraman et al., 1988, p. 23). The current study adopts these definitions. 2.2.2. The research model and hypotheses The research model is presented in Fig. 1 As can be seen, the literature suggests that usability is related to responsiveness, customer satisfaction, and reliability, as indicated by H1, H2, and H3. Subsequently, responsiveness and reliability are related to customer satis- faction, as indicated by H4 and H5. Finally, these three concepts are related to loyalty, as indicated by H6, H7, and H8. The eight hypotheses are developed below. In the banking sector, usability will likely enhance speed, ease site navigation, and increase user control (Casaló et al., 2007, 2008; Flavian et al., 2006). Usability can offer var- ious benefits to customers (Calisir & Gumussoy, 2008), such as the ability to get banking help in various critical situations (Gumussoy, 2016), to access a user-friendly system (Hussien & Aziz, 2013), and to use a variety of communication channels (Laukkanen, 2007). Offering a high level of usability will likely lead to good responsiveness (Raza, Jawaid, & Hassan, 2015). Accordingly, this study proposes the following hypothesis in the MBA context: Hypothesis 1 (H1): The higher the usability, the higher the responsiveness is likely to be. Generally, usability can lead to a pleasant user experience (Nielsen, 1994), affect cus- tomer expectations (Bhattacherjee, 2001), and ensure customer satisfaction. Theoretical arguments and empirical results have emphasized the importance of usability for customer decisions to use certain technological applications (e.g., Hoehle & Venkatesh, 2015). In the online banking context, empirical results indicate that usability can significantly affect M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 151 customer satisfaction (Casaló et al., 2008; Flavian et al., 2006; Hussien & Aziz, 2013). Similarly, Thakur (2014), when studying MBAs in India, finds that usability affects cus- tomer satisfaction. The following hypothesis is accordingly formulated: Hypothesis 2 (H2): The higher the usability, the higher the customer satisfaction is likely to be. Usability is also considered a key factor in e-business success (Lee & Kozar, 2012), and high usability ensures fewer difficulties in using a certain system (Davis, 1989), promotes ease of use of that system (Nielsen, 1994), and reduces possible errors (Sanchez-Franco & Rondan-Cataluña, 2010). In contrast, low usability generates payment-related issues (Flavian et al., 2006). Since high MBA usability ensures trustworthy financial services in terms of transfer- ring money, obtaining account information, and paying bills (Mohammadi, 2015), it can be argued that usability drives reliability (cf. Benlian & Hess, 2011). Hence, it is hypothesized that: Hypothesis 3 (H3): The higher the usability, the higher the reliability is likely to be. Responsiveness is the ability to provide help and instant services to customers, that is, pro- vide fast replies regarding their bank accounts (Raza et al., 2015), in turn increasing the cus- tomer satisfaction (Iberahim et al., 2016). Previous studies in the banking sector have presented contrasting observations about this relationship. While Raza et al. (2015) and Saleem, Zahra, Ahmad, and Ismail (2016) state that there is a significant relationship between responsiveness and customer satisfaction, other studies (Kassim & Asiah Abdullah, 2010; Munusamy, Chelliah, & Mun, 2010) report contrary results. Based on theoretical assump- tions and more recent empirical studies, the current study suggests the following hypothesis: Hypothesis 4 (H4): The higher the responsiveness, the higher the customer satisfaction is likely to be. Bauer, Falk, and Hammerschmidt (2006) conclude that reliability is the most critical fac- tor driving customer satisfaction. In investigating reliability in mobile payment services, Arvidsson (2014) finds that consumers highly rate the importance of reliability. Similarly, Calisir and Gumussoy (2008) emphasize the role of reliability in banking, and Raza et al. (2015) demonstrate that reliability has a considerable effect on customer satisfaction. Munusamy et al. (2010) investigate this relationship in the banking sector in Malaysia and Fig. 1. Research model. 152 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 report no significant relationship, and Wen and Hilmi (2011) find the same lack of relation- ship in another Malaysian study. Overall, the results reported by most of the above studies lead to the following hypothesis: Hypothesis 5 (H5): The higher the reliability, the higher the customer satisfaction is likely to be. Quick responses to customer questions are seen as a factor leading to customer loyalty (Srinivasan, Anderson, & Ponnavolu, 2002). Loyalty can be ensured by offering a variety of communication channels (Verhoef & Donkers, 2005) and by providing embedded ways to ask for help (Awwad & Awad Neimat, 2010). Previous studies report that responsiveness could well affect loyalty (Marimon, Yaya, & Casadesus Fa, 2012; Moorthy, Chee, Yi, Ying, Woen, & Wei, 2017). In a study of mobile commerce, Lin (2012) finds a significant relation- ship between responsiveness and loyalty. This leads to the following hypothesis: Hypothesis 6 (H6): The higher the responsiveness, the higher the loyalty is likely to be. Customer satisfaction is an important issue for any company (Santouridis & Trivellas, 2010), and banks are no exception. It explains post-purchase perceived performance (Fornell, 1992) and ensures customer retention and profitability (Strandberg, Wahlberg, & Öhman, 2012). Previous studies report a strong relationship between customer satisfaction and loyalty (Lin & Wang, 2006; Liébana-Cabanillas et al., 2017; Thakur, 2014). Fornell (1992, p. 7) describes this relationship as follows: “Loyal customers are not necessarily satisfied cus- tomers, but satisfied customers tend to be loyal customers.” The current study emphasizes this relationship, and formulates the following hypothesis: Hypothesis 7 (H7): The higher the customer satisfaction, the higher the loyalty is likely to be. Reliability enhances the ability of MBAs to perform the promised customer services dependably and accurately (Jun & Palacios, 2016). Previous studies have investigated reli- ability as a dimension of service quality, and empirical results support the relationship between reliability and loyalty (e.g., Karatepe, 2011). Other studies of reliability have reached similar conclusions. Ho and Lee (2007) suggest that reliability is a crucial factor for retaining customers, and Moorthy et al. (2017) conclude that reliability is significantly and positively related to loyalty. However, in mobile retailing, Lin (2012) finds no relationship between reliability and loyalty. In a similar vein, Zhou, Lu, and Wang (2010) suggest that reliability might not be as important for YBCs as for older bank customers. Nevertheless, the following hypothesis is based on most previous research: Hypothesis 8 (H8): The higher the reliability, the higher the loyalty is likely to be. 3. Method 3.1. Measure development The items in the preliminary questionnaire were adopted from previous studies to ensure content validity (see the Appendix). Usability was measured by items (Usa 1–5) from M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 153 Casaló et al. (2008). Responsiveness items (Res 1–3) and reliability items (Rel 1–3) were adopted from Lin (2013). Customer satisfaction items (Sat 1–2) were adopted from Aydin and Özer (2005) and Yoon (2010), and loyalty items (Loy 1–2) from Chaudhuri and Holbrook (2001) and Wirtz, Mattila, and Lwin (2007). Two focus group interviews were conducted with four and five YBCs, respectively. All participants belonged to the target age group, that is, 18–29 years, and had at least one year’s experience of MBA use in Sweden. The focus group interviews contributed to the detailed improvement of some items in the preliminary questionnaire. Back translation was conducted to ensure that the items had good consistency (cf. Brislin, 1970), and certain language-related revisions were made as a result. The last step was to send the preliminary questionnaire to two experienced YBCs to check for readability, and minor revisions were made based on their feedback. The final questionnaire was based on a seven-point Likert scale ranging from 1= strongly disagree to 7 = strongly agree. The background variables included were age, gender, and MBA experience. 3.2. Sample, data collection, and data analysis procedures The final questionnaire was sent in electronic form to 500 students at a university in the Mid- Sweden region in late 2018. These students studied business administration, political science, or sociology, were aged 18–29years, and differed in the MBA usage duration and the number of MBAs used. In addition, the students were diverse in terms of socioeconomic class, gender, and cultural background. The main criterion for selecting these students was use of MBAs for at least one year, which requires a Swedish bank account. Sampling university students enabled the current study to avoid limitations related to a sample associated with a single bank (e.g., Strandberg et al., 2012), because the present respondents were customers of several banks. Harm to participants, confidentiality of information provided, confidentiality of collected data, and data-storage issues were among the ethical concerns of the current study, and cer- tain processes were used to address these concerns and the general limitations associated with questionnaires (cf. Grinyer, 2009). Approval to send out the questionnaire was obtained from responsible persons at the university program and course levels. Brief information about the study was presented to the students, including advising that completing the ques- tionnaire was voluntary and that financial information would not be gathered for the study. The anonymity of responses was ensured by using online software complying with the EU’s General Data Protection Regulation. Initially, 129 completed questionnaires were received; after two reminders, the total num- ber of completed questionnaires increased to 146, that is, a response rate of 29.2%. Following the suggestion of Pohlmann (2004), an analysis was conducted comparing the results of those responding before and after the first reminder; no notable differences were found between these two groups. Descriptive statistics, sample adequacy, and common method bias tests were calculated. In a further step, confirmatory factor analysis (CFA) was utilized to test how well the observed variables represent the latent variables (cf. Hair, Black, Babin, & Anderson, 2014). The current study used CFA to delete unnecessary items and refine the measurement model; it was also used to address reliability and validity issues. Subsequently, structural equation 154 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 modeling (SEM) was used to test the research model and the hypotheses. Both CFA and SEM were performed using LISREL 9.30. 4. Results 4.1. Descriptive statistics The characteristics of the sample are presented in the Appendix. Most participants were 18–23 years of age, and the sample was fairly equally distributed in terms of gender. Only a small percentage of participants used more than three MBAs. Regarding usage experience, a large majority had two or more years of MBA experience, and almost half the participants perceived themselves as highly experienced. 4.2. Sample adequacy and common method bias Exploratory factor analysis was used to assess sample adequacy and common method bias. Kaiser-Meyer-Olkin (KMO; cf. Sharma, 1996) and Harmon’s single-factor tests (cf. Podsakoff, MacKenzie, Lee, & Podsakoff, 2003) were conducted. The KMO value was 0.809 (KMO >0.8), indicating that the sample adequacy is good. Harmon’s single-factor test showed that there was no maximum variance explained by a single factor 4.3. Measurement model The initial results of the measurement model, that is, the model that contains all the items included in the research model, did not meet the suggested thresholds. To refine the model, the suggestions of the modification indices in LISREL 9.30 were applied (cf. Jöreskog & Sörbom, 1993). This resulted in three factors (i.e., Usa 4, Usa 5, and Rel 3) being eliminated. The results of the final measurement model with standardized factor loadings and t-values are presented in Fig. 2 Observed variables are represented by rec- tangles; the standardized factor loading values are indicated before the slashes and the t- values after the slashes. The final measurement model shows that x2 = 55.07 (with 44 degrees of freedom). The x2/df ratio equals 1.25 (x2/ df > 2), which is considered a good fit (cf. Jöreskog, Olsson, & Wallentin, 2016). The root mean square error of approximation (RMSEA) is 0.0428 (RMSEA > 0.8), which indicates good fit (cf. Bagozzi & Yi, 1988). The results of the goodness of fit index, normed fit index, non-normed fit index, and comparative fit index were all >0.9, which is the recommended threshold (cf. Jöreskog et al., 2016). Table 1 shows that the overall fit indices of the measurement model meet the recom- mended values. CFA was used to measure the reliability, convergent validity, and discriminant validity of the measurement model. The current study uses two tests to assess reliability: (1) squared multiple correlations (SMC), that is, the degree to which the observed variable’s variance is M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 155 explained by a latent variable, and (2) composite reliability (CR), to assess the internal con- sistency (Hair et al., 2014). CR was calculated from the squared sum of factor loadings (Li) for each latent variable and for the sum of the error variance terms for the latent variables, as shown in Eq. (1). Table 2 shows that the SMCs of all observed variables are higher than 0.5, except for Usa 1 and Res 1, which are below the cutoff value. Table 2 indicates that the CR values are above 0.6 for all latent variables. Fig. 2. The results of the measurement model with standardized factor loadings and t-values. x2 = 55.07, p-value = 0.12243, RMSEA = 0.042; RMSEA = root mean square error of approximation. 156 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 CR ¼ o n i¼1 Li � �2 o n i¼1 Li � �2 þ o n i¼1 ei � � (1) To assess convergent validity, this study used the average variance extracted (AVE), standardized factor loadings, and t-values. AVE was computed from the mean variance of the item loadings on a latent variable, as shown in Eq. (2): AVE ¼ o n i¼1 Li 2 n (2) In Table 2, the computations indicate that AVE is above 0.5 and that all standardized fac- tor loadings exceed 0.6 (cf. Hair et al., 2014). All the t-values are significant. To assess discriminant validity, a confidence interval of 62 standard errors around the standardized correlations between latent variables was calculated based on LISREL output (cf. Hansen, Samuelsen, & Sallis, 2013). The calculations indicated that the confidence inter- val was within the acceptable range, that is, not more than 1 or less than –1. The measurement purification was confirmed by the good results of assessing the good- ness of fit (cf. Jöreskog et al., 2016) and by the reliability and validity of the variables (cf. Fornell & Larcker, 1981). Overall, it can be assumed that the reliability (cf. Bagozzi & Yi, 1988; Hair et al., 2014), convergent validity, and discriminant validity are good (cf. Fornell & Larcker, 1981). 4.4. Testing the research model SEM was performed using LISREL 9.30 (using maximum likelihood and covariance mat- rices) to test whether the empirical data support the research model. All fit indices corre- spond to the recommended values (cf. Jöreskog et al., 2016). The calculations indicate that there are five significant relationships (p< .01), while three hypotheses are not supported. Table 1 The fit indices of the measurement model Fit indices Result x2/df 1.25 Root mean square error of approximation (RMSEA) 0.042 Goodness of fit index (GFI) 0.942 Normed fit index (NFI) 0.943 Non-normed fit index (NNFI) 0.978 Comparative fit index (CFI) 0.986 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 157 Table 3 presents the standardized loadings, t-values, hypothesis outcomes, and fit indices of the model. The structural model shows that usability is directly related to responsiveness (in line with H1) and customer satisfaction (in line with H2), and that responsiveness and customer satisfaction are directly related to loyalty (in line with H6 and H7, respectively). In this sense, an indirect relationship appears between usability and loyalty (see Fig. 3). It should be mentioned that there is a direct relationship between usability and reli- ability (in line with H3), but not between reliability and loyalty (in contrast to H8). In contrast to H4, there is no relationship between responsiveness and customer satisfac- tion, and in contrast to H5, there is no relationship between reliability and customer satisfaction. Table 2 Standardized factor loading, t-value, SMC, AVE, and CR for the measurement model Latent variables Observed variables Standardized factor loading t- value SMC AVE CR Usability Usa 1 0.69 9.09 0.48 0.64 0.84 Usa 2 0.88 12.84 0.78 Usa 3 0.82 11.54 0.68 Responsiveness Res 1 0.66 8.20 0.44 0.74 0.85 Res 2 0.74 9.43 0.55 Res 3 0.78 10.15 0.62 Customer satisfaction Sat 1 0.84 11.14 0.72 0.69 0.82 Sat 2 0.82 10.68 0.63 Reliability Rel 1 0.92 11.47 0.84 0.53 0.77 Rel 2 0.80 9.89 0.64 Loyalty Loy 1 0.72 8.22 0.51 0.52 0.68 Loy 2 0.72 8.26 0.53 Note: SMC = squared multiple correlations; AVE = average variance extended; CR = composite reliability. Table 3 Structural model results Hypothesis Standardized loading t-Value Outcome H1 Usability ! Responsiveness 0.70 6.26* Supported H2 Usability ! Customer satisfaction 0.49 3.22* Supported H3 Usability ! Reliability 0.58 6.49* Supported H4 Responsiveness ! Customer satisfaction 0.13 0.98 Not supported H5 Reliability ! Customer satisfaction 0.16 1.57 Not supported H6 Responsiveness ! Loyalty 0.44 3.27* Supported H7 Customer satisfaction ! Loyalty 0.37 2.77* Supported H8 Reliability ! Loyalty 0.37 0.28 Not supported x2/df = 1.21, RMSEA= 0.038, p= .15, GFI = 0.942, NFI = 0.933, NNFI = 0.982, CFI = 0.987 Note: RMSEA = root mean square error of approximation; GFI = goodness of fit index; NFI = normed fit index; NNFI = non-normed fit index; CFI = comparative fit index. *p-value < 0.01. 158 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 5. Discussion 5.1. Conclusion This study investigates how YBCs perceive the relationships between the usability, responsiveness, customer satisfaction, and reliability antecedents and loyalty in the context of MBAs. Based on the empirical results, it can be argued that usability has a direct relation- ship with responsiveness, customer satisfaction, and reliability and an indirect relationship with loyalty via responsiveness and customer satisfaction. The finding that usability has an indirect relationship with loyalty through customer satisfaction is in line with the findings of Casaló et al. (2008) and Flavian et al. (2006). Responsiveness is significantly related to loyalty, but not to customer satisfaction. The lat- ter finding was not as hypothesized, but is in line with the findings of Kassim and Asiah Abdullah (2010) and Munusamy et al. (2010). These results draw attention to the argument of Fornell (1992, p. 7) that “loyal customers are not necessarily satisfied customers.” As a consequence, the lack of relationship between responsiveness and customer satisfaction might cause MBAs to lose YBCs in the long term. Several previous studies (Arvidsson, 2014; Bauer et al., 2006; Calisir & Gumussoy, 2008; Raza et al., 2015) find a significant relationship between reliability and customer satisfac- tion. However, as in the studies of Munusamy et al. (2010) and Wen and Hilmi (2011), our empirical results do not significantly support this relationship, calling into question whether reliable MBAs can increase the satisfaction of YBCs. This lack of relationship could be at- tributable to YBCs’ perceptions of reliability in the MBA context, and to YBCs’ search for more than just a reliable MBA, for example, a usable one. Previous studies have stressed that reliability might not be as important for YBCs as for older bank customers (Zhou at al., 2010). This also corresponds to our finding that no significant relationship exists between reliability and loyalty, which is in line with the conclusion of Lin (2012), who find the same lack of relationship in mobile retailing. It is also possible that the two dimensions of SERVQUAL used here, that is, responsiveness and reliability, have different roles regarding YBC experience of MBAs. It was no surprise to find a significant relationship between customer satisfaction and loy- alty in the MBA context. The more satisfied YBCs are, the more loyal they could be. It is claimed that YBCs, who will likely be significant for the future of financial services, prefer FinTech companies (Gomber et al., 2018) and tend to change banks more than any other age group (Accenture, 2015). The exclusive offering of financial services in traditional banks will likely weaken due to the attempts of FinTech companies to offer and promote improved Fig. 3. Usability–loyalty model of young bank customers (YBCs) on mobile bank applications (MBAs). M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 159 services (Nicoletti, 2017). Therefore, cultivating the loyalty of YBCs is considered a top pri- ority for traditional banks. 5.2. Theoretical and practical implications The present findings have theoretical as well as practical implications. Compared with previous studies in the banking sector that highlighted service quality as a key to customer satisfaction and loyalty (e.g., Kassim & Asiah Abdullah, 2010; Santouridis & Trivellas, 2010), we argue that in today’s digital world, usability can also ensure satisfied and loyal customers. In other words, it can be concluded that the higher the usability a certain MBA offers, the more satisfied YBCs will be. This is of interest because our empirical findings regarding YBCs’ perceptions in the MBA context demonstrate that both responsiveness and customer satisfaction are directly related to loyalty. Usability is a relatively unstudied phenomenon in the financial services context, but the rise of FinTech has drawn attention to investigations of usability-related issues. Previous studies have acknowledged both ease of use and usefulness (Mohammadi, 2015), and finan- cial services studies such as those of Casaló et al. (2007, 2008) and Flavian et al. (2006) sug- gest that ease of use is likely the proper way to articulate usability. The current study is in line with this suggestion, because ease of use was found to represent a single usability construct. That banks have made the largest information technology (IT) investments across all industries (Puschmann, 2017) means that IT-related costs represent a significant percentage of bank expenditures. The results of the current study might be used to prioritize such IT investments, especially those related to usability, because three usability attributes were found to be particularly important: it should be (1) easy to use the MBA the first time, (2) easy to find information, and (3) easy to navigate the MBA. Mobile application technology represents a promising opportunity for traditional banks and FinTech companies, because a mobile application can be an independent financial serv- ice provider, for example, an MOB. This is unique compared with the earlier versions of mo- bile banking. Traditional banks need to take this development into consideration because YBCs can easily move to other types of financial service providers. 5.3. Limitations and future research Some limitations of this study can be seen as potential areas for future research. The study was conducted in a specific country and the number of responses was limited. It is therefore suggested that cross-cultural studies be conducted in the future, as cultural differences repre- sent a crucial factor in the banking sector, and that more respondents be included. Additional studies are also important because of the general limitations of questionnaire research (cf. Sharma & Sidhu, 2001), including social desirability bias when data are self- reported and the risk of measuring respondents’ recalled rather than “lived” perceptions. Another suggestion is accordingly to conduct “big data” studies focusing on text conversa- tions in social media. 160 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 Raza et al. (2015) find that reliability is related to customer satisfaction, which was not supported by the current study. A suggested area for future studies would accordingly be to explore additional aspects of reliability in the MBA context. It is also recommended that fur- ther studies should cover related issues such as privacy and security. Notes 1 Including four large private banks: Deutsche Bank, Dresdner Bank, Hypoverein Bank, and Commerzbank. 2 Since some respondents use more than one MBA, the original questionnaire asked those to answer Part II based on their experience on the main MBA they use. M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 161 The Appendix: The final questionnaire Part I. Background including demographic variables Variables Frequency (number) Frequency (%) Cumulative (%) Age 18–23 years 105 71.9% 71.9% 24–29 years 41 28.1% 100% Total 146 100% Gender Male 63 43.1% 43.1% Female 81 55.5% 98.6% Prefer not to say 2 1.4% 100% Total 146 100% How many MBAs do you use? 1 55 37.7% 37.7% 2 42 28.7% 66.4% 3 40 27.4% 93.8% 4 or more 9 6.2% 100% Total 146 100% How long have you usedMBAs? 1 year–under 2 years 14 9.6% 9.6% 2 years–under 3 years 33 22. 6% 32.2% 3 years–under 4 years 35 23.9% 56.1% 4 years or more 64 43.9% 100% Total 146 100% Part II. Usability, responsiveness, customer satisfaction, reliability, and loyalty2 Usability Usa 1 It was easy to use the MBA when I used it for the first time. Casaló et al. (2008) Usa 2 It is easy to find the information I need from the MBA. Usa 3 It is easy to navigate in the MBA. Usa 4 It is easy to carry out transactions in the MBA. Usa 5 Transactions can be carried out quickly in the MBA. Responsiveness Res 1 The MBA responds quickly to my questions. Lin (2013) and Malaquias and Hwang (2019) Res 2 The different communication channels in the MBA help me to solve my problems. Res 3 The MBA provides opportunities to ask for help. Customer satisfaction Sat 1 The MBA always meets my expectations. Aydin and Özer (2005)Sat 2 I am very pleased with the MBA. Reliability Rel 1 It is reliable to transfer money in the MBA. Lin (2013) Rel 2 I can trust that the account information in the MBA is correct. Rel 3 It is reliable to pay bills in the MBA. Loyalty Loy 1 I am committed to the MBA. Chaudhuri and Holbrook (2001) Loy 2 I carry out all my banking transactions via the MBA. Wirtz et al. (2007) Note: MBA = mobile bank applications. 162 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 Acknowledgment A preliminary version of this study was presented at the 10th Nordic Workshop on Relationship Dynamics – NoRD2018, Karlstad, Sweden, September 19–21, 2018, under the title: “Mobile bank applications: An empirical study of young bank customers.” The authors would like to thank the conference participants for their valuable comments. Also, the authors would like to thank the two anonymous reviewers for their helpful comments. References Accenture. (2015). Banking Shaped by the Customer. (available at https://www.accenture.com/us-en/;/media/ accenture/conversion-assets/microsites/documents17/accenture-2015-north-america-consumer-banking-survey.pdf). Akturan, U., & Tezcan, N. (2012). Mobile banking adoption of the youth market: Perceptions and intentions. Marketing Intelligence & Planning, 30, 444-459. Amin, M. (2016). Internet banking service quality and its implication on e-customer satisfaction and e-customer loyalty. International Journal of Bank Marketing, 34, 280-306. Arvidsson, N. (2014). Consumer attitudes on mobile payment services–results from a proof of concept test. International Journal of Bank Marketing, 32, 150-170. Aydin, A. E., & Akben Selcuk, E. (2019). An investigation of financial literacy, money ethics and time preferen- ces among college students: A structural equation model. International Journal of Bank Marketing, 37, 880- 900. Aydin, S., & Özer, G. (2005). National customer satisfaction indices: An implementation in the Turkish mobile telephone market.Marketing Intelligence & Planning, 23, 486-504. Awwad, M., & Awad Neimat, B. (2010). Factors affecting switching behavior of mobile service users: The case of Jordan. Journal of Economic and Administrative Sciences, 26, 27-51. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16, 74-94. Bauer, H. H., Falk, T., & Hammerschmidt, M. (2006). eTransQual: A transaction process-based approach for capturing service quality in online shopping. Journal of Business Research, 59, 866-875. Benlian, A., & Hess, T. (2011). The signaling role of it features in influencing trust and participation in online communities. International Journal of Electronic Commerce, 15, 7-56. Berraies, S., Ben Yahia, K., & Hannachi, M. (2017). Identifying the effects of perceived values of mobile bank- ing applications on customers. International Journal of Bank Marketing, 35, 1018-1038. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation-confirmation model. MIS Quarterly, 25, 351-370. Brislin, R. W. (1970). Back-translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1, 185-216. Broderick, A. J., & Vachirapornpuk, S. (2002). Service quality in Internet banking: The importance of customer role.Marketing Intelligence & Planning, 20, 327-335. Calisir, F., & Gumussoy, C. A. (2008). Internet banking versus other banking channels: Young consumers’ view. International Journal of Information Management, 28, 215-221. Carlander, A., Gamble, A., Garling, T., Hauff, J. C., Johansson, L., & Holmen, M. (2018). The role of perceived quality of personal service in influencing trust and satisfaction with banks. Financial Services Review, 27, 83-98. Casaló, L. V., Flavián, C., & Guinalı́u, M. (2007). The role of security, privacy, usability and reputation in the de- velopment of online banking. Online Information Review, 31, 583-603. Casaló, L. V., Flavián, C., & Guinalı́u, M. (2008). The role of perceived usability, reputation, satisfaction and consumer familiarity on the website loyalty formation process. Computers in Human Behavior, 24, 325-345. Chakraborty, S., & Sengupta, K. (2013). An exploratory study on determinants of customer satisfaction of lead- ing mobile network providers – case of Kolkata. India. Journal of Advances in Management Research, 10, 279-298. M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 163 Chaudhuri, A., & Holbrook, M. B. (2001). The chain of effects from brand trust and brand affect to brand per- formance: The role of brand loyalty. Journal of Marketing, 65, 81-93. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13, 319-340. Demirguc-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2018). The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution. The World Bank. (available at https://elibrary. worldbank.org/doi/abs/10.1596/978-1-4648-1259-0). Flavian, C., Guinalı́u, M., & Gurrea, R. (2006). The role played by perceived usability, satisfaction and consumer trust on website loyalty. Information and Management, 43, 1-14. Fornell, C. (1992). A national customer satisfaction barometer: The Swedish experience. Journal of Marketing, 56, 6-21. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and mea- surement error. Journal of Marketing Research, 18, 39-50. Foscht, T., Maloles, C., Schloffer, J., Chia, S. L., & “Jay” Sinha, I. (2010). Banking on the youth: The case for finer segmentation of the youth market. Young Consumers, 11, 264-276. Gimpel, H., Rau, D., & Röglinger, M. (2018). Understanding FinTech start-ups–a taxonomy of consumer-ori- ented service offerings. Electronic Markets, 28, 245-264. Gomber, P., Koch, J.-A., Siering, M., & Siering, M. (2017). Digital finance and FinTech: current research and future research directions. Journal of Business Economics, 87, 537-580. Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of Management Information Systems, 35, 220-265. Grinyer, A. (2009). The anonymity of research participants: Assumptions, ethics, and practicalities. Pan-Pacific Management Review, 12, 49-58. Gumussoy, C. A. (2016). Computers in human behavior usability guideline for banking software design. Computers in Human Behavior, 62, 277-285. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2014).Multivariate Data Analysis.Malaysia: Person. Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., Jalilvand, M., & Tabar, S. (2014). Mobile-banking adoption by Iranian bank clients. Telematics and Informatics, 31, 62-78. Hansen, H., M. Samuelsen, B., & Sallis, J. E. (2013). The moderating effects of need for cognition on drivers of customer loyalty. European Journal of Marketing, 47, 1157-1176. Ho, C.-I., & Lee, Y.-L. (2007). The development of an e-travel service quality scale. Tourism Management, 28, 1434-1449. Hoehle, H., & Venkatesh, V. (2015). Mobile application usability: Conceptualization and instrument develop- ment.MIS Quarterly, 39, 435-471. Hussien, M. I., & Aziz, R. A. E. (2013). Investigating e-banking service quality in one of Egypt’s banks: A stake- holder analysis. The TQM Journal, 25, 557-576. Iberahim, H., Mohd Taufik, N. K., Mohd Adzmir, A. S., & Saharuddin, H. (2016). Customer satisfaction on reli- ability and responsiveness of self service technology for retail banking services. Procedia Economics and Finance, 37, 13-20. International Organization for Standardization. (1998). Ergonomic Requirements for Office Work With Visual Display Terminals – Part 11: Guidance on Usability. (Document No. 9241–11). Jöreskog, K. G., & Sörbom, D. (1993). LISREL 8: Structural Equation Modeling With the SIMPLIS Command Language. Chicago, IL: Scientific Software International. Jöreskog, K. G., Olsson, U. H., & Wallentin, F. Y. (2016). Multivariate Analysis With LISREL. Basel, Switzerland: Springer. Jun, M., & Palacios, S. (2016). Examining the key dimensions of mobile banking service quality: An exploratory study. International Journal of Bank Marketing, 34, 307-326. Kandampully, J., Zhang, T. (Christina), & Bilgihan, A. (2015). Customer loyalty: A review and future directions with a special focus on the hospitality industry. International Journal of Contemporary Hospitality Management, 27, 379-414. 164 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 Kang, H., Lee, M. J., & Lee, J. K. (2012). Are you still with us? A study of the post-adoption determinants of sus- tained use of mobile-banking services. Journal of Organizational Computing and Electronic Commerce, 22, 132-159. Karatepe, O. M. (2011). Service quality, customer satisfaction and loyalty: The moderating role of gender. Journal of Business Economics and Management, 12, 278-300. Kassim, N., & Asiah Abdullah, N. (2010). The effect of perceived service quality dimensions on customer satis- faction, trust, and loyalty in e-commerce settings: A cross cultural analysis. Asia Pacific Journal of Marketing and Logistics, 22, 351-371. Kaur, A., & Medury, Y. (2011). Impact of the internet on teenagers’ influence on family purchases. Young Consumers, 12, 27-38. Killins, R. N. (2017). The financial literacy of Generation Y and the influence that personality traits have on fi- nancial knowledge: Evidence from Canada. Financial Services Review, 26, 143-165. Koenig-Lewis, N., Palmer, A., & Moll, A. (2010). Predicting young consumers’ take up of mobile banking serv- ices.Marketing Intelligence and Planning, 28, 410-432. Laforet, S., & Li, X. (2005). Consumers’ attitudes towards online and mobile banking in China. International Journal of Bank Marketing, 23, 362-380. Larsson, A., & Viitaoja, Y. (2017). Building customer loyalty in digital banking. International Journal of Bank Marketing, 35, 858-877. Laukkanen, T. (2007). Internet vs mobile banking: Comparing customer value perceptions. Business Process Management Journal, 13, 788-797. Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with Computers, 21, 385-392. Lee, Y., & Kozar, K. A. (2012). Understanding of website usability: Specifying and measuring constructs and their relationships. Decision Support Systems, 52, 450-463. Lee, Y. K., Park, J. H., Chung, N., & Blakeney, A. (2012). A unified perspective on the factors influencing usage intention toward mobile financial services. Journal of Business Research, 65, 1590-1599. Liébana-Cabanillas, F., Alonso-Dos-Santos, M., Soto-Fuentes, Y., & Valderrama-Palma, V. A. (2017). Unobserved heterogeneity and the importance of customer loyalty in mobile banking. Technology Analysis & Strategic Management, 29, 1015-1032. Lin, H.-H. (2012). The effect of multi-channel service quality on mobile customer loyalty in an online-and-mo- bile retail context. The Service Industries Journal, 32, 1865-1882. Lin, H. F. (2013). Determining the relative importance of mobile banking quality factors. Computer Standards & Interfaces, 35, 195-204. Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & Management, 43, 271-282. Lu, M. T., Tzeng, G. H., Cheng, H., & Hsu, C. C. (2015). Exploring mobile banking services for user behavior in intention adoption: Using new hybrid MADM model. Service Business, 9, 541-565. Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21, 873-891. Lundell, S. (2019). Lunar Way startar bank i Sverige – ska erbjuda både bolån och kreditkort. (available at https://www.breakit.se/artikel/21392/lunar-way-startar-bank-i-sverige-ska-erbjuda-bade-bolan-och-kreditkort). Malaquias, R. F., & Hwang, Y. (2019). Mobile banking use: A comparative study with Brazilian and U.S. partici- pants. International Journal of Information Management, 44, 132-140. Mallat, N., Rossi, M., & Tuunainen, V. K. (2004). Mobile banking services. Communications of the Acm, 47, 42-46. Marimon, F., Petnji Yaya, L. H., & Casadesus Fa, M. (2012). Impact of e-Quality and service recovery on loy- alty: A study of e-banking in Spain. Total Quality Management & Business Excellence, 23, 769-787. Mohammadi, H. (2015). A study of mobile banking usage in Iran. International Journal of Bank Marketing, 33, 733-759. Moorthy, K., Chee, L. E., Yi, O. C., Ying, O. S., Woen, O. Y., & Wei, T. M. (2017). Customer loyalty to newly opened cafés and restaurants in Malaysia. Journal of Foodservice Business Research, 20, 525-541. M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 165 Munusamy, J., Chelliah, S., & Mun, H. W. (2010). Service quality delivery and its impact on customer satisfac- tion in the banking sector in Malaysia. International Journal of Innovation, Management and Technology, 1, 398-404. Nicoletti, B. (2017). Future of FinTech. Basingstoke, UK: Palgrave Macmillan. Nielsen, J. (1994). Usability Engineering. San Francisco, CA: AP Professional. Nourallah, M., Strandberg, C., & Öhman, P. (2019). Understanding the relationship between trust and satisfaction on mobile bank application. In 3rd International Conference on E-commerce, E-Business and E-Government, in Lyon, France. June 18-21, 2019. Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404- 414. Parasuraman, A., Berry, L. L., & Zeithaml, V. A. (1988). SERVQUAL: A multi item scale for measuring con- sumer perception of service. Journal of Retailing, 64, 12-40. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. The Journal of Applied Psychology, 88, 879-903. Pohlmann, J. T. (2004). Use and interpretation of factor analysis in the journal of educational research: 1992- 2002. The Journal of Educational Research, 98, 14-23. Puschmann, T. (2017). Fintech. Business & Information Systems Engineering, 59, 69-76. Raza, A. S., Jawaid, S. T., & Hassan, A. (2015). Internet banking and customer satisfaction in Pakistan. Qualitative Research in Financial Markets, 7, 24-36. Saleem, M. A., Zahra, S., Ahmad, R., & Ismail, H. (2016). Predictors of customer loyalty in the Pakistani bank- ing industry: A moderated-mediation study. International Journal of Bank Marketing, 34, 411-430. Sampaio, C. H., Ladeira, W. J., & Santini, F. D. O. (2017). Apps for mobile banking and customer satisfaction: A cross-cultural study. International Journal of Bank Marketing, 35, 1133-1151. Sanchez-Franco, M. J., & Rondan-Cataluña, F. J. (2010). Virtual travel communities and customer loyalty: Customer purchase involvement and web site design. Electronic Commerce Research and Applications, 9, 171-182. Santouridis, I., & Trivellas, P. (2010). Investigating the impact of service quality and customer satisfaction on customer loyalty in mobile telephony in Greece. The TQM Journal, 22, 330-343. Scornavacca, E., & Hoehle, H. (2007). Mobile banking in Germany: A strategic perspective. International Journal of Electronic Finance, 1, 304-320. Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32, 129-142. Shaikh, A. A., & Karjaluoto, H. (2019). Mobile financial services. In A. A. Shaikh & H. Karjaluoto (Eds.), Marketing and Mobile Financial Services: A Global Perspective on Digital Banking Consumer Behaviour (pp. 1-26). New York, NY: Routledge. Sharma, S. (1996). Applied Multivariate Techniques. New York, NY: Wiley. Sharma, D. S., & Sidhu, J. (2001). Professionalism vs commercialism: The association between non-audit serv- ices (NAS) and audit independence. Journal of Business Finance & Accounting, 28, 563-594. Sindwani, R., & Goel, M. (2015). The impact of technology based self service banking dimensions on customer satisfaction. International Journal of Business Information Systems Strategies, 4, 1-13. Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-commerce: An exploration of its antecedents and consequences. Journal of Retailing, 78, 41-50. Strandberg, C., Wahlberg, O., & Öhman, P. (2012). Challenges in serving the mass affluent segment: Bank cus- tomer perceptions of service quality.Managing Service Quality: An International Journal, 22, 359-385. Strandberg, C., Wahlberg, O., & Öhman, P. (2015). Effects of commitment on intentional loyalty at the person- to-person and person-to-firm levels. Journal of Financial Services Marketing, 20, 191-207. Suoranta, M., & Mattila, M. (2004). Mobile banking and consumer behaviour: New insights into the diffusion pattern. Journal of financial services Marketing, 8, 354-366. 166 M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 Sum Chau, V., & Ngai, L. W. L. C. (2010). The youth market for internet banking services: Perceptions, attitude and behaviour. Journal of Services Marketing, 24, 42-60. Sun, Y., Wang, M., & Wang, N. (2015). Technology Leadership, Brand Equity, and Customer Loyalty in Mobile Banking: Moderating Role of Need for Uniqueness. DIGIT 2015 Proceedings, 14. Tam, C., & Oliveira, T. (2017). Literature review of mobile banking and individual performance. International Journal of Bank Marketing, 35, 1044-1067. Tan, E., & Lau, J. L. (2016). Behavioural intention to adopt mobile banking among the millennial generation. Young Consumers, 17, 18-31. Thakur, R. (2014). What keeps mobile banking customers loyal? International Journal of Bank Marketing, 32, 628-646. Verhoef, P. C., & Donkers, B. (2005). The effect of acquisition channels on customer loyalty and cross-buying. Journal of Interactive Marketing, 19, 31-43. Wen, C. H., & Hilmi, M. F. (2011). Exploring service quality, customer satisfaction and customer loyalty in the Malaysian mobile telecommunication industry. In 2011 IEEE Colloquium on Humanities, Science and Engineering in Penang, Malaysia Wirtz, J., Mattila, A. S., & Lwin, M. O. (2007). How effective are loyalty reward programs in driving share of wallet? Journal of Service Research, 9, 327-334. Yeh, C. H., Wang, Y. S., & Yieh, K. (2016). Predicting smartphone brand loyalty: Consumer value and con- sumer-brand identification perspectives. International Journal of Information Management, 36, 245-257. Yoon, C. (2010). Antecedents of customer satisfaction with online banking in China: The effects of experience. Computers in Human Behavior, 26, 1296-1304. Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26, 760-767. M. Nourallah et al. / Financial Services Review 29 (2021) 147–167 167