Banks can achieve success and recover significant investments in m-banking services only by retaining their users (Foroughi et al., 2019). ECM is focused on the factors of post-adoption and post-consumption expectations (e.g. Foroughi et al., 2019; Rahi et al., 2018). In the context of m-banking services, user satisfaction reflects the level of their expectations that match the experience (Susanto et al., 2016).
In other words, users will be satisfied when m-banking meets their expectations they had towards m-banking (Foroughi et al., 2019). For example, Foroughi et al. 2019, p. 1019) postulated that users may also view security and privacy poorly. In the original UTAUT, performance expectancy is the strongest predictor of behavioral intention (Beh et al., 2019).
The importance of privacy in the online domain in general has been extensively researched (Damghanian et al., 2016). Moreover, privacy concerns have been indicated as an efficient predictor of continued intention to use m-banking services (e.g. Choudrie et al., 2018; Farah et al., 2018). Furthermore, perceived security and privacy will drive user satisfaction and repeat use of m-banking services (e.g. Hanafizadeh et al., 2014; Susanto et al., 2016).
Facilitating conditions means that users have the necessary resources and expertise to use a particular technology (Cheng et al., 2020).
Methodology
PSP1 I believe that m-banking services have mechanisms to ensure secure transmission of its users' information. CON3 The expectations I have from M-banking services were correct CON4 Overall, most of my expectations from using m-banking services were. PE1 I find m-banking services useful in my daily life. 2012) PE2 Using m-banking services increases my chances of achieving things that are.
PT3 Bank's legal and technological policies sufficiently protect me from problems during operation of m-banking services. SEF1 I can carry out my banking needs using m-banking services, even if there is no one to help me. Suh and Han (2002) SEF2 I can perform my banking needs using m-banking services with only a simple. manual or online help for reference.
SEF3 I am confident in my ability to fulfill my banking needs using m-banking services. Multiple-choice questions were related to demographics, including gender, age, m-banking experience, m-banking usage, and monthly income. In both questionnaires, an explanatory section was included to explain the study's purpose, describe the functionality and features of m-banking, provide examples of m-banking applications.
Participants were also assured that confidentiality of responses would be maintained, and only summary results would be used and reported. Most respondents have used m-banking services for 3-5 years with 42 percent, while only 10 percent have used m-banking services for less than a year. Regarding the monthly income of the respondents, the majority of the respondents, about 33 percent, have less than 1,000 Kuwaiti dinars (KD) as monthly income, while only 16 percent have more than 3,000 KD as monthly income .
Finally, we use a common latent factor (CLF) with all indicators of the constructs included in the model.
Results
A value that falls below the recommended 50% (McLean et al., 2020), suggesting the improbability of CMB in this study. The justifications for using PLS-SEM in this study are consistent with the justification reported in marketing and information systems research (Hair et al., 2012; Henseler et al. a) suggested that the first stage in PLS-SEM is to specify a path model linking the measurement items to the constructs. After specifying the research model, Hair et al. 2017a) argued that both reliability and validity should be verified during measurement model assessment.
The reliability of the measurement model is measured using both Cronbach's alpha (CA) and composite reliability (CR) Hair et al. The loadings of all measurement items (ie, indicators) must be .50 or above on their hypothesized construct and they must be significant (p < .05) Hair et al. Discriminant validity refers to "the degree to which a construct is empirically separated from other constructs in the path model" (Sarstedt et al., 2014, p. 108), and can be assessed by the Fornell and Larcker (1981) criterion.
Because Fornell and Larcker's criteria exaggerate the presence of discriminant validity, Henseler et al. 2015) recommended using the Heterotrait-Monotrait (HTMT) criterion to test discriminant validity. Using a Monte Carlo simulation study, Henseler et al. 2015) found that HTMT can achieve higher specificity and sensitivity rates (97% to 99%) compared to the Fornell-Lacker (20.82%), the most commonly used method to to assess the discriminant validity. In PLS, the individual path coefficients of the structural model can be interpreted as standardized beta coefficients of ordinary least squares regressions (Henseler et al., 2009, p. 304).
In addition, path coefficients must be significant at least at the 0.050 level (Henseler et al., 2009; Urbach and Ahlemann, 2010). In PLS, R2 values represent “the amount of variance in the construct in question that is explained by the model” (Chin, 2010, p. 674). Similar to the f 2 effect size, the q2 effect size indicates the contribution of the exogenous variable to the Q2 value of the endogenous variable (Hair et al., based on a 30-year review, showed that the average effect size in tests of moderation.
PLS-IPMA tests the overall effect of an exogenous variable on a specific target endogenous variable (i.e. importance) by averaging the latent variable score of the exogenous construct (i.e. performance) (Hair et al., 2017b). The purpose of this test is to detect the exogenous variable that more effectively improved the value of the target endogenous variable (i.e., behavioral intensity in this case) with its relatively high importance and low effectiveness (Hock et al., 2010). lt; Figure 4>. Furthermore, as suggested by Hair et al. 2017a), the structural model was also assessed using the following measures: average path coefficient (APC), average R-squared (ARS), and average variance inflation factor (AVIF).
Discussion and Implications
Also, the empirical results of this study supported the significant relationship between performance expectation and users' continued intention to use m-banking services (H7). This result implies that our survey respondents appreciated the usefulness, benefits and convenience of m-banking services and this promoted their continued intention to use such services. Statistical results also recognized the important role of satisfaction in influencing customers' continued intention to use m-banking services (H8).
This will be interpreted as customers' satisfaction with m-banking services will have a great influence on their intention to continue using such services. It is argued that only when users have high confidence in the security and privacy offered by a bank, they will trust the bank enough to use its m-banking services (Susanto et al., 2016). On the contrary, we argue that security and privacy are not guaranteed in the banking sector, and since banking transactions involve critical financial information, it is important to assure users that it is secured to perform various m-banking services.
These assurances will affect user satisfaction with m-banking services (e.g., Albashrawi and Motiwalla, 2019; Hanafizadeh et al., 2014). Perceived trust was also found to be an important construct in predicting customers' continued intention to use m-banking services (H13). The empirical results of the current study failed to confirm the role of facilitating conditions in predicting customers' continued intention to use m-banking services (H14).
Finally, the statistical results empirically confirmed the significant impact of self-efficacy on users' further intention to use m-banking services (H15). This means that users' abilities to perform and complete certain tasks using m-banking services will influence their continued intention to use such services (Foroughi et al., 2019; Susanto et al., 2016). We also assessed how expected performance, expected effort, perceived trust, satisfaction, self-efficacy and facilitated conditions predict customers' continued intention to use m-banking services.
Perceived trust was found to be a very important factor in predicting Kuwaiti customers' continued intention to use m-banking services. Moreover, the results of this study confirmed that only when Kuwaiti customers have high confidence in the security and privacy that a bank offers, will they trust the bank enough to use its m-banking services (Susanto et al., 2016). Therefore, Kuwaiti banks should focus their attention on every aspect related to these factors to motivate Kuwaiti customers to continue using m-banking services.
Limitations and Future Research
Finally, to achieve the roles of effort expectancy and self-efficacy, banks can conduct attractive awareness campaigns to demonstrate the usefulness, as well as ease of use and learning, of m-banking services, design an easy service to use and high quality. and customizable user interfaces, and post videos explaining how to use m-banking services and to increase user familiarity with various features of these services.
Conclusion
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The impact of privacy, accuracy and perceived security on the adoption of mobile self-payment systems. An Empirical Investigation of Mobile Data Service Continuity: Incorporating the Theory of Planned Behavior into the Expectancy-Confirmation Model. An empirical study on service quality perceptions and continuance intention in the context of mobile banking in India.
Mediating between perceived usefulness and perceived ease of use: the case of mobile banking in Yemen. Exploring Mobile Wallet Adoption in the FinTech Era: An Empirical Study from Kuwait. Investigating the role of trust and quality dimensions in the actual use of mobile banking: an empirical investigation.
Understanding individual mobile banking performance: The Delone and Mclean model and the moderating effects of individual culture.
