The effect of individual factors on user behaviour and the moderating role of trust: an empirical investigation of consumers’ acceptance of electronic banking in the Kurdistan Region of Iraq
Financial Innovation volume 6, Article number: 43 (2020)
The popularity of self-service technologies, particularly in the banking industry, more precisely with electronic banking channel services, has undergone a major change as individuals' lifestyles develop. This change has affected individuals’ decisions about accepting any new Information Technology, and Information Communications Technology services that are electronically mediated, for example, E-Banking channel services. This study investigates the effect of Individual Factors on User Behaviour, and the moderating role of Trust in the relationship between Individual Factors, and User Behaviour based on the Unified Theory of Acceptance and Use of Technology. This research proposes a model, with a second-order components research framework. It improves current explanations of the acceptance of electronic banking channel services. Furthermore, this study highlights the role of trust on the acceptance of electronic banking channel services, which is the most crucial consideration in customers’ decisions to accept electronic banking channels services. Thus, trust is the spine of the system in the Kurdistan Region of Iraq. Data were collected using an online questionnaire that received 476 valid responses from academic staff who work at the University of Sulaimani. The model tested data using the Partial Least Squares-Structural Equation Modelling approach. The results show that Individual Factors have a positive effect on User Behaviour. Besides, results show that trust moderates the relationship between Individual Factors and User Behaviour.
The Kurdistan Region of Iraq (KRI) has developed in multiple sectors over the past two decades. Specifically, in Information Technology (IT), Information Communications Technology (ICT) (Wang et al. 2017) and the financial industry. The banking industry, however, still operates in a traditional way in the KRI. Nonetheless, the lack of transparency and the existence of corruption in all sectors, particularly in the financial sector, has prevented the growth of Electronic Banking services in the Kurdistan Region, and is one of the weak points of the government system. However, the Kurdistan Region Government (KRG) wants to have an Electronic Government. E-Banking still does not operate in the KRI and unfortunately, Kurdish society is still a cash society.
The banking system in the Kurdistan region of Iraq operates in traditional ways (Riffai et al. 2011), with no challenging features existing to meet the requirement of this century. The central bank of the Kurdistan region of Iraq has two offices in the KRI, which are located in Erbil, and Sulaymaniyah; however, none of them has a branch that customers can use or belongs to the central bank of Iraq, which is located in Baghdad, and is controlled by the Iraqi government. The two offices are responsible all financial procedures, for example, distributing government employees’ salaries and other banking activities in the KRI.
E-Banking is essential, for the KRI, for customers, and Banks nowadays; however, this is the first time that research has been done on it's’ use in the KRI. The acceptance of E-Banking service in the KRI can be a new area for research; however, E-Banking itself is not a new topic as many studies have been carried out on it, using different theories. For example, The Technology Acceptance Model (TAM 1, 2, and 3, Theory of Reasoned Action (TRA), Theory of Planned Behaviour (TPB), Unified Theory of Acceptance, and Use of Technology (UTAUT 1, and 2), and a self-designed model used by most of the researchers. (Hama Khan 2019; Khan 2018; Hamakhan 2020).
Electronic banking services are a new kind of reform in banking services and play an essential role in establishing electronic government, and e-commerce (Sohail and Shanmugham 2002; Huang et al. 2011). Electronic banking includes all banking services based on the implementation of the electronic system. E-Banking has become a crucial phenomenon in the banking industry, and it will continue as more progress is made in information technology. Thus, the financial industry is gradually experiencing a transformation from a cash-based system to a “paperless” system, which is more convenient and reliable.
In addition, customers’ satisfaction with banking services depends on there trust; whether the banking service is offline, or online (Kingshott et al. 2018). Trust affects the commitment and loyalty that E-Banking can attract significantly (López-Miguens and Vázquez 2017), whether the bank is local, national, or foreign branded in the country (Kingshott et al. 2018).
E-Banking is defined as the automated delivery of new and traditional banking products and services directly to customers through electronic and interactive communication channels. According to Hoehle et al. (2012), E-Banking has four channels. These are Automated Teller Machines (ATMs) (Dabholkar 1996), Telephone banking services (Ahmad and Buttle 2002), Internet banking (Tan and Thompson 2000; Bhattacherjee 2001; Pikkarainen et al. 2004; George 2018), and Mobile banking (Hoehle and Lehmann 2008; Tam and Oliveira 2017; Chawla and Joshi 2018).
This paper aims to examine the proposed second-order components research model and highlights the role of trust in the acceptance of Electronic Banking channel services. This is the key concern that affects consumers’ willingness to accept Electronic Banking channels services, and trust is the spine of the system in the Kurdistan Region of Iraq. Moreover, it brings up trust as a moderator in the relationship between individual factors, and user behaviour based on the Unified Theory of Acceptance and Use of Technology.
Literature review, and research hypothesis
Beyond the significant references of TAM 1 introduced by Davis et al. (1989) and Davis (1989) as an extension of TRA (Fishbein and Ajzen 1975), TAM 2 (Venkatesh and Davis 2000; Venkatesh 2000), TAM 3 (Venkatesh and Bala 2008), UTAUT 1 (Venkatesh et al. 2003), and UTAUT 2 (Venkatesh et al. 2012), which are related to accepting new technology, researchers are still citing, and extending them in it's researches’ model. The most widely used theories can be TAM 1, 2, and 3, and UTAUT 1, and 2 in order to determine the factors that can influence the user’s decision about accepting particular new technology, for instance, E-Banking services. Thus, users can establish a barrier to highly successful of E-Banking services. However, the operations of E-Banking services in the KR are still out of the system, where many factors beyond the initiation of this technology. Venkatesh et al. (2004) created a differentiation among acceptance, adoption, and usage decisions. The authors described acceptance as the people’s initial decision to interact with the technology. Furthermore, adoption occurs after having some direct experience with the technology, and after the decision to accept the technology is made. Usage decisions refer to judgments about continuing to use the technology subsequent to significant direct experience with it, and where an individual has acquired significant knowledge of the technology (Chao et al. 2021).
According to Venkatesh and Brown (2001), E-Banking should be accepted, trusted, adopted, and used. In order to shed light on each step, a bunch of studies tested each step by employing different theories. Giovanis et al. (2019) investigated, which of four well-established theoretical models (i.e., TAM) (Munoz-Leiva et al. 2017; Alalwan et al. 2018a, b), the theory of planned behaviour (Lee 2008; Yadav et al. 2015), UTAUT (Cao and Niu 2019), the decomposed theory of planned behaviour (DTPB) best explains potential users’ behavioural intentions (Shareef et al. 2018) to adopt mobile banking (MB) services. Moreover, other factors affect each of the steps, respectively, action by the technological leadership, e-trust (Salem et al. 2019), e-loyalty (Esterik-Plasmeijer and Raaij 2017; Berraies et al. 2016), customers’ value, for online personalization, customers’ concern, for privacy, and the propensity of technology adoption (Rahi et al. 2019). The best prediction of the use of new technologies may require the testing of the principal factors in order to learn about the customers’ satisfaction (Thakur 2014; Sharma and Sharma 2019), customer loyalty (Shankar and Jebarajakirthy 2019), word-of-mouth (WOM) (Sampaio et al. 2017) intention, and adoption (Alalwan et al. 2018a, b; Siyal et al. 2019; Chauhan et al. 2019), how customer use the system (Baabdullah et al. 2019a, b), and focusing on the role of users’ commitment (Yuan et al. 2019), which is called self-service technologies (Chaouali and El-Hedhli 2019). Table 1 presents a summary of the main findings of selected empirical studies based on TAM.
Moreover, Venkatesh et al. (2003) presented the Unified Theory of Acceptance, and Use of Technology UTAUT 1 as the integration of eight different models of acceptance and use of technology. UTAUT is a definitive model that synthesizes, what is known and provides a foundation to guide future research in this area (Venkatesh et al. 2003). Furthermore, from a theoretical perspective, UTAUT provided a refined view of how the determinants of intention and behaviour evolve (Venkatesh et al. 2003).
Venkatesh et al. (2003) found that the influence of performance expectancy on behavioural intention will be moderated by gender, and age (Mahmoud 2019; Aboobucker and Bao 2018), such that the effect will be stronger, for men, and particularly, for younger men. The influence of effort expectancy on behavioural intention will be moderated by gender, age, and experience, such that the effect will be stronger, for women, particularly younger women, and particularly at early stages of the experience. The impact of social influence on behavioural intention will be moderated by gender, age, voluntariness, and experience, such that the effect will be more reliable, for women, particularly older women, particularly in mandatory settings in the early stages of the experience. The influence of facilitating conditions on usage will be moderated by age, and experience, such that the effect will be more reliable, for older workers, particularly, with increasing experience.
UTAUT is another extension of the TAM that integrates constructs, including performance expectancy, effort expectancy, and Facilitating Conditions.
Social influence is defined as the degree to which an individual perceives the importance of the beliefs of others that he or she should use the new system (Venkatesh et al. 2003; Yaseen and El Qirem 2018).
Facilitating conditions is defined as the degree to, which an individual believes that an organisational, and technical infrastructure exists to support the use of the system (Venkatesh et al. 2003). Individual-level technology adoption is one of the most mature streams of IS research (Venkatesh et al. 2007). Thus, in this study, Individual Factors is crated as second-order (higher-order components) contained from four sub-dimension indicators (lower-order components), which are including: performance expectancy, effort expectancy, and Facilitating Conditions (Venkatesh and Zhang 2010; Venkatesh et al. 2008, 2011a, b, 2016). In terms of the UTAUT 1, and 2. Table 2 presents a summary of the main findings of selected empirical studies based on UTAUT.
From the above discussion, the researcher hypothesised as follows:
Individual Factors have a positive effect on User Behaviour.
Moderating effect of trust
According to Pavlou and Fygenson (2006) Trust is defined as the belief that the trustee will act cooperatively to fulfil the trustor’s expectations without exploiting it's vulnerabilities. Johnson (2007) defines trust in technology as consumers’ expectations of technically competent, reliable, and dependable performance.
Trust is one of the crucial and influence indicators in this field and reinforces aspects to be considered by banks, and mobile device developers to expand mobile banking adoption (Malaquias and Hwang 2019), trust is considered a barrier key of acceptance, and adoption for any new technology. Besides, trust can facilitate the adoption of mobile banking services in a cross-cultural context (Hama Khan 2019; Khan 2018). Many studies conducted have investigated trust using different theories, and in different countries as the researcher reviewed trust in the literature review in this study, and there is enough literature about it (Chaouali et al. 2016; Afshan and Sharif 2015; Jan and Abdullah 2014; Zhou 2012; Hanafizadeh et al. 2012; Huang et al. 2011; Yap et al. 2010; Luo et al. 2010; Alaarj et al. 2016; Alaaraj et al. 2018).
In this study, trust is a moderator variable, for examining the user’s behaviour concerning the acceptance of new technology, which is E-Banking in the KRI. Thus, trust is the most influential factor in determining success in E-Banking services (López-Miguens and Vázquez 2017).
Yiga and Cha (2016) introduced perceived trustworthiness as one of the beliefs that may significantly influence Internet banking adoption. On the other hand, trust is an influential factor that can create the most significant e-competitive advantage for E-Banking (Hammoud et al. 2018; Namahoot and Laohavichien 2018). Banks are affected by electronic lifestyle (Hussain et al. 2018; Chawla and Joshi 2019), whether IT (Salhieh et al. 2011), or ICT (Wang et al. 2017), and trust is considered an external factor in the electronic business environment. Making mistakes leads to a lack of trust, or initial trust (Susanto et al. 2013; Kaabachi et al. 2017) in electronic payment. Customers are afraid to make mistakes while they are making payments even when they use an ATM or different E-Banking services. Trust is a service for non-banking organisations. However, it is more crucial for the Banks, and more than just a service, particularly in E-Banking services, according to a banking and financial institutions interview summary by USAID (2008). In that report of (Economic Development Assessment Kurdistan Region 2008) of banking sector issues, lack of trust in the banking system by both customers, and bankers got 88 cumulative scores (out of 100) in the Kurdistan Region, which can be considered as offline trust. This result was obtained from interviews with bankers and clients.
The trust in Banks can be divided into two types: trust in the offline, or physical bank (Chaouali et al. 2016), and trust in the online, or E-Banking services. Usually, offline trust is the basis, for the online trust since customers will not use the online services of a bank whose physical services they do not trust (but may be prepared to use the online services of a bank they do trust). It means customers’ experience with a bank can let customers accept the E-Banking services of the bank (Chaouali et al. 2016; Shen et al. 2020).
According to McKnight et al. (2002), trust can include three beliefs (Competence, Benevolence, and Integrity). Competence includes the ability of the trustee to do, what the truster needs, capability, and positive judgment. The authors measured perceptions of how well the vendor did it's job, or how knowledgeable the vendor was (expertise/competence). Benevolence includes the trustee caring, and it's motivation to act in the truster’s interests, favourable motives, and not acting opportunistically, or manipulatively. Here the authors focused on the vendor acting in the customer’s best interest, trying to help, and being genuinely concerned. Integrity includes trustee honesty and promise-keeping. Here the authors captured perceptions of vendor, honesty, truthfulness, sincerity, and keeping commitments (reliability/dependability) (Arcand et al. 2017). In order to generate competence among Banks, in terms of the E-Banking services activity, there is a need to build trust. Thus, it is fundamental to reduce risk perceptions (Faroughian et al. 2011; Wen et al. 2019) of E-Banking services (Zhao et al. 2010a, b). Table 3- presents a summary of the main findings of selected empirical studies related to Trust.
From the above discussion, the researcher hypothesised the moderating effect as:
Trust will moderate the relationship between Individual Factors and User Behaviour.
Research model and hypothesis
According to Dahlberg et al. (2008), the framework is used to classify past research, to analyse research findings of classified studies, and to propose meaningful questions, for future research, for each factor.
In this study, the research model is based on TAM and UTAUT. Besides, the framework was constituted by reflective-formative types of higher-order constructions, which consisted of three latent variables named (Individual Factors as an independent variable, Trust as a moderator, and User Behaviour as dependent variable). Individual Factors (second-order components) included four sub-dimensions (lower-order components), which are performance expectancy, effort expectancy, social influence, and Facilitating Conditions. Individual Factors was more concreated when it was second-order, and conceptually more reliable, besides second-order components that reduce the number of paths in the model, where there is only one path from the Independent Variable to the Dependent Variable (Sarstedt et al. 2019). To empirically test the model, the researcher applied a partial least square structural equation modelling (PLS-SEM) approach by SmartPLS (V. 3.2.8) (Ringle et al. 2015). Figure 1 shows the evaluation of the measurement model.
Data collection and sample selection
The data sample collected through electronic questionnaires in the local language (Kurdish\Sorani), in order to make it clearer, for the participants. The participants are from the academic staff at the University of Sulaimani, which is located in Sulaimani city in the KRI. Respondents were given two months to complete the survey. The data accessed on Google Forms was than downloaded in Microsoft Excel. A total of 476 usable questionnaires were collected. Since the questionnaires were electronic, they were handed out by email and there were no incomplete questionnaires. After the data downloaded in a Microsoft Excel file, they were coded. Thereby, the data were analysed by two pieces of software: SPSS (V.26), and SmartPLS (V 3.3.2) via some steps, which show in the next section.
Results and discussions
Since the research model in this study is a complex one, comprising reflective-formative types of second-order components (Mode B) (Sarstedt et al. 2019), and because of the characteristics of the research model, as presented in Fig. 1, the author decided to use PLS path modelling. The data in this research are so-called nonparametric, or scattered data. For example, CB-SEM is unable to give accurate, and decisive results (Jöreskog and Wold 1982), which is an appropriate approach, for this study, and allows second-order components (Hair et al. 2017a, b).
Furthermore, in this study, the research model passed through both stages, which are the measurement model and the structural model. Besides, the author followed (extended) the repeated indicators approach to analyse the higher-order constructs, measurement models, and the structural model since the sample size is sufficiently large (Sarstedt et al. 2019). Following on (Hamakhan 2020) in this study, the hypothesis direction is clear, which is more appropriate in order to minimise the type II error. Thus, the hypothesis is tested by using the one-tailed test instead of the two-tailed test.
The Demographic Information was calculated, for the sample (n = 476) used by the staff of the University of Sulaimani, for this study by SPSS V.26. The sample characteristics reveal that most of the respondents were young participants (n = 366, 76.9%). The majority of the respondents were female (n = 290, 60.9%) with (n = 440, 92.4%) holding a postgraduate degree. Most of the respondents had an online bank account (n = 428, 89.9%) and accessed there bank account (n = 446, 93.7%). Most of the respondents used there bank account (1–15) times a month (n = 208, 43.7%). The majority of the respondents had been using electronic banking (1–10) years (n = 336, 70.6%). Table 4 shows the demographic information of the respondents.
According to Hair et al. (2017a, b), PLS-SEM should come up with two steps, which are called the measurement model and the structural model. For the first step (Measurement Model), some tests must be found by PLS Algorithm, which was done in this study by SmartPLS (V 3.3.2) through running the function (PLS Algorithm), by outer loading (Factor Loading), Cronbach's Alpha, average variance extracted (AVE), composite reliability (CR) and rho_A, Discriminant Validity Measurement, the heterotrait-monotrait ratio (HTMT), in order to determine the inner validity, and reliability based on the PLS-SEM method (Henseler et al. 2009).
The first test is indicator reliability, which should be done by researchers to assess the evaluation of measurement models in PLS-SEM, and for the purpose of testing the inner validity, and reliability, of the model in this study. According to Hair et al. (2017a, b), the measurement model is intended to assess the validity (convergent, and discriminant), and reliability of each indicator forming latent constructs. After the PLS Algorithm run, first, the average variance extracted (AVE) must be checked. A general rule of thumb, for AVE is (≥ + 0.5) (Hair et al. 2017a, b, p. 138). In reflective models, outer loading must be checked. Outer loadings represent the absolute contribution of the indicator to the definition of it's latent variable (David Garson 2016, p. 60). The rule of thumb, for outer loadings above 0.708 is acceptable (Hair et al. 2019a, b); hence, some indicators below 0.708. For example, (SI14, SI15, SI16, FC21, FC23R, T46, T47R, T48 & UB68_Group) removed. According to Hulland (1999, p. 198), in social science studies, it is possible to have outer loadings (< 0.70) particularly, since the newly developed scales are used. According to Hair et al. (2017a, b, p. 136), Cronbach’s alpha is a traditional method of judging criterion inner reliability based on the PLS-SEM method, which can provide an evaluation of the reliability based on the intercorrelations of the observed indicator variables. A general rule of thumb, for Cronbach’s alpha is (> 0.7) (Hair et al. 2017a). Composite reliability is another measurement of the inner reliability based on the PLS-SEM method. The rule of thumb, for composite reliability is (> 0.7) (Gefen et al. 2000). According to Hair et al. (2017a, b, 2019a, b), rho_A is the most crucial inner reliability measurement based on the PLS-SEM method, the rule of thumb, for rho_A is (> 0.7). All the results are acceptable. Table 5 shows the outer loadings, Cronbach’s Alpha, rho_A, composite reliability (CR, and average variance extracted (AVE) values.
Discriminant validity measurement
The heterotrait-monotrait ratio (HTMT) is the measurement used to test discriminant validity. According to Hair et al. (2017a, b), Discriminant validity is defined as the extent to which a construct is truly distinct from other constructs by empirical standards. Besides, Hair et al. (2017a, b, p. 140) proposed that there is a true correlation between two constructs if they were well measured, and disattenuated correlation can be referred to that true correlation. A disattenuated correlation between two constructs higher than 0.90 shows a lack of discriminant validity. HTMT does not apply to relationships between Lower-order component LOCs, and the Higher-order component HOC. The repeated measures approach assumes they are highly correlated. Correlation values of relationships between LOCs, and the HOC are used to measure the contribution of the individual LOCs to calculating the contribution of the individual LOCs in calculating the HOC construct score. Thus, it did not present it (Sarstedt et al. 2019).
Evaluation of the structural model in PLS-SEM
Based on the PLS-SEM method, from the time when the researcher confirmed that the construct measures are reliable, and valid, which was the first step (Measurement Model), than the second step is an evaluation of the structure of the model. The most crucial evaluation metrics, for the structural model are Collinearity Statistics (Inner VIF), R2 value (explained variance), F2 value, Q2 (predictive relevance), F2, and Q2 Effect Size, and the size, and statistical significance of the structural path coefficients (Hair et al. 2017a, b).
Testing collinearity statistics (inner VIF)
Testing collinearity is the first test that should be done by the researcher for the evaluation of the structural model in the PLS-SEM domain. Hair et al. (2011) defined Collinearity as a potential issue in the structural model and stated that if the variance inflation factor (VIF), the rule of thumb, for the VIF, is the value of 5, or above, usually, it can be a problem. The term VIF, which is derived from it's square root, is the degree to which the standard error increased due to the presence of collinearity. Table 6 shows the results of the structural model. In this study, the results, for all variables are below 5, which is acceptable.
R2 value (R2) adjusted
In order to obtain F2 Effect size, scholars required to obtain R2 value first based on the application of PLS-SEM. The R2 value is the most crucial approach to evaluating the structural model that can measure the coefficient of determination R2 Square value. According to Hair et al. (2017a, b, p. 209), the coefficient of determination R2 Square is a measure of the model’s predictive power, and is calculated as the squared correlation between a specific endogenous construct’s actual, and predicted values, and the rule of thumb, for the R2 value is between 0, and 1. On the other hand, Falk and Miller (1992) propose an R2 value of 0.10 as a minimum acceptable level, while values ranging from 0.33 to 0.67 are moderate, whereas values between 0.19 and 0.33 are weak, and any R2 value less than 0.19 are unacceptable. Nevertheless (Henseler et al. 2009; Hair et al. 2017a, 2019a, b) suggested the rule of thumb for the R2 values of 0.75, 0.50, and 0.25 can be considered substantial, moderate, and weak. This is presented in Table 6.
F2 Square value is another most crucial measurement to evaluate the structural model that should be found by scholars based on the application of PLS-SEM, F2′s value indicates an exogenous construct’s small, medium, or large effect, respectively, on an endogenous construct (Hair et al. 2017a, b, p. 216). Results indicated that the Individual Factors (IF) to User Behaviour (UB) is large, which is (1.094). This is presented in Table 6.
Predictive relevance Q2
Following the previous other tests, there is another step, for the researcher to find Predictive Relevance Q2, which is the most crucial evaluation metric based on the application of PLS-SEM to evaluate the structural model. In this study, Q2 value is obtained by using the blindfolding function with omission distance 7 (D = 7). Since my data sample is (N = 476), normally D values between 5, and 10, D should not be an integer when the number of observations used in the model estimation is divided by the omission distance. The blindfolding procedure is usually applied to endogenous constructs that have a reflective measurement model specification as well as to endogenous single item constructs (Hair et al. 2017a, b, p. 212). On the other hand, Q2 value of 0, and below are suggested as a lack of predictive relevance. Hair et al. (2019a, b) suggested that Q2 values larger than zero are meaningful. Nevertheless, Q2 values higher than 0, 0.25, and 0.50 depict small, medium, and large predictive relevance of the PLS-path model. According to Henseler et al. (2009). Q2 values can be as: 0.35 (Large), 0.15 (Medium), and 0.02 (Small). Table 6 shows the results of this study, where all endogenous variables are larger than 0, which is acceptable considering that predictive relevance is based on the rule of thumb.
Before the final step, scholars are required to assess the PLSpredict approach instead of reporting a model fit proposed by Shmueli et al. (2016), which is a set of procedures, for prediction with PLS path models, and the evaluation of it's predictive performance. Recently the PLS-SEM domain has been rapidly extended and updated; therefore, researchers are required to be aware of any progress on the application of PLS-SEM domain (Hair et al. 2019a, b; Sharma et al. 2019; Evermann and Tate 2016). However, the data are not out-of-sample in this study, in contrast, Shmueli et al. (2016) proposed a PLSpredict, for the out-of-sample by estimating the model with predictive analytic, which are the mean absolute error (MAE), the mean absolute percentage error (MAPE), and the root mean squared error (RMSE). In this study, PLSpredict is assessed by running PLSpredict with K = 10. Shmueli et al. (2019) recommended that setting (k = 10). The PLSpredict procedure generates k-fold cross-validation. A fold is a subgroup of the total sample, and k is the number of subgroups. Since the data, for this study is non-normal (non-symmetrically distributed), the mean absolute error (MAE) prediction metric is taken according to Shmueli et al. (2019). The results show that the model lacks predictive power, based on Shmueli et al. (2019) rule of thumb when “PLS-SEM < LM for none of the indicators. If the PLS-SEM analysis (compared to the LM) yields lower prediction errors in terms of the MAE (or the RMSE), for none of the indicators, this indicates that the model lacks predictive power”. Table 7 illustrates the results of this study that was achieved based on Shmueli et al. (2019) who suggested a recommendation setting in the application of the PLSpredict approach.
Hypothesis testing: bootstrapping direct effect results
The final step illustrates the path coefficients and the path diagram for the structural model. Hypothesis testing is obtained, for the structural model, for this study by the Bootstrapping procedure using the one-tailed test, rather than the two-tailed, with 5000 samples, Mode B (Sarstedt et al. 2019), and Bias-Corrected, and Accelerated (BCa), as presented in Tables 8, and 9. Testing the hypothesis using the one-tailed test is more appropriate when the hypothesis direction is clear to minimise the type II error (Hamakhan 2020). Bootstrapping is a resampling approach that draws random samples (with replacement) from the data. It uses these samples to estimate the path model multiple times under slightly changed data constellations (Hair et al. 2017a, b, p. 191). In short, p value, and t-value are achieved, among other results, which are crucial to determining, whether the path coefficient is significant, or not by running the Bootstrapping function. A p value is equal to the probability of obtaining a t-value at least as extreme as the one observed, conditional on the null hypothesis being supported. In other words, the p value is the probability of erroneously rejecting a true null hypothesis (i.e., assu ming a significant path coefficient when in fact it is not significant) (Hair et al. 2017a, b, p. 206), the rule of thumb, for p value is (***p < 0.001, **p < 0.01, *p < 0.05), and for empirical t-value is above 1.96. As presented in Table 8, the following four lower-order components influenced Individual Factors significantly: PE (β = 0.286, t = 3.650), EE (β = 0.692, t = 11.106), SI (β = − 0.188, t = 2.649), and FC (β = 0.254, t = 3.533).
From the Bootstrapping result of the structural model, the following hypothesis can be derived:
(IF) has a positive effect on User Behaviour. IF → UB (β = 0.730, t = 23.825, p < 0.00).
Trust will moderate the relationship between Individual Factors, and User Behaviour. T * IF → UB (β = − 0.100, t = 2.807, p < 0.05).
The last test is testing moderation in this study. Since the Moderator analysis is similar to multigroup analysis, scholars are required to decide whether to test a model as a moderator model, or not. In addition, the moderator analysis is something completely different, which requires different analyses, and interpretation of results (Henseler and Chin 2010; Henseler et al. 2012; Hair et al. 2017a, b, p. 246; Becker, Ringle and Sarstedt 2018; Kou et al. 2014). Hair et al. (2017a, b, p. 246) described Moderation as “a situation, in which the relationship between two constructs is not constant; however, depends on the values of a third variable, referred to as a moderator variable”. Furthermore, the Moderator variable can affect the relationship between the independent and dependent variables directly. In this study, the structural model tested once with the moderator (Trust). Rigdon et al. (2010) proposed bootstrapping with 5000 samples, and Bias-Corrected, and Accelerated (BCa) to analyse moderators; meanwhile, accordingly (Chin et al. 2003; Hair et al. 2019a, b) suggested the two-stage approach to moderator analysis. Table 9 shows the Direct Relationship, for Hypothesis testing included (Std Beta, Std Erro, t-value, p value, 5% lower bounds, and 95% upper bounds). Figure 2 shows the evaluation of the structural model. Figure 3 shows a simple slope analysis (Trust * Individual Factors).
Regarding academic implications, UTAUT, which is combined from other models, is the most cited fundamental, and guidance model for research in ICT (Wang et al. 2017), and IT services (Haider et al. 2018; Salhieh et al. 2011). It is a significant theoretical framework that can be used to elaborate on the acceptance of any new technology service. This study, turning more concrete from theoretical, aims to reduce the number of hypotheses in the path. Individual Factors built as second-order components highlight the effect of Trust that it increased as a moderator in the research framework aimed at understanding, and the acceptance factors of E-Banking services as a new technology service in the KRI. Since this is the first empirical study tested in the KRI, it provides a foundation for future studies, and it creates a valuable contribution to the existing literature of E-Banking. Furthermore, researchers should test more factors in order to create a more significant impact on this area. Moreover, the findings show the requirement to employ Trust as a moderator and recommend even more factors with UTAUT in the future researches (Hamakhan 2020; Hama Khan 2019; Khan 2018).
Several significant practical and managerial implications can be addressed from the results of this research, which are useful for banks’ managers, bankers, and strategic decision-makers willing to employ E-Banking services. Moreover, this research suggests that bank managers should consider becoming more trustworthy and reliable via different methods. For example, training, or publishing some videos on the Bank’s website or sending personal emails to it's customers, in order to increase there knowledge about how to learn about and use E-Banking channel services safely. Particularly, it is crucial to approach different generations and to avoid there losing cost and time by travelling to banks’ branches (Wang et al. 2020; Nazaritehrani and Mashali 2020). It is true that a previous study proved that Trust should be earned by providing the highest quality traditional banking services (for example, ATM, Internet Banking, Mobile Banking, and Application Banking) at the physical bank's branches (offline banking) (Chaouali et al. 2016; Alhassany and Faisal 2018; Chen et al. 2017), to build a reputation and a respectable image and consequently attract existing, and potential customers into the system. Trust is one of the key aspects that can reach out to more customers and convince them. In such a way, it can give those who have it a significant competitive advantage. In short, the results suggest that Banks should pay more attention to marketing strategy and guidelines. For example, increasing the number and accessibility of ATMs, and making them free, simplicity, using social media for sharing and improves experience rather than only advertising (YouTube channel services, Facebook, Twitter, Instagram, and so on), 24/7 Customer Services (Call Centers) via free Skype services or cost-free phone numbers, Kurdish Language, and lower rates of interest for Loans or Mortgages can increase Trust. Finally, this study recommends that banks be always ready to tap there customers complaints and opinions through Research, and Development (R&D), and (Strength, Weakness, Opportunities, and Threats) SWOT analyses. This study emphatically recommends Banks mangers to develop strong Trust in order to gain acceptance of E-Banking services. E-Banking is a key concern affecting economic growth and contributes to a sustainable economy and a sustainable environmental future in the KRI.
There are several limitations to this research that should be addressed in future studies. First, this study only tested Trust as a moderator and many other factors beyond the domain of this study that can also work as moderators. For example, (Attitude, Security, Privacy, and so on) (Hama Khan 2019; Khan 2018). Second, UTAUT is the only theory that the research framework is based on. Other theories can be used as bases to build the research frameworks on, for example, TRA, TPB, TAM 1, 2, and 3, UTAUT 2, and so on (Hamakhan 2020; Hama Khan 2019; Khan 2018). Third, the data are non-normally distributed, which is not suitable for a Covariance Based Structural Equation Modelling approach (CB-SEM), and the sample size is not large. The reliability between independent latent variables and dependent latent variables, depending on the sample size. Thus, it probably leads to an increase in the reliability between all latent variables. Finally, the data were collected from the academic university staff only at the University of Sulaimani through an online questionnaire, which is considered a self-reporting bias. This is a general problem in the methodology’s researches for scholars.
Conclusions and future research
This study has two stages: the first stage provides a systematic review of the relevant literature, which consists of 103 empirical studies from various journals about E-Banking and it's channels. The literature review builds a robust theoretical research framework for this study. It helps researchers in there future work by using different methodologies and theories in order to build a more robust research framework. The review provides an overview of the E-Banking services that explains how researchers can combine the different points of view and results fitting together as part of the big picture. The review mainly focuses on those factors that can influence the acceptance and adopting of new information technology. None of those studies has as yet used Trust as a moderator in it's research frameworks. In this study, one of the key contributions is that Trust is recruited as a moderator in the research framework. The research framework in this study contributes by providing new insights into the relationship between the Individual Factors to User Behaviour moderated by Trust, since, undoubtedly, there is a lack of trust in the KRI. Besides, this is the first study in the KRI in English, which is why it will serve as a valuable basis for future studies.
The second stage provides an empirical examination of the research framework model by using PLS-SEM methods, in order to test the research framework based on PLS-SEM by using SmartPLS. The empirical results show that Individual Factors have a positive impact on User Behaviour, and that Trust has a positive effect on the relation between Individual Factors, and User Behaviour as a moderator.
Availability of data and materials
Abrahão, Moriguchib, Andrade (2016) Intention of adoption of mobile payment: an analysis in the light of the unified theory of acceptance, and use of technology (UTAUT). Revista de Administração e Inovação RAI-21, p 10
Aboobucker I, Bao Y (2018) What obstruct customer acceptance of internet banking? Security and privacy, risk, trust, and website usability, and the role of moderators. J High Technol Manag Res. https://doi.org/10.1016/j.hitech.2018.04.010
AbuShanab E, Pearson JM (2007) Internet banking in Jordan. J Syst Inf Technol 9(1):78–97
Afshan S, Sharif A (2015) Acceptance of mobile banking framework in Pakistan. Telemat Inform 33(2016):370–387
Ahmad R, Buttle F (2002) Retaining telephone banking customers at Frontier Bank. Int J Bank Mark 20(1):5–16. https://doi.org/10.1108/02652320210415944
Akturan U, Tezcan N (2012) Mobile banking adoption of the youth market. Mark Intell Plan 30(4):444–459
Alaarj S, Abidin-Mohamed Z, Bustamam US (2016) Mediating role of trust on the effects of knowledge management capabilities on organizational performance. Procedia Soc Behav Sci 235:729–738. https://doi.org/10.1016/j.sbspro.2016.11.074
Alaaraj S, Abidin-Mohamed ZA, Bustamam US (2018) External growth strategies, and organizational performance in emerging markets: the mediating role of inter-organizational trust. Rev Int Bus Strat 28(2):206–222. https://doi.org/10.1108/RIBS-09-2017-0079
Alalwan AA, Dwivedi YK, Rana NP (2017) Factors influencing adoption of mobile banking by Jordanian bank customers: extending UTAUT2 with trust. Int J Inf Manag 37(3):99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002
Alalwan AA, Baabdullah A, Rana NP, Tamilmani K, Dwivedi YK (2018a) Examining adoption of mobile internet in Saudi Arabia: extending TAM with perceived enjoyment, innovativeness, and trust. Technol Soc. https://doi.org/10.1016/j.techsoc.2018.06.007
Alalwan AA, Dwivedi YK, Rana NP, Algharabat R (2018b) Examining factors influencing Jordanian customers’ intentions, and adoption of internet banking: extending UTAUT2 with risk. J Retail Consum Serv 40:125–138. https://doi.org/10.1016/j.jretconser.2017.08.026
Alhassany H, Faisal F (2018) Factors influencing the internet banking adoption decision in North Cyprus: an evidence from the partial least square approach of the structural equation modeling. Financ Innov 4:29. https://doi.org/10.1186/s40854-018-0111-3
Al-Qeisi K, Dennis C, Alamanos E, Jayawardhena C (2014) Website design quality, and usage behavior: unified theory of acceptance, and use of technology. J Bus Res 67(2014):2282–2290. https://doi.org/10.1016/j.jbusres.2014.06.016
Arcand M, PromTep S, Brun I, Rajaobelina L (2017) Mobile banking service quality, and customer relationships. Int J Bank Mark 35(7):1068–1089. https://doi.org/10.1108/IJBM-10-2015-0150
Baabdullah AM, Alalwan AA, Rana NP, Patil P, Dwivedi YK (2019a) An integrated model for m-banking adoption in Saudi Arabia. Int J Bank Mark. https://doi.org/10.1108/IJBM-07-2018-0183
Baabdullah AM, Alalwan AA, Rana NP, Kizgin H, Patil P (2019b) Consumer use of mobile banking (M-Banking) in Saudi Arabia: towards an integrated model. Int J Inf Manag 44:38–52. https://doi.org/10.1016/j.ijinfomgt.2018.09.002
Baptista G, Oliveira T (2015) Understanding mobile banking: the unified theory of acceptance, and use of technology combined with cultural moderators. Comput Hum Behav 50:418–430
Barkhordari M, Nourollah Z, Mashayekhi H, Mashayekhi Y, Ahangar MS (2017) Factors influencing adoption of e-payment systems: an empirical study on Iranian customers. Inf Syst e-Bus Manag 15:89. https://doi.org/10.1007/s10257-016-0311-1
Bashir I, Madhavaiah C (2014) Determinants of young consumers’ intention to use internet banking services in India. Vision 18(3):153–163. https://doi.org/10.1177/0972262914538369
Bashir I, Madhavaiah C (2015) Trust, social influence, self-efficacy, perceived risk, and internet banking acceptance: an extension of technology acceptance model in Indian context. Metamorphosis 14(1):25–38. https://doi.org/10.1177/0972622520150105
Becker J-M, Ringle CM, Sarstedt M (2018) Estimating moderating effects in PLS-SEM, and PLSc-SEM: interaction term generation*data treatment. J Appl Struct Equ Model 2(2):1–21
Berraies S, Yahia KB, Hannachi M (2016) Identifying the effects of perceived values of Mobile banking applications on customers: comparative study between baby boomers, generation X, and generation Y. Int J Bank Mark. https://doi.org/10.1108/IJBM-09-2016-0137
Bhatiasevi V (2016) An extended UTAUT model to explain the adoption of mobile banking. Inf Dev 32(4):799–814. https://doi.org/10.1177/0266666915570764
Bhattacherjee A (2001) Understanding information systems continuance: an expectation–confirmation model. MIS Q 25(3):351–370. https://doi.org/10.2307/3250921
Boateng H, Adam DR, Okoe AF, Anning-Dorson T (2016) Assessing the determinants of internet banking adoption intentions: a social cognitive theory perspective. Comput Hum Behav 65:468–478. https://doi.org/10.1016/j.chb.2016.09.017
Boonsiritomachai W, Pitchayadejanant K (2017) Determinants affecting mobile banking adoption by generation Y based on the unified theory of acceptance, and use of technology model modified by the technology acceptance model concept. Kasetsart J Soc Sci. https://doi.org/10.1016/j.kjss.2017.10.005
Butt MM, Aftab M (2013) Incorporating attitude towards Halal banking in an integrated service quality, satisfaction, trust, and loyalty model in online Islamic banking context. Int J Bank Mark 31(1):6–23. https://doi.org/10.1108/02652321311292029
Cao Q, Niu X (2019) Integrating context-awareness, and UTAUT to explain Alipay user adoption. Int J Ind Ergon 69:9–13. https://doi.org/10.1016/j.ergon.2018.09.004
Chao X, Kou G, Peng Y, Viedma EH (2021) Large-scale group decision-making with non-cooperative behaviors and heterogeneous preferences: an application in financial inclusion. Eur J Oper Res 288(1):271–293. https://doi.org/10.1016/j.ejor.2020.05.047
Chaouali W, El-Hedhli K (2019) Toward a contagion-based model of mobile banking adoption. Int J Bank Mark 37(1):69–96. https://doi.org/10.1108/IJBM-05-2017-0096
Chaouali W, Yahia IB, Souiden N (2016) The interplay of counter-conformity motivation, social influence, and trust in customers' intention to adopt Internet banking services: the case of an emerging country. J Retail Consum Serv 28:209–218. https://doi.org/10.1016/j.jretconser.2015.10.007
Chauhan V, Yadav R, Choudhary V (2019) Analyzing the impact of consumer innovativeness, and perceived risk in internet banking adoption: a study of Indian consumers. Int J Bank Mark 37(1):323–339. https://doi.org/10.1108/IJBM-02-2018-0028
Chawla D, Joshi H (2017) High versus low consumer attitude, and intention towards adoption of mobile banking in India: an empirical study. Vision 21(4):410–424. https://doi.org/10.1177/0972262917733188
Chawla D, Joshi H (2018) The moderating effect of demographic variables on mobile banking adoption: an empirical investigation. Glob Bus Rev 19(3 suppl):S90–S113. https://doi.org/10.1177/0972150918757883
Chawla D, Joshi H (2019) Scale development, and validation for measuring the adoption of mobile banking services. Glob Bus Rev 20(2):434–457. https://doi.org/10.1177/0972150918825205
Chen Z, Li Y, Wu Y et al (2017) The transition from traditional banking to mobile internet finance: an organizational innovation perspective: a comparative study of Citibank and ICBC. Financ Innov 3:12. https://doi.org/10.1186/s40854-017-0062-0
Chin WW, Marcolin BL, Newsted PR (2003) A partial least squares latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study, and an electronic-mail emotion/adoption study. Inf Syst Res 14(2):189–217. https://doi.org/10.1080/10705510903439003
Chong AY, Ooi KB, Lin B, Tan BI (2010) Online banking adoption: an empirical analysis. Int J Bank Mark 28(4):267–287. https://doi.org/10.1108/02652321011054963
Dabholkar PA (1996) Consumer evaluations of new technology-based self-service options: an investigation of alternative models of service quality. Int J Res Mark 13(1):29–51. https://doi.org/10.1016/0167-8116(95)00027-5
Dahlberg T, Mallat N, Ondrus J, Zmijewska A (2008) Past, present, and future of mobile payments research: a literature review. Electron Commer Res Appl 7(2):165–181. https://doi.org/10.1016/j.elerap.2007.02.001
Davis FD (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q 13(3):319–340. https://doi.org/10.2307/249008
Davis FD, Bagozzi RP, Warshaw PR (1989) User acceptance of computer technology: a comparison of two theoretical models. Manag Sci 35:982–1003. https://doi.org/10.1287/mnsc.35.8.982
Evermann J, Tate M (2016) Assessing the predictive performance of structural equation model estimators. J Bus Res 69(10):4565–4582. https://doi.org/10.1016/j.jbusres.2016.03.050
Falk RF, Miller NB (1992) A primer for soft modeling. University of Akron Press, Akron, OH
Farah MF, Hasni MJ, Abbas AK (2018) Mobile banking adoption: empirical evidence from the banking sector in Pakistan. Int J Bank Mark. https://doi.org/10.1108/IJBM-10-2017-0215
Faroughian FF, Kalafatis SP, Ledden L, Samouel P, Tsogas MH (2011) Value, and risk in business-to-business e-banking. Ind Mark Manag 41:68–81
Fishbein M, Ajzen I (1975) Belief, attitude, intention, and behavior: an introduction to theory, and research. Addison-Wesley, Reading. https://doi.org/10.2307/3033786
Garson GD (2016) Partial least squares: regression and structural equation models (2016 edition). Statistical Associates Publishing. www.statisticalassociates.com
Gefen D, Straub DW, Boudreau MC (2000) Structural equation modeling techniques and regression: guidelines for research practice. Commun AIS 1:1–78
George A (2018) Perceptions of internet banking users: a structural equation modeling (SEM) approach. End-to-end J. https://doi.org/10.1016/j.iimb.2018.05.007
George A, Kumar GSG (2013) Antecedents of customer satisfaction in internet banking: technology acceptance model (TAM) redefined. Glob Bus Rev 14(4):627–638. https://doi.org/10.1177/0972150913501602
Giovanis A, Athanasopoulou P, Assimakopoulos C, Sarmaniotis C (2019) Adoption of mobile banking services: a comparative analysis of four competing theoretical models. Int J Bank Mark. https://doi.org/10.1108/IJBM-08-2018-0200
Haider MJ, Changchun G, Akram T, Hussain ST (2018) Exploring gender effects in intention to islamic mobile banking adoption: an empirical study. Arab Econ Bus J 13(1):25–38. https://doi.org/10.1016/j.aebj.2018.01.002
Hair JF, Ringle CM, Sarstedt M (2011) PLS-SEM: indeed a silver bullet. J Mark Theory Pract 19:139–151
Hair JF, Hult GTM, Ringle CM, Sarstedt M (2017a) A primer on partial least squares structural equation modeling (PLS-SEM), 2nd edn. Sage, Thousand Oaks
Hair J, Hollingsworth CL, Randolph AB, Chong AY (2017b) An updated, and expanded assessment of PLS-SEM in information systems research. Ind Manag Data Syst 117(3):442–458. https://doi.org/10.1108/IMDS-04-2016-0130
Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019a) When to use, and how to report the results of PLS-SEM. Eur Bus Rev 31(1):2–24. https://doi.org/10.1108/EBR-11-2018-0203
Hair JF, Risher JJ, Sarstedt M, Ringle CM (2019b) Rethinking some of the rethinking of partial least squares. Eur J Mark. https://doi.org/10.1108/EJM-10-2018-0665
Hama Khan YM (2019) An essential review of internet banking services in developing countries. e-Finanse 15(2):73–86. https://doi.org/10.2478/fiqf-2019-0013
Hamakhan YM (2020) An empirical investigation of e-banking in the Kurdistan Region of Iraq: the moderating effect of attitude. Financ Internet Q 16(1):45–66. https://doi.org/10.2478/fiqf-2020-0006
Hamidi H, Safareyeh M (2018) A model to analyze the effect of mobile banking adoption on customer interaction, and satisfaction: a case study of m-banking in Iran. Telemat Informat. https://doi.org/10.1016/j.tele.2018.09.008
Hammoud J, Bizri RM, El Baba I (2018) The impact of e-banking service quality on customer satisfaction: evidence from the Lebanese banking sector. SAGE Open. https://doi.org/10.1177/2158244018790633
Hanafizadeh P, Behboudi M, Koshksaray AA, Tabar MJ (2012) Mobile-banking adoption by Iranian bank clients. Telemat Inform 31(2014):62–78
Henseler J, Chin WW (2010) A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Struct Equ Model Multidiscipl J 17(1):82–109
Henseler J, Ringle CM, Sinkovics RR (2009) The use of partial least squares path modeling in international marketing. In: Sinkovics RR, Ghauri PN (eds) Advances in international marketing. Bingley, Emerald, pp 277–320. https://doi.org/10.1108/S1474-7979(2009)0000020014
Henseler J, Fassott G, Dijkstra TK, Wilson B (2012) Analysing quadratic effects of formative constructs by means of variance-based structural equation modelling. Eur J Inf Syst 21(1):99–112
Hoehle H, Lehmann H (2008) Exploring the state-of-the-art of mobile banking literature. In: 7th Global mobility roundtable conference, proceedings published by University of Auckland, New Zeal
Hoehle H, Scornavacca E, Huff S (2012) Three decades of research on consumer adoption, and utilization of electronic banking channels: a literature analysis. Decis Support Syst 54(1):122–132. https://doi.org/10.1016/j.dss.2012.04.010
Huang SM, Shen WC, Yen DC, Chou LY (2011) IT governance: objectives and assurances in internet banking. Adv Acc Inc Adv Int Acc 27:406–414
Hulland J (1999) Use of partial least squares (PLS) in strategic management research: a review of four recent studies. Strateg Manag J 20:195–204
Hussain M, Mollik AT, Johns R, Rahman MS (2018) M-payment adoption for bottom of pyramid segment: an empirical investigation. Int J Bank Mark. https://doi.org/10.1108/IJBM-01-2018-0013
Jan MT, Abdullah K (2014) The impact of technology CSFs on customer satisfaction, and the role of trust: an empirical study of the banks in Malaysia. Int J Bank Mark 32(5):429–447. https://doi.org/10.1108/IJBM-11-2013-0139
Johnson D (2007) Achieving customer value from electronic channels through identity commitment, calculative commitment, and trust in technology. J Interact Mark 21(4):2–22. https://doi.org/10.1002/dir.20091
Jöreskog KG, Wold H (1982) The ML, and PLS techniques for modeling with latent variables: historical, and comparative aspects. In: Jöreskog KG, Wold H (eds) Systems under indirect observation: causality, structure, prediction, vol. 1. North Holland, Amsterdam, pp 263–270
Kaabachi S, Mrad SB, Petrescu M (2017) Consumer initial trust towards internet-only banks in France. Int J Bank Mark. https://doi.org/10.1108/IJBM-09-2016-0140
Kesharwani A, Bisht SS (2012) The impact of trust, and perceived risk on internet banking adoption in India: an extension of technology acceptance model. Int J Bank Mark 30(4):303–322. https://doi.org/10.1108/02652321211236923
Khan YH (2018) A short review of the electronic banking system. Reg Bus Stud 10(1):13–37. https://doi.org/10.33568/rbs.2333
Kingshott RPJ, Sharma P, Chung HFL (2018) The impact of relational versus technological resources on e-loyalty: a comparative study between local, national, and foreign branded banks. Ind Mark Manag 72:48–58. https://doi.org/10.1016/j.indmarman.2018.02.011
Koksal MH (2016) The intentions of Lebanese consumers to adopt mobile banking. Int J Bank Mark 34(3):327–346
Kou G, Peng Y, Wang G (2014) Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf Sci 275:1–12. https://doi.org/10.1016/j.ins.2014.02.137
Kumar VVR, Lall A, Mane T (2017) Extending the TAM model: intention of management students to use mobile banking: evidence from India. Glob Bus Rev 18(1):238–249. https://doi.org/10.1177/0972150916666991
Kumar A, Adlakaha A, Mukherjee K (2018) The effect of perceived security, and grievance redressal on continuance intention to use M-wallets in a developing country. Int J Bank Mark. https://doi.org/10.1108/IJBM-04-2017-0077
Lee MC (2008) Factors influencing the adoption of internet banking: an integration of TAM, and TPB with perceived risk, and perceived benefit. Electron Commer Res Appl 8(2009):130–141
Liébana-Cabanillas F, Muñoz-Leiva F, Sánchez-Fernández J et al (2016) The moderating effect of user experience on satisfaction with electronic banking: empirical evidence from the Spanish case. Inf Syst E-Bus Manag 14:141. https://doi.org/10.1007/s10257-015-0277-4
Lin FT, Wu HY, Tran TNN (2015) Internet banking adoption in a developing country: an empirical study in Vietnam. Inf Syst e-Bus Manag 13(2):267–287. https://doi.org/10.1007/s1025
López-Miguens MJ, Vázquez EG (2017) An integral model of e-loyalty from the consumer's perspective. Comput Hum Behav 72:397–411. https://doi.org/10.1016/j.chb.2017.02.003
Luo X, Li H, Zhang J, Shim JP (2010) Examining multi-dimensional trust, and multi-faceted risk in initial acceptance of emerging technologies: an empirical study of mobile banking services. Decis Support Syst 49:222–234
Mahmoud MA (2019) Gender, e-banking, and customer retention. J Glob Mark. https://doi.org/10.1080/08911762.2018.1513108
Malaquias RF, Hwang Y (2015) An empirical study on trust in mobile banking: a developing country perspective. Comput Hum Behav 54(2016):453–461
Malaquias FF, Hwang Y (2016) Trust in mobile banking under conditions of information asymmetry: empirical evidence from Brazil. Inf Dev 32(5):1600–1612. https://doi.org/10.1177/0266666915616164
Malaquias RF, Hwang Y (2019) Mobile banking use: a comparative study with Brazilian, and U.S. participants. Int J Inf Manag 44:132–140. https://doi.org/10.1016/j.ijinfomgt.2018.10.004
Marakarkandy B, Yajnik N, Dasgupta C (2017) Enabling internet banking adoption: an empirical examination with an augmented technology acceptance model (TAM). J Enterp Inf Manag 30(2):263–294. https://doi.org/10.1108/JEIM-10-2015-0094
Martins C, Oliveira T, Popovič A (2013) Understanding the Internet banking adoption: a unified theory of acceptance, and use of technology, and perceived risk application. Int J Inf Manag 34:1–13
McKnight DH, Choudhury V, Kacmar C (2002) Developing 125 and validating trust measures for e-commerce: an integrative typology. Inf Syst Res 13(3):334–359
Munoz-Leiva F, Climent-Climent S, Liébana-Cabanillas F (2017) Determinants of intention to use the mobile banking apps: an extension of the classic TAM model. Span J Mark ESIC 21:25–38. https://doi.org/10.1016/j.sjme.2016.12.001
Namahoot KS, Laohavichien T (2018) Assessing the intentions to use internet banking: the role of perceived risk, and trust as mediating factors. Int J Bank Mark. https://doi.org/10.1108/IJBM-11-2016-0159
Nazaritehrani A, Mashali B (2020) Development of E-banking channels and market share in developing countries. Financ Innov 6:12. https://doi.org/10.1186/s40854-020-0171-z
Oliveira T, Faria M, Thomas MA, Popovič A (2014) Extending the understanding of mobile banking adoption: when UTAUT meets TTF and ITM. Int J Inf Manag 34:689–703
Ooi KB, Tan GW (2016) Mobile technology acceptance model: an investigation using mobile users to explore smartphone credit card. Expert Syst Appl 59:33–46
Pavlou PA, Fygenson M (2006) Understanding, and predicting electronic commerce adoption: an extension of the theory of planned behaviour. MIS Q 30:115–143
Pikkarainen T, Pikkarainen K, Karjaluoto H, Pahnila S (2004) Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Res. https://doi.org/10.1108/10662240410542652
Rahi S, Ghani MA, Ngah AH (2019) Integration of unified theory of acceptance, and use of technology in internet banking adoption setting: evidence from Pakistan. Technol Soc. https://doi.org/10.1016/j.techsoc.2019.03.003
Riffai MM, Grant K, Edgar D (2011) Big TAM in Oman: exploring the promise of on-line banking, it's adoption by customers, and the challenges of banking in Oman. Int J Inf Manag 32:239–250
Rigdon EE, Ringle CM, Sarstedt M (2010) Structural modeling of heterogeneous data with partial least squares. In: Malhotra NK (ed) Review of marketing research. Sharpe, Armonk, pp 255–296. https://doi.org/10.1108/S1548-6435(2010)0000007011
Ringle CM, Wende S, Becker J-M (2015) "SmartPLS 3." Boenningstedt: SmartPLS GmbH. https://www.smartpls.com
Rodrigues LF, Oliveira A, Costa CJ (2016) Playing seriously: how gamification, and social cues influence bank customers to use gamified e-business applications. Comput Hum Behav 63:392–407. https://doi.org/10.1016/j.chb.2016.05.063
Roy SK, Balaji MS, Kesharwani A, Sekhon H (2017) Predicting Internet banking adoption in India: a perceived risk perspective. J Strat Mark 25:5–6. https://doi.org/10.1080/0965254X.2016.1148771
Saji TG, Paul D (2018) Behavioural intention to the use of mobile banking in Kerala: an application of extended classical technology acceptance model. Metamorphosis 17(2):111–119. https://doi.org/10.1177/0972622518792802
Salem MZ, Baidoun S, Walsh G (2019) Factors affecting Palestinian customers’ use of online banking services. Int J Bank Mark. https://doi.org/10.1108/IJBM-08-2018-0210
Salhieh L, Abu-Doleh J, Hijazi N (2011) The assessment of e-banking readiness in Jordan. Int J Islamic Middle Eastern Financ Manag 4(4):325–342
Sampaio CH, Ladeira WJ, Santini FD (2017) Apps for mobile banking, and customer satisfaction: a cross-cultural study. Int J Bank Mark. https://doi.org/10.1108/IJBM-09-2015-0146
Sánchez-Torres JA, Arroyo X, Sandoval AV, Sánchez-Alzate JA (2018) E-banking in Colombia: factors favouring it's acceptance, online trust, and government support. Int J Bank Mark 36(1):170–181. https://doi.org/10.1108/IJBM-10-2016-0145
Sarstedt M, Hair JF, Cheah J-H et al (2019) How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australas Mark J. https://doi.org/10.1016/j.ausmj.2019.05.003
Shankar A, Jebarajakirthy C (2019) The influence of e-banking service quality on customer loyalty: a moderated mediation approach. Int J Bank Mark. https://doi.org/10.1108/IJBM-03-2018-0063
Shareef MA, Baabdullah A, Dutta S, Kumar V, Dwivedi YK (2018) Consumer adoption of mobile banking services: an empirical examination of factors according to adoption stages. J Retail Consum Serv 43:54–67. https://doi.org/10.1016/j.jretconser.2018.03.003
Sharma SK, Sharma M (2019) Examining the role of trust, and quality dimensions in the actual usage of mobile banking services: an empirical investigation. Int J Inf Manag 44:65–75. https://doi.org/10.1016/j.ijinfomgt.2018.09.013
Sharma PN, Shmueli G, Sarstedt M, Danks N, Ray S (2019) Prediction-oriented model selection in partial least squares path modeling. Decis Sci. https://doi.org/10.1111/deci.12329
Shen F, Zhao X, Kou G (2020) Three-stage reject inference learning framework for credit scoring using unsupervised tractor learning and three-way decision theory. Decis Support Syst 137:113366. https://doi.org/10.1016/j.dss.2020.113366
Shmueli G, Ray S, Velasquez Estrada JM, Chatla SB (2016) The Elephant in the room: evaluating the predictive performance of PLS models. J Bus Res 69(10):4552–4564. https://doi.org/10.1016/j.jbusres.2016.03.049
Shmueli G, Sarstedt M, Hair JF, Cheah J-H, Ting H, Vaithilingam S, Ringle CM (2019) Predictive model assessment in PLS-SEM: guidelines for using PLSpredict. Eur J Mark. https://doi.org/10.1108/EJM-02-2019-0189
Sikdar P, Kumar A, Makkad M (2015) Online banking adoption: a factor validation, and satisfaction causation study in the context of Indian banking customers. Int J Bank Mark 33(6):760–785. https://doi.org/10.1108/IJBM-11-2014-0161
Sinha I, Mukherjee S (2016) Acceptance of technology, related factors in use of off branch e-banking: an Indian case study. J High Technol Manag Res 27(1):88–100. https://doi.org/10.1016/j.hitech.2016.04.008
Siyal AW, Donghong D, Umrani WA, Siyal S, Bhand S (2019) Predicting mobile banking acceptance, and loyalty in Chinese bank customers. SAGE Open. https://doi.org/10.1177/2158244019844084
Sohail MS, Shanmugham B (2002) E-banking, and customer preferences in Malaysia: an empirical investigation. Inf Sci 150(2003):207–217
Susanto A, Lee H, Zo H, Ciganek AP (2013) User acceptance of Internet banking in Indonesia: initial trust formation. Inf Dev 29(4):309–322. https://doi.org/10.1177/0266666912467449
Szopiński TS (2016) Factors affecting the adoption of online banking in Poland. J Bus Res 08992:6
Tam C, Oliveira T (2016a) Performance impact of mobile banking: using the task-technology fit (TTF) approach. Int J Bank Mark 34(4):434–457
Tam C, Oliveira T (2016b) Understanding the impact of m-banking on individual performance: DeLone & McLean and TTF perspective. Comput Hum Behav 61:233–244
Tam C, Oliveira T (2017) Literature review of mobile banking, and individual performance. Int J Bank Mark 35(7):1042–1065. https://doi.org/10.1108/IJBM-09-2015-0143
Tan E, Lau JL (2016) Behavioural intention to adopt mobile banking among the millennial generation. Young Consum 17(1):18–31
Tan M, Thompson SHT (2000) Factors influencing the adoption of Internet banking. J Assoc Inf Syst 1(July):1–43
Thakur R (2014) What keeps mobile banking customers loyal? Int J Bank Mark 32(7):628–646. https://doi.org/10.1108/IJBM-07-2013-0062
USAID (2008) Kurdistan region, economic development assessment final report, RTI-International December 2008. This publication was produced for review by the United States Agency for International Development
van Esterik-Plasmeijer PW, van Raaij WF (2017) Banking system trust, bank trust, and bank loyalty. Int J Bank Mark 35(1):97–111. https://doi.org/10.1108/IJBM-12-2015-0195
Venkatesh V (2000) Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Inf Syst Res 11(4):342–365. https://doi.org/10.1287/isre.11.4.342.11872
Venkatesh V, Bala H (2008) Technology acceptance model 3, and a research agenda on interventions. Decis Sci 39:273–315. https://doi.org/10.1111/j.1540-5915.2008.00192.x
Venkatesh V, Brown A (2001) A longitudinal investigation of personal computers in homes: adoption determinants, and emerging challenges. MIS Q 25(1):71–102
Venkatesh V, Davis FD (2000) A theoretical extension of the technology acceptance model: four longitudinal field studies. Manag Sci 46(2):186–204. https://doi.org/10.1287/mnsc.22.214.171.12426
Venkatesh V, Zhang X (2010) Unified theory of acceptance, and use of technology: U.S. vs. China. J Glob Inf Technol Manag 13(1):5–27. https://doi.org/10.1080/1097198X.2010.10856507
Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: toward a unified view. MIS Q 27(3):425–478
Venkatesh V, Morris MG, Sykes TA, Ackerman PL (2004) Individual reactions to new technologies in the workplace: the role of gender as a psychological construct. J Appl Soc Psychol 34:445–467
Venkatesh V, Davis FD, Morris MG (2007) Dead or alive? The development, trajectory, and future of technology adoption research. J Assoc Inf Syst 8:267–286
Venkatesh V, Brown SA, Maruping LM, Bala H (2008) Predicting different conceptualizations of system use: the competing roles of behavioural intention, facilitating conditions, and behavioural expectation. MIS Q 32(3):483–502
Venkatesh V, Thong JY, Chan FK, Hu PJ, Brown SA (2011a) Extending the two-stage information systems continuance model: incorporating UTAUT predictors, and the role of context. Inf Syst J 21:527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x
Venkatesh V, Sykes TA, Zhang X (2011b) Just what the doctor ordered': a revised UTAUT for EMR system adoption, and use by doctors. In: 44th Hawaii international conference on system sciences, Kauai, HI, pp 1–10. https://doi.org/10.1109/HICSS.2011.1
Venkatesh V, Thong JYL, Xu X (2012) Consumer acceptance, and use of information technology: extending the unified theory of acceptance and use of technology. MIS Q 36:157–178. https://doi.org/10.2307/41410412
Venkatesh V, Thong J, Xu X (2016) Unified theory of acceptance, and use of technology: a synthesis, and the road ahead. J Assoc Inf Syst 17(5):328–376
Wang M, Cho S, Denton T (2017) The impact of personalization, and compatibility with past experience on e-banking usage. Int J Bank Mark 35(1):45–55. https://doi.org/10.1108/IJBM-04-2015-0046
Wang H, Kou G, Peng Y (2020) Multi-class misclassification cost matrix for credit ratings in peer-to-peer lending. J Oper Res Soc. https://doi.org/10.1080/01605682.2019.1705193
Warsame MH, Ireri EM (2018) Moderation effect on mobile microfinance services in Kenya: an extended UTAUT model. J Behav Exp Financ. https://doi.org/10.1016/j.jbef.2018.01.008
Wen F, Xu L, Ouyang G, Kou G (2019) Retail investor attention and stock price crash risk: evidence from China. Int Rev Financ Anal 65:101376. https://doi.org/10.1016/j.irfa.2019.101376
Yadav R, Chauhan V, Pathak GS (2015) Intention to adopt internet banking in an emerging economy: a perspective of Indian youth. Int J Bank Mark 33(4):530–544. https://doi.org/10.1108/IJBM-06-2014-0075
Yap KB, Wong DH, Loh C, Bak R (2010) Offline, and online banking – where to draw the line when building trust in e‐banking?. Int J Bank Market 28(1):27-46. https://doi.org/10.1108/02652321011013571
Yaseen SG, El Qirem IA (2018) Intention to use e-banking services in the Jordanian commercial banks. Int J Bank Mark 36(3):557–571. https://doi.org/10.1108/IJBM-05-2017-0082
Yiga C, Cha KJ (2016) Toward understanding the importance of trust in influencing Internet banking adoption in Uganda. Inf Dev 32(3):622–636. https://doi.org/10.1177/0266666914563359
Yu CS, Asgarkhani M (2015) An investigation of trust in e-banking. Manag Res Rev 38(12):1267–1284
Yuan Y, Lai F, Chu Z (2019) Continuous usage intention of Internet banking: a commitment-trust model. Inf Syst e-Bus Manag 17(1):1–25. https://doi.org/10.1007/s10257-018-0372-4
Zhang Y, Weng Q, Zhu N (2018) The relationships between electronic banking adoption, and it's antecedents: a meta-analytic study of the role of national culture. Int J Inf Manag 40:76–87. https://doi.org/10.1016/j.ijinfomgt.2018.01.015
Zhao AL, Koenig-Lewis N, Hanmer-Lloyd S, Ward P (2010a) Adoption of internet banking services in China: is it all about trust? Int J Bank Mark 28(1):7–26. https://doi.org/10.1108/02652321011013562
Zhao X, Lynch JG, Chen Q (2010b) Reconsidering Baron and Kenny: myths and truths about mediation analysis. J Consum Res 37(2):197–206
Zhou T (2012) Understanding users’ initial trust in mobile banking: an elaboration likelihood perspective. Comput Hum Behav 28:1518–1525
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M. Hamakhan, Y.T. The effect of individual factors on user behaviour and the moderating role of trust: an empirical investigation of consumers’ acceptance of electronic banking in the Kurdistan Region of Iraq. Financ Innov 6, 43 (2020). https://doi.org/10.1186/s40854-020-00206-0