Customer behaviour in using livebank service in digital transformation of banking industry in vietnam

Nội dung text: Customer behaviour in using livebank service in digital transformation of banking industry in vietnam

1. CUSTOMER BEHAVIOUR IN USING LIVEBANK SERVICE IN DIGITAL TRANSFORMATION OF BANKING INDUSTRY IN VIETNAM Do Thi Binh1 – Vu Tuan Duong2 Abstract: In the time of digital transformation, consumer behavior and expectations are ever-evolving, forcing banks to deeply understand behavior of customers. This paper applied the technology acceptance model (TAM) to examine factors that affect customer behavior in using LiveBank service in the Vietnamese market. Through a data set from 450 respondents, structural equation modeling (SEM) analysis showed that e–service quality had positive impact on attitude towards use, perceived usefulness and perceived ease of use; perceived usefulness had positive impact on both attitude towards use and intention to use. However, while there was a positive link between perceived ease of use and perceived usefulness, the relationship between perceived ease of use and attitude towards use was not clear. Finally, attitude towards use positively affected to intention to use LiveBank service in digital transformation of bank industry. These findings provided implications for both banks’ managers and policy-makers to promote digital transformation in bank industry in Vietnam. Keywords: Digital transformation of the Banking industry; Customer behaviour; LiveBank; TAM model. 1. INTRODUCTION Industrial Revolution 4.0 is showing an enormous influence on economic activities on a global scale. The rapidly changing technology environment has created digital transformation requirements for business operations in general and the banking industry in particular. Many banks have considered digital transformation to be an important development goal in the future. Services such as internet banking, electronic payment wallets, mobile banking applications on smartphones have been widely deployed in the banking system, bringing many advantages to customers. The digitization of the banking system is also known as an effective solution to improve the overall service quality. Vietnam is a potential market for digital banking with a population of over 96 million people, in which the proportion of young people accounts for nearly 70%. Also, more than 62% of the population owns a smartphone, and 64 million internet users nationwide. The variety of connection channels from customers is also a potential opportunity for banks to deploy digital transformation service. LiveBank service appeared in Vietnam at the end of 2017 and has many advantages compared to traditional ATMs, linked with the ability to service 24/7, the ability to perform complex types of transaction such as opening accounts, depositing cash into accounts. LiveBank service is capable of helping customers perform transactions that require human elements without contacting tellers. The interaction and communication between the customer and the staff are performed through the camera. All implementation processes are strictly designed and ensure information security requirements. After more than three years in the market, the LiveBank transaction system has reached 150 transaction points and helped customers make nearly 2 million transactions. Especially up to 60% of transactions are completed outside office hours. 1 Faculty of Business Administration, Trade University. Email: binhdt@gmail.com. 2 Faculty of Business Administration, Trade University. Email: vutuanduong@tmu.edu.vn. 435
2. Abundant previous studies have evaluated factors affecting customer behavior when using banking service such as Lee et al. (1970), Malek et al. (1970), Giovanis et al. (2012). In these studies, technology acceptance models (TAM), UTAUT model, or TBP model are applied popularly. However, assessing the impact of these factors on customer behavior has not been considered in LiveBank service in Vietnam. Therefore, this paper objective is to examine factors that affect customer behavior in using LiveBank service by adopting technology acceptance model (TAM). 2. LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT 2.1. The TAM model The technology acceptance research model (TAM) was firstly proposed by Davis (1989). In this model, the usage is directly affected by the intention to use, and attitude towards use influence indirectly. Besides, factors including perceived ease of use, perceived usefulness, and external variables are also mentioned in the TAM model. The specific relationships of the elements in the TAM model are illustrated in Figure 1. Figure 1. Technology Acceptance Model (TAM) Source: Davis (1989) The TAM model is adapted and adopted in many types of research in service industries. Liu et al. (2010) developed the TAM model to explore the factors influencing online learning behavior intention. Meanwhile, Tung et al. (2008) applied this model to research the application of logistics information systems. Doherty et al. (2006) used the TAM model for online retail in financial services. The TAM model is also widely applied to studies of banking services such as Giovanis et al. (2012), Malek et al. (1970), Lule et al. (2012), Yousafzai et al. (2010). 2.2. Hypotheses Development 2.2.1. E-Service Quality Traditional service quality studies suggest that good quality units are packaged in a product or service (Ghobadian et al., 1994). Service quality is the measurement of the difference between the services provided and customer service expectations (Lewis and Booms, 1983). E-Service Quality covers all phases of customer interaction with the company: the degree to which electronic systems facilitate efficiency and effectiveness in delivering services (Prasuraman et al., 2005). Santos (2003) defined E-Service Quality as customer assessments and conclusions about excellence and quality of electronic services. O’Cass (2010) pointed out that E-Service quality has an impact on the usage attitude of customers. Ayo et al. (2016) indicated that E-Service Quality has a positive impact on customers' attitudes when using banking services. Fishbein & Ajzen (1975) and Ajzen (2005) argued that value derived from an object's ability (such as a policy or an action) has a significant effect on attitudes. Meanwhile, service quality in the majority of studies are concerning factors perceived value, satisfaction, behavioral intention (Cronin, 2000; Lai et al. 2009; Andreassen et al. 1998). The results of 436
3. these researches indicated that quality has a positive effect on the customer's perceptions. From the above analysis, the authors propose the following research hypothesis: H1: E–Service Quality has a positive impact on attitude towards use of customer when using LiveBank. The relationship between E-service quality, service quality, perceived usefulness, and perceived ease of use is mentioned in the studies of Tan et al. (2018), Chomchalao et al. (2013), Liao et al. (2009). Rodzi et al. (2016) with a wide range of service environments. As for banking services, Asmah (2015) has mentioned the relationship between system quality, information quality to perceived usefulness, and perceived ease of use. Khrais (2017) pointed out the relevance of connection quality to perceived usefulness and perceived ease of use. Besides, it was Venkatesh and Davis (2000) that extended the TAM model proposed in the past with the additional element is Output Quality. From the above arguments, two following research hypotheses are proposed: H2: E–service quality has a positive impact on the perceived usefulness of customers when using the LiveBank service. H3: E–service quality has a positive impact on customers' perceived ease of use when using LiveBank service. 2.2.2. Perceived Usefulness The perceived usefulness in the TAM model is one of two main factors influencing attitude towards use and also affecting the intention to use. In Electronic Banking activities, Perceived Usefulness is widely recognized through numerous studies (Guriting and Ndubisi, 2006; Jaruwachirathanakul et al., 2005). According to Davis et al. (1992), perceived usefulness refers to the consumer's perception of the outcome of an experience. Perceived usefulness is the degree to which a person perceives a particular system to drive job performance (Mathwick et al., 2001). Jahangir et al. (2008) examined perceived usefulness's relationship to customer attitude with electronic banking services. This relationship is also mentioned in the studies of banking services by Rose et al. (2006); Chau et al. (2003). The relationship between perceived usefulness and customer intention was verified in the studies of Gu et al. (2009); Wang et al. (2003); Shang et al. (2005). From the above analysis, hypothesis 4 and hypothesis 5 are proposed as follow: H4: Perceived usefulness has a positive impact on customers' attitude towards use when using LiveBank. H5: Perceived usefulness has a positive impact on the intention to use of the customer when using the LiveBank service. 2.2.3. Perceived Ease of Use Perceived ease of use is the degree to which consumers perceive a new product or service better than its replacement (Rogers, 1983). Zeithaml et al. (2002) rated ease of use as an innovation that ensures ease of understanding and use. Perceived ease of use is argued to be closely related to costs and benefits (Mathieson, 1991; Consult, 2002). Perceived ease of use, according to Doll et al. (1998) it is the level of confidence that believes using the system will not take much effort. Same as perceived usefulness, perceived ease of use is an important element in the TAM model. Perceived ease of use also has an impact on Attitude Towards Use and perceived usefulness. The studies of Eriksson et al. (2005), Gu et al. (2009), Lule et al. (2012) have verified the relationship of these three factors to electronic banking services. From the above analysis, following research hypotheses are proposed: H6: Perceived ease of use has a positive impact on attitude towards use when using LiveBank service. 437
4. H7: Perceived ease of use has a positive impact on perceived usefulness when using LiveBank service. 2.2.4. Attitude Towards Use Attitude is considered as one of the three important factors to consider human behavior in addition to demographics and beliefs (Assael, 1981). Ajzen and Fishbein (1980) argued that when individuals have a favorable attitude, it will be easier to buy products/services. Outstanding studies on customer behavior such as that of Davis (1989) with the TAM model, Ajzen (1991) with the TBP model, or research on the TRA model by Fishbein et al. (1975) all suggest mention attitude as an important factor influencing customer intention. Research by Yeow et al. (2008) has verified the relationship between attitude towards use to Behavioral Intention of customers when using online banking services in Australia. Wessels et al. (2010) pointed out that attitude shifting from consumer perception to their intention to use M–banking. The relationship between attitude towards use and intention to use in banking services is also pointed out in the studies of Liao et al. (1999), Oni et al. (2010) From the above analysis, we proposed hypothesis 8 as: H8: Attitude towards use has a positive impact on intention to use of customers when using LiveBank service. 2.3. Research Framework Based on above mentioned 8 hypotheses, he theoretical research framework is described in Figure 2: Figure 2. Research Model Source: Adapted from TAM model (Davis, 1989) 3. RESEARCH METHODOLOGY 3.1. Measures The scale of the study is referenced and applied from past banking service studies. The authors selected three factors that constitute E-Service Quality, including Responsiveness (Ibrahim et al., 2006; Lee and Lin, 2005; Jun et al., 2004; Johnston, 1995); Security (Yang et al., 2004; Jun et al., 2004; Yang and Jun 2002; Sohail et al., 2008) and Graphic quality (Fassnacht and Koese, 2006; Sohail et al., 2008; Zeithaml et al., 2000). These three groups of factors are commonly mentioned in studies on E-service quality and are consistent with the characteristics of LiveBank service in Vietnam. The perceived usefulness, perceived ease of use, and intention to use scales have applied the scales from studies of David's (1989) and Agarwal et al. (2000). The attitude towards use scale was adapted by Shih and Fang (2004). Since the questions in the survey are referenced from foreign studies, the contents of the questions will be translated into Vietnamese under the supervision of two professional translators. Before collecting data, the survey content was checked and reviewed by 2 PhDs in Marketing and 3 438
5. Managers in charge of LiveBank services at several commercial banks in Vietnam. The survey questionnaire is designed with three parts structure, part 1 includes the introduction and some notes for the respondents before answering the questionnaire. Part 2 includes questions with answers designed on a 7–point Likert scale that reflect a point of view from 1. Strongly disagree to 7. Strongly agree. Part 3 includes information related to demographic information and acknowledgments. 3.2. Data collection and Sample Data were collected from July to September 2020 using convenient sampling methods. Researchers stood in front of LiveBank transaction offices and request the help of customers to complete the survey information. Before answering, the researcher had questions to confirm the respondents' willingness to participate in the response. If respondents have problems with about the content they will receive the support of research researchers. To motivate respondents, each respondent will receive VND 25.000 (1 USD) after answering the survey. After 2 months of data collection, the number of respondents who responded to the survey was 479. After conducting the review and examination, 29 surveys were rejected because of errors in the lack of data. The research sample includes 450 surveys. Details on characteristics of the sample are described in Table 1. Table 1. Demographic profile of the respondents. Demographic Characteristic Frequency % Gender Male 216 48.00 Female 234 52.00 Age 18–30 Years old 242 53.78 30–45 Years old 159 35.33 > 45 Years old 49 10.89 Education level High school or lesser 8 1.78 Professional degree 42 9.33 College degree 106 23.56 University undergraduate 220 48.89 Postgraduate 74 16.44 Marital status Single 87 19.33 Currently married 202 44.89 Widowed 122 27.11 Divorced 39 8.67 Monthly income Under 10,000,000 VNĐ 115 25.56 10,000,000 – 20,000,000 VNĐ 203 45.11 20,000,000 – 30,000,000 VNĐ 74 16.44 Over 30,000,000 VNĐ 58 12.89 439
6. 3.3. Data Analysis Method The primary data of the study were analyzed by IBM SPSS24 and IBM AMOS 23 software. Cronbach Alpha, EFA, descriptive statistical analysis was analyzed with IBM SPSS 22 software. CFA confirmation factor, research hypothesis testing through SEM model analysis performed by IBM AMOS 23 software. 4. DATA ANALYSIS 4.1. Statistical Approaches The study tested the scale according to the standards of Hair et al. (2010), Cronbach Alpha test, and CFA confirmation factor analysis was performed to test the research model and evaluate the overall reliability, convergence standards. The research hypotheses are tested through the analysis of SEM models. The suitability level of the model is assessed through important indicators including χ2/df (Chi– square to degree–of–freedom ratio), GFI (Goodness of fit index), AGFI (Adjusted Goodness of fit index), CFI (Comparative fit index), TLI (Tucker and Lewis index) and RMSEA (Root mean square error of approximation). Testing standards according to Hu and Bentler studies; Hair et al. (2010), the criteria include: TLI > 0.9; GFI > 0.9; CFI > 0.9, AGFI > 0.9; χ2/df < 3 and RMSEA < 0.08. 4.2. Measurement Model, Construct Reliability and Validity Figure 3. Result of CFA Analysis 440
7. Analysis results show that the model includes 428 degrees of freedom. χ2/df = 2.243 (less than 3). GFI index = 0.949; TLI = 0.969; CFI = 0.975; AGFI = 0.912 (greater than 0.9), RMSEA = 0.035 (less than 0.08), so we can conclude that the model is suitable with the collected data. Table 2. Items, reliability and convergent validity Items FLs α CR AVE Security (Sohail et al., 2008) 0.817 0.818 0.600 Information regarding my banking activities is protected 0.756 My personal information is not shared with other sites 0.801 Easy options to cancel a transaction are provided 0.768 Graphic Quality (Zeithaml et al., 2000) 0.761 0.769 0.527 The text in the interface is very clear 0.654 The characters are easily recognizable 0.829 Images and instruction information are displayed properly 0.687 Responsiveness (Jun et al. 2001) 0.791 0.802 0.578 Service has fast operating speed 0.676 Ability to quickly handle customer problems 0.675 The service is really convenient 0.908 Perceived Usefulness (David, 1989; Agarwal et al , 2000) 0.772 0.775 0.536 Using LiveBank will increase the efficiency of my banking transactions. 0.847 Using LiveBank has many convenience in terms of time 0.664 Using LiveBank makes my transactions easier 0.684 Perceived ease of use (David, 1989) 0.839 0.843 0.644 Operation to use LiveBank is very easy 0.721 The process of learning the steps to use LiveBank is easy 0.886 Interaction using the service is easy to understand and clear 0.791 Attitude Towards Use (Shih and Fang, 2004) 0.765 0.771 0.529 Using LiveBank is a smart solution 0.625 Using LiveBank is a good idea 0.844 I really enjoy using LiveBank 0.705 Intention to use (David, 1989) 0.761 0.764 0.519 I intend to use LiveBank regularly 0.754 I have plan to use LiveBank service 0.657 I expect to use LiveBank in the future 0.746 Note: FLs: factor loadings; α: Cronbach’s alpha; CR: composite reliability; AVE: average variance extracted. 441
8. According to Hair et al. (2010), to evaluate the model's structure should consider the factors of Convergent and Discriminant Validity. In which, the Standardized Factor Loading should be greater than 0.5; CR (Composite Reliability) should be greater than 0.7, the Average Variance Extracted (AVE) should be greater than 0.5. SQRAVE index is larger than the Inter – Construct Correlation index. The results in Table 2 and Table 3 show that the results reached the standards of the testing requirements. Also, the results of Cronbach Alpha coefficient ranged from 0.761 to 0.839, all indexes correlated with the total variable (Item-to-Total Correlations) were greater than 0.5, so the model guarantees reliability (Hair et al., 2010). Table 3. Descriptive statistics and discriminant validity. Constructs Mean SD PU SEC RES GRAP ATT PEOU INT PU – Perceived Usefulness 4.5667 0.659 0.732 SEC – Security 4.7430 0.736 0.503 0.775 RES – Responsiveness 4.2785 0.674 0.210 0.325 0.760 GRAP – Graphic Quality 4.6652 0.690 0.318 0.498 0.401 0.726 ATT – Attitude Towards Use 4.8496 0.749 0.611 0.456 0.335 0.395 0.727 PEOU – Perceived Ease Of 4.4593 0.807 0.539 0.525 0.304 0.421 0.530 0.802 Use INT – Intention To Use 4.6378 0.648 0.469 0.719 0.641 0.554 0.493 0.573 0.720 Note: Diagonal value indicates the square root of AVE of construct; SD: standard deviation. 4.3. Hypothesis Testing Figure 4. Result of SEM model analysis 442
9. Structural equation modeling (SEM) analysis was applied to test 8 research hypotheses. The analysis results show that the model includes 178 degrees of freedom, χ2/df = 2.628 (less than 3). GFI index = 0.916; TLI = 0.910; CFI = 0.924; AGFI = 0.903 (greater than 0.9), RMSEA = 0.06 (less than 0.08); P–value = 0.000 (less than 0.05). The results also show that 7 out of 8 proposed research hypotheses are accepted with p–value significance less than 0.05. The hypothesis that perceived ease of use affects perceived usefulness is rejected with P–value = 0.184 (greater than 0.05). Details of the impact levels of the factors are described in Table 4. Table 4. SEM (structural equation modelling) results and hypotheses testing p– Hypotheses  S.E. C.R. Findings value E–Service Quality Attitude Towards Use 0.483 0.180 4.129 Supported E–Service Quality Perceived Usefulness 0.432 0.135 4.070 Supported E–Service Quality Perceived Ease Of Use 0.663 0.144 7.552 Supported Perceived Usefulness Attitude Towards Use 0.272 0.097 3.395 Supported Perceived Usefulness Intention To Use 0.221 0.088 2.771 0.006 Supported Perceived Ease of Use Attitude Towards Use 0.109 0.077 1.328 0.184 Not supported Perceived Ease of Use Perceived Usefulness 0.270 0.068 3.070 0.002 Supported Attitude Towards Use Intention To Use 0.474 0.077 5.563 Supported Note: p < 0.001; S.E: standard error. The analysis results show that, of the three constituent factors of E–Service Quality, Security plays the most important of the components of E–service quality with  = 0.76, and E–service quality has a significant impact on perceived usefulness and perceived ease of use with  respectively 0.432 and 0.483. Among the factors affecting attitude towards use, E–service quality has the strongest impact with  = 0.483. However, perceived ease of use does not affect attitude towards use (p–value = 0.184). Both perceived usefulness and attitude towards use have an impact on intention to use but attitude towards use has a much greater impact with  = 0.474. The adjusted R2 index of the model is 0.40, showing that the independent variables that explain 40% of the variation of the dependent variable are intention to use. 5. DISCUSSION, IMPLICATION AND LIMITATION 5.1. Discussion The research completed the objectives when the proposed research hypotheses are verified. In general, all relationships between the factors were accepted, except the relationship between perceived ease of use and attitude towards use is not clear. Also, the element of E–service quality with three constituent factors inclusive of security, graphic quality, and responsiveness showed the impact on perceived usefulness, perceived ease of use, and attitude towards use. This relationship is similar to the research results of Liao et al. (2009) or Tan et al. (2018). The security factor was identified as the most important factor for E–service quality with = 0.76 and a high average value (Mean = 4.7430). These findings showed that even with digital services applying high technologies, customers are still very 443
10. concerned about security issues. Not only LiveBank service but also with some researches of Jun et al. (2004); Yang and Jun (2002) with other types of banking services also indicated that the factor of security is the factor that customers care. E–service quality had an impact on perceived ease of use as well as attitude towards use, but the relationship between perceived ease of use and attitude towards use was not clear. Besides, customer reviews with perceived ease of use were at a low value (Mean = 4.4593). This problem can be explained for two reasons as follows. Firstly, customers in the Vietnam market highly appreciate the quality factors of LiveBank service but do not feel the ease of use and manipulation when experiencing this type of service. Besides, the ease of using the LiveBank service is not enough to affect the positive attitudes of customers. Secondly, customers have a comparison between LiveBank service and other services and recognized that modern technological elements equipped for LiveBank have not received a high assessment from customers and are not effective in solving customer needs. 5.2. Implications From the research findings, the study proposed several implications and policies for banks' managers and policymakers in the context of the need to promote digital transformation for the banking industry. Firstly, banks should improve E–service quality that had an important impact on both perceived usefulness, perceived ease of use, and attitude towards use. In particular, the security factor considers focused on because this is an issue that customers expect to be better. However, the application of modern science and technology to the LiveBank service should be considered to suit the usage and exploitation capacity of customers. The LiveBank service in the Vietnamese market has not yet fully utilized its equipped modern functions. Partly because customers do not want to exploit, banks have not been able to assist customers in discovering all the preeminent features of this new service. So the banks' manager needs solutions to introduce and guide customers to use these modern features intelligently. Secondly, the problem of poor customer feedback has also been shown. Digital transformation is intended to increase the convenience and ability to serve customers, but it has not been highly appreciated also raises some problems to overcome in the future related to the speed of information processing of the system and the ability to interact with customers of online consulting staff. Thirdly, for the policymaker, the research results have also shown positive signals about the effectiveness of the LiveBank service in terms of customer perceptions. The perceived usefulness factor shows a clear impact on the attitude towards use and intention to use of customers. Besides, promoting the speed of digital transformation through the application of modern technologies needs to suit the usability of customers to be implemented. Fourthly, the policies to apply technology to drive digital transformation should be considered with the convenience of customers. These types of technologies need to be easy to manipulate and do not take much time to do them properly. 5.3. Limitation The research still has limitations that need to complement in future studies. Firstly, LiveBank service is a new type of service and the range of application is still narrow, mainly concentrated in big cities. Therefore, to evaluate the effectiveness of this type of service as well as its impact on digital transformation, it is necessary to expand research with a larger sample when the LiveBank service develops. Secondly, multi–group tests have not been conducted to clarify the impact of factors on different sample groups. Thirdly, the research's R2 adjusted coefficient for the dependent variable was 444
11. at 0.40 so there still exist other factors affecting 60% of the change of intention to use of customers using LiveBank service. The studies in the future should add some variables to the role of independent variables such as personal value, subjective norms, or barriers variables. REFERENCE 1. Andreassen. W, T. and Lindestad, B. (1998), Customer loyalty and complex services: The impact of corporate image on quality, customer satisfaction and loyalty for customers with varying degrees of service expertise, International Journal of Service Industry Management, Vol. 9 No. 1, pp. 7–2334. 2. Agarwal, R. and Prasad, J. (2000), A field study of the adoption of software process innovations by information systems professionals, IEEE Transactions on Engineering Management, Vol. 47 No. 3, pp. 298–308. 3. Ajzen, I. and Fishbein, M. (1980), Understanding Attitudes and Predicting Social Behavior, Prentice– Hall, New York, NY. 4. Ajzen, I., 1991. The Theory of Planned Behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211 5. Amin, H. (1970). Internet banking adoption among young intellectuals. The Journal of Internet Banking and Commerce, 12(3), 1–13. 6. Assael, H. (1981), Consumer Behavior and Marketing Action, 3rd ed., PWS–Kent Publishing Company, Boston, MA. 7. Asmah, A. E. (2015). Factors that Affect Banks’ Acceptance of Electronic Cheque Clearing System: Evidence from Ghana (Doctoral dissertation, University of Ghana). 8. Ayo, C. k., Oni, A. A., Adewoye, O. J., & Eweoya, I. O. (2016). E–banking users’ behaviour: e– service quality, attitude, and customer satisfaction. International Journal of Bank Marketing, 34(3), 347–367. 9. Chau, P. Y., & Lai, V. S. (2003). An empirical investigation of the determinants of user acceptance of internet banking. Journal of organizational computing and electronic commerce, 13(2), 123–145. 10. Davis, F.D. (1989), Perceived usefulness, perceived ease of use and user of acceptance of information technology, MIS Quarterly, Vol. 3 No. 3, pp. 319–40. 11. Doherty, N. F., Ellis‐Chadwick, F., McKechnie, S., Winklhofer, H., & Ennew, C. (2006). Applying the technology acceptance model to the online retailing of financial services. International Journal of Retail & Distribution Management. 12. Doll, W.J., Hendrickson, A. and Deng, X. (1998), Using Davis’s perceived usefulness and ease–of– use instruments for decision making: a confirmatory and multigroup invariance analysis, Decision Sciences Journal, Vol. 29 No. 4, pp. 839–69. 13. Eriksson, K., Kerem, K., & Nilsson, D. (2005). Customer acceptance of internet banking in Estonia. International Journal of Bank Marketing, 23(2), 200–216. 14. Fassnacht, M., Koese, I., 2006. Quality of electronic services: conceptualizing and testing a hierarchical model. Journal of Service Research 9 (1), 19–37. 15. Fishbein, M. and Ajzen, I. (1975), Belief, Attitude, IntentionandBehavior:AnIntroductionto Theory and Research, Addison–Wesley, Reading, MA. 16. Ghobadian, A., Speller, S., Jones, M., (1994). Service quality concepts and models. Int. J. Qual. Reliability Manage. 11, 43–66. 17. Giovanis, A. N., Binioris, S., & Polychronopoulos, G. (2012). An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece. EuroMed Journal of Business. 445
12. 18. Gu, J. C., Lee, S. C., & Suh, Y. H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605–11616. 19. Guriting, P., & Ndubisi, N. O. (2006). Borneo online banking: evaluating customer perceptions and behavioural intention. Management research news. 20. Hair, J.F., Anderson, R.E., Babin, B.J. & Black, W.C. (2010), Multivariate Data Snalysis: A global Perspective, Upper Saddle River, Pearson, N.J., USA. 21. Ibrahim, E.E., Joseph, M., Ibeh, K.I.N., 2006. Customers’ perception of electronic service delivery in the UK retail banking sector. International Journal of Bank Marketing 24 (7), 475–493. 22. Jahangir, N., & Begum, N. (2008). The role of perceived usefulness, perceived ease of use, security and privacy, and customer attitude to engender customer adaptation in the context of electronic banking. African journal of business management, 2(2), 032–040. 23. Jun, M., Yang, Z., Kim, D., 2004. Customers’ perceptions of online retailing service quality and their satisfaction. International Journal of Quality and Reliability Management 2004 21 (8), 817–840. 24. Jun, M., & Cai, S. (2001). The key determinants of internet banking service quality: a content analysis. International journal of bank marketing. 25. Khrais, L. T. (2017). Framework for measuring the convenience of advanced technology on user perceptions of Internet banking systems. Journal of internet banking and commerce, 22(3), 1–17. 26. Lee, K. S., Lee, H. S., & Kim, S. Y. (1970). Factors influencing the adoption behavior of mobile banking: a South Korean perspective. The Journal of Internet Banking and Commerce, 12(2), 1–9. 27. Lee, G., Lin, H., 2005. Customer perceptions of e-service quality in online shopping. International Journal of Retail and Distribution Management 33 (2), 161–176. 28. Liao, C. H., & Tsou, C. W. (2009). User acceptance of computer-mediated communication: The SkypeOut case. Expert Systems with Applications, 36(3), 4595–4603. 29. Liao, S., Shao, Y. P., Wang, H., & Chen, A. (1999). The adoption of virtual banking: an empirical study. International journal of information management, 19(1), 63–74. 30. Liu, I. F., Chen, M. C., Sun, Y. S., Wible, D., & Kuo, C. H. (2010). Extending the TAM model to explore the factors that affect Intention to Use an Online Learning Community. Computers & education, 54(2), 600–610. 31. Lule, I., Omwansa, T. K., & Waema, T. M. (2012). Application of technology acceptance model (TAM) in m–banking adoption in Kenya. International Journal of Computing & ICT Research, 6(1). 32. Malek, A. M., & Mat, N. K. N. (1970). Modeling the antecedents of internet banking service adoption (IBSA) in Jordan: A Structural Equation Modeling (SEM) approach. The Journal of Internet Banking and Commerce, 16(1), 1–15. 33. Mathwick C, Rigdon Malhotra NK (2001). The effect of dynamic retail experiences on experiential perceptions of value: an Internet and catalog comparison. J. Retailing, 78(1): 51–60. 34. Oni, A. A., & Ayo, C. K. (2010). An empirical investigation of the level of users’ acceptance of e- banking in Nigeria. Journal of internet banking and commerce, 15(1), 1–13. 35. Parasuraman, A., Zeithaml, V. A. and Malhotra A. M. (2005), E–S–QUAL: a multiple-item scale for assessing electronic service quality. Journal of Service Research, Vol. 7, pp. 213–233. 36. Rodzi, F. N. A., Nasir, E. A. M., Azmi, A. L. M., Abdullah, D., Azmi, A., & Kamal, S. B. M. (2016). The role of compatibility, information quality and e–service quality in predicting mobile hotel booking adoption: A conceptual framework. International Academic Research Journal of Business and Technology, 2(2), 123–128. 37. Rose, J., & Fogarty, G. J. (2006). Determinants of perceived usefulness and perceived ease of use in the technology acceptance model: senior consumers' adoption of self–service banking technologies. In Proceedings of the 2nd Biennial Conference of the Academy of World Business, Marketing and 446
13. Management Development: Business Across Borders in the 21st Century (Vol. 2, pp. 122–129). Academy of World Business, Marketing and Management Development. 38. Santos, J. (2003). e–Service quality: a model of virtual service quality dimensions. Managing Service Quality, 13, 233–246. 39. Shang, R. A., Chen, Y. C., & Shen, L. (2005). Extrinsic versus intrinsic motivations for consumers to shop on–line. Information and Management, 42(3), 401–413. 40. Shih, Y. Y., & Fang, K. (2004). The use of a decomposed theory of planned behavior to study Internet banking in Taiwan. Internet research. 41. Sohail, M. S., & Shaikh, N. M. (2008). Internet banking and quality of service. Online Information Review. 42. Supaporn Chomchalao and Thanakorn Naenna. (2013). Influence of System Traits and Personal Traits on the Acceptance of e–Government Service. Information Technology Journal, 12: 880–893. 43. Tan, P. J. B., & Hsu, M. H. (2018). Management of Educational Needs of Employees in the Electronics Industry Using English e–Learning Website Programs. In Management of Information Systems. IntechOpen. 44. Tung, F. C., Chang, S. C., & Chou, C. M. (2008). An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International journal of medical informatics, 77(5), 324–335. 45. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425–478. 46. Venkatesh,V. (2000). Determinants of perceived ease of use: integrating control, intrinsic motivation, and emotion into the technology acceptance model. Information Systems Research, 11(4), 342–365. 47. Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46 (2), 186–204. 48. Wang, Y. S., Wang, Y. M., Lin, Y. M., & Tang, T. I. (2003). Determinants of user acceptance of internet banking: An empirical study. International Journal of Service Industry Management, 14(5), 501–519. 49. Wessels, L., & Drennan, J. (2010). An investigation of consumer acceptance of M‐banking. International Journal of bank marketing. 50. Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2010). Explaining internet banking behavior: theory of reasoned action, theory of planned behavior, or technology acceptance model?. Journal of applied social psychology, 40(5), 1172–1202. 51. Yang, Z., Jun, M., Peterson, R.T., 2004. Measuring customer perceived online service quality: scale development and managerial implications. International Journal of Operations and Production Management 21 (11), 1149–1174. 52. Yang, Z., Jun, M., 2002. Consumer perception of e–service quality: form purchaser and non purchaser perspectives. Journal of Business Strategies 19 (1), 19–41. 53. Yeow, P. H., Yuen, Y. Y., Tong, D. Y. K., & Lim, N. (2008). User acceptance of online banking service in Australia. Communications of the IBIMA, 1(22), 191–197. 54. Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2000). A conceptual framework for understanding e–service quality: implications for future research and managerial practice (No. 115). Cambridge, MA: Marketing Science Institute. 447