Segmentation of customers based on behavioral intention to use multi-channel banking and experience

Abstract

This study aims to segment customers in an emerging economy based on behavioral intention to use multi-channel banking and experience. The study also profiles the customer segments in terms of perceived ease of use, perceived usefulness, perceived risk, and innovativeness by using the Extented Technology Acceptance Model. An online survey was distributed by using convenience sampling in Turkey, and a total of 164 financial customers participated in the survey. A two-step cluster analysis was conducted to segment customers on behavioral intention to use multi-channel banking and experience scores. The profiles of the clusters were then examined according to perceived ease of use, perceived usefulness, perceived risk, and innovativeness. As a result of clustering analysis, financial customers were divided into three segments including “enthusiastic experts”, which had the share of nearly half of the sample, “reluctant experts” and “reluctant amateurs”. A non-parametric one-way ANOVA on ranks revealed differences among the segments related to perceived ease of use, perceived usefulness, perceived risk, and innovativeness. The findings of this study can respond to international investors who would be interested in the banking industry in a developing country. This study also offers essential insights to understand distinct consumer segments according to behavioral intention to use multi-channel banking and experience.

Keywords

• Multi-channel banking,intention,experience,technology acceptance model,cluster analysis,emerging economy

Çok kanallı bankacılığı kullanma davranışsal niyetine ve deneyime göre müşterilerin kümelenmesi

Öz

Bu çalışma, gelişmekte olan bir ekonomideki müşterileri çok kanallı bankacılığı kullanma davranışsal niyetine ve deneyime göre kümelere ayırmayı amaçlamaktadır. Çalışma aynı zamanda, Genişletilmiş Teknoloji Kabul Modeli kullanarak müşteri kümelerinin, algılanan kullanım kolaylığı, algılanan kullanışlılık, algılanan risk ve yenilikçilik açısından da profilini çizmektedir. Türkiye’de kolayda örnekleme yöntemi kullanılarak çevrimiçi bir anket dağıtılmış ve toplamda 164 finansal müşteri ankete katılmıştır. Müşterileri çok kanallı bankacılığı kullanma davranışsal niyetine ve deneyim skorlarına göre kümelere ayırmak için iki aşamalı kümeleme analizi yapılmıştır. Kümelerin profilleri daha sonra algılanan kullanım kolaylığı, algılanan kullanışlılık, algılanan risk ve yenilikçiliğe göre incelenmiştir. Kümeleme analizi sonucunda finansal müşteriler,“isteksiz uzmanlar”, “isteksiz amatörler” ve örneklemin yaklaşık yarısını oluşturan “hevesli uzmanlar” olmak üzere üç bölüme ayrılmıştır. Parametrik olmayan tek yönlü ANOVA ile algılanan kullanım kolaylığı, algılanan kullanışlılık, algılanan risk ve yenilikçiliğe bağlı olarak kümeler arasındaki farklılıklar ortaya çıkarılmıştır. Bu çalışmanın bulguları, gelişmekte olan bir ülkede bankacılık endüstrisine ilgi duyacak uluslararası yatırımcılara fikir verebilir. Bu çalışma aynı zamanda çok kanallı bankacılığı kullanma davranışsal niyetine ve deneyime göre farklı tüketici segmentlerini anlamak için önemli öngörüler sunmaktadır.

Anahtar Kelimeler

• Çok kanallı bankacılık,niyet,deneyim,teknoloji kabul modeli,kümeleme analizi,gelişmekte olan ekonomi

Kaynakça

1. Abadi, H. R. D., Ranjbarian, B. and Zade, F. K. (2012) ‘Investigate the Customers’ Behavioral Intention to Use Mobile Banking Based on TPB, TAM and Perceived Risk (A Case Study in Meli Bank)’, International Journal of Academic Research in Business and Social Sciences.
2. Abbas, S. K. et al. (2018) ‘What are the key determinants of mobile banking Adoption in Pakistan?’, International Journal of Scientific & Engineering Research. doi: 10.14299/ijser.2018.02.012.
3. Adams, D. A., Nelson, R. R. and Todd, P. A. (1992) ‘Perceived Usefulness, Ease of Use, and Usage of Information Technology: A Replication’, MIS Quarterly, 16(2), p. 227. doi: 10.2307/249577.
4. Alalwan, A. A. et al. (2016) ‘Consumer adoption of mobile banking in Jordan’, Journal of Enterprise Information Management. doi: 10.1108/jeim-04-2015-0035.
5. Alalwan, A. A. et al. (2018) ‘Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk’, Journal of Retailing and Consumer Services. doi: 10.1016/j.jretconser.2017.08.026.
6. Aldás-Manzano, J. et al. (2009) ‘The role of consumer innovativeness and perceived risk in online banking usage’, International Journal of Bank Marketing. doi: 10.1108/02652320910928245.
7. Alsamydai, M. J. (2014) ‘Adaptation of the Technology Acceptance Model (TAM) to the Use of Mobile Banking Services’, International Review of Management and Business Research.
8. Alsheikh, L. and Bojei, J. (2014) ‘Determinants affecting customer’s intention to adopt mobile banking in Saudi Arabia’, International Arab Journal of e-Technology.

9. Amin, H. et al. (2008) ‘The Adoption of Mobile Banking in Malaysia: The Case of Bank Islam Malaysia Berhad (Bimb)’, International Journal of Business and Society.
10. Arora, S. and Sahney, S. (2018) ‘Antecedents to consumers’ showrooming behaviour: an integrated TAM-TPB framework’, Journal of Consumer Marketing. doi: 10.1108/JCM-07-2016-1885.
11. Baabdullah, A. M. et al. (2019) ‘Consumer Adoption of Self-Service Technologies in the Context of the Jordanian Banking Industry: Examining the Moderating Role of Channel Types’, Information Systems Management. Taylor & Francis, 36(4), pp. 286–305. doi: 10.1080/10580530.2019.1651107.
12. Banking Regulation and Supervision Agency (2019) Indicators of Turkish Banking Industry. Available at: https://www.bddk.org.tr/ContentBddk/dokuman/duyuru_0789_01.pdf.
13. Bartlett, M. S. (1954) ‘A note on multiplying factors for various chi-squared approximations’, Joural of the Royal Statistical Society.
14. Bauer, R. A. (1960) ‘Consumer behavior as risk taking’, Risk Taking and Information Handling in Consumer Behavior.
15. Brown, I. et al. (2003) ‘Cell phone banking: Predictors of adoption in South Africa - An exploratory study’, International Journal of Information Management. doi: 10.1016/S0268-4012(03)00065-3.
16. Chaoqun Han and Shuiqing Yang (2010) ‘Value-based adoption of mobile banking service: A multi-channel perspective’, in 2010 International Conference on Information, Networking and Automation (ICINA). IEEE, pp. V2-506-V2-510. doi: 10.1109/ICINA.2010.5636459.
17. Chau, P. Y. K. (1996) ‘An Empirical Assessment of a Modified Technology Acceptance Model’, Journal of Management Information Systems. doi: 10.1080/07421222.1996.11518128.
18. Chauhan, V., Yadav, R. and Choudhary, V. (2019) ‘Analyzing the impact of consumer innovativeness and perceived risk in internet banking adoption’, International Journal of Bank Marketing, 37(1), pp. 323–339. doi: 10.1108/IJBM-02-2018-0028.
19. Chawla, D. and Joshi, H. (2017) ‘Consumer perspectives about mobile banking adoption in India – a cluster analysis’, International Journal of Bank Marketing, 35(4), pp. 616–636. doi: 10.1108/IJBM-03-2016-0037.
20. Chen, C. S. (2013) ‘Perceived risk, usage frequency of mobile banking services’, Managing Service Quality. doi: 10.1108/MSQ-10-2012-0137.
21. Chin, W. W. and Todd, P. A. (1995) ‘On the use, usefulness, and ease of use of structural equation modeling in mis research: A note of caution’, MIS Quarterly: Management Information Systems. doi: 10.2307/249690.
22. Chong, A. Y. L. et al. (2010) ‘Online banking adoption: An empirical analysis’, International Journal of Bank Marketing. doi: 10.1108/02652321011054963.
23. Coelho, F. and Easingwood, C. (2008) ‘A model of the antecedents of multiple channel usage’, Journal of Retailing and Consumer Services. doi: 10.1016/j.jretconser.2007.03.002.
24. Cortiñas, M., Chocarro, R. and Villanueva, M. L. (2010) ‘Understanding multi-channel banking customers’, Journal of Business Research. Elsevier Inc., 63(11), pp. 1215–1221. doi: 10.1016/j.jbusres.2009.10.020.
25. Dalla Pozza, I. et al. (2018) ‘Multichannel segmentation in the after-sales stage in the insurance industry’, International Journal of Bank Marketing, 36(6), pp. 1055–1072. doi: 10.1108/IJBM-11-2016-0174.
26. Davis, F. D. (1989) ‘Perceived usefulness, perceived ease of use, and user acceptance of information technology’, MIS Quarterly: Management Information Systems. doi: 10.2307/249008.
27. Davis, F. D. (1993) ‘User acceptance of information technology: system characteristics, user perceptions and behavioral impacts’, International Journal of Man-Machine Studies, 38(3), pp. 475–487. doi: 10.1006/imms.1993.1022.
28. Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989) ‘User Acceptance of Computer Technology: A Comparison of Two Theoretical Models’, Management Science. doi: 10.1287/mnsc.35.8.982.
29. Deb, M. and Lomo-David, E. (2014) ‘An empirical examination of customers’ adoption of m-banking in India’, Marketing Intelligence and Planning. doi: 10.1108/MIP-07-2013-0119.
30. Ennew, C. and Waite, N. (2007) Financial Services Marketing, Financial Services Marketing: An International Guide to Principles and Practice. Elsevier Ltd. doi: 10.4324/9780080465609.
31. Featherman, M. S. and Pavlou, P. A. (2003) ‘Predicting e-services adoption: a perceived risk facets perspective’, International Journal of Human-Computer Studies, 59(4), pp. 451–474. doi: 10.1016/S1071-5819(03)00111-3.
32. Frasquet, M., Ruiz-Molina, M. E. and Molla-Descals, A. (2015) ‘The role of the brand in driving online loyalty for multichannel retailers’, International Review of Retail, Distribution and Consumer Research. doi: 10.1080/09593969.2015.1081100.
33. Frimpong, K. et al. (2017) ‘Effect of inherent innovativeness and consumer readiness on attitudes to mobile banking’, Journal of Financial Services Marketing. doi: 10.1057/s41264-017-0037-2.
34. Giovanis, A. N., Binioris, S. and 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. doi: 10.1108/14502191211225365.
35. Gu, J. C., Lee, S. C. and Suh, Y. H. (2009) ‘Determinants of behavioral intention to mobile banking’, Expert Systems with Applications. doi: 10.1016/j.eswa.2009.03.024.
36. Guriting, P. and Oly Ndubisi, N. (2006) ‘Borneo online banking: Evaluating customer perceptions and behavioural intention’, Management Research News. doi: 10.1108/01409170610645402.
37. Hair, J. et al. (2010) ‘Multivariate Data Analysis: A Global Perspective’, in Multivariate Data Analysis: A Global Perspective.
38. Heinhuis, D. and De Vries, E. J. (2009) ‘Modelling customer behaviour in multi-channel service distribution’, in Lecture Notes in Business Information Processing. doi: 10.1007/978-3-642-01197-9_4.
39. Hua, G. (2008) ‘An experimental investigation of online banking adoption in China’, in 14th Americas Conference on Information Systems, AMCIS 2008.
40. Jaruwachirathanakul, B. and Fink, D. (2005) ‘Internet banking adoption strategies for a developing country: the case of Thailand’, Internet Research, 15(3), pp. 295–311. doi: 10.1108/10662240510602708.
41. Jham, V. (2009) ‘An empirical investigation of customer satisfaction with multi channel banking’, International Journal of Electronic Customer Relationship Management. doi: 10.1504/IJECRM.2009.025283.
42. Joo, Y. J., Lee, H. W. and Ham, Y. (2014) ‘Integrating user interface and personal innovativeness into the TAM for mobile learning in Cyber University’, Journal of Computing in Higher Education. doi: 10.1007/s12528-014-9081-2.
43. K Chau, P. Y. and K Lai, V. S. (2003) ‘An Empirical Investigation of the Determinants of User Acceptance of Internet Banking’, Journal Of Organizational Computing And Electronic Commerce.
44. Kaiser, H. F. (1974) ‘An index of factorial simplicity’, Psychometrika. doi: 10.1007/BF02291575.
45. Kesharwani, A. and Singh Bisht, S. (2012) ‘The impact of trust and perceived risk on internet banking adoption in India’, International Journal of Bank Marketing. Edited by S. K. Roy, 30(4), pp. 303–322. doi: 10.1108/02652321211236923.
46. De Keyser, A., Schepers, J. and Konuş, U. (2015) ‘Multichannel customer segmentation: Does the after-sales channel matter? A replication and extension’, International Journal of Research in Marketing. doi: 10.1016/j.ijresmar.2015.09.005.
47. Kim, J. and Lee, H. H. (2008) ‘Consumer product search and purchase behaviour using various retail channels: The role of perceived retail usefulness’, International Journal of Consumer Studies. doi: 10.1111/j.1470-6431.2008.00689.x.
48. Konuş, U., Verhoef, P. C. and Neslin, S. A. (2008) ‘Multichannel Shopper Segments and Their Covariates’, Journal of Retailing. doi: 10.1016/j.jretai.2008.09.002.
49. Koufaris, M. (2002) ‘Applying the Technology Acceptance Model and flow theory to online Consumer Behavior’, Information Systems Research. doi: 10.1287/isre.13.2.205.83.
50. Kumar, V. and Venkatesan, R. (2005) ‘Who are the multichannel shoppers and how do they perform?: Correlates of multichannel shopping behavior’, Journal of Interactive Marketing. doi: 10.1002/dir.20034.
51. Lassar, W. M., Manolis, C. and Lassar, S. S. (2005) ‘The relationship between consumer innovativeness, personal characteristics, and online banking adoption’, International Journal of Bank Marketing. doi: 10.1108/02652320510584403.
52. Laukkanen, T. (2007) ‘Customer preferred channel attributes in multi-channel electronic banking’, International Journal of Retail and Distribution Management. doi: 10.1108/09590550710743744.
53. Lee, E. J., Kwon, K. N. and Schumann, D. W. (2005) ‘Segmenting the non-adopter category in the diffusion of internet banking’, International Journal of Bank Marketing. doi: 10.1108/02652320510612483.
54. Lee, M. C. (2009) ‘Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit’, Electronic Commerce Research and Applications. doi: 10.1016/j.elerap.2008.11.006.
55. Lee, S. C. and Im, K. H. (2015) ‘Banking behavior in security and multi-channel environment’, Journal of Computer Virology and Hacking Techniques. doi: 10.1007/s11416-014-0235-6.
56. Lee, Y., Kozar, K. A. and Larsen, K. R. T. (2003) ‘The Technology Acceptance Model: Past, Present, and Future’, Communications of the Association for Information Systems. doi: 10.17705/1cais.01250.
57. Liao, Z. and Cheung, M. T. (2002) ‘Internet-based e-banking and consumer attitudes: An empirical study’, Information and Management. doi: 10.1016/S0378-7206(01)00097-0.
58. Lu, J., Yao, J. E. and Yu, C.-S. (2005) ‘Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology’, The Journal of Strategic Information Systems, 14(3), pp. 245–268. doi: 10.1016/j.jsis.2005.07.003.
59. Luo, X. et al. (2010) ‘Examining multi-dimensional trust and multi-faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services’, Decision Support Systems. doi: 10.1016/j.dss.2010.02.008.
60. Mann, B. J. S. and Sahni, S. K. (2012) ‘Profiling Adopter Categories of Internet Banking in India: An Empirical Study’, Vision: The Journal of Business Perspective, 16(4), pp. 283–295. doi: 10.1177/0972262912460187.
61. Manser Payne, E., Peltier, J. W. and Barger, V. A. (2017) ‘Omni-channel marketing, integrated marketing communications and consumer engagement: A research agenda’, Journal of Research in Interactive Marketing, 11(2), pp. 185–197. doi: 10.1108/JRIM-08-2016-0091.
62. Mathieson, K. (1991) ‘Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior’, Information Systems Research. doi: 10.1287/isre.2.3.173.
63. Mattsson, J. and Helmersson, H. (2005) ‘Internet banking: Modelling the e-competence of customers with a text-analytic CIT approach’, International Journal of Bank Marketing. doi: 10.1108/02652320510619594.
64. Mbama, C. I. and Ezepue, P. O. (2018) ‘Digital banking, customer experience and bank financial performance’, International Journal of Bank Marketing, 36(2), pp. 230–255. doi: 10.1108/IJBM-11-2016-0181.
65. Meidan, A. (1984) Bank Marketing Management, Bank Marketing Management. London: Macmillan Education UK. doi: 10.1007/978-1-349-86033-3.
66. Midgley, D. F. and Dowling, G. R. (1978) ‘Innovativeness: The Concept and Its Measurement’, Journal of Consumer Research. doi: 10.1086/208701.
67. Mutahar, A. M. et al. (2018) ‘The effect of awareness and perceived risk on the technology acceptance model (TAM): mobile banking in Yemen’, International Journal of Services and Standards. doi: 10.1504/IJSS.2018.091840.
68. Natarajan, T., Balasubramanian, S. A. and Kasilingam, D. L. (2017) ‘Understanding the intention to use mobile shopping applications and its influence on price sensitivity’, Journal of Retailing and Consumer Services, 37, pp. 8–22. doi: 10.1016/j.jretconser.2017.02.010.
69. Nistor, N. and Heymann, J. O. (2010) ‘Reconsidering the role of attitude in the TAM: An answer to Teo (2009a)’, British Journal of Educational Technology. doi: 10.1111/j.1467-8535.2010.01109.x.
70. Nunnally, J. (1978) Psychometric methods. New York: McGraw-Hill.
71. Paluri, R. A. and Mehra, S. (2016) ‘Financial attitude based segmentation of women in India: an exploratory study’, International Journal of Bank Marketing, 34(5), pp. 670–689. doi: 10.1108/IJBM-05-2015-0073.
72. Patel, K. and Brown, I. (2016) ‘Towards a Theory of Multi-Channel Banking Adoption amongst Consumers’, The Electronic Journal Information Systems Evaluation, 19(3), pp. 137–157.
73. Pavlou, P. A. (2003) ‘Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model’, International Journal of Electronic Commerce. doi: 10.1080/10864415.2003.11044275.
74. Pertiwi, E., Guihua, N. and Pingfeng, L. (2016) ‘The Influence of Omni-Channel Retailing on Indonesian SMEs Online and Offline Business Operations’, in PROCEEDINGS OF THE 13TH INTERNATIONAL CONFERENCE ON INNOVATION AND MANAGEMENT, VOLS I & II.
75. Pikkarainen, T. et al. (2004) ‘Consumer acceptance of online banking: An extension of the technology acceptance model’, Internet Research. doi: 10.1108/10662240410542652.
76. Plé, L. (2006) ‘Managing multichannel coordination in retail banking: The influence of customer participation’, International Journal of Bank Marketing. doi: 10.1108/02652320610681756.
77. Polasik, M. and Wisniewski, T. P. (2009) ‘Empirical analysis of internet banking adoption in Poland’, International Journal of Bank Marketing. doi: 10.1108/02652320910928227. Rajaobelina, L., Brun, I. and Ricard, L. (2019) ‘A classification of live chat service users in the banking industry’, International Journal of Bank Marketing, 37(3), pp. 838–857. doi: 10.1108/IJBM-03-2018-0051.
78. Ramayah, T. et al. (2003) ‘Receptiveness of Internet Banking By Malaysian Consumers : the Case of Penang’, Asian Academy of Management Journal.
79. Rogers, E. M. and Shoemaker, F. F. (1971) Communication of innovations: A cross-cultural approach, 2nd ed., Communication of innovations: A cross-cultural approach, 2nd ed.
80. Sanayei, A. and Bahmani, E. (2012) ‘Integrating TAM and TPB with perceived risk to measure customers’ acceptance of internet bankinga’, International Journal of Information Science and Management.
81. Shankar, A. and Kumari, P. (2016) ‘Factors affecting mobile banking adoption behavior in India’, Journal of Internet Banking and Commerce, 21(1).
82. Shanmugam, A., Savarimuthu, M. T. and Wen, T. C. (2014) ‘Factors affecting Malaysian behavioral intention to use mobile banking with mediating effects of attitude’, Academic Research International.
83. Sharma, S. K. et al. (2017) ‘A multi-analytical model for mobile banking adoption: a developing country perspective’, Review of International Business and Strategy, 27(1), pp. 133–148. doi: 10.1108/RIBS-11-2016-0074.
84. Siu, N. Y. and Chang, L. M. (2015) ‘A Study of Service Quality, Perceived Risk and Personal Innovativeness in Internet Banking’, in Revolution in Marketing: Market Driving Changes. Springer, pp. 78–83.
85. Slade, E. L. et al. (2015) ‘Modeling Consumers’ Adoption Intentions of Remote Mobile Payments in the United Kingdom: Extending UTAUT with Innovativeness, Risk, and Trust’, Psychology and Marketing. doi: 10.1002/mar.20823.
86. Sousa, R. and Voss, C. (2012) ‘The impacts of e-service quality on customer behaviour in multi-channel e-services’, Total Quality Management and Business Excellence. doi: 10.1080/14783363.2012.661139.
87. Sousa, R. and Voss, C. A. (2006) ‘Service quality in multichannel services employing virtual channels’, Journal of Service Research. doi: 10.1177/1094670506286324.
88. Swaminathan, J. M. and Tayur, S. R. (2003) ‘Models for supply chains in e-business’, Management Science. doi: 10.1287/mnsc.49.10.1387.17309.
89. Szajna, B. (1996) ‘Empirical evaluation of the revised technology acceptance model’, Management Science. doi: 10.1287/mnsc.42.1.85.
90. Teo, T., Faruk Ursavaş, Ö. and Bahçekapili, E. (2011) ‘Efficiency of the technology acceptance model to explain pre‐service teachers’ intention to use technology’, Campus-Wide Information Systems, 28(2), pp. 93–101. doi: 10.1108/10650741111117798.
91. Thakur, R. and Srivastava, M. (2014) ‘Adoption readiness, personal innovativeness, perceived risk and usage intention across customer groups for mobile payment services in India’, Internet Research. doi: 10.1108/IntR-12-2012-0244.
92. The Banks Association of Turkey (2020) Digital, Internet, and Mobile Banking Statistics. Available at: https://www.tbb.org.tr/Content/Upload/istatistikiraporlar/ekler/1289/Dijital-Internet-Mobil_Bankacilik_Istatistikleri-Mart_2020.pdf.
93. Thompson, R. L., Higgins, C. A. and Howell, J. M. (1991) ‘Personal computing: Toward a conceptual model of utilization’, MIS Quarterly: Management Information Systems. doi: 10.2307/249443.
94. Trading Economics (2020) GDP Growth Rate. Available at: https://tradingeconomics.com/country-list/gdp-growth-rate (Accessed: 10 March 2020).
95. Venkatesan, R., Kumar, V. and Ravishanker, N. (2007) ‘Multichannel shopping: Causes and consequences’, Journal of Marketing. doi: 10.1509/jmkg.71.2.114.
96. Venkatesh, V. et al. (2003) ‘User acceptance of information technology: Toward a unified view’, MIS Quarterly: Management Information Systems. doi: 10.2307/30036540.
97. Venkatesh, V. and Davis, F. D. (1996) ‘A Model of the Antecedents of Perceived Ease of Use: Development and Test’, Decision Sciences. doi: 10.1111/j.1540-5915.1996.tb01822.x.
98. Venkatesh, V., Thong, J. Y. L. and Xu, X. (2012) ‘Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology’, MIS Quarterly: Management Information Systems. doi: 10.2307/41410412.
99. Wang, S. W. et al. (2014) ‘Virtually Compatible or Risky Business? Investigating Consumers’ Proclivity Toward Online Banking Services’, Journal of Marketing Channels. doi: 10.1080/1046669X.2013.832466.
100. Wang, Y. S. et al. (2003) ‘Determinants of user acceptance of Internet banking: An empirical study’, in International Journal of Service Industry Management. doi: 10.1108/09564230310500192.
101. Wang, Y. S., Lin, H. H. and Luarn, P. (2006) ‘Predicting consumer intention to use mobile service’, Information Systems Journal. doi: 10.1111/j.1365-2575.2006.00213.x.
102. Wessels, L. and Drennan, J. (2010) ‘An investigation of consumer acceptance of M‐banking’, International Journal of Bank Marketing, 28(7), pp. 547–568. doi: 10.1108/02652321011085194.
103. Xu, X. and Jackson, J. E. (2019) ‘Examining customer channel selection intention in the omni-channel retail environment’, International Journal of Production Economics. Elsevier B.V., 208, pp. 434–445. doi: 10.1016/j.ijpe.2018.12.009.
104. Yoon, C. (2010) ‘Antecedents of customer satisfaction with online banking in China: The effects of experience’, Computers in Human Behavior, 26(6), pp. 1296–1304. doi: 10.1016/j.chb.2010.04.001.
105. Zhou, T. (2012) ‘Examining mobile banking user adoption from the perspectives of trust and flow experience’, Information Technology and Management. doi: 10.1007/s10799-011-0111-8.