Measuring Client’s Feelings on Mobile Banking
Keywords:
mobile banking, mind genomics, statistical modeling
Abstract
Mobile banking is relatively a new service offered by banks around the world. Banks are obliged to keep investing in new technologies as otherwise, they would lose competitiveness and market share. It is interesting to know how clients react to these innovation efforts.
This study aims to understand the client’s responses to bank innovation, especially to mobile banking technology in Albania. An online experiment is conceived based on Experimental Design Principles. Participants evaluate combinations of messages (elements) about mobile banking and rate each combination. The collected data are used to create individual models and later a general model to calculate the statistical relevance of each of the messages. The models use ordinary least squares regression and advanced data mining techniques (k—means clustering) to analyze the data and classify participants accordingly.
At the end of the analyses, a set of two or three mindsets are depicted to show what pushes participants in the study in their decision-making process. The mindsets help banks understand client’s reactions and allow banks to address different issues to serve their clients better.
References
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[23] Muñoz-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. Spanish Journal of Marketing - ESIC, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001
[24] Nagaraj, S., & Singh, S. (2017). Adoption of Internet Banking Services in India: An Empirical Study. Global Journal of Management and Business, 17(2-E).
[25] Oyedemi, T. (2015). Participation, citizenship and internet use among South African youth. Telematics and Informatics, 32(1), 11-22.
[26] Ribaj, A., & Ilollari, O. (2019). Asymmetric information versus banks’ costumer trust, Albania case linked with SEE countries. Albania Case Linked with SEE Countries, 24.
[27] Ribaj, Artur, Ilollari, O., & Scalera, F. (2020). The Unethical Banking Costs Distrust of Bank Customers (Albania Case as a Model for SEE Countries). WSEAS Transactions on Business and Economics, 16(65), 582–592.
[28] Sanader, D. (2014). Mobile banking: New trend in the contemporary banking sector. Bankarstvo, 45(3), 86–109.
[29] Saulo, A., Moskowitz, V., Gere, A., Papajorgji, P., & Ettinger Lieberman, L. Feuerwerker, D. (2019). Linking Food Endorsement Labels & Messaging to Perceived Price and Emotions. A Mind Genomics ® Exploration. Advances in Nutrition and Food Science, 2019(5).
[30] Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142. https://doi.org/10.1016/j.tele.2014.05.003
[31] Tan, G. W. H., Chong, C. K., Ooi, K. B., & Chong, A. Y. L. (2010). The Adoption of Online Banking in Malaysia: An Empirical Analysis. The International Journal of Business and Management, 3(2), 169-193.
[32] Todri, A., Papajorgji, P., Moskowitz, H. R., & Scalera, F. (2020). Perceptions regarding Distance Learning in Higher Education, Smoothing the Transition. Contemporary Educational Technology, 12(1). https://doi.org/10.30935/cedtech/9274
[33] Xiao, Y., Sukumar, A. P. C., Tipi, L., & David, E. (2017). Factors influencing people’s intention to adopt e-banking: an empirical study of consumers in Shandong Province, China. Asian Journal of Computer and Information Systems, 5(3), 26–43.
[34] Yee‐Loong Chong, A., Ooi, K., Lin, B., & Tan, B. (2010). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267–287. https://doi.org/10.1108/02652321011054963
[35] Yu, C. (2012). Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the Utaut Model. Journal of Electronic Commerce Research, 13(2).
[36] Zdaniuk, B. (2014). Ordinary Least-Squares (OLS) Model. In Encyclopedia of Quality of Life and Well-Being Research (2014. Edit). https://doi.org/https://doi.org/10.1007/978-94- 007-0753-5_2008
[37] Zemel, R., Gan Choudhuri, S., Gere, A., Upreti, H., Deitel, Y., Papajorgji, P., & Moskowitz, H. (2019). Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive Viewpoint Typing. In Handbook of Modern Dairy Science and Technology. IntechOpen. https://doi.org/10.5772/intecopen.85532
[38] Zemel, R., Gere, A., Papajorgji, P., Zemel, G., & Moskowitz, H. (2018). Uncovering Consumer Mindsets Regarding Raw Beverages. Food and Nutrition Sciences, 9(3), 259–267. https://doi.org/10.4236/fns.2018.93020
[39] Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27–37. https://doi.org/10.1007/s10799-011-0111-8
[2] Akpojivi, U., & Bevan-Dye, A. (2015). Mobile advertisements and information privacy perception amongst South African Generation Y students. Telematics and Informatics, 32(1), 1–10.
[3] Arvidsson, N. (2014). Consumer attitudes on mobile payment services – results from a proof of concept test. International Journal of Bank Marketing, 32(2), 150–170. https://doi.org/10.1108/IJBM-05-2013-0048
[4] Boar, B. H. (2002). The art of strategic planning for information technology. John Wiley & Sons.
[5] Butler, P., & Peppard, J. (1998). Consumer purchasing on the Internet: Processes and prospects. European Management Journal, 16(5), 600–610.
[6] Chuang, C. C. (2019). Adopt m-banking as a successful business strategy. International Research Journal of Applied Finance, 10(4), 160–169.
[7] Gere, A., Zemel, R., Papajorgji, P., & Moskowitz, H. (2018). Weak Signals and Mind-Sets of Consumers: The Case of Milk. Journal of Food Science and Engineering, 8, 125–136. https://doi.org/10.17265/2159-5828/2018.03.004
[8] Gjino., G., & Ilollari (Findiku)., O. (2014). Mobile banking: near future of banking. Review of Applied Socio-Economic Research, 7(1), 43–51.
[9] Gofman, A. (2012). Origins of RDE and the Role of Experimentation in Consumer- Driven Innovation. In H. R. Gofman, A., Moskowitz (Ed.), Rule Developing Experimentation: A Systematic Approach to Understand & Engineer the Consumer Mind (pp. 3–33). Bentham ebooks. https://doi.org/10.2174/97816080528441120101
[10] Gofman, A., & Moskowitz, H. R. (2010). ISOMORPHIC PERMUTED EXPERIMENTAL DESIGNS AND THEIR APPLICATION IN CONJOINT ANALYSIS. Journal of Sensory Studies, 25(1), 127–145. https://doi.org/https://doi.org/10.1111/j.1745-459X.2009.00258.x
[11] Gomber, P., Kauffman, R. J., Parker, C., & Weber, B. W. (2018). On the Fintech Revolution: Interpreting the Forces of Innovation, Disruption and Transformation in Financial Services. Journal of Management Information Systems, 35(1), 220–265.
[12] Illollari, O., & Gjino, G. (2013). Which forces drive the banks to new investments? Innovation mechanisms banks use to succeed challenges. Review of Applied Socio-Economic Research, 6(2), 121–130.
[13] Ilollari, O., Papajorgji, P., & Civici, A. (2020). Understanding client’s feelings about mobile banking in Albania. UNICART2, International Conference, Academic Research & Tourism.
[14] Ilollari, O., Papajorgji, P., Gere, A., Zemel, R., & Moscowitz, H. R. (2019). Using Mind Genomics to Understand the Specifics of a Customers Mind.
[15] Jehan, S. N., & Ansari, Z. A. (2018). Internet Banking Adoption in Saudi Arabia: An Empirical Study. International Journal of Marketing Studies, 10(57).
[16] L’Hostis, A., & Wannemacher, P. (2015). The State Of Mobile Banking. Landscape: The Mobile Banking Strategy Playbook. Forrester Research.
[17] Milutinovic, V., & Salom, J. (2016). A Guide to Data-Driven Marketing Strategy (1st ed. Ed). SpringerBriefs in Business.
[18] Mortimer, G., Neale, L., Hasan, S. F. E., & Dunphy, B. (2015). Investigating the factors influencing the adoption of m-banking: a cross cultural study. International Journal of Bank Marketing, 33(4), 545–570. https://doi.org/10.1108/IJBM-07-2014-0100
[19] Moskowitz, H. R., Beckley, J., & Ashman, H. (2006). Founding A New Science: Mind Genomics. Journal of Sensory Studies. https://doi.org/https://doi.org/10.1111/j.1745-459X.2004.00066.x
[20] Moskowitz, H. R., Wren, J., & Papajorgji, P. (2020). Mind Genomics and the Law (1st editio). Lambert Publishing.
[21] Mucherino, A., Papajorgji, P., & Pardalos, P. M. (2009). A survey of data mining techniques applied to agriculture. Operational Research, 9(2). https://doi.org/10.1007/s12351-009-0054-6.
[22] Mucherino, A., Papajorgji, P., & Pardalos, P. (2009). Introduction to Data Mining. In Data Mining in Agriculture (pp. 1–20).
[23] Muñoz-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. Spanish Journal of Marketing - ESIC, 21(1), 25–38. https://doi.org/10.1016/j.sjme.2016.12.001
[24] Nagaraj, S., & Singh, S. (2017). Adoption of Internet Banking Services in India: An Empirical Study. Global Journal of Management and Business, 17(2-E).
[25] Oyedemi, T. (2015). Participation, citizenship and internet use among South African youth. Telematics and Informatics, 32(1), 11-22.
[26] Ribaj, A., & Ilollari, O. (2019). Asymmetric information versus banks’ costumer trust, Albania case linked with SEE countries. Albania Case Linked with SEE Countries, 24.
[27] Ribaj, Artur, Ilollari, O., & Scalera, F. (2020). The Unethical Banking Costs Distrust of Bank Customers (Albania Case as a Model for SEE Countries). WSEAS Transactions on Business and Economics, 16(65), 582–592.
[28] Sanader, D. (2014). Mobile banking: New trend in the contemporary banking sector. Bankarstvo, 45(3), 86–109.
[29] Saulo, A., Moskowitz, V., Gere, A., Papajorgji, P., & Ettinger Lieberman, L. Feuerwerker, D. (2019). Linking Food Endorsement Labels & Messaging to Perceived Price and Emotions. A Mind Genomics ® Exploration. Advances in Nutrition and Food Science, 2019(5).
[30] Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129–142. https://doi.org/10.1016/j.tele.2014.05.003
[31] Tan, G. W. H., Chong, C. K., Ooi, K. B., & Chong, A. Y. L. (2010). The Adoption of Online Banking in Malaysia: An Empirical Analysis. The International Journal of Business and Management, 3(2), 169-193.
[32] Todri, A., Papajorgji, P., Moskowitz, H. R., & Scalera, F. (2020). Perceptions regarding Distance Learning in Higher Education, Smoothing the Transition. Contemporary Educational Technology, 12(1). https://doi.org/10.30935/cedtech/9274
[33] Xiao, Y., Sukumar, A. P. C., Tipi, L., & David, E. (2017). Factors influencing people’s intention to adopt e-banking: an empirical study of consumers in Shandong Province, China. Asian Journal of Computer and Information Systems, 5(3), 26–43.
[34] Yee‐Loong Chong, A., Ooi, K., Lin, B., & Tan, B. (2010). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267–287. https://doi.org/10.1108/02652321011054963
[35] Yu, C. (2012). Factors Affecting Individuals to Adopt Mobile Banking: Empirical Evidence from the Utaut Model. Journal of Electronic Commerce Research, 13(2).
[36] Zdaniuk, B. (2014). Ordinary Least-Squares (OLS) Model. In Encyclopedia of Quality of Life and Well-Being Research (2014. Edit). https://doi.org/https://doi.org/10.1007/978-94- 007-0753-5_2008
[37] Zemel, R., Gan Choudhuri, S., Gere, A., Upreti, H., Deitel, Y., Papajorgji, P., & Moskowitz, H. (2019). Consumers, and Dairy: Applying Artificial Intelligence, Mind Genomics, and Predictive Viewpoint Typing. In Handbook of Modern Dairy Science and Technology. IntechOpen. https://doi.org/10.5772/intecopen.85532
[38] Zemel, R., Gere, A., Papajorgji, P., Zemel, G., & Moskowitz, H. (2018). Uncovering Consumer Mindsets Regarding Raw Beverages. Food and Nutrition Sciences, 9(3), 259–267. https://doi.org/10.4236/fns.2018.93020
[39] Zhou, T. (2012). Examining mobile banking user adoption from the perspectives of trust and flow experience. Information Technology and Management, 13(1), 27–37. https://doi.org/10.1007/s10799-011-0111-8
Published
2022-06-27
How to Cite
Ilollari, O., PAPAJORGJI, P., Civici, A., & Moskowitz, H. (2022). Measuring Client’s Feelings on Mobile Banking . Review of Applied Socio-Economic Research, 23(1), 28-39. https://doi.org/10.54609/reaser.v23i1.135
Section
Articles
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