Structural Equation Model: Intention To Use Mobile Banking of Bottom of Pyramid Customer

Dian Kusumaningrum, Dewi Savitri Saraswati, Seprianus Seprianus

Abstract


The economy is shifting into the digital economy and to overcome it, the banking industry competes through innovation and digital strategy. Smartphone-based mobile banking is the key component of the digital strategy with 70% of the banks agree to focus their strategy on mass customer segment (PWC, 2017).

The purposes of the study are to identify the predicting factors influencing the intention to use mobile banking and empirically validate a model explaining the behavioral intention to use it, especially on the Bottom of Pyramid (BOP) segment. The model used was Structural Equation Model (SEM) based on Partial Least Square (PLS). The data used for developing the model was based on a survey to 100 BOP households.

The results of this study show that the variables that have the highest significant effect on BOP’s customer intention to use mobile banking are involuntary barriers, followed by perceived risk, and attitude. This result can be further used by researchers and mobile banking providers to evaluate the existing mobile banking services to improve its contribution in providing better market penetration and more appropriate financial services for BOP and ultimately financial inclusion in Indonesia.

Keywords: Mobile Banking, Intention, Structural Equation Model

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References


Allen, F., Kunt, A. D., Klapper, L., Soledad, M., & Peria, M. (2016). The foundations of financial inclusion: Understanding ownership and use of formal accounts. Journal of Financial Intermediation, volume 27, 1-30.

Ates, A. and Bititci, U. (2011). Change process: a key enabler for building resilient SMEs. International Journal for Production Research, 49(18) (2011), pp 5601-5618. DOI:10.1080/00207543.2011.563825.

Bank Mandiri. (2018). Membentang Asa Sebagai Kebanggaan Bangsa Laporan Tahunan 2017. Jakarta: Bank Mandiri. Retrieved October 2018, from https://www.bankmandiri.co.id/documents/38265486/38265681/Bank+Mandiri+2017+Annual+Report+-+Indonesia.pdf/7cf15373-9eab-5f22-3dfb-c50e424386a3

Baptista, G., & Oliveira, T. (2016). A weight and a meta-analysis on mobile banking acceptance research. Computers in Human Behavior, Vol 63, 480-489.

Bungin, B. (2010). Penelitian Kualitatif. Prenada Media Group, Jakarta.

Christensen, J.F. (1995). Assets profiles for technological innovation. Research Policy, 24 (5) (1996), pp 727-745. DOI: http://dx.doi.org/10.1016/0048-7333(94)00794-8.

Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. 2nd ed. New York: Psychology Press.

Davis, F. D. (1989). Perceived usefullness, perceived ease of use, and user acceptance of information tehcnology. MIS Quarterly 13(3), pp. 319-340.

Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, pp. 213-236.

Joe F. Hair Jr, Marko Sarstedt, Lucas Hopkins, Volker G. Kuppelwieser, (2014) "Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research", European Business Review, Vol. 26 Issue: 2, pp.106-121, https://doi.org/10.1108/EBR-10-2013-0128

Hair, J. F., Hult, T. M., Ringle, C. M., & e Sarstedt, M. A. (2014). Primer on Partial Least Squares Structural Equation Modelling (PLS-SEM). Los Angeles: SAGE.

Henseler, J., Sarstedt, C. M., & Sinkovics, R. R. (2009). The use of partial leaset squares path modeling in international marketing. Advances in International Marketing, Vol. 20, 277-319.

Hew, J. J., Lee, V. H., Ooi, K. B., & Wei, J. (2015). What catalyses mobile apps usage intention: An empirical analysis. Industrial Management & Data Systems, 115(7), 1269 - 1291.

International Finance Corporation. (2010). Mobile Banking in Indonesia, Assessing the Market Potential for Mobile Technology to Extend Banking to the Unbanked and Underbanked. Jakarta: IFC. Retrieved from https://www.ifc.org/wps/wcm/connect/1a5695804723d0248b21ab2b131bed2a/Mobile%2BBanking%2BFinal%2BReport.pdf?MOD=AJPERES

Kesharwani, A., & Bisht, S. S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International Journal of Bank Marketing Vol. 30 Iss: 4, 302-322. doi:http://dx.doi.org/10.1108/02652321211236923

Kilara, T., & Rhyne, E. (2014, June). Customer-Centricity for Financial Inclusion. CGAP, pp. 1-4.

Liébana-Cabanillas, F., Sánchez-Fernández, J., & Munoz-Leiva, F. (2014). Antecedents of the adoption of the new mobile payment systems : The moderating effect of age. Computers in Human Behaviour, 464 - 478.

Lin, H. F. (2011). An empirical investigation of mobile banking adoption: The effect of innovation attributes and knowledge-based trust. International Journal of Information Management, 252 - 260. doi:https://doi.org/10.1016/j.ijinfomgt.2010.07.006

Ludwig, A. M., & Leisch, F. (2012, May). SEM PLS: Structural Equation Modeling Using Partial Least Squares. Journal of statistical software volume 48 issue 3.

Munoz-Leiva, F., Climent-Climent, S., & Liébana-Cabanillas, F. (2016). Determinants of intention to use the mobile banking apps: An extension of the classic TAM model. Spanish Journal of Marketing - ESIC. doi:http://dx.doi.org/10.1016/j.sjme.2016.12.001

Pavlou, P. A. (2002). A theory of planned behavior perspective to the consumer adoption of electronic commerce. MIS Quarterly 30 (1), 115-143.

Pham , T.-T. T., & Ho, J. C. (2015, November). The effects of product-related, personal-related factors and attractiveness of alternatives on consumer adoption of NFC-based mobile payments. Technology in Society, Volume 43, 159-172. doi:https://doi.org/10.1016/j.techsoc.2015.05.004

PWC. (2018). PwC Survey: Digital Banking in Indonesia 2018. PWC. Retrieved from https://www.pwc.com/id/en/publications/assets/financialservices/digital-banking-survey-2018-pwcid.pdf

Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics (32), 129 - 142. doi:http://dx.doi.org/10.1016/j.tele.2014.05.003

Susanto, A., Lee, H., & Zo, H. (2011). Factors Influencing Initial Trust Formation in Adopting Internet Banking in Indonesia. ICACSIS. Retrieved Oktober 23, 2018, from https://www.researchgate.net/publication/233389618_Factors_Influencing_Initial_Trust_Formation_in_Adopting_Internet_Banking_in_Indonesia/download

Tenenhaus, M., Vinzi, V. E., Chatelin, Y., & Lauro, C. (2005). PLS Path Modeling, Computational Statistics & Data Analysis (Vol. 48). 159-205

Wetzels, M., Odekerken-Schroder, G., & Oppen, C. V. (2009). Using PLS path modelling for assessing hierarchical construct models: guidelines and empirical illustration. MIS Quarterly, volume 33, n.1, pp. 177-195.

Zhou, T. (2011). An empirical examination of initial trust in mobile banking. Internet Research, Vol. 21 Issue: 5, pp.527-540. doi:https://doi.org/10.1108/10662241111176353

Zhao, A. L., Lloyd, , S. H., Ward, P., & Goode, M. M. (2008). Perceived risk and Chinese consumers'internet banking services adoption. International Journal of Bank Marketing, Vol. 26 Issue: 7, 505-525. doi:https://doi.org/10.1108/02652320810913864

Davila , T., Epstein, M.J., and Shelton, R. (2006). MakingInnovation Work: How to manage it, measure it and profit from it. Upper Saddler River, New Jersey.

Wang, C.L., and Ahmed, P. K. (2004). The development and validation of the organizational innovativeness construct using confirmatory factor analysis. European Journal of Innovation Management, 7 (4) (2004), pp. 303-313.




DOI: http://dx.doi.org/10.14203/STIPM.2019.156

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