Análise do impacto da influência social na aceitação de aplicativos bancários móveis por consumidores no Brasil

Autores

DOI:

https://doi.org/10.5585/remark.v22i4.23729

Palavras-chave:

Marketing de serviços bancários, Comportamento do consumidor, Mobile Banking

Resumo

Objetivo: Analisar o impacto da influência social na aceitação de aplicativos bancários móveis.

Metodologia: Foi realizada uma pesquisa descritiva do tipo survey, com 371 usuários de aplicativos bancários, maiores de 18 anos. A análise foi realizada por meio de Equação Estrutural - Mínimo Quadrado Parcial (Smart-PLS 4.0).

Originalidade: Esta pesquisa é um esforço pioneiro de aplicação do TAM com a inclusão da influência social, no contexto da pandemia de COVID-19 no Brasil, para analisar a aceitação de mobile banking.

Resultados: O modelo conceitual apresenta bom poder explicativo. A pesquisa trouxe evidências empíricas de que a adoção do mobile banking é impactada pela influência social. Este trabalho reforça a premissa de que a intenção do sujeito de adotar serviços bancários móveis é influenciada pelas pessoas importantes para si. Dessa forma, os esforços de marketing devem levar em consideração esses grupos de referência em suas estratégias, a fim promover a tecnologia em questão. 

Contribuições: O Modelo Conceitual, ajustado empiricamente e com as escalas correspondentes, serve de orientação aos desenvolvedores de aplicativos e à gestão de marketing bancário. Devido à escassez de trabalhos empíricos sobre o tema no contexto brasileiro e a crescente utilização de aplicativos de bancos, este estudo amplia o conhecimento existente e fornece suporte empírico para estudos posteriores.

Downloads

Não há dados estatísticos.

Biografia do Autor

Maria José Isac, Universidade Estadual Paulista Júlio de Mesquita Filho– Unesp

Graduada em Administração

 

Sheila Farias Alves Garcia, Universidade Estadual Paulista Júlio de Mesquita Filho– Unesp

Doutora

Dirceu da Silva, Universidade Estadual de Campinas - Unicamp

Livre-docente

 

Referências

Ahadzadeh, A. S., Sharif, S. P., Ong, F. S., & Khong, K. W. (2015). Integrating Health Belief Model and Technology Acceptance Model: An Investigation of Health-Related Internet Use. Journal of Medical Internet Research, 17(2), e45. 10.2196/jmir.3564

Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211.

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Williams, M. D. (2016). Consumer adoption of mobile banking in Jordan: Examining the role of usefulness, ease of use, perceived risk and self-efficacy. Journal of Enterprise Information Management, 29(1), 118-139. 10.1108/JEIM-04-2015-0035

Al-Somali, S. A., Gholami, R., & Clegg, B. (2009). An investigation into the acceptance of online banking in Saudi Arabia. Technovation, 29(2), 130-141. https://doi.org/10.1016/j.technovation.2008.07.004

App Annie & Liftoff. (2021). Mobile Finance Apps Report - Banking on the future of fintech. APP ANNIE. Retrieved Abril 18, 2021, from http://bit.ly/3JjrttP

Baabdullah, A. M., Alalwan, A. A., Rana, N. P., Patil, P., & Dwivedi, Y. K. (2019). An integrated model for m-banking adoption in Saudi Arabia. International Journal of Bank Marketing, 37(2), 452-478, from http://bit.ly/3jaWwNG

Bido, D. de S., & Silva, D. da (2019). SmartPLS 3: especificação, estimação, avaliação e relato. Administração: Ensino e Pesquisa, 20(2), 488-536. https://doi.org/10.13058/raep.2019.v20n2.1545

Bradley, J. (2012). If We Build It They Will Come? The Technology Acceptance Model. Information Systems Theory. Integrated Series in Information Systems, 1(28), 19-36. https://doi.org/10.1007/978-1-4419-6108-2_2

Bryan, A. D., Aiken, L. S., & West, S. G. (1997). Young Women's Condom Use: The Influence of Acceptance of Sexuality, Control Over the Sexual Encounter, and Perceived Susceptibility to Common STDs. Health Psychology, 16(5), 468-479. 0278-6133/97/S3.00

Callow, M. A., Callow, D. D., & Smith, C. (2020). Older Adults’ Intention to Socially Isolate Once covid-19 Stay-at-Home Orders Are Replaced With “Safer-at-Home” Public Health Advisories: A Survey of Respondents in Maryland. Journal of Applied Gerontology, 39(11), 1175-1183. https://doi.org/10.1177/0733464820944704

Carpenter, C. J. (2010). A Meta-Analysis of the Effectiveness of Health Belief Model Variables in Predicting Behavior. Health Communication, 25(8), 661-669. https://doi.org/10.1080/10410236.2010.521906

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. https://doi.org/10.1207/S15327744JOCE1302_3

Chawla, D., & Joshi, H. (2019). Consumer attitude and intention to adopt mobile wallet in India – An empirical study. International Journal of Bank Marketing, 37(2), 1590-1618. https://doi.org/10.1108/IJBM-09-2018-0256

Chin, W. W. The partial least squares approach for structural equation modeling. In Marcoulides, G.A. (Ed.). Modern methods for business research. London: Lawrence Erlbaum Associates, p. 295-236, 1998.

Chong, A. Y.-L., Darmawan, N., Ooi, K.-B., & Lin, B. (2010a). Adoption of 3G services among Malaysian consumers: an empirical analysis. International Journal of Mobile Communications, 8(2), 129-149. 10.1504/IJMC.2010.031444

Chong, A. Y.-L., Ooi, K.-B., Lin, B., & Tan, B.-I. (2010b). Online banking adoption: an empirical analysis. International Journal of Bank Marketing, 28(4), 267-287. 10.1108/02652321011054963

Davis, F. D. (1986). A TECHNOLOGY ACCEPTANCE MODEL FOR EMPIRICALLY TESTING NEW END-USER INFORMATION SYSTEMS: THEORY AND RESULTS. Sloan School of Management, M.I.T. in December.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://doi.org/249008

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management Science, 35(8), 982-1003.

Dela Coleta, M. F. (1999). o modelo de crenças em saúde (HBM): uma análise de sua contribuição à psicologia da saúde. Temas em Psicologia, 7(2), 175-182. http://pepsic.bvsalud.org/pdf/tp/v7n2/v7n2a07.pdf

Devellis, R. F. (2003). Scale development : theory and applications. Thousand Oaks : SAGE Publications.

Dou, K., Yu, P., Deng, N., Liu, F., Guan, Y., Li, Z., Ji, Y., Du, N., Lu, X., & Duan, H. (2017). Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test. JMIR Mhealth Uhealth, 5(12), e177. 10.2196/mhealth.7886

Dror, A. A., Eisenbach, N., Taiber, S., Morozov, N. G., Mizrachi, M., Zigron, A., Srouji, S., & Sela, E. (2020). Vaccine hesitancy: the next challenge in the fight against covid-19. European Journal of Epidemiology, 35, 775–779. https://doi.org/10.1007/s10654-020-00671-y

Elhajjar, S., & Ouaida, F. (2020). An analysis of factors affecting mobile banking adoption. International Journal of Bank Marketing, 38(2), 352-367. https://doi.org/10.1108/IJBM-02-2019-0055

Farah, M. F., Hasni, M. S., & Abbas, A. K. (2018). Mobile-banking adoption: empirical evidence from the banking sector in Pakistan. International Journal of Bank Marketing, 36(7), 1386-1413. 10.1108/IJBM-10-2017-0215

FEBRABAN - Federação Brasileira de Bancos. (2020, dezembro). Destaques de 2020. Expectativas para 2021. FEBRABAN. Retrieved Abril 18, 2021, from http://bit.ly/3HapB44

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research. Addison-Wesley.

Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39-50. https://doi.org/10.2307/3151312

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (9th ed.). Hampshire: Cengage Learning.

Hair Jr., J. F., Celsi, M. W., Ortinau, D. J., & Bush, R. P. (2014b). Fundamentos de Pesquisa de Marketing (3ª ed.). Porto Alegre: AMGH.

Hair JR., J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014a). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications.

Icek, A., & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888–918. https://doi.org/10.1037/0033-2909.84.5.888

Janz, N. K., & Becker, M. H. (1984, Março 1). The Health Belief Model: A Decade Later. Health Education Quarterly, 11(1), 1-47. 10.1177 / 109019818401100101

Johns Hopkins University (JHU), covid-19 Dashboard by the Center for Systems Science and Engineering (CSSE). (2022, April 17). Coronavírus covid-19 (2019-nCoV). Retrieved January 21, 2023, from http://bit.ly/40if13I

Jones, C. L., Jensen, J. D., Scherr, C. L., Brown, N. R., Christy, K., & Weaver, J. (2015). The Health Belief Model as an Explanatory Framework in Communication Research: Exploring Parallel, Serial, and Moderated Mediation. Health Communication, 30(6), 566-576. https://doi.org/10.1080/10410236.2013.873363

Kim, J., & Park, H.-A. (2012). Development of a Health Information Technology Acceptance Model Using Consumers’ Health Behavior Intention. JOURNAL OF MEDICAL INTERNET RESEARCH, 14(5), 1-14. 10.2196/jmir.2143

Koenig-Lewis, N., Palmer, A., & Moll, A. (2010). Predicting young consumers' take up of mobile banking services. International Journal of Bank Marketing, 28(5), 410-432. https://doi-org.ez87.periodicos.capes.gov.br/10.1108/02652321011064917

Koksal, M. H. (2016). The intentions of Lebanese consumers to adopt mobile banking. International Journal of Bank Marketing, 34(3), 327-346. 10.1108/IJBM-03-2015-0025

Lin, S.-P. (2011). Determinants of adoption of Mobile Healthcare Service. International Journal of Mobile Communications (IJMC), 9(3), 298-315. 10.1504 / IJMC.2011.040608

López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2008). An assessment of advanced mobile services acceptance: Contributions from TAM and diffusion theory models. Information & Management, 45(6), 359-364. https://doi.org/10.1016/j.im.2008.05.001

Luarn, P., & Lin, H.-H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21, 873–891. 10.1016/j.chb.2004.03.003

Makanyeza, C. (2017). Determinants of consumers’ intention to adopt mobile banking services in Zimbabwe. International Journal of Bank Marketing, 35(6), 997-1017. 10.1108/IJBM-07-2016-0099

Mobile Time & Opinion Box. (2020, Maio). Panorama Mobile Time/Opinion Box. Opinion Box. Retrieved Abril 18, 2021, from http://bit.ly/3kLGqe1

Mohallem, D. F., Tavares, M., Silva, P. L., Guimarães, E. C., & Freitas, R. F. (2008, Abril). Avaliação do coeficiente de variação como medida da precisão em experimentos com frangos de corte. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 60(2), 449-453. http://dx.doi.org/10.1590/s0102-09352008000200026

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. 10.1108/IJBM-07-2014-0100

Mousa, A. H., Mousa, S. H., Aljshamee, M., & Nasir, I. S. (2021). Determinants of customer acceptance of e-banking in Iraq using technology acceptance model. TELKOMNIKA Telecommunication, Computing, Electronics and Control, 19(2), 421-431. 10.12928/TELKOMNIKA.v19i2.16068

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. SPANISH JOURNAL OF MARKETING - ESIC, 21(1), 25-38. https://doi.org/10.1016/j.sjme.2016.12.001

Park, J., Ahn, J., Thavisay, T., & Ren, T. (2019). Examining the role of anxiety and social influence in multi-benefits of mobile payment service. Journal of Retailing and Consumer Services, 47, 140-149. https://doi.org/10.1016/j.jretconser.2018.11.015

Patel, K. J., & Patel, H. J. (2018). Adoption of internet banking services in Gujarat An extension of TAM with perceived security and social influence. International Journal of Bank Marketing, 36(1), 47-169. 10.1108/IJBM-08-2016-0104

Pikkarainen, T., Pikkarainen, K., Karjaluoto, H., & Pahnila, S. (2004). Consumer acceptance of online banking: an extension of the technology acceptance model. Internet Research, 14(3), 224–235. 10.1108/10662240410542652

Rana, N. P., Dwivedi, Y. K., & Williams, M. D. (2013). Evaluating alternative theoretical models for examining citizen centric adoption of e-government. Transforming Government: People, Process and Policy., 7(1), 27-49. 10.1108/17506161311308151

Reiter, P. L., Pennell, M. L., & Katz, M. L. (2020). Acceptability of a covid-19 vaccine among adults in the United States: How many people would get vaccinated? Vaccine, 38(42), 6500-6507. https://doi.org/10.1016/j.vaccine.2020.08.043

Ringle, C. M., Silva, D. D., & Bido, D. d. S. (2014, Maio 01). Structural Equation Modeling with the Smartpls. Revista Brasileira de Marketing, 13(02), 56-73. http://dx.doi.org/10.5585/remark.v13i2.2717

Rosenstock, I. M. (1974). Historical Origins of the Health Belief Model. Health Education Monographs, 2(4), 328-335. 10.1177 / 109019817400200403

Shaikh, A. A., & Karjaluoto, H. (2015, February). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129-142. https://doi.org/10.1016/j.tele.2014.05.003

Sharma, S. K., Govindaluri, S. M., Al-Muharrami, S., & Tarhini, A. (2017). A multi-analytical model for mobile banking adoption: a developing country perspective. Review of International Business and Strategy, 27(1), 133-148. 10.1108/RIBS-11-2016-0074

Sreelakshmi, C. C., & Prathap, S. K. (2020). Continuance adoption of mobile based payments in covid-19 context: an integrated framework of health belief model and expectation confirmation model. International Journal of Pervasive Computing and Communications, 16(4), 351-369. 10.1108/IJPCC-06-2020-0069

Sudarsono, H., Nugrohowati, R. N. I., & Tumewang, Y. K. (2020). The Effect of covid-19 Pandemic on the Adoption of Internet Banking in Indonesia: Islamic Bank and Conventional Bank. The Journal of Asian Finance, Economics and Business, 7(11), 789-800. doi:10.13106/jafeb.2020.vol7.no11.789

Sukendro, S., Habibi, A., Khaeruddin, K., Indrayana, B., Syahruddin, S., Makadada, F. A., & Hakim, H. (2020). Using an extended Technology Acceptance Model to understand students’ use of e-learning during covid-19: Indonesian sport science education context. Heliyon, 6(11), 1-9. https://doi.org/10.1016/j.heliyon.2020.e05410.

Teo, T. (2011). Technology acceptance research. In educationIn: Technology Acceptance in Education. SensePublishers. https://doi.org/10.1007/978-94-6091-487-4_1

Tobbin, P. (2012). Towards a model of adoption in mobile banking by the unbanked: a qualitative study. info, 14(5), 74-88, from http://bit.ly/3HzG9ne

Tsai, C.-H. (2014). The Adoption of a Telehealth System: The Integration of Extended Technology Acceptance Model and Health Belief Model. Journal of Medical Imaging and Health Informatics, 4(3), 448-455(8). https://doi.org/10.1166/jmihi.2014.1278

Wessels, L., & Drennan, J. (2010). An investigation of consumer acceptance of M‐banking. International Journal of Bank Marketing,, 28(7), 547-568. https://doi.org/10.1108/02652321011085194

Zhao, Y., & Bacao, F. (2021). How Does the Pandemic Facilitate Mobile Payment? An Investigation on Users’ Perspective under the covid-19 Pandemic. International Journal of Environmental Research and Public Health, 18(3), 1016-1038. https:// doi.org/10.3390/ijerph18031016

Zhou, T., Lu, Y., & Wang, B. (2010, July). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in Human Behavior, 26(4), 760-767. https://doi.org/10.1016/j.chb.2010.01.013

Downloads

Publicado

18.12.2023

Como Citar

Isac, M. J., Garcia, S. F. A., & da Silva, D. (2023). Análise do impacto da influência social na aceitação de aplicativos bancários móveis por consumidores no Brasil. ReMark - Revista Brasileira De Marketing, 22(4), 1709–1763. https://doi.org/10.5585/remark.v22i4.23729

Edição

Seção

Special Issue: Applications of neurosciences to the marketing field