References
- Abdul Aziz, N. A., L. J. Wen, H. Azman, and A. Sufian. 2020. The impact of consumers’ attitude towards mobile payment feasibility. Journal of Computational and Theoretical Nanoscience 17 (2):1127–42. [Online] doi: https://doi.org/10.1166/jctn.2020.8777.
- Ajzen, I. 1991. The theory of planned behavior. Organizational Behavior and Human Decision Processes 50 (2):179–211. doi: https://doi.org/10.1016/0749-5978(91)90020-T.
- Ajzen, I., and M. Fishbein. 1973. Attitudinal and normative variables as predictors of specific behavior. Journal of Personality and Social Psychology 27 (1):41–57. doi: https://doi.org/10.1037/h0034440.
- Alalwan, A. A., Y. K. Dwivedi, and N. P. Rana. 2017. Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management 37 (3):99–110. doi: https://doi.org/10.1016/j.ijinfomgt.2017.01.002.
- Antón, C., C. Camarero, and J. Rodríguez. 2013. Usefulness, enjoyment, and self-image congruence: The adoption of e-book readers. Psychology & Marketing 30 (4):372–84. doi: https://doi.org/10.1002/mar.20612.
- Arif, I., S. Afshan, and A. Sharif, 2016. Resistance to adopt mobile banking in a developing country: Evidence from modified TAM. Journal of Finance and Economics Research 1 (1):23–40.
- Arvidsson, N. 2014. Consumer attitudes on mobile payment services – results from a proof of concept test. International Journal of Bank Marketing 32 (2):150–70. doi: https://doi.org/10.1108/IJBM-05-2013-0048.
- Awa, H. O., O. U. Ojiabo, and B. C. Emecheta. 2015. Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science & Technology Policy Management 6 (1):76–94. doi: https://doi.org/10.1108/JSTPM-04-2014-0012.
- Bagozzi, R. P., and Y. Yi. 1988. On the evaluation of structural equation models. Journal of the Academy of Marketing Science 16 (1):74–94. doi: https://doi.org/10.1007/BF02723327.
- Bailey, A. A., I. Pentina, A. S. Mishra, and M. S. Ben Mimoun. 2017. Mobile payments adoption by US consumers: An extended TAM. International Journal of Retail & Distribution Management 45 (6):626–40. doi: https://doi.org/10.1108/IJRDM-08-2016-0144.
- Baishya, K., and H. V. Samalia. 2020. Extending unified theory of acceptance and use of technology with perceived monetary value for smartphone adoption at the bottom of the pyramid. International Journal of Information Management 51:102036. doi: https://doi.org/10.1016/j.ijinfomgt.2019.11.004.
- Barki, E. E. R., and J. G. Parente. 2006. Consumer behaviour of the base of the pyramid market in Brazil. [online]. http://bibliotecadigital.fgv.br/dspace/handle/10438/21989 (accessed December 27, 2020).
- BBVA. 2015. Keys to the growth of mobile banking in Colombia | BBVA. NEWS BBVA [online]. https://www.bbva.com/en/keys-growth-mobile-banking-colombia/ (accessed June 26, 2021).
- Bhatiasevi, V. 2016. An extended UTAUT model to explain the adoption of mobile banking. Information Development 32 (4):799–814. doi: https://doi.org/10.1177/0266666915570764.
- Bogliacino, F., L. Jiménez Lozano, and D. Reyes. 2018. Socioeconomic stratification and stereotyping: Lab-in-the-field evidence from Colombia. International Review of Economics 65 (1):77–118. doi: https://doi.org/10.1007/s12232-017-0285-4.
- Chang, T.-K., and F.-H. Yeh. 2020. Using the same payword chains associated with a single account from multiple mobile devices. Wireless Communications and Mobile Computing 2020:1–6. doi: https://doi.org/10.1155/2020/8882655.
- Chemingui, H., and H. B. Lallouna. 2013. Resistance, motivations, trust and intention to use mobile financial services. International Journal of Bank Marketing 31 (7):574–92. doi: https://doi.org/10.1108/IJBM-12-2012-0124.
- Cheng, C. H., C. H. Chen, Y. S. Chen, H. L. Guo, and C. K. Lin. 2019. Exploring Taiwanese’s smartphone user intention: An integrated model of technology acceptance model and information system successful model. International Journal of Social and Humanistic Computing 3 (2):97–107. doi: https://doi.org/10.1504/IJSHC.2019.10023073.
- Chica-Olmo, J., A. Sánchez, and F. H. Sepúlveda-Murillo. 2020. Assessing Colombia’s policy of socio-economic stratification: An intra-city study of self-reported quality of life. Cities 97:102560. doi: https://doi.org/10.1016/j.cities.2019.102560.
- Chin, W. W. 1998. The partial least squares approach for structural equation modeling. In Modern methods for business research. Methodology for business and management, ed. George A. Marcoulides, 295–336. Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
- Chouk, I., and Z. Mani. 2016. Les objets connectés peuvent-ils susciter une résistance de la part des consommateurs ? Une étude netnographique. Décisions Marketing (84):19–42. doi: https://doi.org/10.7193/DM.084.19.41.
- Chouk, I., and Z. Mani. 2019. Factors for and against resistance to smart services: Role of consumer lifestyle and ecosystem related variables. Journal of Services Marketing 33 (4):449–62. doi: https://doi.org/10.1108/JSM-01-2018-0046.
- Correa, M. K. 2020. Ministra TIC Colombia : Fortalecer cobertura y comercio electrónico: Los encargos de Duque a la MinTIC. W Radio. May 5. https://www.wradio.com.co/noticias/actualidad/fortalecer-cobertura-y-comercio-electronico-los-encargos-de-duque-a-la-mintic/20200505/nota/4035945.aspx (accessed June 26, 2021).
- Dahana, W. D., T. Kobayashi, and A. Ebisuya. 2018. Empirical study of heterogeneous behavior at the base of the Pyramid: The influence of demographic and psychographic factors. Journal of International Consumer Marketing 30 (3):173–91. doi: https://doi.org/10.1080/08961530.2017.1399308.
- Davis, F. D. 1989. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly 13 (3):319–40. doi: https://doi.org/10.2307/249008.
- Davis, F. D., R. P. Bagozzi, and P. R. Warshaw. 1989. User acceptance of computer technology: A comparison of two theoretical models. Management Science 35 (8):982–1003. doi: https://doi.org/10.1287/mnsc.35.8.982.
- de Best, R. 2020. Mobile payment usage worldwide. [online]. https://www.statista.com/topics/4872/mobile-payments-worldwide/ (accessed May 15, 2021).
- Kerviler, G. d., N. T. M. Demoulin, and P. Zidda. 2016. Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers? Journal of Retailing and Consumer Services 31 (C):334–44.
- Luna, I. R.d., F. Liébana-Cabanillas, J. Sánchez-Fernández, and F. Muñoz-Leiva 2019. Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change 146 (September):931–44. doi:https://doi.org/10.1016/j.techfore.2018.09.018
- Escalas, J. E., and J. R. Bettman. 2003. You are what they eat: The influence of reference groups on consumers’ connections to brands. Journal of Consumer Psychology 13 (3):339–48. doi: https://doi.org/10.1207/S15327663JCP1303_14.
- Escalas, J. E., and J. R. Bettman. 2005. Self-construal, reference groups, and brand meaning. Journal of Consumer Research 32 (3):378–89. doi: https://doi.org/10.1086/497549.
- Farah, M. F., M. J. S. Hasni, and A. K. Abbas. 2018. Mobile-banking adoption: Empirical evidence from the banking sector in Pakistan. International Journal of Bank Marketing 36 (7):1386–413. doi: https://doi.org/10.1108/IJBM-10-2017-0215.
- Farhat, R., and B. M. Khan. 2012. Effect of brand image & self image congruency on brand preference & customer satisfaction. International Journal of Marketing and Technology 2 (3):92.
- Featherman, M. S., and P. A. Pavlou. 2003. Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human-Computer Studies 59 (4):451–74. doi: https://doi.org/10.1016/S1071-5819(03)00111-3.
- Fielding, N. G., R. M. Lee, and G. Blank (eds.). 2008. The SAGE handbook of online research methods, 1st ed. Los Angeles, CA: SAGE Publications Ltd.
- Fishbein, M., and I. Ajzen. 1975. Predicting and changing behavior, 1st ed. London, UK: Routledge.
- Flavian, C., M. Guinaliu, and Y. Lu 2020. Mobile payments adoption – Introducing mindfulness to better understand consumer behavior. International Journal of Bank Marketing 38 (7):1575–99. doi: https://doi.org/10.1108/IJBM-01-2020-0039.
- Fornell, C., and D. F. Larcker. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18 (1):39–50. doi: https://doi.org/10.1177/002224378101800104.
- Forsythe, S. M., and B. Shi. 2003. Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research 56 (11):867–75. doi: https://doi.org/10.1016/S0148-2963(01)00273-9.
- Franke, G., and M. Sarstedt. 2019. Heuristics versus statistics in discriminant validity testing: A comparison of four procedures. Internet Research 29 (3):430–47. doi: https://doi.org/10.1108/IntR-12-2017-0515.
- Geisser, S. 1974. A predictive approach to the random effect model. Biometrika 61 (1):101–7. doi: https://doi.org/10.1093/biomet/61.1.101.
- Giménez-Santana, A., J. M. Caplan, and G. Drawve. 2018. Risk terrain modeling and socio-economic stratification: Identifying risky places for violent crime victimization in Bogotá, Colombia. European Journal on Criminal Policy and Research 24 (4):417–31. doi: https://doi.org/10.1007/s10610-018-9374-5.
- Giovanis, A., P. Athanasopoulou, C. Assimakopoulos, and C. Sarmaniotis. 2019. Adoption of mobile banking services: A comparative analysis of four competing theoretical models. International Journal of Bank Marketing 37 (5):1165–89. doi: https://doi.org/10.1108/IJBM-08-2018-0200.
- Graeff, T. R. 1996. Image congruence effects on product evaluations: The role of self-monitoring and public/private consumption. Psychology & Marketing13 (5):481–99.
- Grubb, E. L., and H. L. Grathwohl. 1967. Consumer self-concept, symbolism and market behavior: A theoretical approach. Journal of Marketing 31 (4):22–7. doi: https://doi.org/10.1177/002224296703100405.
- Guevara, J. D., and R. Shields. 2019. Spatializing stratification: Bogotá. Ardeth. A Magazine on the Power of the Project 4 (March):223–36.
- Gupta, S., and P. Srivastav. 2016. An exploratory investigation of aspirational consumption at the bottom of the pyramid. Journal of International Consumer Marketing 28 (1):2–15. doi: https://doi.org/10.1080/08961530.2015.1055873.
- Hair, J. F., M. Sarstedt, L. Hopkins, and V. G. Kuppelwieser 2014. Partial least squares structural equation modeling (PLS-SEM): An emerging tool in business research. European Business Review 26 (2):106–21. doi: https://doi.org/10.1108/EBR-10-2013-0128.
- Hair, J. F., M. Sarstedt, C. M. Ringle, and S. P. Gudergan 2017. Advanced issues in partial least squares structural equation modeling, 1st ed. Los Angeles, CA: SAGE Publications, Inc.
- Hasan, M. R., S. M. R. Shams, M. Rahman, and S. E. Haque. 2020. Analysing pro-poor innovation acceptance by income segments. Management Decision 58 (8):1663–74. doi: https://doi.org/10.1108/MD-09-2019-1301.
- Hee, O. C., K. N. Ying, T. O. Kowang, and L. L. Ping 2020. what influences urbanites’ mobile payment adoption? The moderating roles of demographic divides. Social Sciences & Humanities 28 (4):3253–76.
- Heidenreich, S., and T. Kraemer. 2015. Passive innovation resistance: The curse of innovation? Investigating consequences for innovative consumer behavior. Journal of Economic Psychology 51 (December):134–51. doi:https://doi.org/10.1016/j.joep.2015.09.003
- Heidenreich, S., and P. Spieth. 2013. Why innovations fail—the case of passive and active innovation resistance. International Journal of Innovation Management 17 (05):1350021. doi: https://doi.org/10.1142/S1363919613500217.
- Henseler, J., C. M. Ringle, and M. Sarstedt. 2015. A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science 43 (1):115–35. doi: https://doi.org/10.1007/s11747-014-0403-8.
- Hong, S. C. 2020. Digital Bible and innovation resistance. Journal of Media and Religion 19 (1):24–34. doi: https://doi.org/10.1080/.2020.1728187.
- Huang, D., A. Coghlan, and X. Jin 2020. Understanding the drivers of Airbnb discontinuance. Annals of Tourism Research 80 (January):102798. doi:https://doi.org/10.1016/j.annals.2019.102798
- Hulland, J., H. Baumgartner, and K.M. Smith 2018. Marketing survey research best practices: evidence and recommendations from a review of JAMS articles. Journal of the Academy of Marketing Science 46 (1):92–108.
- Hussain, J., A. Ul Hassan, H. S. Muhammad Bilal, R. Ali, M. Afzal, S. Hussain, J. Bang, O. Banos, and S. Lee. 2018. Model-based adaptive user interface based on context and user experience evaluation. Journal on Multimodal User Interfaces 12 (1):1–16. doi: https://doi.org/10.1007/s12193-018-0258-2.
- Hussain, M., A. T. Mollik, R. Johns, and M. S. Rahman. 2019. M-payment adoption for bottom of pyramid segment: An empirical investigation. International Journal of Bank Marketing 37 (1):362–81. doi: https://doi.org/10.1108/IJBM-01-2018-0013.
- Jagtap, S. 2019. Key guidelines for designing integrated solutions to support development of marginalised societies. Journal of Cleaner Production 219 (May):148–65. doi:https://doi.org/10.1016/j.jclepro.2019.01.340
- Jeon, H. M., H. J. Sung, and H. Y. Kim. 2020. Customers’ acceptance intention of self-service technology of restaurant industry: Expanding UTAUT with perceived risk and innovativeness. Service Business 14 (4):533–51. doi: https://doi.org/10.1007/s11628-020-00425-6.
- Joachim, V., P. Spieth, and S. Heidenreich. 2018. Active innovation resistance: An empirical study on functional and psychological barriers to innovation adoption in different contexts. Industrial Marketing Management 71:95–107. doi: https://doi.org/10.1016/j.indmarman.2017.12.011.
- Johar, J. S., and M. J. Sirgy. 1991. Value-expressive versus utilitarian advertising appeals: When and why to use which appeal. Journal of Advertising 20 (3):23–33. doi: https://doi.org/10.1080/00913367.1991.10673345.
- Kang, Y. S., S. Hong, and H. Lee. 2009. Exploring continued online service usage behavior: The roles of self-image congruity and regret. Computers in Human Behavior 25 (1):111–22. doi: https://doi.org/10.1016/j.chb.2008.07.009.
- Kansal, P. 2016. Factors affecting adoption of mobile banking at the bottom of the Pyramid in India. International Journal of Marketing and Business Communication 5 (3):8–19. http://www.publishingindia.com/ijmbc/49/factors-affecting-adoption-of-mobile-banking-at-the-bottom-of-thepyramid-in-india/507/3624/.
- Karahanna, E., and D. W. Straub. 1999. The psychological origins of perceived usefulness and ease-of-use. Information & Management 35 (4):237–50. doi: https://doi.org/10.1016/S0378-7206(98)00096-2.
- Karjaluoto, H., A. A. Shaikh, and S. Saraniemi 2019. How perceived value drives the use of mobile financial services apps. International Journal of Information Management 47 (August):252–61. doi:https://doi.org/10.1016/j.ijinfomgt.2018.08.014
- Kaur, P., A. Dhir N. Singh, G. Sahu, and M. Almotairi, 2020. An innovation resistance theory perspective on mobile payment solutions. Journal of Retailing and Consumer Services 55 (July):102059. doi:https://doi.org/10.1016/j.jretconser.2020.102059
- Khalilzadeh, J., A. B. Ozturk, and A. Bilgihan. 2017. Security-related factors in extended UTAUT model for NFC based mobile payment in the restaurant industry. Computers in Human Behavior 70 (C):460–74. doi: https://doi.org/10.1016/j.chb.2017.01.001.
- Klabi, F. 2020. Self-image congruity affecting perceived quality and the moderation of brand experience: The case of local and international brands in the Kingdom of Saudi Arabia. Journal of Global Marketing 33 (2):69–83. doi: https://doi.org/10.1080/08911762.2019.1614242.
- Kleijnen, M., K. de Ruyter, and T. W. Andreassen. 2005. Image congruence and the adoption of service innovations. Journal of Service Research 7 (4):343–59. doi: https://doi.org/10.1177/1094670504273978.
- Kressmann, F., M. J. Sirgy, A. Herrmann, F. Huber, S. Huber, and D.-J. Lee. 2006. Direct and indirect effects of self-image congruence on brand loyalty. Journal of Business Research 59 (9):955–64. doi: https://doi.org/10.1016/j.jbusres.2006.06.001.
- Lappeman, J., K. Ransome, and Z. Louw. 2019. Not one segment: Using global and local BoP characteristics to model country-specific consumer profiles. European Business Review 31 (3):317–36. doi: https://doi.org/10.1108/EBR-01-2018-0027.
- Laukkanen, T. 2016. Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research 69 (7):2432–9. doi: https://doi.org/10.1016/j.jbusres.2016.01.013.
- 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 8 (3):130–41. doi: https://doi.org/10.1016/j.elerap.2008.11.006.
- Li, B., S. D. Hanna, and K. T. Kim 2020. Who uses mobile payments: Fintech potential in users and non-users. Journal of Financial Counseling and Planning. 31 (1):83–100. doi:https://doi.org/10.1891/JFCP-18-00083
- Liébana-Cabanillas, F., V. Marinkovic, I. R. de Luna, and Z. Kalinic 2018. Predicting the determinants of mobile payment acceptance: A hybrid SEM-neural network approach. Technological Forecasting and Social Change 129 (April):117–30. doi:https://doi.org/10.1016/j.techfore.2017.12.015
- Luarn, P., and H.-H. Lin. 2005. Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior 21 (6):873–91. doi: https://doi.org/10.1016/j.chb.2004.03.003.
- Malek, B. A., S. Mohtar, and A. S. Ariffin. 2017. The factor that affects the effectiveness of agent banking characteristics on financial inclusion performance: A study from Malaysian government-owned banks in Negeri Sembilan. Journal of Advanced Research in Business and Management Studies 7 (1):91–102.
- Martins, C., T. Oliveira, and A. Popovič. 2014. Understanding the Internet banking adoption: A unified theory of acceptance and use of technology and perceived risk application. International Journal of Information Management 34 (1):1–13. doi: https://doi.org/10.1016/j.ijinfomgt.2013.06.002.
- Mathur, M. K., R. Mehta, S. Swami, and S. Bhatnagar 2018. Exploring the urban BoP Market. In Bottom of the Pyramid marketing: Making, shaping and developing BoP markets. Marketing in emerging markets,ed. R. Singh, 199–212. Bingley, UK: Emerald Publishing Limited. doi: https://doi.org/10.1108/978-1-78714-555-920181012.
- Moghavvemi, S., et al. 2021. Drivers and barriers of mobile payment adoption: Malaysian merchants’ perspective. Journal of Retailing and Consumer Services 59 (March):102364. doi:https://doi.org/10.1016/j.jretconser.2020.102364
- Mohammadi, H. 2015. A study of mobile banking usage in Iran. International Journal of Bank Marketing 33 (6):733–59. doi: https://doi.org/10.1108/IJBM-08-2014-0114.
- Mostafa, R. B. 2020. Mobile banking service quality: A new avenue for customer value co-creation. International Journal of Bank Marketing 38 (5):1107–32. doi: https://doi.org/10.1108/IJBM-11-2019-0421.
- Muñoz-Leiva, F., S. Climent-Climent, and F. Liébana-Cabanillas. 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. doi: https://doi.org/10.1016/j.sjme.2016.12.001.
- Mutahar, A. M., N. M. Daud, T. Ramayah, O. Isaac, and A. H. Aldholay. 2018. The effect of awareness and perceived risk on the technology acceptance model (TAM): mobile banking in Yemen. International Journal of Services and Standards 12 (2):180. doi: https://doi.org/10.1504/IJSS.2018.091840.
- Natarajan, T., S. A. Balasubramanian, and D. L. Kasilingam 2017. Understanding the intention to use mobile shopping applications and its influence on price sensitivity. Journal of Retailing and Consumer Services 37 (July):8–22. doi:https://doi.org/10.1016/j.jretconser.2017.02.010.
- Nepomuceno, M. V., M. Laroche, and M.-O. Richard. 2014. How to reduce perceived risk when buying online: The interactions between intangibility, product knowledge, brand familiarity, privacy and security concerns. Journal of Retailing and Consumer Services 21 (4):619–29. doi: https://doi.org/10.1016/j.jretconser.2013.11.006.
- Nunnally, J. C., and I. Bernstein. 1994. Psychometric theory, 3rd ed. New York: McGraw-Hill Higher Education.
- Nur, T., and R. R. Panggabean. 2021. Factors influencing the adoption of mobile payment method among generation Z: The extended UTAUT approach. https://papers.ssrn.com/abstract=3824425 (accessed May 15, 2021).
- Onkvisit, S., and J. Shaw. 1987. Self‐concept and image congruence: Some research and managerial implications. Journal of Consumer Marketing. 4 (1):13–23. doi:https://doi.org/10.1108/eb008185
- Ooi, K.-B., and G. W.-H. Tan. 2016. Mobile technology acceptance model: An investigation using mobile users to explore smartphone credit card. Expert Systems with Applications 59 (October):33–46. doi:https://doi.org/10.1016/j.eswa.2016.04.015
- Pal, A., T. Herath, R. De, and H. R. Rao. 2020. Contextual facilitators and barriers influencing the continued use of mobile payment services in a developing country: Insights from adopters in India. Information Technology for Development 26 (2):394–420. doi: https://doi.org/10.1080/02681102.2019.1701969.
- Palau, M. 2020. Pandemic draws more latin american poor into banking system | Business News | US News. Associated Press, November 13. http://www.usnews.com/news/business/articles/2020-11-13/pandemic-draws-more-latin-american-poor-into-banking-system (accessed June 26, 2021).
- Parasuraman, A., and C. L. Colby. 2014. An updated and streamlined technology readiness index: TRI 2.0. Journal of Service Research 18 (1):59–74. doi:https://doi.org/10.1177/1094670514539730.
- Patil, P., K. Tamilmani, N. P. Rana, and V. Raghavan. 2020. Understanding consumer adoption of mobile payment in India: Extending Meta-UTAUT model with personal innovativeness, anxiety, trust, and grievance redressal. International Journal of Information Management 54:102144. doi: https://doi.org/10.1016/j.ijinfomgt.2020.102144.
- Pavlou, P. A. 2003. Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce 7 (3):101–34.
- Pipitwanichakarn, T., and N. Wongtada. 2019. Mobile commerce adoption among the bottom of the pyramid: A case of street vendors in Thailand. Journal of Science and Technology Policy Management 10 (1):193–213. doi: https://doi.org/10.1108/JSTPM-12-2017-0074.
- Quester, P. G., A. Karunaratna, and L. K. Goh 2000. Self-congruity and product evaluation: A cross-cultural study. Journal of Consumer Marketing 17: 525–35. https://researchbank.swinburne.edu.au/items/0d2aa08c-1b1d-4a20-a257-8ce2ba1ff2cc/1/.
- Technology acceptance among micro-entrepreneurs in marginalized social strata: The case of social innovation in Bangladesh. Technological Forecasting and Social Change 118 (May):236–45. doi:https://doi.org/10.1016/j.techfore.2017.01.027
- Ram, S., and J. N. Sheth. 1989. Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing 6 (2):5–14. doi: https://doi.org/10.1108/EUM0000000002542.
- Rammile, N., and J. Nel. 2012. Understanding resistance to cell phone banking adoption through the application of the technology acceptance model (TAM). African Journal of Business Management 6 (1):86–97.
- Raza, S. A., A. Umer, and N. Shah. 2017. New determinants of ease of use and perceived usefulness for mobile banking adoption. International Journal of Electronic Customer Relationship Management 11 (1):44–65. doi: https://doi.org/10.1504/IJECRM.2017.10007744.
- Reddy, A., and A. Ahmad. 2020. A study on self-image congruence and perceived quality with respect to symbolic purchase in sport at Bangalore City. International Journal of Innovations in Management, Engineering and Science (IJIMES) 6 (2):4–8.
- Ríos, A. M. 2021. Change in the number of mobile banking services in Colombia from 2016 to 2019. https://www.statista.com/statistics/980701/mobile-banking-services-change-colombia/ (accessed June 26, 2021).
- Rogers, E. 1995. Diffusion of innovations, 5th ed. New York, NY: Free Press.
- Rootman, C., and J. Krüger. 2020. Increasing customer adoption of the mobile payment technology zapper in South Africa. Journal of African Business 21 (4):509–28. doi: https://doi.org/10.1080/15228916.2020.1790915.
- Sirgy, M. J., D. Grewal, T. F. Mangleburg, J.-O. Park, K.-S. Chon, C. B. Claiborne, J. S. Johar, and H. Berkman. 1997. Assessing the predictive validity of two methods of measuring self-image congruence. Journal of the Academy of Marketing Science 25 (3):229–41. doi: https://doi.org/10.1177/0092070397253004.
- Stone, M. 1974. Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological) 36 (2):111–47. doi: https://doi.org/10.1111/j.2517-6161.1974.tb00994.x.
- Streukens, S., and S. Leroi-Werelds. 2016. Bootstrapping and PLS-SEM: A step-by-step guide to get more out of your bootstrap results. European Management Journal 34 (6):618–32. doi: https://doi.org/10.1016/j.emj.2016.06.003.
- Talke, K., and S. Heidenreich. 2014. How to overcome pro-change bias: Incorporating passive and active innovation resistance in innovation decision models. Journal of Product Innovation Management 31 (5):894–907. doi: https://doi.org/10.1111/jpim.12130.
- Taylor, S., and P. A. Todd. 1995. Understanding information technology usage: A test of competing models. Information Systems Research 6 (2):144–76. doi: https://doi.org/10.1287/isre.6.2.144.
- Tenenhaus, M., V. E. Vinzi, Y.-M. Chatelin, and C. Lauro. 2005. PLS path modeling. Computational Statistics & Data Analysis 48 (1):159–205. doi: https://doi.org/10.1016/j.csda.2004.03.005.
- Uribe-Mallarino, C. 2008. Estratificación social en Bogotá: De la política pública a la dinámica de la segregación social. Universitas Humanística 65 (65):139–71.
- van Klyton, A., J. F. Tavera-Mesías, and W. Castaño-Muñoz. 2021. Innovation resistance and mobile banking in rural Colombia. Journal of Rural Studies 81 (January):269–80. doi:https://doi.org/10.1016/j.jrurstud.2020.10.035.
- Venkatesh, V., J. Thong, X. Xu. 2012. Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly 36 (1):157–78. doi: https://doi.org/10.2307/41410412.
- Venkatesh, V., M. G. Morris, G.B. Davis, and F. D. Davis. 2003. User acceptance of information technology: Toward a unified view. MIS Quarterly 27 (3):425–78. doi:https://doi.org/10.2307/30036540
- Venkatesh, V., and H. Bala. 2008. Technology acceptance Model 3 and a research agenda on interventions. Decision Sciences 39 (2):273–315. doi: https://doi.org/10.1111/j.1540-5915.2008.00192.x.
- Venkatesh, V., and F. D. Davis. 2000. A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science 46 (2):186–204. doi: https://doi.org/10.1287/mnsc.46.2.186.11926.
- Wong, D., H. Liu, Y. Meng-Lewis, Y. Sun, and Y. Zhang. 2021. Gamified money: Exploring the effectiveness of gamification in mobile payment adoption among the silver generation in China. Information Technology & People. 34: 1–35. doi: https://doi.org/10.1108/ITP-09-2019-0456.
- Wu, S., M. Ren, A. H. Pitafi, and T. Islam. 2020. Self-image congruence, functional congruence, and mobile app intention to use. Mobile Information Systems 2020:1–17. doi: https://doi.org/10.1155/2020/5125238.
- Yang, Q., C. Pang, L. Liu, D. C. Yen, and J.M. Tarn. 2015. Exploring consumer perceived risk and trust for online payments: An empirical study in China’s younger generation. Computers in Human Behavior 50 (September):9–24. doi:https://doi.org/10.1016/j.chb.2015.03.058.
- Yu, C.-S., C.-K. Li, and W. Chantatub. 2015. Analysis of consumer E-lifestyles and their effects on consumer resistance to using mobile banking: empirical surveys in Thailand and Taiwan. International Journal of Business & Information 10 (2):198–232.
- Yu, C.-S., and W. Chantatub. 2016. Consumer resistance to using mobile banking- Evidence from Thailand and Taiwan. International Journal of Electronic Commerce Studies 7 (1):21–38. doi: https://doi.org/10.7903/ijecs.1375.
- Zhang, J., Y. Luximon, and Y. Song. 2019. The role of consumers’ perceived security, perceived control, interface design features, and conscientiousness in continuous use of mobile payment services. Sustainability 11 (23):6843. doi: https://doi.org/10.3390/su11236843.