Contactless digital financial innovation and global contagious COVID-19 pandemic in low income countries: Evidence from Uganda

References

• Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–41.
   Web of Science ®Google Scholar
• Agur, I., Peria, S. M., & Rochon, C. (2020 Digital financial services and the pandemic: Opportunities and risks for emerging and developing economies). and : and and . International Monetary Fund Special Series on COVID-19, Transactions, Vol. 1, pp. 2–1. IMF Publications, Washington DC. Retrieved from https://www.imf.org/en/Publications/SPROLLs/covid19-special-notes.
   Google Scholar
• Alliance for Financial Inclusion. (2011). “Measuring financial inclusion: a core set of indicators”. AFI Financial Inclusion Data Working Group Paper, available at: http://www.afiglobal.org.
   Google Scholar
• Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Aker, J.C., Boumnijel, R., McClelland, A. and Tierney, N. (2016). “Payment Mechanisms and Anti-Poverty Programs: Evidence from a Mobile Money Cash Transfer Experiment in Niger”, Tufts University working paper, Fletcher School and Department of Economics, Tufts University, Medford, MA
   Google Scholar
• Anderson, C. L., & Agarwal, R. (2011). The digitization of healthcare: Boundary risks, emotion, and consumer willingness to disclose personal health information. Information Systems Research, 22(3), 469–490.
   Web of Science ®Google Scholar
• Alliance for Financial Inclusion. (2011). “Measuring financial inclusion: a core set of indicators”. AFI Financial Inclusion Data Working Group Paper, available at: http://www.afiglobal.org.
   Google Scholar
• Anderson, J. C., & Gerbing, D. W. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychol. Bull, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411
   Web of Science ®Google Scholar
• Arner, D. W., J. N. Barberis, J. Walker, R. P. Buckley, A. M. Dahdal, D. and A. Zetzsche. (2020). “Digital Finance & the COVID-19 Crisis.” University of Hong Kong Faculty of Law Research Paper No. 2020/017. https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3558889
   Google Scholar
• Aspinwall, L. G. (2001). Dealing with adversity: self-regulation, coping, adaptation, and health. In A. Tesser & N. Schwarz (Eds.), The Blackwell handbook of social psychology (Vol. 1, pp. 591–614). Malden, MA: Blackwell. Intraindividual processes
   Google Scholar
• Auer, R., Comelli, G., & Frost, J. (2020). COVID-19, Cash, and the Future of Payments. In BIS Bulletin, No. (3). BIS Publications: Basel, Switzerland. https://www.bis.org/publ/bisbull03.pdf
   Google Scholar
• Baganzi, R., & Lau, A. K. W. (2017). Examining trust and risk in mobile money acceptance in Uganda. Sustainability, 9(12), 1–22.
   Web of Science ®Google Scholar
• Bagozzi, R., & Baumgartner, H. (2000). The role of emotions in goal-directed behavior. The Why of Consumption: Contemporary Perspectives on Consumer Motives, Goals, and Desires, 36–58.
   Google Scholar
• Bagozzi, R. P., Gopinath, M., & Nyer, P. U. (1999). “The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184–206. https://doi.org/10.1177/0092070399272005
   Web of Science ®Google Scholar
• Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1),74–94. https://doi.org/10.1007/BF02723327.
   Google Scholar
• Bangura, J. A. (2016). “Saving money, saving lives: A case study on the benefits of digitizing payments to Ebola response workers in sierra Leone.” https://www.betterthancash.org/tools-research/case-studies/saving-money-saving-lives-a-case-study-on-the-benefits-of-digitizing-payments-to-ebola-response-workers-in-sierra-leone
   Google Scholar
• Barrett, K., & Campos, J. (1987). Perspectives on emotional development: II. A functionalist approach to emotions. In J. Osofsky (Ed.), Handbook of infant development (2nd ed., pp. 555–578). New York: Wiley.
   Google Scholar
• Bodenhausen, G. V., Kramer, G. P., & Süsser, K. (1994). Happiness and stereotypic thinking in social judgment. Journal of Personality & Social Psychology, 66, 621–632.
   Web of Science ®Google Scholar
• Bollen, K. A. (1989). Structural equations with latent variables. John Wiley & Sons.
   Google Scholar
• Bower, G. H. (1981). Mood and memory. The American Psychologist, 36, 129–148.
   PubMed Web of Science ®Google Scholar
• Bower, G. H. (1991). Mood congruity of social judgment. In J. Forgas (Ed.), Emotion and social judgment (pp. 31–54). Oxford, UK: Pergamon.
   Google Scholar
• Chin, W. W. (1998). The partial least squares approach for structural equation modeling: Methodology for business and management. Erlbaum
   Google Scholar
• Chin, W. W., & Newsted, P. R. (1999). Structural equation modeling analysis with small samples using partial least squares. In R. H. Hoyle (Ed.), Statistical strategies for small sample research (pp. 307–341). Sage Publications, Inc.: Thousand Oaks.
   Google Scholar
• Cronbach, L. J. (1971). “Test Validation.” In Educational Measurement, edited by R. L. Thorndike, 443–507. Washington, DC: IGI Publishing, American Council on Education.
   Google Scholar
• Consultative Group to Assist the Poor-CGAP. (2020). COVID-19 (coronavirus) – Insights for inclusive finance. CGAP Publications, Washington, DC.
   Google Scholar
• Churchill, G.A., & Iacobucci, D. (2004). Marketing research: Methodological foundations, 9th ed, Thomson South-Western, Ohio.
   Google Scholar
• Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum.
   Google Scholar
• 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/10.2307/249008
   Web of Science ®Google Scholar
• 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. https://doi.org/10.1287/mnsc.35.8.982
   Web of Science ®Google Scholar
• DeVellis, R. F. (2003). Scale development: Theory and applications (2nd ed.). Sage.
   Google Scholar
• Demirguc-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2020). The Global Findex Database 2017: Measuring financial inclusion and opportunities to expand access to and use of financial services. The World Bank Economic Review, The World Bank.
   Google Scholar
• Demirgüç-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2018). The Global Findex Database 2017: Measuring financial inclusion and the fintech revolution. World Bank. https://globalfindex.worldbank.org/sites/globalfindex/files/2018-04/2017%20
   Google Scholar
• Diener, E. (2000). Subjective well-being: The science of happiness and a proposal for a national index. The American Psychologist, 55(1), 34–43. https://doi.org/10.1037/0003-066X.55.1.34
   PubMed Web of Science ®Google Scholar
• Dumas, T., Frisetti, A., & Radice, H. W. (2017). Harnessing digital technology for cash transfer programming in the Ebola response: Lessons learned from USAID/Office of Food for Peace Partners’ West Africa Ebola Responses (2015–2016).Washington D.C. https://www.alnap.org/help-library/harnessing-digital-technology-for-cash-transfer-programming-in-the-ebola-response
   Google Scholar
• Dupas, P., & Robinson, J. (2013). Savings Constraints and Microenterprise Development: Evidence from a Field Experiment in Kenya. American Economic Journal. Applied Economics, 5(1), 163–192.
   Web of Science ®Google Scholar
• Economides, N., & Jeziorski, P. (2017). Mobile Money in Tanzania. Marketing Science, 36(6), 813–1017.
   Web of Science ®Google Scholar
• Field, A. P., & Hole, G. (2003). How to Design and Report Experiment. London: Sage Publishing.
   Google Scholar
• Field, A.P. (2005). Discovering statistics using SPSS, Sage.
   Google Scholar
• Field, A.P. (2009). Discovering Statistics using SPSS, Sage, London.
   Google Scholar
• Folkman, S. (1997). Positive psychological states and coping with severe stress. Social science & medicine, 45, 1207–1221.
   PubMed Web of Science ®Google Scholar
• Folkman, S., & Moskowitz, J. T. (2000). Positive affect and the other side of coping. The American Psychologist, 55, 647–654.
   PubMed Web of Science ®Google Scholar
• Forgas, J. P. (1995). Mood and judgment: The affect infusion model (AIM). Psychological Bulletin, 117, 39–66.
   PubMed Web of Science ®Google Scholar
• Forgas, J. P., & Bower, G. H. (1987). Mood effects on person-perception judgments. Journal of Personality and Social Psychology, 53(1), 53–60.
   PubMed Web of Science ®Google Scholar
• Fredrickson, B. L. (2000). Cultivating positive emotions to optimize health and well-being. Prevention and Treatment, 3, 1–25.
   Google Scholar
• Fredrickson, B. L. (2001). The role of positive emotions in positive psychology: The broaden and build theory of positive emotions. American Psychology, 56(3), 218–226. https://doi.org/10.1037//0003-066x.56.3.218
   PubMed Web of Science ®Google Scholar
• Frijda, N. H. (1986). The emotions. Cambridge University Press.
   Google Scholar
• Global System for Mobile Communications-GSMA., (2020), “Mobile money recommendations to central banks in response to COVID-19.” https://www.gsma.com/mobilefordevelopment/resources/mobile-money-recommendations-to-central-banks-in-response-to-covid-19
   Google Scholar
• Hair, J. F. J., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2014). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage.
   Google Scholar
• Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2016). A Primer on Partial. Least Squares Structural Equation Modeling (PLS-SEM) (2nd ed.). Sage.
   Google Scholar
• Huang, Y., Lin, C., Wang, P., & Wu, Z. (2020). Saving China from the Coronavirus and Economic Meltdown: Experiences and Lessons. In R. Baldwin (Ed.), Mitigating the COVID Economic Crisis: Act Fast and Do Whatever It TakesMitigating the COVID Economic Crisis: Act Fast and Do Whatever It Takes online book
   Google Scholar
• Isen, A. M., Shalker, T. E., Clark, M., & Karp, L. (1978). Affect, accessibility of material in memory, and behavior: A cognitive loop?. Journal of Personality and Social Psychology, 36(1), 1–12.
   PubMed Web of Science ®Google Scholar
• Johns, R (2010). Likert items and scales. Retrieved from Survey Question Bank website: http://www.surveynet.ac.uk/sqb/datacollection/likertfactsheet.pdf.
   Google Scholar
• Johnson Laird, P. N., & Oatley, K. (1992). Basic emotions, rationality, and folk theory. Cognition & emotion, 6, 201–223.
   Web of Science ®Google Scholar
• Kahneman, D. (1999). Objective happiness. In D. Kahneman, E. Diener, & N. Schwarz (Eds.), Well-being: Foundations of hedonic psychology (pp. 3±25). New York: Russell-Sage.
   Google Scholar
• Kavanagh, D. J., & Bower, G. H. (1985). Mood and self-efficacy: Impact of joy and sadness on perceived capabilities. Cognitive Therapy and Research, 9, 507–525.
   Web of Science ®Google Scholar
• Komiak, S. Y. X., & Benbasat, I. (2006). The effects of personalization and familiarity on trust and adoption of recommendation agents. MIS Quarterly, 30(4), 941–960.
   Web of Science ®Google Scholar
• Lazarus, R. S. (1991a). Cognition and motivation in emotion. American Psychology, 46:352–67
   PubMed Web of Science ®Google Scholar
• Lazarus, R. S. (1991b). Progress on a cognitive-motivational-relational theory of emotion. The American Psychologist, 46(8), 819–834.
   PubMed Web of Science ®Google Scholar
• Lazarus, R. S., Kanner, A. D., & Folkman, S. (1980). Emotions: A cognitrve-phenomenological analysis. In R. Plutchik & H. Kellerman (Eds.), Theories of emotion (pp. 189–217). New York: Academic Press.
   Google Scholar
• Lerner, J. S., Gonzalez, R. M., Small, D. A., & Fischhoff, B. (2003). Effects of fear and anger on perceived risks of terrorism: A national field experiment. Psychological Science, 14, 144–150.
   PubMed Web of Science ®Google Scholar
• Lerner, J. S., & Keltner, D. (2000). Beyond valence: Toward a model of emotion-specific influences on judgement and choice. Cognition & Emotion, 14(No4), 473–493. https://doi.org/10.1080/026999300402763
   Google Scholar
• Lerner, J. S., Small, D. A., & Loewenstein, G. (2004). Heart strings and purse strings: Carryover effects of emotions on economic decisions. Psychological Science, 15(5), 337–340.
   PubMed Web of Science ®Google Scholar
• Levenson, R. W. (1994). Human emotion: A functional view. In P. Ekman & R. J. Davidson (Eds.), The nature of emotion: Fundamental questions (pp. 123–126). New York: Oxford University Press.
   Google Scholar
• Likert, R (1932). A technique for the measurement of attitudes, Archives ofPsychology, 140(1), 44–53.
   Google Scholar
• María, J. C., Lorena, S., & Natalia, T. (2020). Emotions, concerns and reflections regarding the COVID-19 pandemic in Argentina. Ciênc. saúde coletiva, 25(1), 2447–2456. https://doi.org/10.1590/1413-81232020256.1.10472020
   PubMedGoogle Scholar
• 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/10.1177/002224378101800104
   Web of Science ®Google Scholar
• Ministry of Health in Uganda, (2023), Cumulative Number of Covid-19 cases; Cumulative Number of Covid-19 DEATHS: Cumulative Number of Covid-19 RECOVERIES. https://covid19.gou.go.ug/statistics.html
   Google Scholar
• Mulligan, K., & Scherer, K. R. (2012). Toward a working definition of emotion. Emotion Review, 4(4), 345–357. https://doi.org/10.1177/1754073912445818
   Web of Science ®Google Scholar
• Muralidharan, K., Niehaus, P., & Sukhtankar, S. (2016). Building State Capacity: Evidence from Biometric Smartcards in India. The American Economic Review, 106(10), 2895–2929.
   Web of Science ®Google Scholar
• Nalletamby, S. (2020). COVID-19 pandemic bolsters case for technology-based economic resilience. African Development Bank Bulletin, 16. https://www.afdb.org/en/news-and-events/covid-19-pandemic-bolsters-case-technology-basedeconomic-resilience-35255on13/07/2021
   Google Scholar
• Nampewo, D., Tinyinondi, G. A., Kawooya, D. R., & Ssonko, G. W. (2016). Determinants of private sector credit in Uganda: The role of mobile money. Financial Innovation, 2(13), 1–16.
   Google Scholar
• Nunnally, J. C. (1978). Psychometric theory. McGraw–Hill.
   Google Scholar
• Oatley, K., & Johnson Laird, P. N. (1996). The communicative theory of emotions: Empirical tests, mental models, and implications for social interaction. (L. L. Martin & A. Tesser, Eds.). Erlbaum.
   Google Scholar
• Okello Candiya Bongomin, G., & Ntayi, J. M. (2020). Mobile money adoption and usage and financial inclusion: Mediating effect of digital consumer protection. Digital Policy, Regulation and Governance, 22(3), 157–176. https://doi.org/10.1108/DPRG-01-2019-0005
   Web of Science ®Google Scholar
• Okello Candiya Bongomin, G., Yosa, G., & Ntayi, J. M. (2021a). Reimaging the mobile money ecosystem and financial inclusion of MSMEs in Uganda: Hedonic motivation as mediator. International Journal of Social Economics, 48(11), 1608–1628. https://doi.org/10.1108/IJSE-09-2019-0555
   Web of Science ®Google Scholar
• Okello Candiya Bongomin, G., Yourougou, G., Yosa, P., Amani, F., & Ntayi, J. M. (2021b). Psychoanalysis of the mobile money ecosystem in the digital age: Generational cohort and technology generation theoretical approach. Development in Practice. https://doi.org/10.1080/09614524.2021.1937539
   Web of Science ®Google Scholar
• Ozili, P. K. (2018). Impact of digital finance on financial inclusion and stability. Borsa Istanbul Review, 18(4), 329–340. https://doi.org/10.1016/j.bir.2017.12.003
   Web of Science ®Google Scholar
• Pazarbasioglu, C., Mora, A. G., Uttamchandani, M., Natarajan, H., Feyen, E., & Saal, M. (2020). Digital financial services.: World Bank Group. http://pubdocs.worldbank.org/en/230281588169110691/Digital-Financial-Services.pdf
   Google Scholar
• Perugini, M., & Bagozzi, R. (2001). The role of desires and anticipated emotions in goal‐directed behaviours: Broadening and deepening the theory of planned behaviour. British Journal of Social Psychology, 40(1), 79–98.
   PubMed Web of Science ®Google Scholar
• Phillips, D., & Baumgartner, H. (2002). The role of consumption emotions in the satisfaction response. Journal of Consumer Psychology, 12(3), 243–252.
   Web of Science ®Google Scholar
• Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y. & Podsakoff, N.P (2003). “Common method biases in behavioural research: a critical review of the literature and recommended remedies”, Journal of Applied Psychology, 88(5), 879–903.
   PubMed Web of Science ®Google Scholar
• Raghunathan, R., & Pham, M.T. (1999). “All Negative Moods Are Not Equal: Motivational Influences of Anxiety and Sadness on Decision Making,” Organizational Behavior and Human Decision Processes, 79 (1), 56–77.
   PubMed Web of Science ®Google Scholar
• Schwarz, N. (1990). Feelings as information: Informational and motivational functions of affective states. In ET Higgins (Ed.), Handbook of Motivation and Cognition (Vol. 2, pp. 527–561). New York: RM Sorrentino.
   Google Scholar
• Schwarz, N., & Clore, G. L. (1983). Mood, misattribution and judgments of well-being: Informative and directive functions of affective states. Journal of Personality and Social Psychology, 45(3), 513–523.
   Web of Science ®Google Scholar
• Simon, H. A. (1967). Motivational and emotional controls of cognition. Psychological Review, 74, 29–39.
   PubMed Web of Science ®Google Scholar
• Smith, C. A., & Ellsworth, P. C. (1985). Patterns of cognitive appraisal in emotion. Journal of Personality & Social Psychology, 48(4), 813–838.
   PubMed Web of Science ®Google Scholar
• Spector, P.E. (2006). “Method variance in organizational research: truth or urban legend?”, Organizational Research Methods, Vol. 9 No. 2, pp. 221–232.
   Web of Science ®Google Scholar
• Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625–649. https://doi.org/10.3102/0034654308325896
   Web of Science ®Google Scholar
• Suri, T. (2017). Mobile money. Annual Review of Economics, 9(1), 497–520. https://doi.org/10.1146/annurev-economics-063016-103638
   Web of Science ®Google Scholar
• Suri, T., & Jack, W. (2016). The long-run poverty and gender impacts of mobile money. Science, 354(6317), 1288–1292. https://doi.org/10.1126/science.aah5309
   PubMed Web of Science ®Google Scholar
• Tooby, J., & Cosmides, L. (1990). The past explains the present: Emotional adaptations and the structure of ancestral environments. Ethology and Sociobiology, 11, 375–424.
   Web of Science ®Google Scholar
• Tourangeau, R., Rips, L.J. and Rasinski, K. (2000). The Psychology of Survey Response, Cambridge University Press, Cambridge.
   Google Scholar
• Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly, 36(1), 157–178. https://doi.org/10.2307/41410412
   Web of Science ®Google Scholar
• Wood, S., & Moreau, C. (2006). From fear to loathing?: How emotions influence the evaluation and early use of innovation. Journal of Marketing, 70, 44–57.
   Web of Science ®Google Scholar
• Wood, S. L., & Swait, J. (2002). Psychological indicators of innovation adoption: Cross-classification based on need for cognition and need for change. Journal of Consumer Psychology, 12, 1–13.
   Web of Science ®Google Scholar
• World Bank, (2020), Covid-19 and Digital Financial Inclusion in Africa: How to leverage digital technologies during the pandemic. Africa Knowledge in Time Policy Brief October, Issue 1, NO. 4, The World Bank Washington D.C
   Google Scholar
• World Health Organization. (2020). Coronavirus disease (Covid-19) pandemic Dashboard. Daily and weekly update. 13th December, 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019?adgroupsurvey={adgroupsurvey}&gclid=Cj0KCQjwwtWgBhDhARIsAEMcxeBbGvwCcoY-X4MX3g58eaJgpYIhx7ZgNkq5txprZzKPkOx1Mt7D3YoaAuXZEALw_wcB
   Google Scholar
• World Health Organization. (2023). Coronavirus disease (Covid-19) pandemic Dashboard. Daily and weekly update. 16th March, 2023. https://www.who.int/emergencies/diseases/novel-coronavirus-2019?adgroupsurvey={adgroupsurvey}&gclid=Cj0KCQjwwtWgBhDhARIsAEMcxeBbGvwCcoY-X4MX3g58eaJgpYIhx7ZgNkq5txprZzKPkOx1Mt7D3YoaAuXZEALw_wcB
   Google Scholar
• Worldometer. (2023). COVID-19 Coronavirus Statistics: Daily and weekly update. https://www.worldometers.info › coronavirus
   Google Scholar
• Uganda Bureau of Statistics. (2014). The National Population and Housing Census 2014 – Main Report, Kampala, Uganda.
   Google Scholar
• United Nations Capital Development Fund. (2020). Digitizing Payments in the Time of COVID-19 and Beyond. News Letter, April 29, 2020 Brussels, Belgium. https://www.uncdf.org/article/5574/digitizing-payments-in-the-time-of-covid19-and-beyond
   Google Scholar
• United Nations Capital Development Fund. (2020). Digitizing Payments in the Time of COVID-19 and Beyond. News Letter, April 29 2020, Brussels, Belgium. https://www.uncdf.org/article/5574/digitizing-payments-in-the-time-of-covid19-and-beyond
   Google Scholar
• Wright, W. F., & Bower, G. H. (1992). Mood effects on subjective probability assessment. Organizational Behavior and Human Decision Processes, 52, 276–291.
   Web of Science ®Google Scholar
• Zeelenberg, M., Nelissen, R. M. A., Breugelmans, S. M., & Pieters, R. (2008). On emotion specificity in decision-making: Why feeling is for doing. Judgment and Decision Making, 3, 18–27.
   Web of Science ®Google Scholar