2,521
Views
4
CrossRef citations to date
0
Altmetric
GENERAL & APPLIED ECONOMICS

Social networks and technology adoption: Evidence from mobile money in Uganda

& | (Reviewing editor)
Article: 1913857 | Received 26 Mar 2020, Accepted 04 Apr 2021, Published online: 13 Apr 2021

References

  • Abdinoor, A., & Mbamba, U. O. (2017). Factors influencing consumers’ adoption of mobile financial services in Tanzania. Cogent Business & Management, 4(1), 1392273. doi: 10.1080/23311975.2017.1392273.
  • Afawubo, K., Agbaglah, M., Couchoro, M. K., & Gbandi, T. (2017). Socioeconomic determinants of the mobile money adoption process: The case of Togo. Cahier de recherche, 17(03), 1-23. Page couverture WP Logo_GREDI (usherbrooke.ca)
  • Aizer, A., & Currie, J. (2004). Networks or neighbourhoods? correlations in the use of publicly-funded maternity care in California. Journal of Public Economics, 88(12), 2573–20. doi: 10.1016/j.jpubeco.2003.09.003.
  • Albeck, S., & Kaydar, D. (2002). Divorced mothers: their network of friends pre-and post-divorce. Journal of Divorce & Remarriage, 36(3-4), 111–138. https://doi.org/10.1300/J087v36n03_07
  • Angrist, J. D. (2014). The perils of peer effects. Labour Economics, 30, 98–108. doi: 10.1016/j.labeco.2014.05.008.
  • Arai, L. (2007). Peer and neighbourhood influences on teenage pregnancy and fertility: qualitative findings from research in English communities. Health & Place, 13(1), 87–98. https://doi.org/10.1016/j.healthplace.2005.10.003
  • Aslund, O., & Fredriksson, P. (2009). Peer effects in welfare dependence quasi-experimental evidence. Journal of Human Resources, 44(3), 798–825. doi: 10.1353/jhr.2009.0007.
  • Bandiera, O., & Rasul, I. (2006). Social networks and technology adoption in Northern Mozambique. The Economic Journal, 116(514), 869–902. doi: 10.1111/j.1468-0297.2006.01115.x.
  • Banerjee, A., Chandrasekhar, A. G., Duflo, E., & Jackson, M. O. (2013). The diffusion of microfinance. Science, 341(6144). doi: 10.1126/science.1236498.
  • Bertrand, M., Luttmer, E. F., & Mullainathan, S. (2000). Network effects and welfare cultures. The Quarterly Journal of Economics, 115(3), 1019–1055. doi: 10.1162/003355300554971.
  • Burke, M. A., & Sass, T. R. (2013). Classroom peer effects and student achievement. Journal of Labor Economics, 31(1), 51–82. doi: 10.1086/666653.
  • Burns, J., Godlonton, S., & Keswell, M. (2010). Social networks, employment and worker discouragement: evidence from South Africa. Labour Economics, 17(2), 336–344. doi: 10.1016/j.labeco.2009.08.007.
  • Currie, J. (2006). The take-up of social benefits. Public Policy and the Income Distribution, 80.
  • Dahl, G. B., Løken, K. V., & Mogstad, M. (2014). Peer effects in program participation. American Economic Review, 104(7), 2049–2074. doi: 10.1257/aer.104.7.2049.
  • Daponte, B. O., Sanders, S., & Taylor, L. (1999). Why do low-income households not use food stamps? evidence from an experiment. Journal of Human Resources, 34(3), 612–628. doi: 10.2307/146382.
  • Demirguc-Kunt, A., Klapper, L., Singer, D., Ansar, S., & Hess, J. (2018). The Global Findex Database 2017: Measuring financial inclusion and the fintech revolution. The World Bank.
  • Deri, C. (2005). Social networks and health service utilization. Journal of Health Economics, 24(6), 1076–1107. doi: 10.1016/j.jhealeco.2005.03.008.
  • Devillanova, C. (2008). Social networks, information and health care utilization: Evidence from undocumented immigrants in Milan. Journal of Health Economics, 27(2), 265–286. doi: 10.1016/j.jhealeco.2007.08.006.
  • Drouard, J. (2011). Costs or gross benefits?–what mainly drives cross-sectional variance in internet adoption. Information Economics and Policy, 23(1), 127–140. doi: 10.1016/j.infoecopol.2010.12.001.
  • Duflo, E., & Saez, E. (2003). The role of information and social interactions in retirement plan decisions: evidence from a randomized experiment*. The Quarterly Journal of Economics, 118(3), 815–842. doi: 10.1162/00335530360698432.
  • Duggan, M., & Kearney, M. S. (2005). ‘The impact of child ssi enrollment on household outcomes: evidence from the survey of income and program participation’. NBER Working Paper (w11568).
  • Fortin, B., & Yazbeck, M. (2015). Peer effects, fast food consumption and adolescent weight gain. Journal of Health Economics, 42, 125–138. doi: 10.1016/j.jhealeco.2015.03.005.
  • Gichuki, C. N., & Mulu-Mutuku, M. (2018). Determinants of awareness and adoption of mobile money technologies: evidence from women micro entrepreneurs in Kenya. Women’s Studies International Forum, 67, 18–22. doi: 10.1016/j.wsif.2017.11.013.
  • Goh, T. T., & Sun, S. (2014). Exploring gender differences in Islamic mobile banking acceptance. Electronic Commerce Research, 14(4), 435–458. doi: 10.1007/s10660-014-9150-7.
  • GSMA. (2017). State of the industry: Mobile financial services for the unbanked.
  • Heckman, J. J., & Smith, J. A. (2004). The determinants of participation in a social program: evidence from a prototypical job training program. Journal of Labour Economics, 22(2), 243–298. doi: 10.1086/381250.
  • InterMedia (2012). Mobile money in Uganda: Use, barriers and opportunities. Washington, DC. Retrieved from http://www.intermedia.org/wp-content/uploads/2013/11/FITS_YearEndReport_11-8-134P.pdf
  • Khan, M. R., & Blumenstock, J. (2017). Determinants of Mobile Money Adoption in Pakistan. arXiv preprint arXiv:1712.01081.
  • Kiconco, R. I., Rooks, G., & Snijders, C. (2020). Learning mobile money in social networks: comparing a rural and urban region in Uganda. Computers in Human Behavior, 103, 214–225. doi: 10.1016/j.chb.2019.09.005.
  • Lu, J., Yao, J. E., & Yu, C.-S. (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245–268. doi: 10.1016/j.jsis.2005.07.003.
  • Maddala, G. S. (1983). Limited-dependent and Qualitative Variables in Econometrics. Cambridge University press.
  • Maertens, A., & Barrett, C. B. (2012). Measuring social networks’ effects on agricultural technology adoption. American Journal of Agricultural Economics, 95(2), 353–359. doi: 10.1093/ajae/aas049.
  • Mallat, N. (2007). Exploring consumer adoption of mobile payments–A qualitative study. The Journal of Strategic Information Systems, 16(4), 413–432. doi: 10.1016/j.jsis.2007.08.001.
  • Manski, C. F. (1993). Identification of endogenous social effects: the reflection problem. The Review of Economic Studies, 60(3), 531–542. doi: 10.2307/2298123.
  • Manski, C. F. (2000). Economic analysis of social interactions. Journal of Economic Perspectives, 14(3), 115–136. doi: 10.1257/jep.14.3.115.
  • McVicar, D., & Polanski, A. (2014). Peer effects in UK adolescent substance use: never mind the classmates?. Oxford Bulletin of Economics and Statistics, 76(4), 589–604. doi: 10.1111/obes.12030.
  • Mukong, A. K. (2017). Peer networks and tobacco consumption in South Africa. South African Journal of Economics, 85(3), 341–367. doi: 10.1111/saje.12166.
  • Mukong, A. K., & Burns, J. (2015). Social Networks and Maternal Health Care Utilisation in Tanzania. Technical report, Economic Research Southern Africa.
  • Mukong, A. K., & Burns, J. (2020). Social networks and antenatal care utilisation in Tanzania. Scientific African. doi: 10.1016/j.sciaf.2020.e00535.
  • Munshi, K. (2003). Networks in the modern economy: Mexican migrants in the US labor market. The Quarterly Journal of Economics, 118(2), 549–599. doi: 10.1162/003355303321675455.
  • Munyegera, G. K., & Matsumoto, T. (2016). Mobile money, remittances, and household welfare: panel evidence from rural Uganda. World Development, 79, 127–137. doi: 10.1016/j.worlddev.2015.11.006.
  • Murendo, C., Wollni, M., De Brauw, A., & Mugabi, N. (2018). Social network effects on mobile money adoption in Uganda. The Journal of Development Studies, 54(2), 327–342. doi: 10.1080/00220388.2017.1296569.
  • Narayanasamy, K., Rasiah, D., & Tan, T. M. (2011). The adoption and concerns of e-finance in Malaysia. Electronic Commerce Research, 11(4), 383. doi: 10.1007/s10660-011-9081-5.
  • Okello Candiya Bongomin, G., Ntayi, J. M., Munene, J. C., & Malinga, C. A. (2018). Mobile money and financial inclusion in sub-Saharan Africa: The moderating role of social networks. Journal of African Business, 19(3), 361–384. doi: 10.1080/15228916.2017.1416214.
  • Oreopoulos, P. (2003). The long-run consequences of living in a poor Neighborhood. The Quarterly Journal of Economics, 118(4), 1533–1575. doi: 10.1162/003355303322552865.
  • Portes, A. (1995). The economic sociology of immigration. New York: Russell Sage Foundation, 29, 11–12.
  • Portes, A., & Sensenbrenner, J. (1993). Embeddedness and immigration: notes on the social determinants of economic action. American Journal of Sociology, 98(6), 1320–1350. doi: 10.1086/230191.
  • Tobbin, P., & Kuwornu, J. K. (2011). Adoption of mobile money transfer technology: Structural equation modelling approach. European Journal of Business and Management, 3(7), 59–77. http://www.iiste.org
  • Topa, G. (2001). Social interactions, local spill-overs and unemployment. Review of Economic Studies, 68(2), 261–295. doi: 10.1111/1467937X.00169.
  • Wooldridge, J. M. (2002). Econometric analysis of cross section and panel data (pp. 108). MIT press.
  • Wydick, B., Hayes, H. K., & Kempf, S. H. (2011). Social networks, neighbourhood effects, and credit access: Evidence from rural Guatemala. World Development, 39(6), 974–982. doi: 10.1016/j.worlddev.2009.10.015.
  • Zhang, Y., Lin, N., & Li, T. (2012). Markets or networks: households’ choice of financial intermediary in Western China. Social Networks, 34(4), 68–670. doi: 10.1016/j.socnet.2012.08.003.