{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,22]],"date-time":"2026-01-22T06:37:10Z","timestamp":1769063830171,"version":"3.49.0"},"reference-count":48,"publisher":"Association for Computing Machinery (ACM)","issue":"4","license":[{"start":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T00:00:00Z","timestamp":1545868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["Proc. ACM Interact. Mob. Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,12,27]]},"abstract":"<jats:p>Access to financial institutions is difficult in developing economies and especially for the poor. However, the widespread adoption of mobile phones has enabled the development of mobile money systems that deliver financial services through the mobile phone network. Despite the success of mobile money, there is a lack of quantitative studies that unveil which factors contribute to the adoption and sustained usage of such services. In this paper, we describe the results of a quantitative study that analyzes data from the world's leading mobile money service, M-Pesa. We analyzed millions of anonymized mobile phone communications and M-Pesa transactions in an African country. Our contributions are threefold: (1) we analyze the customers' usage of M-Pesa and report large-scale patterns of behavior; (2) we present the results of applying machine learning models to predict mobile money adoption (AUC=0.691), and mobile money spending (AUC=0.619) using multiple data sources: mobile phone data, M-Pesa agent information, the number of M-Pesa friends in the user's social network, and the characterization of the user's geographic location; (3) we discuss the most predictive features in both models and draw key implications for the design of mobile money services in a developing country. We find that the most predictive features are related to mobile phone activity, to the presence of M-Pesa users in a customer's ego-network and to mobility. We believe that our work will contribute to the understanding of the factors playing a role in the adoption and sustained usage of mobile money services in developing economies.<\/jats:p>","DOI":"10.1145\/3287035","type":"journal-article","created":{"date-parts":[[2018,12,27]],"date-time":"2018-12-27T19:28:03Z","timestamp":1545938883000},"page":"1-18","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Mobile Money"],"prefix":"10.1145","volume":"2","author":[{"given":"Simone","family":"Centellegher","sequence":"first","affiliation":[{"name":"Fondazione Bruno Kessler, University of Trento, Vodafone Research, Trento, Italy"}]},{"given":"Giovanna","family":"Miritello","sequence":"additional","affiliation":[{"name":"Vodafone Research, London, United Kingdom"}]},{"given":"Daniel","family":"Villatoro","sequence":"additional","affiliation":[{"name":"Vodafone Research, London, United Kingdom"}]},{"given":"Devyani","family":"Parameshwar","sequence":"additional","affiliation":[{"name":"Vodafone Group, London, United Kingdom"}]},{"given":"Bruno","family":"Lepri","sequence":"additional","affiliation":[{"name":"Fondazione Bruno Kessler, Trento, Italy"}]},{"given":"Nuria","family":"Oliver","sequence":"additional","affiliation":[{"name":"Vodafone Research, London, United Kingdom"}]}],"member":"320","published-online":{"date-parts":[[2018,12,27]]},"reference":[{"key":"e_1_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0138098"},{"key":"e_1_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btq134"},{"key":"e_1_2_2_3_1","unstructured":"Fernando Aportela. 1999. Effects of financial access on savings by low-income people.  Fernando Aportela. 1999. Effects of financial access on savings by low-income people."},{"key":"e_1_2_2_4_1","volume-title":"Predicting poverty and wealth from mobile phone metadata. Science 350, 6264","author":"Blumenstock Joshua","year":"2015","unstructured":"Joshua Blumenstock , Gabriel Cadamuro , and Robert On. 2015. Predicting poverty and wealth from mobile phone metadata. Science 350, 6264 ( 2015 ), 1073--1076. Joshua Blumenstock, Gabriel Cadamuro, and Robert On. 2015. Predicting poverty and wealth from mobile phone metadata. Science 350, 6264 (2015), 1073--1076."},{"key":"e_1_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/2369220.2369225"},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/2663204.2663254"},{"key":"e_1_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1257\/0002828054201242"},{"key":"e_1_2_2_9_1","volume-title":"What makes a successful mobile money implementation. Learnings from M-PESA in Kenya and Tanzania","author":"Camner Gunnar","year":"2009","unstructured":"Gunnar Camner , Emil Sj\u00f6blom , and Caroline Pulver . 2009. What makes a successful mobile money implementation. Learnings from M-PESA in Kenya and Tanzania ( 2009 ). Gunnar Camner, Emil Sj\u00f6blom, and Caroline Pulver. 2009. What makes a successful mobile money implementation. Learnings from M-PESA in Kenya and Tanzania (2009)."},{"key":"e_1_2_2_10_1","volume-title":"The power of social networks to drive mobile money adoption. Technical report","author":"CGAP.","year":"2013","unstructured":"CGAP. 2013. The power of social networks to drive mobile money adoption. Technical report ( 2013 ). CGAP. 2013. The power of social networks to drive mobile money adoption. Technical report (2013)."},{"key":"e_1_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3151470.3151473"},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.5408"},{"key":"e_1_2_2_13_1","volume-title":"Understanding congested travel in urban areas. Nature communications 7","author":"\u00c7olak Serdar","year":"2016","unstructured":"Serdar \u00c7olak , Antonio Lima , and Marta C Gonz\u00e1lez . 2016. Understanding congested travel in urban areas. Nature communications 7 ( 2016 ). Serdar \u00c7olak, Antonio Lima, and Marta C Gonz\u00e1lez. 2016. 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