JOURNAL ARTICLE
Mobile Payment Services, Government Involvement, and Mobile Network Operator Performance.
Published In: Manufacturing & Service Operations Management (M&SOM) (INFORMS), 2023, v. 25, n. 6. P. 2002 1 of 3
Database: Business Source Ultimate 2 of 3
Authored By: Dong, Yan; Song, Sining; Zou, Fan 3 of 3
Abstract
This article examines the impact of government involvement in mobile payment services (MPS) on the market performance of mobile network operators (MNOs), focusing on mobile-government (m-government) initiatives that include person-to-government (P2G) and government-to-person (G2P) payment services. Using proprietary industry data and employing difference-in-differences and changes-in-changes estimation methods, the study finds that government participation in MPS launches significantly expands MNO user bases, with larger MNOs and those offering a variety of government-involved services benefiting most due to economies of scale and scope. Additionally, government involvement enhances the effectiveness of microloan offerings linked to MPS and, as regulators, governments' financial inclusion policies and agent network regulations further amplify MNO market growth. The findings highlight governments' dual role as both business partners and regulators in promoting financial inclusion and improving MNO market outcomes, with implications for policymakers and MNO managers aiming to leverage government partnerships in mobile financial ecosystems.
Additional Information
- Source:Manufacturing & Service Operations Management (M&SOM) (INFORMS). 2023/11, Vol. 25, Issue 6, p2002
- Document Type:Article
- Subject Area:Social Sciences and Humanities
- Publication Date:2023
- ISSN:1523-4614
- DOI:10.1287/msom.2021.1068
- Accession Number:173670157
- Copyright Statement:Copyright of Manufacturing & Service Operations Management (M&SOM) (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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