Mobile Money Transfer services have eased the means of transferring money from one mobile phone user to another in Kenya. Since the introduction of the services there is disparity in adoption of different mobile money transfer platforms in Kenya. In this study Structural Equation Modeling was used to create a model of factors that influence the adoption and usage of Mobile Money Transfer services in Kenya. The findings in this study provide useful information to Mobile Network Operators that they can use in implementation of their Mobile Money Transfer service. The study was conducted in Juja Township. The study established that the independent variable namely, Performance Expectancy, Effort Expectancy and Social Influence had significant influence on Behavioral Intention towards the use of a given Mobile Money Transfer service. This means that the MMT’s users would continue to use a given Mobile Money Transfer service they have chosen. Facilitating Conditions was found to be a significant factor in predicting adoption and use of Mobile Money Transfer for males and females where gender was used as moderating factor. Also Behavioral intention was a significant determinant of Use Behavior of Mobile Money Transfer services. In conclusion the research model was found to be important in determining factors that influence the adoption and use of a given Mobile Money Transfer service.
Published in | American Journal of Theoretical and Applied Statistics (Volume 4, Issue 6) |
DOI | 10.11648/j.ajtas.20150406.22 |
Page(s) | 513-526 |
Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Mobile Money Transfer Services (MMT’s), Mobile Network Operators (MNO), Structural Equation Model (SEM)
[1] | Awwad, Mohammad Suleiman (2009), ‘Application of structural equation modeling to investigate factors affecting the intention to adopt internet banking in jordan’, Jordan Journal of Business Administration Vol 5(2). |
[2] | Baker, S.R. (2007), ‘Testing a conceptual model of oral health: a structural equation modeling approach’, International and American Associations for Dental Research Vol 86(8), 708–712. |
[3] | Barrett, P (2007), ‘Structural equation modeling: Adjudging model fit’, Personality and Individual Differences 42 (5), 815–24. |
[4] | Bollen, K. A (1989), ‘Structural equations with latent variables’. |
[5] | Bryne, B. M (2013), Structural Equation Modeling With Amos: Basic Concepts, Applications, and Programming, Second Edition., Routledge Press. |
[6] | Byrne, B (1998), Structural equation modelling with LISREL, PRECIS, and SIMPLIS., Hillsdale, NJ: Lawrence Erlbaum. |
[7] | Chin, W. W. and P. A. Todd (1995), "On the Use, Usefulness, and Ease of Use of Structural Equation Modeling in MIS Research: A Note of Caution," MIS Quarterly. |
[8] | Diamantopoulos, A. and J. A Siguaw (2000), Introducing LISREL, London: Sage Publications. |
[9] | Gefen, D., W. Straub and M. Boudreau (2000), ‘Structural equation modeling techniques and regression: Guidelines for research practice’, Communications of AIS Volume 4, Article 7 2. |
[10] | Hooper, D., J. Coughlan and R Mullen, M. (2008), ‘Structural equation modeling: Guidelines for determining model fit.’, The Electronic Journal of Business Research Methods Volume 6(Issue 1), pp. 53 – 60. |
[11] | Hu, L.T. and P.M Bentler (1999), ‘Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives’, Structural Equation Modeling. |
[12] | Joreskog, K. G. and D Sorbom (1993), ‘Lisrel 8: Structural equation modeling with the simplis command language’, Chicago: Scientific Software International. |
[13] | Kabbucho, Kamau, Cerstin Sander and Peter Mukwana (2003), ‘"passing the buck- money transfer systems: The practice and potential for products in Kenya"’, MicroSave Africa Report. |
[14] | Kaplan, D. (2000), Structural Equation Modeling; Foundations and Extensions., Sage, Newbury Park, CA. |
[15] | Mason, M. and O. Lineth (2007), ‘Poverty reduction through enhanced rural access to financial services in Kenya. Institute for policy analysis and research (ipar)’, Southern and Eastern Africa Policy Research Network (SEAPREN) Working Paper No. 6. |
[16] | Maurer, B., J. Kendall and C. Veniard (2011), ‘An emerging platform: From money transfer system to mobile money ecosystem’, Innovations: Technology, Governance, Globalization 6(4), 49–64. |
[17] | Mbiti, Isaac and David N. Weil (2011), ‘Mobile banking: The impact of M-pesa in Kenya’, NBER WORKING PAPER SERIES. |
[18] | Rosseel, Yves (2012), ‘lavaan: An r package for structural equation modeling.’ Journal of Statistical Software 48(2), 1–36. |
[19] | Schumaker, R. E. and R. G. Lomax (2004), ‘A Beginners Guide to Structural Equation Modeling’, Routledge. |
[20] | Tobbin, P. E (2010), Modeling adoption of mobile money transfer: A consumer behavior analysis, in ‘Paper presented at The 2nd International Conference on Mobile Communication Technology for Development, Kampala, Uganda. General’. |
[21] | Tobbin, Peter (2011), ‘Adoption of mobile money transfer technology: Structural equation modeling approach’, European Journal of Business and Management 3(7). |
[22] | Ullman, J. B. (2006), ‘Structural equation modeling: Reviewing the basics and moving forwad.’ Journal of Personality Assessment 85(1). |
[23] | Ullman, J.B (1996), ‘Structural equation modeling (in: Using multivariate statistics, third edition, b.g. tabachnick and l.s. fidell, eds.)’, HarperCollins College Publishers. New York, NY. pp. 709–819. |
[24] | 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), 425–478. |
[25] | Yamane, Taro (1967), Statistics: An Introductory Analysis, 2nd Ed., New York: Harper and Row. |
APA Style
Joseph Kuria Waitara, Anthony Gichuhi Waititu, Anthony Kibera Wanjoya. (2015). Modeling Adoption and Usage of Mobile Money Transfer Services in Kenya Using Structural Equations. American Journal of Theoretical and Applied Statistics, 4(6), 513-526. https://doi.org/10.11648/j.ajtas.20150406.22
ACS Style
Joseph Kuria Waitara; Anthony Gichuhi Waititu; Anthony Kibera Wanjoya. Modeling Adoption and Usage of Mobile Money Transfer Services in Kenya Using Structural Equations. Am. J. Theor. Appl. Stat. 2015, 4(6), 513-526. doi: 10.11648/j.ajtas.20150406.22
AMA Style
Joseph Kuria Waitara, Anthony Gichuhi Waititu, Anthony Kibera Wanjoya. Modeling Adoption and Usage of Mobile Money Transfer Services in Kenya Using Structural Equations. Am J Theor Appl Stat. 2015;4(6):513-526. doi: 10.11648/j.ajtas.20150406.22
@article{10.11648/j.ajtas.20150406.22, author = {Joseph Kuria Waitara and Anthony Gichuhi Waititu and Anthony Kibera Wanjoya}, title = {Modeling Adoption and Usage of Mobile Money Transfer Services in Kenya Using Structural Equations}, journal = {American Journal of Theoretical and Applied Statistics}, volume = {4}, number = {6}, pages = {513-526}, doi = {10.11648/j.ajtas.20150406.22}, url = {https://doi.org/10.11648/j.ajtas.20150406.22}, eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajtas.20150406.22}, abstract = {Mobile Money Transfer services have eased the means of transferring money from one mobile phone user to another in Kenya. Since the introduction of the services there is disparity in adoption of different mobile money transfer platforms in Kenya. In this study Structural Equation Modeling was used to create a model of factors that influence the adoption and usage of Mobile Money Transfer services in Kenya. The findings in this study provide useful information to Mobile Network Operators that they can use in implementation of their Mobile Money Transfer service. The study was conducted in Juja Township. The study established that the independent variable namely, Performance Expectancy, Effort Expectancy and Social Influence had significant influence on Behavioral Intention towards the use of a given Mobile Money Transfer service. This means that the MMT’s users would continue to use a given Mobile Money Transfer service they have chosen. Facilitating Conditions was found to be a significant factor in predicting adoption and use of Mobile Money Transfer for males and females where gender was used as moderating factor. Also Behavioral intention was a significant determinant of Use Behavior of Mobile Money Transfer services. In conclusion the research model was found to be important in determining factors that influence the adoption and use of a given Mobile Money Transfer service.}, year = {2015} }
TY - JOUR T1 - Modeling Adoption and Usage of Mobile Money Transfer Services in Kenya Using Structural Equations AU - Joseph Kuria Waitara AU - Anthony Gichuhi Waititu AU - Anthony Kibera Wanjoya Y1 - 2015/10/30 PY - 2015 N1 - https://doi.org/10.11648/j.ajtas.20150406.22 DO - 10.11648/j.ajtas.20150406.22 T2 - American Journal of Theoretical and Applied Statistics JF - American Journal of Theoretical and Applied Statistics JO - American Journal of Theoretical and Applied Statistics SP - 513 EP - 526 PB - Science Publishing Group SN - 2326-9006 UR - https://doi.org/10.11648/j.ajtas.20150406.22 AB - Mobile Money Transfer services have eased the means of transferring money from one mobile phone user to another in Kenya. Since the introduction of the services there is disparity in adoption of different mobile money transfer platforms in Kenya. In this study Structural Equation Modeling was used to create a model of factors that influence the adoption and usage of Mobile Money Transfer services in Kenya. The findings in this study provide useful information to Mobile Network Operators that they can use in implementation of their Mobile Money Transfer service. The study was conducted in Juja Township. The study established that the independent variable namely, Performance Expectancy, Effort Expectancy and Social Influence had significant influence on Behavioral Intention towards the use of a given Mobile Money Transfer service. This means that the MMT’s users would continue to use a given Mobile Money Transfer service they have chosen. Facilitating Conditions was found to be a significant factor in predicting adoption and use of Mobile Money Transfer for males and females where gender was used as moderating factor. Also Behavioral intention was a significant determinant of Use Behavior of Mobile Money Transfer services. In conclusion the research model was found to be important in determining factors that influence the adoption and use of a given Mobile Money Transfer service. VL - 4 IS - 6 ER -