Research article of American Journal of Computer Sciences and Applications
A Quantitative Model of Smartphone Adoption Among Urban and Rural Nigerians: The Role of Social Context In Ensuring Continuous Usage
Iyanda O. A. PhD, MSc. MBA, FCA
Tristate Healthcare System, Agodi GRA, Ibadan, Oyo State, Nigeria
Rapid diffusion and adoption of smartphone technologies could accelerate development in developing nations like Nigeria. Much is known about the factors that influence technology adoption and continuous use in general, but these theories of socio-economic impact have rarely been tested in Nigeria. Additionally, there may be differences in technology adoption and use behaviors between urban and rural settings. Such differences could influence strategies for encouraging widespread smartphone adoption in Nigeria. Therefore, the present quantitative, survey study sought to test a theoretical model of smartphone adoption and continuous use based on the unified theory of acceptance and use of technologies as well as on expectation confirmation theory. Using factor analysis and partial least squares analysis, this research tested a set of hypotheses related to intention to use smartphones and continuous use, exploring the moderating role of urban versus rural locations. The study discovered that, among Nigerians with high performance expectancy, those who lives in urban areas are more likely to intend to use smartphones when compared with their rural counterparts. The study concluded that social, socio-economic, and infrastructure factors enable urban dwellers to utilize smartphones, whereas context factors make smartphone adoption more difficult in rural areas. That is social influence was positively correlated with intention to use smartphones. Additionally, there is a strong positive correlation between intent to use and continuous usage of mobile technologies, justifying intent to use as an important outcome variable in research on technology adoption in Africa. Based on the results of the finding, this work recommends amongst others, that adequate Information Technology Infrastructure should be planned on a continuous basis for deployment in the rural areas.
Keywords: Qualitative Model, Smartphones Adoption, Mobile Technologies, Social Context, Performance Expectancy, Expectation Confirmation Theory, Unified Theory of Acceptance
How to cite this article:
Iyanda O. A. A Quantitative Model of Smartphone Adoption Among Urban and Rural Nigerians: The Role of Social Context In Ensuring Continuous Usage. American Journal of Computer Sciences and Applications, 2017; 1:5.DOI: 10.28933/ajcsa-2017-09-13-01
1.  G.V. Pereira, L.R. de Oliveira, M.G. Testa, Government’s Implementation of Information and Communication Technology in Developing Countries: An Analysis of Human Development Outcomes, (2015).
2.  A. Majchrzak, M. Markus, J. Wareham, Special Issue—Call for Papers—ICT & societal challenges, MIS Quarterly (2014) 1-3.
3.  P.M. Napoli, J.A. Obar, Mobile Leapfrogging and Digital Divide Policy: assessing the limitations of mobile Internet access, Fordham University Schools of Business research paper (2263800) (2013).
4.  V. Venkatesh, M.G. Morris, G.B. Davis, F.D. Davis, User acceptance of information technology: Toward a unified view, MIS quarterly (2003) 425-478.
5.  N.P. Rana, Y.K. Dwivedi, M.D. Williams, A meta-analysis of existing research on citizen adoption of e-government, Information Systems Frontiers 17(3) (2015) 547-563.
6.  V. Venkatesh, T.A. Sykes, X. Zhang, ‘Just what the doctor ordered’: a revised UTAUT for EMR system adoption and use by doctors, System Sciences (HICSS), 2011 44th Hawaii International Conference on, IEEE, 2011, pp. 1-10.
7.  Y. Kim, K. Crowston, Technology adoption and use theory review for studying scientists’ continued use of cyber‐infrastructure, Proceedings of the American Society for Information Science and Technology 48(1) (2011) 1-10.
8.  S. Halilovic, M. Cicic, Antecedents of information systems user behaviour–extended expectation-confirmation model, Behaviour & Information Technology 32(4) (2013) 359-370.
9.  T.-C. Lin, S. Wu, J.S.-C. Hsu, Y.-C. Chou, The integration of value-based adoption and expectation–confirmation models: An example of IPTV continuance intention, Decision Support Systems 54(1) (2012) 63-75.
10.  Y.M. Kang, C. Cho, S. Lee, Analysis of factors affecting the adoption of smartphones, Technology Management Conference (ITMC), 2011 IEEE International, IEEE, 2011, pp. 919-925.
11.  T.D. Thomas, L. Singh, K. Gaffar, The Utility of the UTAUT Model in Explaining Mobile Learning Adoption in Higher Education in Guyana, International Journal of Education and Development using Information and Communication Technology 9(3) (2013) 71-87.
12.  P.A. Nuq, B. Aubert, Towards a better understanding of the intention to use eHealth services by medical professionals: The case of developing countries, International Journal of Healthcare Management 6(4) (2013) 217-236.
13.  M. Ursula Paola Torres, K. Gohar Feroz, M. Junghoon, R. Jae Jeung, E-learning motivation and educational portal acceptance in developing countries, Online Information Review 35(1) (2011) 66-85.
14.  G.E. Phillips-Wren, O.M. Ferreiro, G. Forgionne, H. Desai, Adoption of decision support systems (DSS) in a developing country, Journal of Decision Systems 16(4) (2007) 425-449.
15.  P.K. Nair, F. Ali, L.C. Leong, Factors Affecting Acceptance & Use of ReWIND: Validating the Extended Unified Theory of Acceptance and Use of Technology, Interactive Technology and Smart Education 12(3) (2015) 183-201.
16.  E.J.Y. Liew, S. Vaithilingam, M. Nair, Facebook and socio-economic benefits in the developing world, Behaviour & Information Technology 33(4) (2014) 345-360.
17.  A.N. Islam, Sources of satisfaction and dissatisfaction with a learning management system in post-adoption stage: A critical incident technique approach, Computers in Human Behavior 30 (2014) 249-261.
18.  Y. Kim, D.A. Briley, M.G. Ocepek, Differential innovation of smartphone and application use by sociodemographics and personality, Computers in Human Behavior 44 (2015) 141-147.
19.  S.S. Kim, J.-Y. Son, Out of dedication or constraint? A dual model of post-adoption phenomena and its empirical test in the context of online services, MIS quarterly (2009) 49-70.
20.  R.A. Duncombe, Understanding the impact of mobile phones on livelihoods in developing countries, Development Policy Review 32(5) (2014) 567-588.
21.  H.K. Chavula, Telecommunications development and economic growth in Africa, Information Technology for Development 19(1) (2013) 5-23.
22.  J. Hellström, P.-E. Tröften, The innovative use of mobile applications in East Africa, Swedish international development cooperation agency (Sida)2010.
23.  M.L. Smith, R. Spence, A.T. Rashid, Mobile phones and expanding human capabilities, Information Technologies & International Development 7(3) (2011) pp. 77-88.
24.  W. Cochran, Sampling techniques. 2nd, New York. Wiley publications in statistics, 1963.
25.  J. de Carvalho, F.O. Chima, Applications of structural equation modeling in social sciences research, (2014).
26.  J.F. Hair, W.C. Black, B.J. Babin, R.E. Anderson, R.L. Tatham, Multivariate data analysis, Pearson Prentice Hall Upper Saddle River, NJ2006.
27.  V. Venkatesh, J.Y. Thong, X. Xu, Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology, MIS quarterly 36(1) (2012) 157-178.
28.  P. Jones, P. Beynon-Davies, I. Apulu, A. Latham, R. Moreton, Factors affecting the effective utilisation and adoption of sophisticated ICT solutions: Case studies of SMEs in Lagos, Nigeria, Journal of Systems and Information Technology 13(2) (2011) 125-143.
29.  P. Carmody, The informationalization of poverty in Africa? Mobile phones and economic structure, Information Technologies & International Development 8(3) (2012) pp. 1-17.
30.  N. Jentzsch, Implications of mandatory registration of mobile phone users in Africa, Telecommunications Policy 36(8) (2012) 608-620.
31.  A.S. Sife, E. Kiondo, J.G. Lyimo-Macha, Contribution of mobile phones to rural livelihoods and poverty reduction in Morogoro Region, Tanzania, The Electronic Journal of Information Systems in Developing Countries 42 (2010).
32.  A. Bhattacherjee, Understanding information systems continuance: an expectation-confirmation model, MIS quarterly (2001) 351-370.
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