A two-stage structural equation modeling-neural network approach for understanding and predicting the determinants of m-government service adoption

Published date11 November 2019
Date11 November 2019
AuthorShamim Talukder,Raymond Chiong,Sandeep Dhakal,Golam Sorwar,Yukun Bao
Subject MatterInformation & knowledge management,Information systems,Information & communications technology
A two-stage structural equation
modeling-neural network
approach for understanding and
predicting the determinants of
m-government service adoption
Shamim Talukder
Department of Management, North South University, Dhaka, Bangladesh
Raymond Chiong and Sandeep Dhakal
School of Electrical Engineering and Computing, The University of Newcastle,
Newcastle, Australia
Golam Sorwar
School of Business and Tourism,Southern Cross University,Bilinga, Australia, and
Yukun Bao
Centre for Big Data Analytics, Jiangxi University of Engineering, Xinyu, China
Purpose Despite the widespread use of mobile government (m-government) services in developed
countries, the adoption and acceptanceof m-government services among citizens in developing countries is
relatively low. The purposeof this study is to explore the most critical determinants of acceptanceand use of
m-governmentservices in a developing country context.
Design/methodology/approach The unied theory of acceptance and use of technology (UTAUT)
extended with perceived mobility and mobile communication services (MCS) was used as the theoretical
framework. Data was collectedfrom 216 m-government users across Bangladeshand analyzed in two stages.
First, structural equation modeling (SEM) was used to identify signicant determinants affecting users
acceptance of m-governmentservices. In the second stage, a neural network model was used to validateSEM
resultsand determine the relative importance of the determinants of acceptanceof m-government services.
Findings The results show thatfacilitating conditions and performance expectancyare the two important
precedents of behavioral intentionto use m-government services, and performance expectancy mediatesthe
relationshipbetween MCS, mobility and the intentionto use m-government services.
Research limitations/implications Academically,this study extended and validated the underlying
concept of UTAUT to capture the adoption behavior of individuals in a different cultural context. In
particular, MCS might be the most critical antecedenttowards mobile application studies. From a practical
perspective, this studymay provide valuable guidelines to government policymakers and systemdevelopers
towardsthe development and effective implementation of m-governmentsystems.
Originality/value This study has contributed to the existing, but limited, literature on m-government
service adoption in the context of a developingcountry. The predictive modeling approach is an innovative
approachin the eld of technology adoption.
Keywords Mobile government, Technology adoption, UTAUT, Developing countries,
Structural equation modeling, Neural network, Bangladesh
Paper type Research paper
Determinants of
Received15 October 2017
Revised2 October 2019
Accepted4 October 2019
Journalof Systems and
Vol.21 No. 4, 2019
pp. 419-438
© Emerald Publishing Limited
DOI 10.1108/JSIT-10-2017-0096
The current issue and full text archive of this journal is available on Emerald Insight at:
1. Introduction
Mobile technologies are being widely adopted by governments around the world for the
delivery of various services to citizens,employees, businesses, and other organizations; this
concept is generally known as mobile government (m-government) (Ahmad and Khalid,
2017). The widespread use of m-government services is not surprising, given that
approximately sevenbillion people (97 per cent of the global population) currently live under
the mobile-cellular network coverage (ITU, 2016). Globally, the total mobile-broadband
penetration exceeds 3.6 billion(47 per cent of the global population), more than the coverage
of PC-based internet (3.2 billion) (ITU, 2016). In this context, there is a growing need to
understand the effectiveness of the acceptanceof services provided through m-government
(Rana and Dwivedi, 2015).
Despite the growing mobile penetration, improved design and usability, and increased
government support,the adoption of m-government services in developing countrieshas not
yet achieved its objectives, and studies have shown similar e-government projects have
either failed or experienced a slow adoption process (Gao et al., 2014;Alshibly and Chiong,
2015;Sharma et al.,2018). For instance, in the context of developing countries, only 15 per
cent of e-government projects were successfully implemented, 50 per cent were partially
completed, and 35 per cent of e-government projects failed to be implemented (Napitupulu
and Sensuse, 2014). Therefore, a key challenge for m-government systems, to date, has not
necessarily been their design, but their utilization (Almarashdeh and Alsmadi, 2017).
However, the utilization or adoption behaviour of users of m-government systems has not
been adequately addressed in the relevant literature, and there is room for more research in
this area (Saxena,2018;Sharma et al.,2018).
Although some recent studies have explored the adoption of m-government services
(Ahmad and Khalid, 2017;Gao et al.,2014;Saxena, 2018;Wang, 2014), they do not include
context-specic predictors such as quality of mobile communication services (MCS) and
perceived mobility. For example, in a developing country context, m-government is still
considered a disruptive technology (Chen et al.,2016); and the importance of MCS might be
the most critical factor in m-government service adoption (Sultan and Steve, 2015). MCS,
therefore, need to be considered in the study of m-government service adoption. This study
attempts to bridge this gap in the literature by adding contextual predictors to the unied
theory of acceptance and use of technology (UTAUT)model and by observing the effect of
some additional contextualconstructs, namely quality MCS and mobility, on the adoptionof
m-government services. It should be noted that, in this study, in order to facilitate the
understanding of the topic, m-government is considered as an overall concept, rather than
focusing on any one particular service, and Bangladesha developing country with a large
population hasbeen selected as the target for this study.
A large majority of prior studies in this eld have employed conventional statistical
methods such as multiple regression analysis and structural equation modeling (SEM) as
their methodological approach. These techniques, however, might not be adequate in
explaining complex interaction effects among multiple predictors, since relationships
between predictors and system usage behaviors could be asymmetric, and conventional
regression techniquesare incompatible with asymmetric relationships (Liuet al.,2017). This
limitation lends credibility to predictive modeling techniques, such as neural networks, as
an alternative approach for exploring information systems (IS) usage behavior (Raymond
et al.,2010;Liuet al., 2017;Woodside, 2013). A neural network offers an alternate avenue for
deducing causal relationshipsbetween the antecedents and outcome of IS adoption behavior
by taking into account interdependencies among the former. Therefore, we address the
limitation discussed above by employing a two-stage predictive modeling approach

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