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

Despite the widespread use of mobile government (m-government) services in developed countries, the adoption and acceptance of m-government services among citizens in developing countries is relatively low. The purpose of this study is to explore the most critical determinants of acceptance and use of m-government services in a developing country context.,The unified 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 collected from 216 m-government users across Bangladesh and analyzed in two stages. First, structural equation modeling (SEM) was used to identify significant determinants affecting users' acceptance of m-government services. In the second stage, a neural network model was used to validate SEM results and determine the relative importance of the determinants of acceptance of m-government services.,The results show that facilitating conditions and performance expectancy are the two important precedents of behavioral intention to use m-government services, and performance expectancy mediates the relationship between MCS, mobility and the intention to use m-government services.,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 antecedent towards mobile application studies. From a practical perspective, this study may provide valuable guidelines to government policymakers and system developers towards the development and effective implementation of m-government systems.,This study has contributed to the existing, but limited, literature on m-government service adoption in the context of a developing country. The predictive modeling approach is an innovative approach in the field of technology adoption.

[1]  Alain Yee-Loong Chong,et al.  A two-staged SEM-neural network approach for understanding and predicting the determinants of m-commerce adoption , 2013, Expert Syst. Appl..

[2]  M. Sarstedt,et al.  A new criterion for assessing discriminant validity in variance-based structural equation modeling , 2015 .

[3]  Douglas R. Vogel,et al.  How to satisfy citizens? Using mobile government to reengineer fair government processes , 2016, Decis. Support Syst..

[4]  Khalizani Khalid,et al.  The adoption of M-government services from the user's perspectives: Empirical evidence from the United Arab Emirates , 2017, Int. J. Inf. Manag..

[5]  Stuti Saxena,et al.  Role of “perceived risks” in adopting mobile government (m-government) services in India , 2018 .

[6]  Ibrahim Almarashdeh,et al.  How to make them use it? Citizens acceptance of M-government , 2017 .

[7]  Anol Bhattacherjee,et al.  Understanding Information Systems Continuance: An Expectation-Confirmation Model , 2001, MIS Q..

[8]  L. Carstensen,et al.  Socioemotional Selectivity Theory and the Regulation of Emotion in the Second Half of Life , 2003 .

[9]  James C. Anderson,et al.  STRUCTURAL EQUATION MODELING IN PRACTICE: A REVIEW AND RECOMMENDED TWO-STEP APPROACH , 1988 .

[10]  Sujeet Kumar Sharma,et al.  Neural network approach to predict mobile learning acceptance , 2018, Education and Information Technologies.

[11]  Yogesh Kumar Dwivedi,et al.  A generalised adoption model for services: A cross-country comparison of mobile health (m-health) , 2016, Gov. Inf. Q..

[12]  Catherine E. Connelly,et al.  Identifying the ideal fit between mobile work and mobile work support , 2010, Inf. Manag..

[13]  Viswanath Venkatesh,et al.  Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology , 2012, MIS Q..

[14]  Tien-Hsiang Chang,et al.  Analyzing the factors that affect the adoption of mobile services in Taiwan , 2014 .

[15]  Marko Sarstedt,et al.  An assessment of the use of partial least squares structural equation modeling in marketing research , 2012 .

[16]  Keng Siau,et al.  Mobile communications and mobile services , 2003, Int. J. Mob. Commun..

[17]  Ingoo Han,et al.  The impact of Web quality and playfulness on user acceptance of online retailing , 2007, Inf. Manag..

[18]  Yu-Ru Lin,et al.  Elucidating user behavior of mobile learning: A perspective of the extended technology acceptance model , 2007, Electron. Libr..

[19]  Martin J. Liu,et al.  Predicting RFID adoption in healthcare supply chain from the perspectives of users , 2015 .

[20]  Peter Trkman,et al.  Analyzing older users' home telehealth services acceptance behavior - applying an Extended UTAUT model , 2016, Int. J. Medical Informatics.

[21]  Mark Srite,et al.  The Role of Espoused National Cultural Values in Technology Acceptance , 2006, MIS Q..

[22]  Matloub Hussain,et al.  Exploring mobile government from the service quality perspective , 2017, J. Enterp. Inf. Manag..

[23]  Gordon B. Davis,et al.  User Acceptance of Information Technology: Toward a Unified View , 2003, MIS Q..

[24]  A. Woodside.  Moving beyond multiple regression analysis to algorithms: Calling for adoption of a paradigm shift from symmetric to asymmetric thinking in data analysis and crafting theory , 2013 .

[25]  Shin-Yuan Hung,et al.  User acceptance of mobile e-government services: An empirical study , 2013, Gov. Inf. Q..

[26]  A. Ant Ozok,et al.  User aspects of electronic and mobile government: results from a review of current research , 2009, Electron. Gov. an Int. J..

[27]  Judy E. Scott,et al.  Cognitive engagement with a multimedia ERP training tool: Assessing computer self-efficacy and technology acceptance , 2009, Inf. Manag..

[28]  Golam Sorwar,et al.  Understanding factors influencing the adoption of mHealth by the elderly: An extension of the UTAUT model , 2017, Int. J. Medical Informatics.

[29]  Yogesh Kumar Dwivedi,et al.  Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology , 2015, Gov. Inf. Q..

[30]  Yogesh Kumar Dwivedi,et al.  Citizen's adoption of an e-government system: Validating extended social cognitive theory (SCT) , 2015, Gov. Inf. Q..

[31]  Bill Anckar,et al.  VALUE CREATION IN MOBILE COMMERCE: FINDINGS FROM A CONSUMER SURVEY , 2002 .

[32]  Raymond Chiong,et al.  Investigating the Critical Success Factors for Implementing Electronic Document Management Systems in Governments: Evidence From Jordan , 2016, Inf. Syst. Manag..

[33]  P. Klarner,et al.  Disentangling the Effects of Team Competences, Team Adaptability, and Client Communication on the Performance of Management Consulting Teams , 2013 .

[34]  Vassilis Kostakos,et al.  Applying configurational analysis to IS behavioural research: a methodological alternative for modelling combinatorial complexities , 2017, Inf. Syst. J..

[35]  Jing Wang,et al.  Adoption of Open Source Software: The role of social identification , 2011, Decis. Support Syst..

[36]  Yukun Bao,et al.  Acceptance and use predictors of fitness wearable technology and intention to recommend , 2019, Ind. Manag. Data Syst..

[37]  Viswanath Venkatesh,et al.  Model of Acceptance with Peer Support: A Social Network Perspective to Understand Employees' System Use , 2009, MIS Q..

[38]  Manisha Sharma,et al.  Examining the role of trust and quality dimensions in the actual usage of mobile banking services: An empirical investigation , 2019, Int. J. Inf. Manag..

[39]  Darmawan Napitupulu,et al.  The Critical Success Factors Study for e-Government Implementation , 2014 .

[40]  Changlin Wang,et al.  Antecedents and consequences of perceived value in Mobile Government continuance use: An empirical research in China , 2014, Comput. Hum. Behav..

[41]  Nripendra P. Rana,et al.  Mobile applications in government services (mG-App) from user's perspectives: A predictive modelling approach , 2018, Gov. Inf. Q..

[42]  C. Fornell,et al.  Evaluating structural equation models with unobservable variables and measurement error. , 1981 .

[43]  Paul Jen-Hwa Hu,et al.  Extending the two‐stage information systems continuance model: incorporating UTAUT predictors and the role of context , 2011, Inf. Syst. J..

[44]  Julían Andrés,et al.  La gestión del conocimiento y su influencia en las capacidades dinámicas: Contrastación empírica en Empresas Colombianas Intensivas en uso de conocimiento , 2020 .

[45]  Eunil Park,et al.  User acceptance of long-term evolution (LTE) services: An application of extended technology acceptance model , 2013, Program.

[46]  Beomjin Choi,et al.  Domain-specific innovativeness and new product adoption: A case of wearable devices , 2017, Telematics Informatics.

[47]  Raymond Chiong,et al.  Customer empowerment: Does it influence electronic government success? A citizen-centric perspective , 2015, Electron. Commer. Res. Appl..