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Predicting the adoption of a mobile government security response system from the user's perspective: An application of the artificial neural network approach

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  • Shahzad, Fakhar
  • Xiu, Guoyi
  • Shafique Khan, Muhammad Aamir
  • Shahbaz, Muhammad

Abstract

The core aim of this study is not only to explore the new phenomenon of mobile government (m-govt) security response systems (SRS) but also to identify some important factors from the user's perspective in order to build a strong and acceptable m-govt SRS for crisis management. In a comprehensive research model, the relationships between constructs were measured by a two-stage approach using structural equation modeling (SEM) and an artificial neural network (ANN) on data collected through a structured questionnaire from 574 valid respondents. The outcomes from the SEM and ANN analyses predicted that awareness, perceived compatibility, perceived response time, and trust are the top four major factors in adopting m-govt SRS, regardless of the factors' order of influence. This finding makes a significant contribution to the current literature on m-govt adoption and has practical significance for understanding various aspects of the development and implementation of m-govt SRS. The current study provides a broad perspective on the successful implementation of m-govt SRS by government agencies and practitioners, particularly in the provision of security services.

Suggested Citation

  • Shahzad, Fakhar & Xiu, Guoyi & Shafique Khan, Muhammad Aamir & Shahbaz, Muhammad, 2020. "Predicting the adoption of a mobile government security response system from the user's perspective: An application of the artificial neural network approach," Technology in Society, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:teinso:v:62:y:2020:i:c:s0160791x19302581
    DOI: 10.1016/j.techsoc.2020.101278
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    3. Albahri, A.S. & Alnoor, Alhamzah & Zaidan, A.A. & Albahri, O.S. & Hameed, Hamsa & Zaidan, B.B. & Peh, S.S. & Zain, A.B. & Siraj, S.B. & Alamoodi, A.H. & Yass, A.A., 2021. "Based on the multi-assessment model: Towards a new context of combining the artificial neural network and structural equation modelling: A review," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    4. Ashaari, Mohamed Azlan & Singh, Karpal Singh Dara & Abbasi, Ghazanfar Ali & Amran, Azlan & Liebana-Cabanillas, Francisco J., 2021. "Big data analytics capability for improved performance of higher education institutions in the Era of IR 4.0: A multi-analytical SEM & ANN perspective," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    5. Shahbaz, Muhammad & Zahid, Rimsha, 2022. "Probing the factors influencing cloud computing adoption in healthcare organizations: A three-way interaction model," Technology in Society, Elsevier, vol. 71(C).
    6. Ghazanfar Ali Abbasi & Noor Fareen Abdul Rahim & Hongyan Wu & Mohammad Iranmanesh & Benjamin Ng Chee Keong, 2022. "Determinants of SME’s Social Media Marketing Adoption: Competitive Industry as a Moderator," SAGE Open, , vol. 12(1), pages 21582440211, January.
    7. Ahmad Nazrul Hakimi Ibrahim & Muhamad Nazri Borhan & Mohd Haniff Osman & Faridah Hanim Khairuddin & Nur Mustakiza Zakaria, 2022. "An Empirical Study of Passengers’ Perceived Satisfaction with Monorail Service Quality: Case of Kuala Lumpur, Malaysia," Sustainability, MDPI, vol. 14(11), pages 1-17, May.
    8. Ahmad Nazrul Hakimi Ibrahim & Muhamad Nazri Borhan & Mohd Haniff Osman & Muhamad Razuhanafi Mat Yazid & Munzilah Md. Rohani, 2022. "The Influence of Service Quality on User’s Perceived Satisfaction with Light Rail Transit Service in Klang Valley, Malaysia," Mathematics, MDPI, vol. 10(13), pages 1-21, June.
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