TY - GEN
T1 - Identifying the Differing Service Maturity Levels of Mobile-based Smart Regency with e-Government Adoption Model (GAM) framework
AU - Darmawan, Aang Kisnu
AU - Siahaan, Daniel Oranova
AU - Susanto, Tony Dwi
AU - Walid, Miftahul
AU - Umam, Busro Akramul
AU - Hidayanto, Achmad Nizar
N1 - Funding Information:
Recognition Our thanks go to the Republic of Indonesia's Ministry of Science, Technology, and Higher Education for supporting this work through funding. This study was performed in conjunction with the University Collaborative Research Scheme with Contract No. 039 / SP2H / LT-MONO / LL7 / 2020.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/11/19
Y1 - 2020/11/19
N2 - Currently, almost every nation struggles with the idea of smart cities to adopt city management. Several previous studies have produced many models to measure the quality of ISS. In previous studies, service quality and experience in smart city applications were measured and assessed. However, a few works still examine the service quality of the Smart District model, which varies quite strongly from the overall smart city concept. This research aims to assess the service quality of mobile-based smart regions or districts. The model and approach used are the E-Government Adoption Model (GAM). This model is a development of the previous model, such as technology adoption model (TAM), diffusion of innovation theory (DOI), and theory of prohibited behavior (TPB). Two hundred eighty-three participants interviewed succeeded in collecting data collection using online and offline survey methods, data processing using AMOS 24.0 software. Research findings show that the variables in the GAM model reflect a positive relationship and are significant in terms of quality measures for intelligent district services. This study contributes to explain the effective use of intelligent regional mobile devices. This research shows that governments and policymakers focus more on important issues affecting the quality of smart cellular service.
AB - Currently, almost every nation struggles with the idea of smart cities to adopt city management. Several previous studies have produced many models to measure the quality of ISS. In previous studies, service quality and experience in smart city applications were measured and assessed. However, a few works still examine the service quality of the Smart District model, which varies quite strongly from the overall smart city concept. This research aims to assess the service quality of mobile-based smart regions or districts. The model and approach used are the E-Government Adoption Model (GAM). This model is a development of the previous model, such as technology adoption model (TAM), diffusion of innovation theory (DOI), and theory of prohibited behavior (TPB). Two hundred eighty-three participants interviewed succeeded in collecting data collection using online and offline survey methods, data processing using AMOS 24.0 software. Research findings show that the variables in the GAM model reflect a positive relationship and are significant in terms of quality measures for intelligent district services. This study contributes to explain the effective use of intelligent regional mobile devices. This research shows that governments and policymakers focus more on important issues affecting the quality of smart cellular service.
KW - E-Government Adoption Model
KW - e-Service Quality
KW - mobile-based application
KW - smart city
KW - smart regency
UR - http://www.scopus.com/inward/record.url?scp=85099792098&partnerID=8YFLogxK
U2 - 10.1109/ICISS50791.2020.9307540
DO - 10.1109/ICISS50791.2020.9307540
M3 - Conference contribution
AN - SCOPUS:85099792098
T3 - 7th International Conference on ICT for Smart Society: AIoT for Smart Society, ICISS 2020 - Proceeding
BT - 7th International Conference on ICT for Smart Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 7th International Conference on ICT for Smart Society, ICISS 2020
Y2 - 19 November 2020 through 20 November 2020
ER -
