Successful factors determining the significant relationship between e-governance system and government operational excellence

Abstract

Every government's major objective is to provide the greatest services in order to establish efficiency and quality of performance. Syria's government has understood how critical it is to go in the direction of information technology. However, there are gaps and poor links across government sectors, which has tainted the image of Syrian e-governance. As a result, one of the main aims of this study is to figure out what factors impact Syrians' acceptance of the e-government system. A total of 600 questionnaires were delivered to Syrian individuals as part of a survey. The data was analysed using the structural equation model (SEM) using AMOS version 21.0. User intention to utilise an e-government system was shown to be influenced by performance expectations, effort expectations, system flexibility, citizens-centricity, and facilitating conditions. Assurance, responsiveness, reliability, tangibles, and empathy are five fundamental factors that have a major impact on government operation excellence. Behavioural Intention is being utilised as a mediator between the government operation excellence (GOE) initiative and the e-government platform.

Proposed Smart E-Government Application Design toward Achieving Operational Excellent in Government

“…According to [32,33,34,35], a data with goodness of fit model depends on the IFI, CFI and TLI indicators that value greater than the 0.9 as a cutoff point (i.e., 0.970, 0.970 and 0.968 respectively). In comparison, the RMSEA was 0.025 which was lower than the 0.1 level as suggested by [36]. In comparison, the absolute CMIN / DF (1.239) was less than 5 and demonstrated a goodness of fit model [37].…”

“…There are three items (i.e., EMP5, FC1 and ASSU1) that had an inappropriate absolute meaning over threshold 2.28 for other items in the model shown during the analysis of SRCs. As suggested by [36] in order to have a goodness of fit model, the researchers' decided to delete these three items from the study. The findings have shown the significance of chi-square was at the 0.000 levels.…”

Citizen-centricity in digital government research: A literature review and integrative framework

“…Predictions and evaluations of use of digital government services. Alalwan, 2013;Alarabiat et al, 2021;Alguliyev et al, 2018;Alomari et al, 2012;Alshibly and Chiong, 2015;Anwer et al, 2016;Aranyossy, 2022;Bhattacharya et al, 2012;Carter and Bélanger, 2005;Chen and Zhang, 2012;Chen, 2010;Dash and Jain, 2022;Gupta and Suri, 2017;Hujran et al, 2020;Lal and Haleem, 2009;Linders et al, 2018;Naswir et al, 2019;Osman et al, 2014;Pawlowski and Scholta, 2023;Rana et al, 2013;Saleh and Alyaseen, 2021;Sanati and Lu, 2010;Scutella et al, 2022;Sepasgozar et al, 2019;Sigwejo and Pather, 2016;Singh and Singh, 2018;Sorn-in et al, 2014;Tan et al, 2013;Tassabehji et al, 2019 counterfactuals, i.e., contrasting types with 'opposite' features to the ideals. This analysis allows us to theorize (Weick, 1995) through the generation of ideal types and thereby capturing different dimensions of citizen-centricity as described in digital government research.…”

“…Several scholars have studied determinants of use of government services in various national contexts (Alomari et al, 2012;Hujran et al, 2020;Urbina & Abe, 2017;Saleh & Alyaseen, 2021), as well as variables related to citizens' use of social media (Alarabiat et al, 2021;Alalwan, 2013). Carter and Bélanger (2005) identified factors, such as ease of use and perceived trust, that they argued were essential for citizen adoption of digital government, and must be understood for successful implementation of citizen-centric digital government.…”