An optimized electronic government services adoption model using structural equation and maximum attribute relative models - Witarsyah, Deden (2018)
Electronic Government (e-gov) and its adoption plays an important role in assisting countries to provide their citizen with various services. However, the literature has shown that the adoption of current e-government services adoption model does not properly precise in fulfilling the user's desires, particularly in developing countries. This is due to the fact that the key factors of the current models are not suitable and properly determined. Specifically, current adoption models have too many factors, thus resulting to difficulties to work on important factors especially when constraints are imposed. In this research, Structural Equation Model (SEM) was used to analyze the effectiveness of recent models. SEM was selected because it allows researchers to test the relationship between complex variables are either recursive or nonrecursive to obtain a thorough overview of the whole model. From the analysis, an optimized model is proposed. Then, Maximum Attribute Relative (MAR) is implemented to determine the most important factors of e-government adoption model. MAR has been chosen because it has the capability to solve the uncertainty information of the respondents’ respond. The proposed model has been tested and passed the t-test and p-value approach where the value of Behavior Intention to User Behavior are 5.584 and 0.000; value of Facilitating Condition to User Behavior are 3.535 and 0.000; value of Information Quality to Performance Expectancy are 2.714 and 0.007; value of Performance Expectancy to Behavior Intention are 6.171 and 0.000; value of System Quality to Performance Expectancy are 2.895 and 0.004; and finally, value of Trust to Behavior Intention are 5.422 and 0.000. The fit test and indices for the model proposed were proven fit enough, where Standardized Root Mean Square Residual (SRMR) was 0.063 that indicated a good fit of the model, and Normed Fit Index (NFI) was 0.778, showing the marginal fit of the model. Meanwhile, computational model analysis using MAR to support the procession of the proposed model showed that Facilitating Condition (FC) has a value of 43. This portrays that the FC variable is the highest in influencing the people to use e-government, followed by Performance Expectancy and Information Quality that resulting in the value of both 35. The findings confirmed the significance of information quality, system quality and trust perceived by the citizens in adopting e-government services, and provide insights into whether an optimization model and computational model using MAR based on the soft set theory should be integrated to explain citizens’ intention to use e-government. Additionally, the optimized model offers the stakeholders a new perspective for dealing with e-government adoption by signifying the importance of support quality perceived by citizens.