The term “E-Government” does not have a single, accepted definition. Some merely characterize it as a means for a government to conduct online business [3]. Others view E-Government as the process of developing websites that provide information on governmental and political topics. E-Government also refers to effectively delivering government information and services through web-based application-specific technologies [3]. Saudi Arabia is situated in middle east Asia and has an area of 2,149,690 Km² [4]. The Saudi Government brought in skilled workers from all over the world to aid in the transformation of this Kingdom into a modern nation because it encourages modernization in all facets of life in the Kingdom [4]. Saudi Arabia has kept many principles of Arab and Islamic traditions, including power and governmental structures, although taking on a new shape. While doing so, it has embraced E-Government and Western technology to make its services more accessible to the general population [5]. The E-Government system that Saudi Arabia implemented required much effort to be put in place to realize the system’s success gradually. Since its year of introduction, the E-Government has been successful in completing several initiatives. The initiatives include the Absher E-services gateway, expanding E-Government learning and capacity building, implementing the E-Government program to communicate change, implementing the comprehensive YESSER program of communications with agencies, and increasing women’s employment in the E-Government workforce [6]. Some of the current efforts of the Saudi Government can be reflected in implementing E-Visa Service, Nifaz, Absher, and Nazah to ensure the E-Governess is implemented in its true face.

Table 1 presents the standing of Saudi Arabia in implanting the E-Government based on the survey conducted by the United Nations in 2021. According to the index defined by the United Nations According to [7] EGDI report, there are 4 rating classes/quartiles; very high EGDI (1.000 < 0.800), High EGDI (0.600 - <0.800), Middle EGDI (0.400 < 0.200), and Low EGDI (0.000 <0.200). It reveals that currently, UAE is on top of the list in implementing the E-Government systems, and Saudi Arabia ranks 3^rd among the Gulf Countries. The data from Table 1 also shows continuous improvement compared to the EGDI value from 2003 to 2020.

There are several issues and challenges that most countries are facing, either implanting E-Government or planning to implement it. Saudi Arabia is one of them, where the Government has faced many challenges while implementing E-Governance. Some of the factors that hurdle the implanting E-Government systems are; a) Accountability [8], there is no perfect system exists to keep a check and balance on implanting the E-Government system, b) Digital Divide [9], there is a lack of professionalism and competency exists, and there in some cases, the end-users are not aware of using technology appropriately, c) Rules and Regulations [10], there have been very complicated rules and procedures implemented that end-users do not wish to use E-Government or face issues, d) Lack of Trained people [11], many of the users of E-Government systems are not adequately trained and lack in many competencies, e) ICT infrastructure [11], there are still some issues related to availability of Internet in many rulers and urban areas where internet service providers have not upgraded their infrastructure to fast access of the Internet, and finally, f) Cyber security [12], many users are afraid of cyber-attack, information theft, and denial of service (DoS), which ultimately restrict the end-users to use E-Government systems.

According to [14] application of the Organizational Environment (TOE) model, relative advantage, Complexity, and compatibility are three technological determinants of adoption. Comparative advantage is the extent to which new technological development offers a greater benefit than a preceding development [15]. The degree to which an innovation is deemed somewhat challenging to

understand and use is called its Complexity [15]. New technologies can be complex, making it difficult for users to grasp them quickly. As a result, the implementation may take a long period. Last, compatibility refers to how well a new technology matches a company’s principles, procedures, and requirements [13] - [21] suggested the key factors in the technology context as; Relative Advantage (X1), IT Infrastructure (X2), Compatibility (X3), Complexity (X4), and Security (X5). Table 2 displays the Technology Hypothesis.

The organizational factor comprises several traits, procedures, and structures that affect an innovation’s likelihood of being adopted and assimilated. The organizational environment of implementing new technologies has been extensively researched. Support from senior management, corporate culture, resources, and business nature have been crucial. Gaining the resources necessary to embrace a new technology requires the assistance of top management [21]. By encouraging a welcoming culture that values change, efficiency, and goal-setting, top management can enhance the acceptance of innovation inside a business. In addition to human resources, hardware, and software, organizational culture is a significant factor in determining whether an organization decides to implement new e-initiatives, including E-Government, among corporate enterprises [22].

Last but not least, the adoption of new technology is influenced by the nature of the business, including firm size. The model takes into account factors such as Management Support (X6), Financial Resources (X7), and Business Nature (X8) in the context of the firm (X8). Therefore, the following hypotheses in Table 3 reflect the factors that should be investigated further in this study.

A more modern explanation for how end-users accept and use information

technology is the Unified Theory of Acceptance and Use of Technology (UTAUT) model of [23]. The theory investigates four key concepts: 1) performance expectancy, 2) effort expectancy, 3) social Influence, and 4) facilitating factors. Gender, age, and experience are proposed as mediating factors in the behavioral component, which addresses effort expectancy, performance expectancy, and social impact. [8] asserts that the perceptions of utilizing E-Government services in terms of perceived advantages, such as enabling contact with the Government, enhancing the quality of government services, and giving people an equal footing on which to conduct business with the Government, might be used to gauge performance expectancy. According to empirical evidence from prior studies, these students’ behavioral intentions were influenced by peer pressure, effort expectations, and performance expectations. Additionally, decision-makers were informed that the usage of E-Government services was dictated by enabling conditions and behavioral intents and that these services needed to be helpful to the intended users.

[9] identified the elements influencing the adoption of E-Government services in Pakistan and developed an integrated model employing UTAUT and initial trust. Performance Expectancy (X9), Effort Expectancy (X10), Social Influence (X11), Perceived Usefulness (X12), and Ease of Use have been taken into consideration as the factors for the behavioral context (X13). Language (X14) and Uncertainty have been included as two moderating factors based on the Cultural Context (X15). The premise based on the behavioral context is shown in Table 4. The underlying hypothesis is shown in Table 5 using the moderating factors.

The value of the loading factor (outer loading) on each indicator is examined for this evaluation. The indication is legitimate if the value is higher than 0.50 [19]. Look at Table 6.

A construct indicator with a loading value below 0.7 should be considered and included in the subsequent analysis as it is legitimate, according to the test results in

Table 6. All indicators after assessment can be used for further investigation since the test findings in Table 6 demonstrate that all build indicator loading values have values above 0.7. By comparing the average extracted variance to the square correlations between the construct and each of the other constructs in the model, discriminant validity is determined. A reverse technique is used to make the computation process nimble. It demonstrates how one construct is unique from the others. The square root of the AVE is determined to assess the construct discriminant validity, and it must be higher than each construct correlation. Table 7 displays construct correlations for each variable in the model as well as the square root of the AVE.

Table 7 presents the values of the root square of AVE in latent variables Relative Advantage (X1) (0.87), IT Infrastructure (X2) (0.80), Compatibility (X3) (0.88), Complexity (X4) (0.85), Security (X5) (0.83), Management Support (X6) (0.92), Finance Resources (X7) (0.88), Business Nature (X8) (0.89), Performance Expectancy (X9) (0.82), Effort Expectancy (X10) (0.88), Social Influence (X11) (0.83), Perceived Usefulness (X12) (0.89), Ease of Use (X13) (0.91), Language (X14) (0.95), Uncertainty (X15) (0.77), and E-Government Adoption (Y) (0.89) are either strongly or significantly correlated to each other, therefore, the model is good to use for the measurement based on the discriminant validity.

The study examined the relationship between the independent variables (X1

through X13, including moderating and without moderating variables) and the dependent variable, Y. A structural model evaluation is employed to examine the role of the independent variable to the dependent variable using the R² value. This is known as the goodness of fit model through the [24] Tenenhaus Goodness of Fit (GoF) value. Therefore, the test was done twice. The first test is done on the model without including the moderating variables. The second test was done on the model that consists of the moderating variable, which aims to determine the effect of moderating variables on the relationship of independent variables with the dependent variable.

The significance of the route coefficient of the partial least squares method is used to test hypotheses (PLS). The path coefficient depicts the impact of one independent variable on the dependent variable. It may be argued that the results supported the study hypothesis if the p-value of the path coefficient is 0.05 (five percent), which indicates that the independent factors have a substantial impact on the dependent variables. Figure 2 and Figure 3 below show the path coefficient for this investigation.

Table 8 and Table 9 reveal the path coefficient value obtained from the Partial Least Square (PLS). The values were applied to test the hypothesis.

Table 8 and Table 9 reveals the results of the path coefficient from Figure 2 and Figure 3 respectively. Hypothesis H1, H2, H4, H8, H9, and, H11 have positive and negative coefficient values but p-value > 0.05 (α = 5%). Thus, hypotheses H1, H2, H4, H8, H9, and H11 are rejected. Hypothesis H3, H5, H6, H7, H9, H10, H12, H13, and H14 have significant path coefficient values and have p-value < 0.05 (α = 5%), and hence hypotheses H3, H5, H6, H7, H9, H10, H12, H13, and H14 are accepted.

Hypothesis H15 tests the moderator path variant coefficient (X13*X14) and shows the result that the variable of the interaction of X13 and X14 has a p-value equal to 0.002 with a path coefficient of −0.107. The p-value is less than 0.05 (α = 5%); hence, Hypothesis H15 is accepted. For testing H16 moderating path variant coefficient (X12*X15) shows the result that in the interaction variable, X12 and X15 found a p-value equal to 0.081 with a path coefficient of −0.052. Since the p-value > 0.05 (α = 5%), hence the hypothesis H16 is rejected. H17 also tested the moderating effect of X15 over X13 and found the path coefficient value of −0.133 and a p-value <0.05. Hence, the H17 is accepted.

Hypothesis H18 and H20 tested the moderating variable X14 with X10 and X12 and found the path coefficient values −0.009 and 0.112, respectively, with p-value >0.05; thus, hypotheses H18 and H20 are rejected. Finally, H19 was tested with the moderating variable X14 with X2 and found path coefficient value −0.098 and p-value < 0.05. Hence H19 is accepted. Table 10 presents the summary of the hypothesis results.