Evaluating Public Sector Employees’ Adoption of E-Governance and Its Impact on Organizational Performance in Angola

Abstract

Angola’s public sector employees’ adoption of e-governance and its impact on organizational performance was the primary objective of this study. The research employed the UTAUT model to conduct an in-depth study and analyze organizational performance, e-governance, and behavioral intention in detail, formulating four hypotheses. To test the hypotheses, a quantitative research method was used to collect data using online surveys sent by SurveyMonkey. A total of 273 individuals participated in the survey, and each survey took around 45 min to complete. Statistical analysis was performed on the acquired data using the SPSS and AMOS programs. The results of the analysis supported three hypotheses and disproved one. The statistical research that resulted in insignificant results revealed that effort expectancy had no direct effect on the behavioral intention of adopting e-governance or the influence on organizational performance. On the other hand, the accepted hypotheses demonstrated that performance expectation, social influence, and facilitating conditions had direct positive effects on organizational performance and a mediating effect on the behavioral intention to adopt e-governance in the public sector of the Angolan state.

1. Introduction

E-governance has been used in various economies due to its innovative approach to public sector administration. As a result of their stronger economic and social resources, developed economies have found it easier to adapt to improvements in information technology and communication and to adopt new methods of advancement [1]. However, implementing technological advancements and the broader concept of e-governance have proven more challenging for developing and undeveloped nations due to several obstacles [2]. One of the biggest obstacles to the widespread adoption of e-governance has been developing and undeveloped nations’ incapacity to adapt their economies and social systems to the new realities ushered in by the phenomenon. Changes brought on by e-governance were met with varying resistance in various nations, and public institutions were slow to adapt to technological changes.

Lack of resources and human competency to adapt to changes by e-governance in the global market have been the primary obstacles for poor and underdeveloped countries to embrace and accept e-governance [2]. Moreover, senior or authoritative personnel’s incapacity to adapt to change, a lack of legitimacy, and, at the same time, a lack of revenue and economic establishment to adapt to change, internal conflicts, foreign obstacles, and inadequate resource allocation are just a few examples of widespread problems [3]. These persistent problems slowed the adoption of e-government in developing and impoverished nations. However, these economies slowly adopted e-governance to facilitate growth and public engagement through increased transparency. In addition, compared to established economies, these nations confront many obstacles when integrating e-governance properly. As a result, they are still behind in fully incorporating all the contemporary aspects of e-governance [2].

One of the main reasons why the changes incorporated by the government to embrace e-governance have been confronting challenges has been employees of the public sector, in addition to the common issues that the economies have addressed in adopting e-governance. Senior personnel or those working in the public sector often fight the changes in developing or undeveloped economies. The current approach exacerbates concerns regarding the complexity of adopting new technologies [4]. Another major barrier to the full adoption of governance in the public sector is the widespread absence of requisite skills among public sector employees [4].

Angola is a Southern African nation and Sub-Saharan Africa’s third-largest economy [5]. Due to its dependence on oil exports, the economy has struggled since 2016. As a result of the economic crisis, the national debt is projected to increase from 57.1% in 2015 to 120.3% in 2020 [5]. The advent of the COVID-19 outbreak has also caused considerable problems for the economy, and the country has been fighting the crisis tenaciously by implementing new market-sustaining measures. In addition, Angola’s political atmosphere has shifted significantly throughout the years; the country only recently ratified a constitution and began functioning as a presidential republic in 2010 [6].

Similarly, the country has labored to hold several conferences to ensure that the public knows its general status and supports its residents [5]. Since the country has been focused on its citizens and has implemented many initiatives to ensure that they are aware of their rights and are developing appropriately, it has also adopted e-governance to meet the needs of its population. The technique of e-governance utilized by the Angolan economy for its residents has been interactive ministry websites. The government has developed these websites to communicate information about the nation’s financial reforms and to provide various forms and other materials that may be useful to the public [5].

However, there are considerable gaps in the existing research, and little is known about the country’s experience with e-governance, even though significant initiatives have been taken to assure the country’s survival in a competitive market and worldwide expansion. The lack of information necessitates more studies to fill the knowledge gap in the country and its strategy for implementing e-governance to maintain a competitive advantage.

2. Aims and Objectives of the Research

The primary aim of the research is to evaluate the public sector employees’ adoption of e-governance and its impact on organizational performance in Angola. The study’s research objectives are in line with the stated aim.

• To identify the factors (perceived usefulness, effort expectancy, social influence, facilitating conditions) that influence the adoption of e-governance in Angola’s public sector.
• To evaluate whether the adoption of e-governance in Angola’s public sector influences organizational performance.
• To highlight the mediating role of behavioral intention to adopt e-governance on the relationship between the acceptance factors (perceived usefulness, effort expectancy, social influence, facilitating conditions) and organizational performance.

3. Literature Review

3.1. Defining Organizational Performance

Rekawani, Lubis, & Utami [7] define organizational performance as the whole work outcome. Johara, Yahya, & Tehseen [8] defined organizational performance as an organization’s capability to complete tasks and accomplish goals through effective organizational behavior. According to Lee, Myeongju, and Bongsoon Cho [9], organizational performance is an effective way for an organization to reach its specified goals and achieve competitive results. The author’s further explained organizational performance as an organization’s overall input in attaining its goals strategically to obtain a long-term competitive advantage. Wholly based on different scholars’ views on “organizational performance”, it may be defined as an organization’s overall behavior to achieve its purpose and provide results that assist it in excelling in the target market.

According to Abolade [10], firms and organizations worldwide are working on integrating more organized and positive work culture into their daily activities to boost organizational performance. A corporation can gain a competitive edge by maximizing its organizational efficiency, which improves company performance [11]. According to Hanaysha [12], a company’s effectiveness is largely dependent on the efforts of its employees and, more broadly, on the company’s culture. Al-dalahmeh, Masa’deh, Abu Khalaf, & Obeidat [13] further noted that a positive work culture inside the system and progress toward achieving organizational goals are fostered by employees’ enthusiasm for and dedication to those goals. It is recommended by Annarelli et al. [14] that when conducting an overarching review of the organization’s performance, both the strategic and operational management facets of the business be considered. It has been established that an organization’s success is impacted positively by the existence of thriving company culture [15]. According to the existing literature, several aspects, such as organizational culture, organizational behavior, strategic and operational management, the work environment, and leadership, are important for sustaining an organization’s good performance.

3.2. Defining E-Governance

According to Heeks [16], modern technology and communication methods have enabled governments worldwide to manage their systems more precisely, which has largely contributed to the emergence of e-government. Neelakantam [17] describes the emergence of e-governance as a concept that is increasingly utilized in ordinary speech in the contemporary world. Likewise, Larsson & Gronlund [18] explain it as a sustainability concept for the future world, as the author believes that e-governance has emerged as the current mode of administration worldwide, enabling governments and organizations to authorize and administer operations more efficiently through the incorporation of useful technology. The use of information and communication technologies (ICTs) in public sector organizations has been found to increase government efficiency, hence reducing costs and boosting service levels for citizens, as outlined by Kaye [19] when doing e-governance research in the public sector or sectors concentrating on the adoption of e-governance, the focus switches from departments and services to holistic strategies that apply to the entire government, according to the author.

Although there are many positive aspects of e-governance, scholars have also addressed the constraints that must be overcome before e-governance can be widely used. For instance, Saxena [20] pointed out that boosting the economy or cutting government spending is not a straightforward goal of e-governance because e-governance is a lengthy process with associated financial costs and political considerations. Major consequences may result from these risks. Organizations confront technical, economic, and social hurdles while trying to implement efficient e-governance, as further articulated by Dash & Pani [21]. Overall, most scholars agree that a lack of resources, lack of knowledge, lack of expertise, and skills within the system, the government’s inability to integrate information technology effectively, and the users’ inability to adopt the mode of e-governance progressively are the greatest challenges to the widespread adoption of e-governance in the public sector.

3.3. Hypothesis Development

3.3.1. Mediating Effect of Behavior Intention of E-Governance Adoption between Performance Expectancy and Organizational Performance

Fedorko, Bačik, & Gavurova [22] demonstrated that e-governance adoption intention positively moderates performance expectations. According to the study, people are more likely to adopt new technologies when they are informed of the benefits and convinced that doing so will improve their data security and boost their confidence in their identification. Consequently, there is a relationship between performance expectations and the application of technology for a particular purpose. Lee, Yen, Peng, & Wu [23] found that users’ performance expectations directly influenced their adoption intentions and positively impacted the organization’s overall performance. According to the study, performance expectations also clearly predict technology adoption behavior. According to Kabra, Ramesh, Akhtar, & Dash [24], there is a positive and direct correlation between performance expectation and behavior intention in the humanitarian practitioner sector. Raman, Don, Khalid, & Rizuan [25] analyzed educators’ use of learning software and found a similar pattern. Numerous studies have demonstrated a link between performance expectations and intended user behavior.

Overall, the emphasized research and literature conclusions reveal a positive association between performance expectations and the behavioral intention to adopt the technology. The lack of research on the relationship between performance expectation, behavior intention, and organizational performance in e-governance or Angola highlights the need for further study. Based on the studied literature and investigations, the following hypothesis has been formulated:

H1. 

Behavior intention of adopting e-governance mediates the relationship between performance expectancy and organizational performance.

3.3.2. Mediating Effect of Behavior Intention of E-Governance Adoption between Effort Expectancy and Organizational Performance

Fedorko, Bačik, & Gavurova [22] also examined users’ expectations regarding how much effort biometric fingerprints require to influence their behavioral intentions. At the time, scholars were examining the financial systems of Uganda. The research revealed that the use of fingerprints and biometric systems enables users to feel more protected and secure with their data; at the same time, it is an easier identification method than the conventional method used by banks; this both increases their expectations of the product and the likelihood of positive user behavior intention towards the product. After analyzing the research data, the authors of this study concluded that effort expectancy influences user behavior positively based on the criteria of the conducted research.

Sung, Jeong, Jeong, & Shin [26] presented that effort expectation influences customers’ desire to adopt mobile commerce through personal innovation. This study explored the relationship between customer expectations of effort and their inclination to adopt mobile commerce. When assessing mobile commerce adoption in Pakistan, effort expectation had a favorable effect on behavioral intention, and the study indicated that effort expectation and perceived usability are equal. The literature review to evaluate the relationship between effort expectancy and behavior found a variety of technologies and contexts in which they have been implemented in various nations. This demonstrates the need for additional research on Angola’s adoption of e-government. Based on the existing literature and the positive association between effort expectation and behavior intention, the following hypothesis has been established:

H2. 

Behavior intention of adopting e-governance mediates the relationship between effort expectancy and organizational performance.

3.3.3. Mediating Effect of Behavior Intention of E-Governance Adoption between Effort Expectancy and Organizational Performance

According to Prabhakaran et al. [27], mobile wallet promotional incentives are insufficient for widespread adoption. The study examined the direct and indirect societal impacts of mobile money usage. To illustrate the effect of promotional incentives on user behavior, various discounts, cash-back restrictions, and regional marketing differences were used. According to the study, peer pressure causes customers to adopt mobile wallets, which utilized the UTAUT model to evaluate user intents. According to the report, positive product impressions, personal influence, and other factors all play a part in customer technology adoption. The study’s results indicate that social influence influences users’ intentions to adopt new technologies positively. Kulviwat et al. [28] examined the influence of social impact on a user’s choice to adopt new technologies. Individuals’ intentions to adopt technological improvements are affected by social context and the opinions of others. According to the study, peer pressure enhances the likelihood that a user will adopt new technology.

Overall, the literature study illuminates a large array of technologies and implementation situations in numerous countries. Additional research is required to assess how social influence impacts the adoption of e-governance, particularly in Angola’s public sector. In light of previous research and the positive correlation between social influence and behavior intention, the following hypothesis has been developed:

H3. 

Behavior intention of adopting e-governance mediates the relationship between social influence and organizational performance.

3.3.4. Mediating Effect of Behavior Intention of E-Governance Adoption between Facilitating Conditions and Organizational Performance

The objective of Ahmed Dhaha & Sheikh Ali’s [29] study at the University of Somalia was to identify the elements influencing the adoption of 3G mobile phones and user satisfaction. The study’s subjects were the students. According to the study’s findings, enabling conditions are one of the most powerful factors positively influencing customers’ intentions to adopt 3G mobile phones. In addition, Hossain et al. [30] evaluated the extent to which customers embraced location-based services and utilized them continuously. They focused on moderating factors’ influence on users’ technological behaviors. Regarding adopting new technologies, the researchers determined that the relationship between behavioral intention and user behavior is affected by enabling settings. According to the research, if students were to embrace location-based services to access the internet or social networks, they would require internet connectivity.

Chen & Aklikokou [31] investigated in greater depth how users’ opinions of the utility and simplicity of e-government affect their intentions to utilize the technology. In addition, the study indicated a positive relationship between the facilitating condition and the user’s intent to accept technology based on their behavior. The analyzed research revealed a positive correlation between the enabling state and behavioral intention in the context of various technologies and services. No studies on the application of e-governance or papers relating to Angola were included in the annotations. Therefore, it was necessary to undertake additional research. Despite this, the study conducted a literature review and developed the following hypothesis:

H4. 

Behavior intention of adopting e-governance mediates the relationship between facilitating conditions and organizational performance.

The following Figure 1 is an overarching framework for the planned research that is based on the hypotheses that have been formulated.

4. Materials and Methods

Since the purpose of this study is to quantify the effect of one construct on another, it is grounded in positivism, which enables the researchers to conduct empirical research and collect quantitative data. In addition, a deductive research approach was chosen because deductive reasoning facilitates comprehension of the link between the variables [32]. In contrast the other research approach, inductive reasoning is the polar opposite of deductive reasoning, as it is founded on the philosophy of building ideas based on acquired evidence in order to drive study findings. Inductive reasoning facilitates the development of new hypotheses depending on the researcher’s or the study’s criteria [33] However, since the researcher used a positivist approach and the study was centered on examining the relationship between variables and testing the hypothesis, the deductive research approach was utilized. As the deductive research method coincided well with the study’s aims and objectives, the study’s findings were effectively driven by this method.

Probability sampling was used with the use of random sampling, since it enables the researcher to randomly select a sample to accurately reflect the entire population. The surveys were prepared using existing studies that have concentrated on analyzing similar variables in different studies. For perceived usefulness, the survey questionnaire was adopted from the study of Davis [34]; for effort expectancy, the scale was adopted from the study of AlAwadhi & Morris [35]; and for social influence, the scale was adopted from the study of Venkatesh et al. (2003). In addition, other scale adopted from other studies were for facilitating conditions [35], behavioral intentions [36], and organizational purpose [37]. Based on these research instruments the survey questionnaire was developed using a five-point Likert scale. The questionnaires were published on SurveyMonkey (an online portal to conduct e-surveys). SurveyMonkey was used to provide a link to a questionnaire that was used to collect data. The link was distributed to around 400 public sector personnel in Angola after it was established online, out of which 287 respondents answered. After collecting the data, numerous analyses were conducted using SPSS and AMOS to conduct multiple statistical tests to evaluate each hypothesis and achieve the aims and objectives of the research.

In total, 437 Angolan employees received the survey link and 287 individuals (65.67%) responded to the survey. After calculating the survey response rate, the researchers conducted data analysis. The researchers searched for missing numbers to evaluate the completeness of the data. If missing values exceed 5%, they must be replaced. There were no missing data in the questionnaires. Consequently, no respondents dropped out. To ensure the accuracy of the data, outliers were also examined. The presence of multivariant outliers further led to calculation of Mahalanobis distance. Using Mahalanobis distance, the researcher calculated standardized probability for which the cutoff value was calculated to be more than 0.01 which indicated the presence of outliers, hence, the researchers the outliers, leaving 273 for data analysis. It was important to remove the outliers for the accuracy of data, as the presence of outliers in the data leads to error in the overall results and may impact the study negatively. Furthermore, to guarantee that the collected data represented “normal” behavior, the researchers employed normality analysis moving forward. The skewness of the data and any noticeable peaks in the data were analyzed to decide whether or not the acquired information could be trusted which did not show any major deviation. Using demographic statistics, the researchers analyzed the participants’ demographic characteristics (age, nationality, experience, and level of education).

5. Results

The researchers further analyzed the common methods of bias. A common technique bias is present when one un-rotated component accounts for over 50% of the overall variance. Common method bias can affect even the simplest of tests. At the same time, it is straightforward to identify and correct this bias. It may be necessary to employ statistical correction for more sophisticated tests. Clearly, using a conventional research technique did not lead to unintended bias because the common method bias result for the variables that were examined showed that just 31.67 percent of the variation was attributable. Following the common method bias, the researchers conducted a reliability analysis. Roberts, Priest, & Traynor [38] state that reliability analysis is performed to determine the internal consistency of the variable. Internal consistency helps the researchers to calculate the validity and accuracy of the collected data. Cronbach’s alpha values were computed to determine the internal consistency of the variables in the current study. Following the analysis, item EE6 was shown to be insignificant and was removed. Hence, the removal of the item showed a great increase in the Cronbach’s alpha value of this variable which is represented in the table below. Other factors, including performance expectancy, social influence, behavior intention, and organizational performance, all had internal consistencies above 0.7, suggesting strong internal consistency, which showed their reliability. The results of the reliability analysis are depicted in the

Table 1. Reliability Analysis.

Variable	Cronbach’s Alpha
PU	0.776
EE	0.882 (EE6 was removed due to low reliability)
SI	0.793
FC	0.822
BI	0.685
OP	0.914

below.

Following the reliability analysis, the researchers conducted an exploratory factor analysis. Data reduction and summarization are important goals of exploratory factor analysis [39]. The steps involved in conducting an EFA are extensive, requiring the researchers to perform numerous calculations (Kaiser-Meyer-Olkin values, Bartlett’s test of sphericity, identification of common values, application of the latent root criterion, identification of the percentage of overall variance, and determination of factor loadings). The steps were completed in order by the researchers. Factor extraction was accomplished using principal components analysis (PCA) and varimax rotation (VMR), as was previously described. Thus, EFA was used by the researchers for analysis, and the dimensionality of the variables was established. The EFA helped determine which factors were most important to examine, thereby narrowing the focus of the research. The proportion of total variance was set to 60%, while the significance level, commonality and factor loadings were set to 0.5.

The researchers strictly adhered to all procedures from the KMO analysis through factor extraction. In the

Table 2. Exploratory Factor Analysis (EFA) for the Research Constructs.

Code	Construct	Bartlett’s Test of Sphericity	KMO	Eighen Value	Percentage of Total Variance	Factor Loading	Communalities
PE1	Performance Expectancy	X2(6) = 289.864, p < 0.01	0.694	2.322	58.066	0.715	0.512
PE2						0.725	0.525
PE3						0.817	0.667
PE4						0.786	0.618
EE1	Effort Expectancy	X2(15) = 798.005, p < 0.01	0.880	3.788	63.139	0.798	0.637
EE2						0.854	0.730
EE3						0.804	0.647
EE4						0.808	0.652
EE5						0.814	0.662
EE7						0.678	0.460
SI1	Social Influence	X2(6) = 317.715, p < 0.01	0.773	2.472	61.809	0.724	0.525
SI2						0.828	0.686
SI3						0.812	0.659
SI4						0.776	0.602
FC2	Facilitating Conditions	X2(6) = 377.220, p < 0.01	0.763	2.578	64.465	0.801	0.642
FC3						0.747	0.559
FC4						0.820	0.673
FC5						0.840	0.705
BI1	Behavior Intention	X2(3) = 130.18, p < 0.01	0.668	1.841	61.368	0.602	0.776
BI2						0.631	0.794
BI3						0.608	0.780
OP3	Organizational Performance	X2(28) = 1069.87 p < 0.01	0.912	4.680	58.508	0.763	0.582
OP4						0.752	0.566
OP5						0.762	0.581
OP6						0.760	0.577
OP9						0.729	0.531
OP10						0.746	0.556
OP11						0.802	0.644
OP12						0.802	0.643

that follows, we will summarize the results. All KMO values for all constructs above the assumed threshold value (0.6) suggest a sufficient number of cases, and KMO values for all constructs were also higher than 0.60. The significance of Bartlett’s test of sphericity for all constructs also attested that the sample size was fine for EFA. All eigenvalues were more than one, and the total variation that could be characterized was over 60%. All communalities were greater than 0.5, suggesting that each construct is unidimensional and has a unique factor solution. Ultimately, all the variables with factor loadings above 0.60 were included into the main dataset.

Once the EFA was conducted, the researcher conducted a confirmatory factor analysis (CFA). Confirmatory factor analysis is a statistical technique that quantifies the congruence between empirical data and a theoretical model [40]. Common metrics used to assess a model’s fitness for the purpose include the CMIN/df, Goodness of Fit Index (GFI), Adjusted Goodness of Fit Index (AGFI), Root Mean Square Error of Approximation (RMSEA), Root Mean Square Residual (RMSR), Standardized Root Mean Square Residual (SRMR), Tucker Lewis Index (TLI), and Comparative Fit Index (CFI). The results of the CFA conducted for the current research are given in

Table 3. Common Factor Analysis.

Model Fit Measure	Description	Pre-Determined Ratio
Chi-square/df (CMIN/DF)	The Chi-square/df (CMIN/DF) represents the level of autonomy of the data with few differences. When the value of Chi-square/df (CMIN/DF) is closer to zero, the model fits more accurately.	<3 is considered to be good. However, if the value is <5, it is also acceptable.
The Goodness of Fit Index (GFI)	The approach used by the research to measure the congruence of the observed matrices covariance and the matrices that have been hypothesized. The index between 1 and 0.8 shows an acceptable fit model (Cheng and Assistant, 2011), while the value closer to zero depicts a poor model.	>0.80 = Acceptable.
Adjusted Goodness of Fit (AGFI)	The AGFI is similar to GFI. The values are asserted to be AGFI when the value of GFI has been modified based on the variable. When measuring AGFI, over 0.90 indicates a reasonably fitting model.	>0.90 = Acceptable
Root Mean Square Error of Approximation (RMSEA)	The Root Mean Square Error of Approximation (RMSEA) is a rigorous calculation that shows the degree of disagreement between the conceptual perspective, the covariance algebraic expressions of the population, and the variable estimations. Values close to 1 are considered acceptable. Values below 0.8 are recommended for model fit, but values below 0.6 are normally preferred.	≤0.08 = Acceptable
Root Mean Square Residual (RMR)	Root mean square can be explained when the residual covariance equals the square root of RMR. If the values are reduced, it represents a more accurate match.	≤0.02 = Acceptable
Tucker Lewis Index (TLI)	The TLI is a relative measure of the model’s position along a continuous spectrum that is unchanged by the sample size. Commuting TLI is often preferred for a small sample size and values greater than 0.9 are considered appropriate.	>0.90 = Acceptable
Comparative Fit Index (CFI)	The CFI measures the gap between the conceptual perspective and the empirical values after taking sample size into account. Values greater than 0.9 are considered appropriate.	>0.90 = Acceptable

below.

A full structural model was employed, the purpose of which is to evaluate hypotheses regarding the relationships between constructs. The researcher-selected structural model includes performance expectancy, effort expectancy, facilitating conditions, social influence, behavior intention, and organizational performance. Despite the baseline model’s poor fit, a satisfactory fit was achieved using modification indices and removing low-loading components. The following Figure 2 shows the structural equation model.

Multicollinearity analysis is necessary to prevent false conclusions. An essential assumption for multivariate analysis is the absence of multicollinearity. In spite of the fact that there are a number of statistical methods for doing so, correlation matrix analysis remains the standard method for gauging multicollinearity. Multicollinearity is not a problem if the correlations in the correlation matrix are lower than the cutoff value of 0.8. In order to evaluate multicollinearity, the variance inflation factor (VIF) and the tolerance value are also utilized in addition to analyzing the correlation matrix. If the VIF is greater than 10 and the tolerance value is less than 0.1, collinearity is a concern. The

Table 4. Multicollinearity analysis.

Model		t	Sig.	Collinearity Statistics
				Tolerance	VIF
1	(Constant)	8.281	0.000
	Performance Expectancy	2.452	0.015	0.465	2.151
	Effort Expectancy	1.029	0.304	0.311	3.218
	Social Influence	2.359	0.019	0.471	2.124
	Facilitating Conditions	−2.218	0.027	0.287	3.480
	Behavioral Intentions	1.657	0.099	0.435	2.301

Dependent Variable: Organizational Performance.

below displays the results of a multicollinearity test that was performed to ensure that the data collected for each variable in this study did not contain any false positives or negatives.

In addition, correlation analysis, group differences, and hypothesis testing were also made as part of the data analysis. H1 indicated, “Behavior intention of adopting e-governance mediates the relationship between performance expectancy and organizational performance”. The results computed showed that behavioral intention influences organizational performance (B = 0.779, p < 0.01). The researcher also assessed behavioral intention’s indirect mediating effect on performance expectancy and organizational performance. Performance expectancy increases organizational performance through behavioral intention (B = 0.042, C.I. = 0.009–0.082). Performance expectancy affects organizational performance directly (B = 0.493, p < 0.01). Overall, behavioral intention somewhat mediates the connection between performance expectancy and organizational performance, which resulted in the acceptance of Hypothesis 1.

For H2, which hypothesized, “Behavior intention of adopting e-governance mediates the relationship between effort expectancy and organizational performance”. Results indicated positively significant effort expectancy and behavioral intention (B = 0.034, p < 0.05) and also proved that behavioral intention influences organizational performance (B = 0.779, p < 0.01). The researcher also assessed behavioral intention’s indirect mediating effect on effort expectancy and organizational performance. It was found that effort expectancy does not affect organizational performance through behavioral intention (B = 0.014, C.I.= −0.015–0.078). Hence, H2 was dismissed.

H3 stated that “Behavior intention of adopting e-governance mediates the relationship between social influence and organizational performance”. Results revealed that social influence enhances behavioral intention (B = 0.147, p < 0.01) and also represented that behavioral intention influences organizational performance (B = 0.779, p < 0.01). The researcher also investigated behavioral intention’s indirect mediating effect on social influence and organizational performance, which showed that social influence does impact organizational performance through behavioral intention (B = 0.061, C.I. = 0.025–0.107). Overall, the results showed that the behavioral intention’s mediating effect on social influence and organizational performance was evident; hence, H3 was accepted.

Lastly, H4 indicated that “Behavior intention of adopting e-governance mediates the relationship between facilitating condition and organizational performance”. The results for this hypothesis showed that facilitating conditions enhance behavioral intention (B = 0.253, p < 0.01) and also that behavioral intention influences organizational performance (B = 0.779, p < 0.01). The researcher also assessed behavioral intention’s indirect mediating influence on facilitating conditions and organizational performance. The results showed that the facilitating conditions enhance organizational performance through behavioral intention (B = 0.110, C.I = 0.060–0.166). Overall, the results revealed that behavioral intention somewhat mediates the connection between performance expectancy and organizational performance; hence, H4 was accepted. To summarize, the correlation analysis results are given in the

Table 5. Correlation Analysis.

Correlations
		Performance Expectancy	Effort Expectancy	Social Influence	Facilitating Conditions	Behavioral Intentions	Organizational Performance
Performance Expectancy	Pearson Correlation	1
	Sig. (2-tailed)
	N	273
Effort Expectancy	Pearson Correlation	0.696 **	1
	Sig. (2-tailed)	0.000
	N	273	273
Social Influence	Pearson Correlation	0.549 **	0.656 **	1
	Sig. (2-tailed)	0.000	0.000
	N	273	273	273
Facilitating Conditions	Pearson Correlation	0.665 **	0.785 **	0.680 **	1
	Sig. (2-tailed)	0.000	0.000	0.000
	N	273	273	273	273
Behavioral Intentions	Pearson Correlation	0.594 **	0.653 **	0.626 **	0.713 **	1
	Sig. (2-tailed)	0.000	0.000	0.000	0.000
	N	273	273	273	273	273
Organizational Performance	Pearson Correlation	0.312 **	0.283 **	0.305 **	0.217 **	0.287 **	1
	Sig. (2-tailed)	0.000	0.000	0.000	0.000	0.000
	N	273	273	273	273	273

** Correlation is significant at the 0.01 level (2-tailed).

below.

The

Table 6. Hypothesis testing.

Hypothesis	Accepted/ Rejected
H1. Behavior intention of adopting e-governance mediates the relationship between performance expectancy and organizational performance.	Accepted
H2. Behavior intention of adopting e-governance mediates the relationship between effort expectancy and organizational performance.	Rejected
H3. Behavior intention of adopting e-governance mediates the relationship between social influence and organizational performance.	Accepted
H4. Behavior intention of adopting e-governance mediates the relationship between facilitating conditions and organizational performance.	Accepted

below displays the study’s overall outcomes based on hypothesis acceptance and rejection.

Overall, based on the numerical values discovered by the research, three hypotheses were validated and one was discredited. In the public sector of the Angolan republic, the findings and acceptance of the hypotheses showed that performance expectancy, social influence, and favorable conditions all directly impact behavioral intentions to adopt e-governance.

6. Discussion

The results of each of the four hypotheses pointed indisputably to the existence of a direct, positive, and significant relationship between behavioral intention and organizational success. Regardless of whether the hypotheses were confirmed, each of the variables analyzed to determine their veracity revealed a direct relationship between behavioral intention and organizational performance. This study’s emphasized findings demonstrated congruence with prior studies examining the beneficial relationship between behavioral intention and organizational effectiveness. These results were emphasized because they demonstrated congruence. Lee, Yen, Peng, and Wu [23], the first of the few studies reflected in Section 2 evaluating each variable, showed that any variable, such as performance expectancy, that positively affects the behavioral intention of adopting new technology or any other measure has a direct impact on the organization’s performance. The results of the study demonstrated that this is the case whenever a variable like performance expectation influences behavioral outcomes positively. Raman and Don [25] reached the same conclusion, namely that behavioral intention directly and positively impacts organizational performance. As a result of enhanced comprehension and a stronger willingness to accept new responsibilities, both individual performance and the organization’s overall performance have increased. According to prior studies, behavioral intention considerably increased organizational effectiveness. This type of information can be understood on a fundamental level. Because it was conducted in various situations, previous research was severely limited regarding the various circumstances and insights it could offer. This study’s core objective, the adoption of e-government in Angola’s public sector, benefited from these findings.

According to the data, performance expectation strongly influences behavioral intention. Individuals’ performance expectations influence their behavior. This study supports experts’ views that performance expectations strongly influence behavior intentions. Fedorko, Baik, and Gavurova [22] demonstrated that performance expectancy and behavior intention are positively mediated. They explained that consumers are likelier to adopt new technologies if they know the benefits and believe it will improve data security and identity trust. This was done because people will be more open to new technologies if they know their benefits. Thus, performance expectations influence technology uptake because they both relate to technological adoption. According to Section 2, the current results matched Wu’s [23] study that demonstrated a positive link between performance expectation and behavior intention. The study found that a user’s expectation of the new technology’s performance impact is one of the most important factors in accepting it. The current study is unique because it focused on the Angolan public sector and analyzed the research findings. Thus, this study is significant. This study shows that performance expectations and behavioral intention are positively correlated. This study was interpreted in light of the public sector’s increased reliance on e-governance. This study shows that performance expectation strongly predicts behavioral intention. Future researchers will benefit from the direct mediation between behavioral intention and performance expectation.

However, the current study’s findings—that effort expectancy has neither a significant nor a direct impact on behavioral intention—make sense, even though both reviewed and original research have shown a favorable association between the two constructs. Even though both evaluated and conducted prior studies demonstrated a positive association between effort expectancy and behavioral intention, the current study’s findings are nevertheless comprehensible. Possible explanations include the thesis study’s focus on the e-governance implementation context and the inherent constraints of the research approach. Since the cited study did not test the hypothesis’s inverse relationship between effort expectancy and behavioral intention in the context of the Angolan public sector and e-governance, and since the cited study’s results were contradictory to those of other studies, the inverse relationship was found to be nonexistent.

In addition, the study found that social influence and behavioral intention are linked. Social influence affects behavioral intention. The current research supported the Section 2 2 research and led to this notion. In Section 2, Prabhakaran et al. [27] examined social impact by proposing incentive programs to increase mobile wallet research use. This study examined direct and indirect social factors that affect user behavior while adopting technology, such as a mobile wallet. Factors include using discounts, cash-back policies, and regional disparities in the promotion’s locations, and it was shown how promotional benefits affect user behavior. The UTAUT model was used to determine users’ intent to embrace mobile wallets, and peer pressure was a prominent factor. The study found that customers’ interest in new technology was influenced by various factors, including their positive views of the product, their sense of importance, and others. The study found that social impact strongly influences users’ adoption of new technology. This research examined the relationship between social influence and behavioral intention by adopting e-governance in the public sector and the republic. Previous studies that found a link between social influence and behavioral intention were conducted in different contexts. This study emphasized the relationship between social influence and behavioral intention in a situation that has never been characterized or explored in detail, highlighting the significance of the current research and its value as previous research for future research.

Lastly, this research also found that e-governance behavior intention mediates the relationship between enabling circumstance and organizational performance. The research and statistical analysis validated the concept. The research discovered that enabling environments had an immediate effect on behavior intention. The research revealed this. The outcomes of the current study were consistent with the researcher’s previous work and the existing literature, which indicated no substantial alterations. In Section 2, the researcher examined a study from Somalia University. Ahmed Dhaha and Sheikh Ali [29] investigated the relationship between 3G mobile phone uptake and customer happiness. The focus of investigations was on students. According to the study, favorable environments substantially increase users’ intent to adopt 3G mobile phones. It was verified. This claim was supported by research. Throughout their discussion, the researchers emphasized the significance of supporting settings in determining user behavior and attitudes toward technology adoption. In Section 2, Chen & Aklikokou [31] offered a similar explanation when they studied how the facilitating conditions of e-government services influences their utilization. Chen & Aklikokou [31] gave a similar understanding. The facilitating condition was also connected with the user’s intent to adopt technology solutions.

This study is successful and advantageous for future research since it can be understood in e-government adoption in the public sector. Current research indicates that enabling condition directly influences behavioral intention, hence this notion is accepted. The study is beneficial because it demonstrates that enabling conditions directly affect behavioral intention. This study is significant since no prior research has demonstrated the effect of favorable circumstances on the intention to adopt e-government in Angola’s public sector. This supports the study further. Current research indicates that enabling conditions have a direct impact on behavior intention. This demonstrated how this data could conceivably be utilized in the future and have real-world consequences, particularly in the public sector and in Angola. This study demonstrated the significance of future theoretical and practical applications of these data, particularly in the public sector and in Angola.

7. Conclusions

7.1. Contribution of the Research

The present research studied e-government adoption and behavioral intention with performance expectations, social influence, effort expectations, and facilitating factors. Based on the adopted methodology, the analysis of the study revealed a positive correlation between performance expectancy, social influence, and facilitating conditions; however, the role of effort expectancy on e-government adoption in Angola’s public sector was refuted in the context of the current study. This indicated that despite the effort required to adopt e-governance, the benefits of e-governance were so great that no additional effort was required to implement e-governance. Since the hypothesis was generated based on a previous study that supported the impact of effort expectancy on the adoption of technology, this study found no impact of effort expectancy on e-governance adoption. This demonstrated that in the context of the Angolan Republic, no specific effort was required to implement e-governance due to the direct benefits it could provide to the Angolan public sector by improving operations, empowering the public sector as a whole, and facilitating faster information access.

Moreover, based on hypothesis testing, variables associated with the UTAUT model were found to have a positive impact on the behavioral intention of e-governance adoption; this was similar to different studies reviewed throughout the study in which different authors described how variables such as facilitation conditions, social influence, and performance expectation have a positive impact on the behavioral intention of technology adoption. This study examined the impact of these characteristics on organizational performance, revealing how the Angolan public sector could implement e-governance for long-term effectiveness. There was no earlier study that explored the adoption of e-governance in the setting of the Angolan republic based on the UTAUT model. Hence this was of added value. Overall, this research helped in understanding how the behavioral intention of e-governance adoption in the republic of Angola can be influenced by different factors of the UTAUT model, which facilitate e-governance adoption in the region.

The current research findings are obvious and instructional about how e-governance behavioral intent may affect an organization’s overall success. This study can help Angola’s public sector adopt e-governance strategically, boosting organizational performance and allowing citizens to benefit from e-governance technologies. The study’s findings helped comprehend how these aspects might affect an organization’s operational performance when related to the behavioral goal of embracing e-governance. This will enable future study and provide a deeper understanding of Angola’s existing position and e-governance ambitions for its private sector. This will help future academics understand Angola’s current situation and e-government plans for its private sector. The current research has both theoretical and practical implications for future studies. It will help the Angolan republic understand the current condition of e-governance in the country and how to use tactics based on acknowledged criteria to increase organizational performance. This research will help future studies and the Angolan government understand e-situation.

7.2. Limitation of the Research

This study had a few limitations, the most prominent of which was its exclusive dependence on quantitative research methods. In other words, even while it was essential to quantify the subject of the study, the researchers did not pause to investigate the how and why of the phenomenon under investigation. As a result, the study’s results were based purely on empirical facts and statistical analysis and were derived from them. Prospective researchers can be persuaded to employ a mixed-methods approach that combines quantitative and qualitative investigation of a phenomenon. Since this study was conducted for limited period of time, future research can conduct longitudinal studies to test similar hypotheses from a mixed-methods perspective and observe how these variables can impact the behavioral intention of adoption of e-governance in the long run. In conclusion, it is important to note that the study’s sample size was rather small, which could make generalizing the results challenging. For this reason, longitudinal samples should be utilized in a future study on e-governance in the Republic of Angola so that the influence of the aforementioned four factors on behavioral intention may be evaluated more precisely.