The Relationship between Accounting Information System Quality and Organizational Performance, The Moderation of Top Management Support in Iraq

The Relationship between Accounting Information System Quality and Organizational Performance, The Moderation of Top Management Support in Iraq

Hayder Ali Ageeb, Nor Hanani Ahamad Rapani, Hazianti Abdul Halim

Department of Accounting, Faculty of Management and Economics, University Pendidikan Sultan Idris, Tanjong Malim, Perak, Malaysia

DOI: https://dx.doi.org/10.47772/IJRISS.2024.ICAME2428

Received: 06 December 2024; Accepted: 10 December 2024; Published: 26 December 2024

ABSTRACT

This study is conducted to examine the relationship between Accounting Information System Quality (AIS Quality) Dimensions and Organizational Performance (OP) among companies listed in the Iraq Stock Exchange (ISE) by focusing on the moderating impact of Top Management Support (TMS). A quantitative approach is employed in this study and a structural equation modelling (SEM) is used to analyze the collected data. The Non-probability sampling technique was chosen for the data collection. A questionnaires were disseminated to a total of 491 respondents. SPSS and Smart PLS were used to analyze the data. The results indicate that AIS quality dimensions significantly enhance OP, with   system quality (β = 0.071, p-value = 0.022), information quality (β = 0.061, p-value = 0.025), service quality (β = 0.590, p-value = 0.000), and user satisfaction (β = 0.108, p-value = 0.041). Meanwhile, TMS fully moderated the relationship between AIS quality and OP. In conclusion, AIS quality dimensions play an important role in increasing OP, and TMS serves as an influential moderator in the relationship between AIS Quality dimensions and OP. Clearly, these findings have the potential to guide the ISE to focus on the appropriate AIS Quality dimensions that enhance its OP and increase its accountants’ knowledge and skills. The study offers practical recommendations for AIS development and highlights the strategic value of AIS for economic growth and sustainable development in Iraq.

Keywords: Accounting Information System quality, Organizational performance, Top  management support, Iraq Stock Exchange.

INTRODUCTION

One of the main concerns of most organizations today is the need to improve their performance (Onunwor, 2022). OP reflects the interaction between organizational behavior and achievements as well as the output value offered by the organization in the form of goods and services (Richard, Devinney, Yip, & Johnson, 2009; Barth, Emrich, & Daumann, 2018). OP also involves the iterative activities of setting organizational goals, monitoring progress toward goals, and making adjustments to achieve these goals more effectively and efficiently. An important element that affects organizational performance is the quality of AIS (Sunarta & Astuti, 2023). According to Algrari & Ahmed, (2019), the AIS quality plays a very important role as a provider of quality accounting information to assist management in carrying out its duties optimally. Thus, the success or failure of an organization in achieving its goals is highly dependent on the quality of the AIS used. Quality accounting information is used to support organizations in making better decisions and improving OP (Ganyam & Ivungu, 2019; Rosa & Purfini, 2019). Within this framework, the quality of AIS encompasses various dimensions such as system quality, information quality, service quality, system usage, user satisfaction, and net benefits, all of which collaboratively contribute to the overall efficacy of these systems (DeLone & McLean, 2016).

In recent years, scholars have recognized that the effectiveness of AIS in improving OP is not solely contingent upon the technical aspects of the system. Instead, the strategic alignment of these systems with organizational objectives, substantially underpinned by top management support, determines their ultimate impact. TMS is a critical enabler, creating an environment where AIS can thrive, aligning technology initiatives with broader business goals, and ensuring adequate resources are allocated for system development and maintenance (Munir, 2018; Kuraesin, Yadiati, Sueb, & Fitrijanti, 2019), when top management backs AIS initiatives, it makes sure that they are in line with the organization’s overall strategic goals and objectives, making sure that the system directly contributes to the organization’s mission and vision.; organizational culture, where trust and commitment from top-level executives foster a culture of data-driven decision-making and accountability within the organization, enabling stakeholders to rely on AIS-generated insights for informed strategic choices (Huy & Phuc, 2020; Alqudah et al., 2023; Alathamneh, 2020). Particularly within the context of Iraq, where economic and technological infrastructures are undergoing rapid transformation, the potential influence of AIS on OP remains an area ripe for exploration(Blass et al., 2014). The role of TMS is pivotal in facilitating this transformation, acting as a catalyst that propels organizations toward leveraging AIS effectively (Zeina & Sari, 2018). This study attempts to fill the gap in existing research by examining how the quality of AIS (system quality, information quality, service quality, system use, user satisfaction, and net benefits) influences organizational outcomes in Iraqi enterprises, with special emphasis on the moderating role of TMS.

The significance of this research lies in its potential to provide strategic insights for scholars and practitioners. By identifying the conditions under which AIS quality translates into enhanced organizational performance, this study hopes to offer tangible strategies for improving efficiency and results in Iraq’s private and public sectors (Al-Ali, 2014; Aldegis, 2018). Furthermore, it contributes to academia by adding a nuanced understanding of how managerial actions can magnify or mitigate the effects of AIS quality, thereby providing a foundation for future research in similar contexts. As Iraq continues to navigate economic challenges and seize growth opportunities, understanding the dynamics between technology systems and management practices becomes imperative for sustained competitive advantage. In addition, this research also contributes to strengthening the information system success model and contingency theory. with the following considerations: The development of financial technology has increasingly increased competition between financial institutions, including ISE.

LITERATURE REVIEW

Organizational Performance

Organizational performance has become the basic concept of all organizations, because of its gross effects (positive and negative) on the survival and growth of the organization and enhances its competitiveness (Jassmy, Bhaya, & Al-Dulaimi, 2016; Silva & Borsato, 2017; Onunwor, 2022). OP is a vital sign of the organization, showing how well activities within a process or the outputs of a process achieve a specific goal (Taşlıyan, Selim Eren, & Yücel, 2018; Adham & Alwan, 2021). In addition, scholars considered OP as the achieved results of the interaction between the activities of the communication and information technology sector and its resources or the difference between the financial goals and the non-financial ones in a specific period of time (Rajneesh & Karamjit, 2014). The contribution of AIS quality in an organization is that it helps to adapt to changing environments, records transactions in a structured way, and ensure competitiveness. It enables continuous information flow to both internal and external parties and helps businesses grow (Grande, Estébanez, & Colomina, 2011). In addition, ample evidence indicates the positive relationship between AIS quality and OP, highlighting favorable outcomes such as cost reduction, improved financial reporting, and enhanced data-driven decision-making processes. However, the interplay between AIS quality and performance is complex and often moderated by various factors, including organizational culture, user competence, and, most significantly, the TMS (Pham, Pham, & Pham, 2016).

TMS emerges from the literature as a pivotal element influencing the deployment and utilization of AIS quality within organizations’ practices (Blass et al., 2014). This support is paramount in the implementation phase and in fostering an organizational culture that encourages the effective use of information systems. Studies have illustrated that top management’s active involvement ensures alignment of AIS quality with strategic objectives, thereby unlocking its full potential to improve OP. In the context of Iraq, a country experiencing significant economic development and industrial diversification, the literature suggests that the quality of AIS could critically enhance national competitiveness. However, the moderation by TMS is less empirically explored within this geopolitical sphere. Thus, this study aims to fill this scholarly void by examining the nuances of this relationship in Iraqi organizations, providing novel insights into how strategic managerial actions can leverage AIS quality to optimize performance outcomes.

Accounting Information System Quality

In today’s corporate world, the ever-increasing need for market improvement, growth, and expansion has forced managers to consider more innovative management techniques aimed at enhancing organizational decision-making. Adoption of information technology within corporate organizations is one of these techniques (Davoren, 2019; Khalid & Kot, 2021). The main advantages of optimal use of AIS quality in an organization are a better adaptation to a changing environment, better management of arm’s length transactions, and a high degree of competitiveness (Shaar, Khattab, Alkaied, & Manna, 2015; Faccia, Mosteanu, Fahed, & Capitanio, 2019). The quality of an AIS is critical in determining its effectiveness and overall contribution to organizational success. Several dimensions are often analyzed to assess AIS quality, including system, information, and service (Davoren, 2019; Khalid & Kot, 2021). Together, these dimensions contribute to the perceived and actual utility of the AIS, influencing user satisfaction, system utilization, and, ultimately, net benefits realized by the organization (Petter, Delone, & McLean, 2013; DeLone & McLean, 2016). The low usage of AIS could contribute to low performance through poor information, which negatively affects decision-making and leads to low levels of operational skills and knowledge (Kareem et al., 2021). Furthermore, the high-quality AIS creates a robust platform that fosters improved business processes, enhances communication and collaboration across departments, and gives management the tools to make informed, data-driven decisions in this way, AIS quality directly links to improved organizational performance metrics such as productivity, efficiency, customer satisfaction, and financial performance (Laudon & Laudon, 2016; Savitri, 2019).

Top Management Support

Top management support is often seen as a critical success factor in the effective implementation and operation of an AIS quality (Carolina, 2017; Tilahun, 2019). The active involvement and commitment of top executives provide the necessary drive that propels technology adoption and integration across organizational layers. When leadership demonstrates a strong commitment to AIS quality initiatives, it sets a precedent for the rest of the organization, creating a culture that values technology-driven decision-making and operational execution(Shien, 2015; Kuraesin et al., 2019). This support is essential during various stages, from the planning and development phase to the deployment and continuous operation of the AIS quality. One of the key aspects of top management support is the allocation of resources. Leaders must ensure adequate financial investment, personnel, and time commitment to AIS projects to enable successful implementation and ongoing maintenance(Ragu Nathan et al., 2004; Štemberger et al., 2011; Nguyen, Chen, & Nguyen, 2021; Al-Raggad et al., 2024).

Moreover, top management acts as a change agent, facilitating the necessary organizational adjustments, such as process reengineering and policy revision, to accommodate the new system. Furthermore, top management’s endorsement enhances user acceptance and reduces resistance to change. By openly advocating for the system, management can shape perceptions and attitudes about the AIS, highlighting its benefits while addressing employee concerns (Ifinedo, 2008; Mir, 2013; Shafi et al., 2019). Additionally, their involvement in communications and training initiatives underscores the system’s importance and reinforces that the new AIS quality aligns with the organization’s strategic objectives. In essence, the endorsement and support from top management smooth the path for AIS quality initiatives and increase the likelihood of achieving desired business outcomes such as improved productivity, efficiency, and competitiveness (Hertati et al., 2021). Thus, as organizations navigate the complexities of digital transformation, sustained top management support becomes indispensable for leveraging the full potential of their AIS quality. 

Relationship between AIS Quality and OP

The development of technology and the pursuit of companies to utilize them in order to achieve organizations’ goals and ambitions led researchers to examine the impact of technology on all activities of organizations and its performance (Kareem, Aziz, Maelah, Yunus, & Dauwed, 2019; Almaliki, Rapani, & Khalid, 2020). AIS quality play a vital role in organizations. AIS quality are designed to assist organizations in processing data into information for reporting purposes, which impacts the economy and OP (Ganyam & Ivungu, 2019; Hendri et al., 2022; Sunarta & Astuti, 2023). The development of AIS quality has a significant impact on OP and effectiveness, because organizational managers need information to support decision-making. The impact of the AIS quality on OP will occur when the organization implements a quality AIS because, with a quality information system, data will be processed quickly, reliably, and consistently so that the information produced will be of high quality; thus, it can be used for support in decision making so that the decisions taken will be better (Algrari & Ahmed, 2019b; Fullana & Ruiz, 2021).

Meanwhile, Rosa & Purfini, (2019), revealed that the company will be able to improve OP by implementing a good and quality AIS. Several previous researchers have studied the importance of a quality AIS in organizations. The results of previous studies show a relationship between AIS quality and OP. For example, a study conducted by Magboul et al., (2024), investigated the factors influencing employees’ use of AIS quality in Sudan’s top five oil companies and investigates how these systems impact both financial and non-financial performance of OP. The results show that the practical application of AISs and their impact on organizational financial and non-financial performance. In addition, Akhter, (2022), the study on private commercial banks of Bangladesh showed that the AIS quality has a positive and significant effect on OP, while Alsmady, (2023), conducted a study on the Amman Stock Exchange, providing empirical evidence that the quality of AIS has a strong positive effect on companies’ performance. Meanwhile, Thuan et al., (2022), investigated the variables influencing the use of AIS quality in small and medium-sized businesses in Vietnam. The PLS-SEM model was used to test the proposed hypotheses using 132 valid replies, and the results were put together as a result. The study’s findings demonstrate that AIS use and efficacy are positively correlated, and that AIS performance is positively correlated with OP. Accordingly, Darmansyah et al., (2022), investigated how the quality of AIS affected business performance. The financial service sector’s regionally owned businesses (BUMDs) in West Java and Banten Province serve as the analysis’s unit of analysis. 38 units of the corporate entity make up the research’s sample size. The findings of this study suggest that raising AIS quality has a considerable impact on enhancing business performance. No study was found about the impact of AIS Quality on OP from the perspective of Iraq. Therefore, this paper aims to study the connection between AIS quality and OP using the IS Success Model, specifically in the context of the Iraq Stock Exchange.

The Relationship Between AIS Quality and OP, The Moderation of TMS

In exploring the interplay between AIS quality and OP, the moderating role of TMS emerges as a pivotal element. While AIS quality intrinsically boosts OP by enhancing the accuracy, reliability, and efficiency of financial reporting and decision-support processes, the extent of this impact is significantly influenced by the level of support provided by top management (Shaar et al., 2015 ; Ahmed, Mohamad, & Azmi, 2016; Daoud et al., 2021). TMS acts as a catalyst in bridging the gap between high-quality AIS and desired organizational outcomes. When senior executives actively endorse and participate in AIS quality initiatives, it sends a strong message about the importance of technology integration and utilization. This endorsement is crucial during the system’s implementation phase and throughout its lifecycle, ensuring sustained commitment to leveraging AIS quality capabilities fully (Marei et al., 2019; Daoud et al., 2021). Moreover, TMS involvement can enhance resource allocation, ensuring sufficient budget, time, and human resources are dedicated to AIS quality projects. This involvement is essential for overcoming potential resistance to change, as employees are more likely to accept and adapt to new systems when they observe unwavering support and advocacy from their leaders. Such leadership fosters an environment where innovation and continuous improvement are prioritized, further amplifying the benefits derived from high-quality AIS (Yigitbasioglu, 2015; Lutfi, 2022).

Furthermore, the strategic vision provided by TMS can guide the alignment of AIS quality functions with organizational goals. By ensuring that the system is not merely a tool for information processing but also a strategic asset, TMS can help maximize the performance-enhancing potential of AIS quality (Hertati et al., 2021). This strategic alignment ensures that AIS quality contributes to achieving competitive advantage by facilitating not just operational efficiencies but also strategic insights and agility (Novianty et al., 2018; Hertati et al., 2021). Based on the description above, the quality of AIS is indicated by the more optimal top management support is given, the higher the success of information systems is achieved, the performance of information systems, and the success of information systems with the participation and involvement of TMS, so it can be said that TMS affects AIS quality and offers advice and alternatives (Shien, 2015).

Ultimately, the moderation effect of TMS support highlights the multifaceted nature of achieving high OP. It underscores the necessity of viewing AIS quality not only through the lens of technical excellence but also as an integral component of broader organizational strategy, heavily reliant on strong executive leadership and involvement. this research emphasizes TMS involvement in strengthening the link between the AIS quality and OP in companies mentioned on the ISE. Accordingly, in this research, TMS is suggested to moderate the relationship between AIS quality (System Quality, Information Quality, Service Quality, System Use, User Satisfaction, and Net Benefits) and OP.

Fundamental Theory Used in this Research

Information system success model of Delone & McLean, (1992), and Delone & Mclean, (2003). In this model, six variables related to the success of an information system are presented: system quality, information quality, service quality, users, user satisfaction, and net benefits. In addition, this model has been used extensively and validated by many researchers in the IS field, giving it the reliability to be used for research that is theoretical and empirical (Xu, Benbasat, & Cenfetelli, 2013; Shagari et al., 2015). According to Fiedler, (1964), the theory states that there is no one best way of leading and that a leadership style that is effective in one situation may not be successful in others. In addition, contingency theory suggests that in order to improve performance, managers of firms must devote particular attention to their use of AIS Quality, taking care to adopt the systems best tailored to their special circumstances. (Mitchell, Biglan, Oncken, & Fiedler, 2017).

Moreover, contingency theory suggests that an AIS Quality should be designed in a flexible manner so as to consider the environment and organizational structure confronting an organization (Samuel, 2013; Dandago & Rufai, 2014). In addition, the contingency approach assumes that the effect of one variable on another depends on a third contingent variable (Badara & Saidin, 2014). However, the contingency theory varies from other theories in its definite suggestions because it assumes a conditional relation between two or more independent variables with a dependent variable (Badara, 2017). Therefore, Therefore, this study tends to the IS success model and contingency theory as they provide the theoretical foundation for the study.

METHODOLOGY

This study adopts a quantitative research methodology to examine the relationships among AIS quality, OP, and the moderating role of TMS in ISE. The research design is structured to collect and analyze data systematically to validate or refute the proposed hypotheses. The research targets a diverse range of sectors listed on the ISE, including banks, insurance, investment, services, industry, tourism and hotels, agriculture, telecommunications, and money transfer companies.  A non-probability sampling was employed, given the need to ensure participants possess relevant knowledge about IS operations within their organizations. From each participating company, a sample size comprised of five key respondents was selected: The Chief Executive Officer, Chief Financial Officer, Human Resource Manager, Internal Audit Manager, and Accountant.

In total, 125 companies were surveyed, resulting in a potential respondent pool of 625 individuals. The valid questionnaires for the study were 491. The percentage of valid questionnaires was (89.27%), were accepted after the review process. The data was collected using a structured questionnaire designed to capture a range of variables, including perceptions of AIS quality, organizational performance metrics, and the extent of top management support. The questionnaire was distributed electronically to ensure efficient data gathering, accommodating respondents’ busy schedules and geographical dispersal. SPSS and Smart PLS were employed to evaluate the collected data.

RESULTS AND DISCUSSIONS

Demographic Profile

A demographic variable is assembled by investigators to illustrate the nature and distribution of the sample employed with inferential statistics. Which discusses the demographic information of the respondents. The demographic characteristics included the age, gender, qualification, etc… of the participants. Table 1 shows the profile of respondents in this study. 85.3% of the respondents are male, and only 14.7% are female, which was a normal tabulation for this sector because, normally, female employees were rare on the Iraq Stock Exchange (IKN, 2011; Aziz, Mohammed, Muhammed, Sadq, & Othman, 2021; Bannay, Hadi, & Amanah, 2020). Another reason is the lack of legal education and awareness of relevant individual rights (Women in Iraq Factsheet). These and various sociocultural barriers are among the reasons that allow organizations operating in Iraq to commit gender discrimination in their job vacancy promotions (IUN, 2013; Aziz et al., 2021). Meanwhile, most of the respondents’ age was in the range of 30-39 years old (39.1%), followed by 50 years and above (33.8%) and 20-29 years old (26.5%).

Table 1. Respondents’ Profile

Measurement Model Assessment (Outer model)

It is crucial to evaluate the accuracy of the measurement model as a preliminary step to the evaluation of the structural model. The main purpose of analyzing the measurement model is to confirm that the indicators used are sound and valid and can better explain the theoretical constructs adequately. The assessment of the measurement model (outer model) consists of determining the internal consistency reliability and both the convergent and discriminant validity (Hair et al., 2014). In the following sub-sections, each criterion was addressed and discussed separately in order to assess the measurement model. To perform the reliability and validity tests, this research referred to the steps proposed by (Hair Jr et al., (2013), as shown in Table 2.

Table 2. Summary of measurement criteria for assessment of the measurement models

Indicator Reliability

The indicator reliability of the measurement model was assessed by examining the factor loadings for all the indicators. To determine that the measurement model has satisfactory Figure 1 For measurement model assessment indicator reliability, each factor loading should have a value of 0.708 and above (Hair et al., 2016; Ramayah et al., 2018). Moreover, higher factor loadings ranging from 0.7 to 0.9 indicate a higher level or degree of confidence that the measurement items congregate in the underlying estimated construct (Henseler & Fassott, 2010). Table 3 shows that there are 70 indicators (items) for the eleven constructs which include Customer Perspective (CP1-CP6); Financial Perspective (FP1-FP6); Internal Process Perspective (IPC1-IPC6); Information Quality (IQ1-IQ6); Learning & Growth Perspective (LGP1 – LGP6); Net Benefits (NB1-NB6); Service Quality (SerQ1 – SerQ6); System Quality (SysQ1 – SysQ6); System Use (USE1-USE6); Top Management Support (TMS1 – TMS10); and User Satisfaction (SATIS1 – SATIS6).

Following the guidelines specified by (Hair Jr et al., 2016), the assessment of indicator reliability of all indicators was performed based on the outer loadings. Three six were below the cut-off of 0.708 and affected the value of AVE (FP6, IPC6, SATIS1, SysQ1, SysQ5, SysQ6). It was recommended that these three items should be removed as their values are below 0.708; the cut-off is 0.708 and affects the value of AVE. Hence, the measurement model with 64 indicators factor loadings is within the recommended benchmark thus the reliability of the indicators has been confirmed as seen Figure 1.

Table 3. Factor loadings of indicators

The measurement model is shown in Figure 1, where the model shows all the items with their outer loading. The higher factor loadings ranging from 0.7 to 0.9 indicate a higher level or degree of confidence that the measurement items congregate in the underlying estimated construct (Henseler & Fassott, 2010).

Figure 1 depicts the results of the measurement model assessment obtained from PLSSEM (Algorith).

Convergent Validity

Convergent validity refers to “the extent to which a measure correlates positively with alternative measures of the same construct” (Hair et al., 2014). According to (Chin, 1998), the Average Variance Extracted (AVE) has a key role in confirming the convergent validity. The average variance extracted (AVE) should be studied to determine the convergent validity. According to Hair Jr et al., (2013), using the common rule of thumb, the AVE values should be higher than (0.5). Table 4 shows that all constructs have AVE values ranging from 0.548 to 0.730, which exceed 0.5 of the recommended threshold value. Based on the results obtained, this study’s measurement model has shown adequate convergent validity.

Table 4. Convergent validity for constructs

Model of Goodness of Fit (GOF)

GOF was explained by Hair Jr et al., (2016), as a worldwide measure of fit and the average of R² of the endogenous variables as well as the geometric mean of AVE. The goal of GOF is to account for the research model’s dependence on both empirical and conceptual levels, particularly the measurement. The GOF value is 0.681 higher than 0.36, as shown in Table 5 Subsequently, Henseler et al., (2014b), defined the GOF criterion for determining if GOF values are large, medium, small, or do not fit the PLS model and consider this to be globally valid. According to the findings, the goodness of fit metric of the model was greater than the sufficient validity of the global PLS model, which is deemed a globally valid PLS model. Moreover, structural models concentrate on the model’s overall performance Henseler et al., (2014b). The GOF model in Table 4.19 produced an R²=71.4 % and the rest of 28.6 % can be clarified by the other independent variable (AIS quality) that are not included in the analysis, such as alignment, adaptability, ease of manipulation, objectivity, reputation, technical competence, tangibles, follow-up service, intention to reuse, thoroughness of use, usage level, motivation of use, knowledge, user feel, achievement of the task, availability of the system, productivity, robustness, job creation, increased transactions, cost savings. The formula for calculating GOF is shown below: 𝐺𝑂𝐹 = √𝑅 ² ∗ AVE.

Table 5. Goodness of Fit of the model GOF

Path Coefficient and Hypotheses Testing

After the collinearity issues were assessed, the next step in evaluating the structure model was to estimate the path coefficient to examine the strength of the relationships between the constructs. The strength of the affiliation between two constructs was measured based on the magnitude of the path coefficient (Urbach & Ahlemann, 2010). As suggested by Hair Jr et al., 2013), a bootstrap procedure with 5000 sub-samples was used to measure the statistical significance of each path coefficient. Bootstrapping is used in evaluating the significance of the coefficient. When the empirical t-value is larger than the critical value, the coefficient is significant at a certain error probability (significant) level. Results from Table 6 reveal that the path coefficient estimates indicate that the relationships among the hypothesized constructs are small, medium, and large since the value ranged from -0.098 to 0.590 at the p= <0.05 level.

Table 6. Results of the structural model path coefficient

Structural Model Assessment (Inner model)

The next crucial step after the measurement models were measured and evaluated successfully was to analyze the structural model. The structural model includes the model’s predictive capabilities and the relationships among the hypothetical constructs. If the outer model (measurement model) is reliable, a valid assessment permits an evaluation of the inner path model’s (structural model) estimates. Following the assessment of the structural model that has been proposed by Hair Jr et al., (2013), several criteria require checking. The first criterion for the assessment structure model concerns collinearity issues. This can be performed using a tolerance VIF test. The second criterion covers the structural model’s significance and relevance, which can be measured using path coefficients and standard errors (t-value), which are acquired by means of bootstrapping. The third criterion is related to assessing the coefficients of determination (R²) of the endogenous latent variables to determine the accuracy of the model’s predictive by calculating the squared correlation between specific endogenous constructs of actual and predicted values (Hair Jr et al., 2021). The fourth criterion is related to the measurement of the effect of the size of different constructs on the dependent construct (F²). Finally, the predictive relevance (Q²) is reported in Table 7, which also summarizes all of the required tests and acceptance criteria for each step in assessing the structural model, as proposed by Hair Jr et al., (2021).

Table 7. Summary of measurement criteria for assessment of the structural models

Figure 2 illustrates the structural model assessment, which analyzes the relationships between different constructs in the study, focusing on how they contribute to OP. The figure shows path coefficients that indicate the strength and direction of the relationships between constructs. These coefficients visually represent how changes in one variable influence others in the model. For example, a positive path coefficient between AIS quality and OP suggests that improvements in AIS quality positively impact OP.

Figure 2 Results of the Structural model assessment

Moderating Analysis

This section explores the moderating effect of TMS on the relationship between System Quality, Information Quality, Service Quality, the Use of the System, User Satisfaction, Net Benefits, and OP in the ISE. Separate analyses were conducted to evaluate the moderating effect of TMS by obtaining the R² value of the main effect model and interaction effect model. The R2 value of the main effect model was (0.714) and the full model with the interaction effect model is (0.794). Besides that, the effect of F² of the model was 0.279, which was considered medium. Table 8, shows the moderation effect of TMS in the relationship between System Quality, Information Quality, Service Quality, the Use of the System, User Satisfaction, Net Benefits, and OP in the ISE.

Table 8. Moderation Effects of Top Management Support

Results show that the path coefficients of TMS* System Quality were -0.093, TMS*Information Quality was -0.111, TMS* Service Quality was -0.062, TMS* System Use, TMS* User Satisfaction was -0.107, and TMS* Net Benefits was -0.106. According to Chin, (2010), and Henseler & Fassott, (2010), ENREF_15; (Ramayah et al., 2018), the significance of the relationships cannot be determined based on the value of path coefficients only. Thus, bootstrapping resampling technique with 5000 samples were employed to test the significance of the relationships by obtaining the t-values. When the t-value is more than 1.645 in one -tail the hypothesis is accepted at 0.05 levels. The results in Table 8, indicate that all the interaction terms were significant as the t values exceeded the cut-off point of 1.645. Therefore, the six hypotheses were supported to be Failed to reject.

Results of Hypotheses Testing

The research hypotheses were tested using the results obtained from the path coefficient assessment in the structural model. This included using the path estimates and T-value with their p-values with the aim of supporting or rejecting the research hypotheses. Following this was a calculation of the path coefficient values (β) by using the PLS algorithm test. Table 9 presents the results of testing the research hypotheses using the path coefficients, t-value, and significance levels. Another analysis has been employed to evaluate the moderating effect of TMS in the relationship between System Quality, Information Quality, Service Quality, the Use of the System, User Satisfaction, Net Benefits, and OP in the ISE. Table 9 presents the results of testing the research hypotheses using the path coefficients, t-value, and significance levels.

Table 9. Hypotheses testing results

Table 9. shows the direct and indirect hypotheses by using AIS Quality dimensions and OP. The role of TMS as a moderator. Thus, the hypotheses reveal the following:

H1. There is a significant relationship between System Quality and Organizational Performance in companies listed in ISE. The results show that there is a significant positive relationship between Information Quality and Organizational Performance (β=0.071, t=2.012). Therefore, the hypothesis is supported. H2. There is a significant relationship between Information Quality and Organizational Performance in companies listed in ISE. The results show that there is a significant positive relationship between Information Quality and Organizational Performance (β=0.061, t=1.966). Therefore, the hypothesis is supported. H3. There is a significant relationship between Service Quality and Organizational Performance in companies listed in ISE. The results show that there is a significant positive relationship between Service Quality and Organizational Performance (β=0.59, t=7.35). Therefore, the hypothesis is supported. H4. There is a significant relationship between System Use and Organizational Performance in companies listed in ISE. The results show that there is no significant positive relationship between System Use and Organizational Performance (β=-0.098, t=1.085). Therefore, the hypothesis is rejected. H5. There is a significant relationship between User Satisfaction and Organizational Performance in companies listed in ISE. The results show that there is a significant positive relationship between User Satisfaction and Organizational Performance (β=0.108, t=1.743). Therefore, the hypothesis is supported. H6. There is a significant relationship between Net Benefits and Organizational Performance in companies listed in ISE. The results show that there is no significant positive relationship between Net Benefits and Organizational Performance (β=0.046, t=0.556). Therefore, the hypothesis is rejected.

H7. The Top Management Support moderates the relationship between System Quality and Organizational Performance in companies listed in ISE (β=0.093, t=2.884). This indicates that there is a significant positive moderate relationship between System Quality and Organizational Performance. Therefore, the hypothesis is supported. H8. The Top Management Support moderates the relationship between Information Quality and Organizational Performance in companies listed in ISE (β=0.111, t=2.826). This indicates that there is a significant positive moderate relationship between Information Quality and Organizational Performance. Therefore, the hypothesis is supported. H9. The Top Management Support moderates the relationship between Service Quality and Organizational Performance in companies listed in ISE (β=0.062, t=2.272). This indicates that there is a significant positive moderate relationship between Service Quality and Organizational Performance. Therefore, the hypothesis is supported. H10. The Top Management Support moderates the relationship between System Use and Organizational Performance in companies listed in ISE (β=0.083, t=2.274). This indicates that there is a significant positive moderate relationship between System Use and Organizational Performance. Therefore, the hypothesis is supported. H11. The Top Management Support moderates the relationship between User Satisfaction and Organizational Performance in companies listed in ISE (β=0.107, t=3.804). This indicates that there is a significant positive moderate relationship between User Satisfaction and Organizational Performance. Therefore, it is supported. H12. The Top Management Support moderates the relationship between Net Benefits and Organizational Performance in companies listed in ISE (β=0.106, t=4.137). This indicates that there is a significant positive moderate relationship between Net Benefits and Organizational Performance. Therefore, the hypothesis is supported. This is highlights the role of strong TMS in maximizing AIS quality in achieving positive organizational outcomes for companies listed on the ISE.

CONCLUSIONS

This study successfully highlights the critical role of AIS quality in enhancing OP, particularly in the context of ISE listed companies. The empirical evidence gathered underscores the pertinence of considering AIS quality as a strategic asset, composed of dimensions which is system quality, information quality, service quality, users, user satisfaction, and net benefits, each contributing significantly to OP. Through a robust measurement model and the use of Structural Equation Modeling (SEM), this research demonstrates the validity and reliability of the constructs involved, ensuring that the study results are credible and resonant with theoretical expectations. TMS emerges as a vital moderating factor, amplifying the positive effects of AIS quality on performance outcomes. This finding stresses the necessity for executives to actively foster an environment that leverages the full capacity of information systems, thus facilitating improved decision-making processes and operational efficiencies. The significance of TMS involvement cannot be understated in achieving performance goals, proving essential for organizations aiming to thrive in competitive and dynamic business landscapes. These conclusions carry profound implications for stakeholders, including policymakers, the Ministry of Finance, and the Ministry of Commerce and Industry in Iraq, by providing actionable insights into how AIS quality can be harnessed as a vehicle for economic growth and efficient market operations.

On a practical level, businesses are encouraged to invest in improving AIS quality components and nurture a supportive organizational culture led by engaged management to harness the system’s full benefits.  In essence, this study not only advances the academic discourse regarding the nexus between AIS quality and OP but also offers practical guidance for enhancing system usage and managerial practices, paving the way for more informed policy-making and strategic implementation in information systems. Furthermore, the integration of emerging technologies, such as artificial intelligence and block chain, into AIS quality presents an exciting frontier for research. Investigating how these advancements can enhance AIS quality and further improve organizational outcomes could provide actionable insights for businesses aiming to stay ahead in technology adoption. Finally, expanding the scope of factors moderating the AIS-performance relationship, including organizational infrastructure, employee training, and technological advancements, could offer a more holistic view of all variables at play. Overall, continuing to explore these dimensions will not only refine theoretical constructs but will also offer practical guidance for practitioners aiming to harness the full potential of AIS quality in diverse organizational landscapes.

REFERENCES

1. Adham, T. H., & Alwan, N. M. (2021). Positive Thinking and Its Relationship to the Organizational Performance of Sports Activity Supervisors in the Directorates of Education in the Middle Euphrates. Indian Journal of Forensic Medicine & Toxicology, 15(3), 5359–5364.
2. Ahmed, R., Mohamad, B., & Azmi, N. (2016). Exploring the relationship between multi-dimensional top management support and project success: An international study. EMJ – Engineering Management Journal, 28(1), 54–67.
3. Akhter, A. (2022). Impact of Accounting Information System on Organizational Performance: Private Commercial Banks of Bangladesh. International Journal of Scientific and Research Publications (IJSRP), 12(4), 307–311.
4. Al-Ali, H. A. K. (2014). Factor Affecting the Efficiency of Accounting Information Systems and Their Role in Rationalizing Expenditure in Jordanian Public Universities. Al Albayt University, Jordan. 12(2), 76–87.
5. Al-Raggad, M., Al-Raggad, A., Al-Raggad, M., Alraggad, A., Al-Mahasneh, A., & Abu Huson, Y. (2024). Examining Bribery as a Financial Crime in the Private Sector within the Framework of Jordanian Penal Legislation. Pakistan Journal of Criminology, 16(1), 635–648.
6. Alathamneh, M. S. (2020). The impact of accounting information systems reliability on enhancing the requirements of planning process at Jordanian commercial banks. Management Science Letters, 10(5), 1043–1050.
7. Aldegis, A. M. (2018). Impact of Accounting Information Systems’ Quality on the Relationship between Organizational Culture and Accounting Information in Jordanian Industrial Public Shareholding Companies. International Journal of Academic Research in Accounting, Finance and Management Sciences, 8(1), 70–80.
8. Algrari, A. Y., & Ahmed, R. M. (2019a). The impact of blockchain on accounting information systems. Journal of Information Technology Management, 7(1), 1–10.
9. Algrari, A. Y., & Ahmed, R. M. (2019b). The impact of blockchain on accounting information systems. Journal of Information Technology Management, 11(3), 62–80.
10. Almaliki, O. J., Rapani, N. H. A., & Khalid, A. A. (2020). The Effect of Accounting Information System Characteristics On The Internal Audit Effectiveness Among Companies Traded In Iraqi Stock Exchange: Testing The Moderating Role Of Experience.
11. Alqudah, H., Lutfi, A., Abualoush, S. habis, Al Qudah, M. Z., Alshira’h, A. F., Almaiah, M. A., … Tork, M. (2023). The impact of empowering internal auditors on the quality of electronic internal audits: A case of Jordanian listed services companies. International Journal of Information Management Data Insights, 3(2), 100–183.
12. Alsmady, A. A. (2023). Accounting Information Quality, Tax Avoidance and Companies’ Performance: the Moderate Role of Political Connection. International Journal of Professional Business Review, 8(1), 1–42.
13. Aziz, A. I., Mohammed, S. S., Muhammed, H. O., Sadq, Z. M., & Othman, B. A. (2021). The Gender-Biased Wording of Recruitment Advertisements in Iraq. UKH Journal of Social Sciences, 5(2), 25–33.
14. Badara, M. S. (2017). The relevant of contingency theory and stewardship theory on the internal audit research. Journal of World Economic Research, 6(2), 17–22.
15. Badara, M. S., & Saidin, S. Z. (2014). Empirical evidence of antecedents of internal audit effectiveness from Nigerian perspective. Middle-East Journal of Scientific Research, 19(4), 460–471.
16. Bannay, D. F., Hadi, M. J., & Amanah, A. A. (2020). The impact of inclusive leadership behaviors on innovative workplace behavior with an emphasis on the mediating role of work engagement. Problems and Perspectives in Management, 18(3), 479–491.
17. Barth, M., Emrich, E., & Daumann, F. (2018). Approaches and methods used for measuring organizational performance in national sport governing bodies from 1986 to 2014. A systematized review. Current Issues in Sport Science (CISS), 3(7), 1–22.
18. Blass, V., Corbett, C. J., Delmas, M. A., & Muthulingam, S. (2014). Top management and the adoption of energy efficiency practices: Evidence from small and medium-sized manufacturing firms in the US. Energy, 65(2), 560–571.
19. Carolina, Y. (2017). Understanding AIS User Knowledge, AIS Quality, and Accounting Information Quality. GATR Accounting and Finance Review, 2(3), 32–37.
20. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.
21. Chin, W. W. (2010). How to write up and report PLS analyses. In Handbook of partial least squares. Springer, Berlin, Heidelberg. In Methods.
22. Dandago, K. I., & Rufai, A. S. (2014). Information Technology and Accounting Information System in the Nigerian Banking Industry. Asian Economic and Financial Review, 4(5), 655–670.
23. Daoud, L., Marei, A., Al-Jabaly, S. M., & Aldaas, A. A. (2021). Moderating the role of top management commitment in usage of computer-assisted auditing techniques. Accounting, 7(2), 457–468.
24. Darmansyah, A., Aswin, R., Tuntun, R., Zen, S., Firmialy, S. D., Darmansyah, Asep, & Rahadi, R. A. (2022). Determining Model of Effect of Quality of Accounting Information Systems on Firm Performance. Central Asia and The Caucasus, 23(1), 3555–3565.
25. Davoren, J. (2019). The Three Fundamental Roles of Information Systems in Business. Retrieved from Hearst Newspapers, LLC: Retrieved April 9, 2019, Human Resource Information Systems,23681.Html, p. 23681.
26. Delone, W. H., & Mclean, E. R. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. Journal of Management Information Systems, 19(4), 9–30.
27. Delone, W. H., & McLean, E. R. (1992). The quest for the dependent variable. Information Systems Research. Information System Research, 3(1), 60–95.
28. DeLone, W. H., & McLean, E. R. (2016). Information Systems Success Measurement. In Foundations and Trends® in Information Systems (Vol. 2).
29. Faccia, A., Mosteanu, N. R., Fahed, M., & Capitanio, F. (2019). Accounting information systems and ERP in the UAE. An assessment of the current and future challenges to handle big data. In Proceedings of the 2019 3rd International Conference on Cloud and Big Data Computing, 90–94.
30. Fiedler, F. E. (1964). A Contingency Model of Leadership Effectiveness. Advances in Experimental Social Psychology, 1(C), 149–190.
31. Fullana, O., & Ruiz, J. (2021). Accounting information systems in the blockchain era. International Journal of Intellectual Property Management, 11(1), 63–80.
32. Ganyam, A. I., & Ivungu, J. A. (2019). Effect of accounting information System on financial performance of firms: A review of literature. Journal of Business and Management, 21(5), 39–49.
33. Grande, E. U., Estébanez, R. P., & Colomina, C. M. (2011). The impact of accounting information systems (AIS) on performance measures: Empirical evidence in spanish SMEs. International Journal of Digital Accounting Research, 11(1), 25–43.
34. Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2014). A primer on partial least squares structural equation modeling (PLS-SEM). In Sage Publications.
35. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM) . Sage publications.
36. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2013). A primer on partial least squares structural equation modeling (PLS-SEM). In Sage Publications.
37. Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM) (2 ed.). In Sage Publications.
38. Hendri, N., Irawan, A., Sa’diah, K., Jodi, I. W. G. A. S., Hidayat, A., Nasution, S. W. P., & Mujiani, S. (2022). Evaluation of Financial Management Information System Using Modification of the Delone & Mclean Model During the Covid-19 Pandemic. International Journal of Professional Business Review, 7(5), 1–20.
39. Henseler, J., & Fassott, G. (2010). Testing moderating effects in PLS path models: An illustration of available procedures. In Handbook of partial least squares. In Handbook of Partial Least Squares.
40. Henseler, Jörg, Dijkstra, T. K., Sarstedt, M., Ringle, C. M., Diamantopoulos, A., Straub, D. W., … Calantone, R. J. (2014). Common Beliefs and Reality About PLS: Comments on Rönkkö and Evermann (2013). Organizational Research Methods, 17(2), 182–209.
41. Hertati, L., Antasari, R., Nazarudin, N., Fery, I., Azwari, P. C., & Safkaur, O. (2021). Top Management Support Functions in Higher Education Management Accounting Information Systems. Ilomata International Journal of Tax and Accounting, 2(1), 1–16.
42. Huy, P. Q., & Phuc, V. K. (2020). The impact of public sector scorecard adoption on the effectiveness of accounting information systems towards the sustainable performance in public sector. Cogent Business and Management, 7(1), 1–23.
43. Ifinedo, P. (2008). Impacts of business vision, top management support, and external expertise on ERP success. Business Process Management Journal, 14(4), 551–568.
44. IKN, I. K. N. (2011). International Labor Organization, United Nations, Baghdad.
45. IUN, I. U. N. (2013). Women in Iraq factsheet, Iraq United Nations.
46. Jassmy, B. A. K., Bhaya, Z. M. A., & Al-Dulaimi, Z. Y. S. (2016). Customer Orientation and Organizational Performance in Iraqi Private Banks. In International Conference on Marketing and Business Development Journal, The Bucharest University of Economic Studies, 2(1), 219–225.
47. Kareem, H. M., Aziz, K. A., Maelah, R., Yunus, Y. M., & Dauwed, M. A. (2019). Enterprises Performance Based Accounting Information System: Success Factors. Asian Journal of Scientific Research, 12(1), 29–40.
48. Kareem, H. M., Dauwed, M., Meri, A., Jarrar, M., Al-Bsheish, M., & Aldujaili, A. A. (2021). The Role of Accounting Information System and Knowledge Management to Enhancing Organizational Performance in Iraqi SMEs. Sustainability, 13(22), 1–13.
49. Khalid, B., & Kot, M. (2021). The impact of accounting information systems on performance management in the banking sector. IBIMA Business Review, 31(7), 1–16.
50. Kuraesin, A. D., Yadiati, W., Sueb, M., & Fitrijanti, T. (2019). The Effect of Management Support on the Quality of Accounting Information Systems and its Impact on the Quality of Accounting Information (In Private Higher Education in Bandung City). International Journal of Mechanical Engineering and Technology (IJMET), 10(3), 450–456.
51. Laudon, K. C., & Laudon, J. P. (2016). Management Information System: Managing the Digital Firm. London: Pearson Education,.
52. Lutfi, A. (2022). Factors Influencing the Continuance Intention to Use Accounting Information System in Jordanian SMEs from the Perspectives of UTAUT: Top Management Support and Self-Efficacy as Predictor Factors. Economies, 10(4), 1–17.
53. Magboul, I., Jebreel, M., Shawtri, F., Qabajeh, M., Shanti, A., Alqudah, M., & Abu Huson, Y. (2024). Factors influencing accounting information system usage by oil companies & performance outcomes. Cogent Business and Management, 11(1), 1–16.
54. Marei, A., Emer, & Iskandar, T. B. M. (2019). The impact of Computer Assisted Auditing Techniques (CAATs) on development of audit process: an assessment of Performance Expectancy of by the auditors. International Journal of Management and Commerce Innovations, 7(2), 1199–1205.
55. Mir, U. R. (2013). Effect of organizational culture and top management support on ERP implementation.
56. Mitchell, T. R., Biglan, A., Oncken, G. R., & Fiedler, F. E. (2017). The Contingency Model: Criticism and Suggestions. Academy of Management Journal, 13(3), 253–267.
57. Munir, M. (2018). Manajemen Pemasaran Pendidikan Dalam Meningkatkan Kuantitas Peserta Didik. Intizam: Jurnal Manajemen Pendidikan Islam, 1(2), 78–94.
58. Nguyen, T., Chen, J. V., & Nguyen, T. P. H. (2021). Appropriation of accounting information system use under the new IFRS: Impacts on accounting process performance. Information & Management, 58(8), 103534.
59. Novianty, I., Mulyani, S., Winarningsih, S., & Farida, I. (2018). Dynamic interactions between major macroeconomic aggregates in Albania. A vector autoregression approach. Journal of Applied Economic Sciences, 13(8), 2196–2215.
60. Onunwor, A. A. (2022). Digital Infrastructure and Organizational Performance of First Bank Plc, in Rivers State. Innovative Journal of Marketing, 8(1), 108–123.
61. Petter, S., Delone, W., & McLean, E. R. (2013). Information systems success: The quest for the independent variables. Journal of Management Information Systems, 29(4), 7–61.
62. Pham, T. K., Pham, C. H., & Pham, L. (2016). Top Management Support, Organizational Learning, Innovative Behavior, Employee Commitment and Organizational Performance of Manufacturing Companies in Hai Phong. International Journal of Financial Research, 7(3), 54–64.
63. Ragu-Nathan, B. S., Apigian, C. H., Ragu-Nathan, T. S., & Tu, Q. (2004). A path analytic study of the effect of top management support for information systems performance. Omega, 32(6), 459–471.
64. Rajneesh, & Karamjit, K. (2014). Knowledge Management and Firm Performance: A Descriptive Study. , International Journal of Advance Research in Computer Science and Management Studies, 2(4), 197–204.
65. Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 3.0: An Updated Guide and Practical Guide to Statistical Analysis (2nd Edition) (2 Ed.). Kuala Lumpur, Malaysia: Pearson., (7), 587–632.
66. Richard, P. J., Devinney, T. M., Yip, G. S., & Johnson, G. (2009). Measuring organizational performance: Towards methodological best practice. Journal of Management, 35(3), 718–804.
67. Rosa, D., & Purfini, A. P. (2019). Analysis Effect Quality of Accounting Information Systems to Support Company Performance. IOP Conference Series: Materials Science and Engineering, 662(3), 1–7.
68. Samuel, N. (2013). Impact of Accounting Information Systems on Organizational Effectiveness of Automobile Companies in Kenya. (Vol. 11).
69. Savitri, E. (2019). Effect of accounting information systems quality end-user satisfaction based accounting software enterprise resource planning perceived usefulness as moderating. International Journal of Scientific and Technology Research, 8(6), 187–192.
70. Shaar, E. M. A. L., Khattab, S. A., Alkaied, R. N., & Manna, A. Q. (2015). The Effect of Top Management Support on Innovation: the Mediating Role of Synergy Between Organizational Structure and Information Technology. International Review of Management and Business Research, 4(2), 499–513.
71. Shafi, K., Ahmad, U. S., Nawab, S., Bhatti, W. K., Shad, S. A., Hameed, Z., … Shoaib, F. (2019). Measuring performance through enterprise resource planning system implementation. IEEE Access, 7(1), 6691–6702.
72. Shagari, S. L., Abdullah, A., & Saat, R. M. (2015). The influence of system quality and information quality on accounting information system (AIS) effectiveness in Nigerian Banks. International Postgraduate Business Journal, 7(2), 58–74.
73. Shien, M. J. (2015). Organizational culture and its impact on the quality of accounting information systems. Journal of Theoretical and Applied Information Technology, 82(2), 0278–0283.
74. Silva, F. A. da, & Borsato, M. (2017). Organizational Performance and Indicators: Trends and Opportunities. Procedia Manufacturing, 11(8), 1925–1932.
75. Štemberger, M. I., Manfreda, A., & Kovačič, A. (2011). Achieving top management support with business knowledge and role of IT/IS personnel. International Journal of Information Management, 31(5), 428–436.
76. Sunarta, I. N., & Astuti, P. D. (2023). Accounting Information System Quality and Organizational Performance: the Mediating Role of Accounting Information Quality. International Journal of Professional Business Review, 8(3), 1–20.
77. Taşlıyan, M., Selim Eren, A., & Yücel, D. K. (2018). Defining the Determinants of Organizational Performance in Turkish Manufacturing Sees. IOSR Journal of Business and Management (IOSR-JBM), 20(5), 26–35.
78. Thuan, P. Q., Khuong, N. V., Anh, N. D. C., Hanh, N. T. X., Thi, V. H. A., Tram, T. N. B., & Han, C. G. (2022). The Determinants of the Usage of Accounting Information Systems toward Operational Efficiency in Industrial Revolution 4.0: Evidence from an Emerging Economy. Economies, 10(4), 1–19.
79. Tilahun, M. (2019). A Review on Determinants of Accounting Information System Adoption. Science Journal of Business and Management, 7(1), 17–22.
80. Urbach, N., & Ahlemann, F. (2010). Structural Equation Modeling in Information Systems Research Using Partial Least Squares. Journal of Information Technology Theory and Application (JITTA), 11(2), 5–40.
81. Xu, J., Benbasat, I., & Cenfetelli, R. T. (2013). Integrating Service Quality with System and Information Quality: An Empirical Test in the E-Service Context. Management Information Systems Research Center, 37(3), 777–794.
82. Yigitbasioglu, O. M. (2015). The role of institutional pressures and top management support in the intention to adopt cloud computing solutions. Journal of Enterprise Information Management, 28(4), 579–594.
83. Zeina, N., & Sari, M. (2018). the Effect Business Process To Quality of Accounting Information Systems With Survey in Bumn Industrial Strategis in Bandung. 5(6), 1–24.