Editorial Board

A Refereed Monthly International Journal of Management

Accountants’ Behavioral Intentions and use Behavior of Accounting Information System

 

Viet Trung Hoang

University of Economics

Technology for Industries, Vietnam

trunghv124@gmail.com

 

Thi Thuy Nguyen

Thang Long University,

Vietnam

nthuy189@gmail.com

Corresponding author

Abstract

Accounting information systems (AIS) are mainly based on computers and software, automating accounting tasks. The application of AIS shows that digitalization has a positive impact on accounting activities. Therefore, using the Unified Theory of Acceptance and Use of Technology (UTAUT2) model and adding the factors of Self-Efficacy and Seamless connectivity to study the accountants' behavioral intention and behavior of using AIS in Vietnam are a suitable research direction, aiming to examine the factors affecting the accountants' behavioral intention and behavior of using AIS, because their behavioral intention and behavior of using AIS can contribute to ensuring the accuracy of accounting data. SEM (Structural Equation Model) model used in this study to analyze and found that the factors Effort Expectancy, Price Value and Cost Saving play a role in promoting and determining the accountants’ behavioral intention of using AIS. At the same time, the factors Habit, Seamless connectivity, Behavioral intentions have positive impacts on the accountants' behavior of using the AIS in Vietnam. The results of this model help accountants and managers have strategies to develop the use of AIS in the increasingly increasing accounting activities in Vietnam.

Keywords: Accounting Information Systems, Behavioral Intention, Use Behavior, UTAUT2.

Introduction

According to Ha Van Duong (2021), an information system is a framework that gathers components in a structure to work together to collect, process, store and distribute information and data towards a common goal. An AIS provides a feedback mechanism to achieve a predetermined goal. Organizations use information systems for many different purposes to achieve the overall goals of the organization. Therefore, AIS can be defined as the components that collect accounting and financial data, process, store and convert into accounting information for decision makers in an organization. Kris (2023) asserts that AIS is a process that combines information technology with accounting principles to collect, process and establish reports to systematically approach and manage the financial transactions of an organization.

Jarah and Jarrah (2022) show that AIS contributes to data integrity. AIS is important, used to make business decisions and contributes to increased operational efficiency. Kris (2023) states that AIS serves as the foundational infrastructure for accounting and financial management, contributing to ensuring accuracy, reliability and ability to support financial management and decision-making, as well as monitoring the entire accounting and financial activities of an organization. AIS is used for financial planning, budgeting and financial management, playing an important role in setting policies, helping with decision-making, compliance reporting and financial analysis, and providing a systematic approach to managing financial transactions. According to Nurul (2025), AIS is a strategic solution that contributes to increased efficiency through automation, accelerated data processing, ensuring data accuracy, leading to faster report generation. This is an important basis for systematic management of accounting activities and financial transactions from more reliable and suitable financial reports for decision-making.

Therefore, studying the accountants’ behavioral intention and behavior of using AIS in Vietnam will contribute to promoting and improving efficiency, processing data faster, ensuring data quality, leading to faster reporting in the digital environment. AIS is the foundation of modern financial management and accounting. Automating AIS tasks contributes to cost savings. AIS integrates seamlessly into the organization's operations, allowing access to real-time or near-real-time financial information, supporting decision-making and enabling faster decision-making. Furthermore, AIS contributes to data integrity and AIS reliability is important to improve the efficiency of all organizational operations, which is an increasingly popular and effective development trend in Vietnam.

Literature and Hypotheses

Behavioral intention and behavior of using AIS and The UTAUT2 Model

In many accounting and finance operations, the adoption of AIS is a growing trend. The automation of AIS tasks contributes to the expansion of accounting and finance operations capabilities. AIS has significantly contributed to the handling of large volume accounting operations with greater accuracy. This shows that AIS is seamlessly integrated into the operations of the organization, which has pushed many operations to focus on digital transformation and modernization journey. Planning for strategic planning, decision support and faster decision-making through the use of AIS. The functions of accounting and finance operations create important roles for AIS, along with increased automation and continuous monitoring of data integrity, which has driven the need for AIS use, driving the behavioral intention of accountants and executives to use AIS to collectively implement more AIS-centric long-term operational strategies (Ha van Duong, 2022).

Zaini et al. (2020) extended the UTAUT2 model by adding two more factors, namely perceived technology fit and perspectives on communication, to study the factors influencing AIS usage. The empirical results revealed that the hypotheses were consistent with previous studies and all the factors of the model were statistically significant.

Pourghanbari et al. (2022a) tested the factors affecting accountants’ behavioral intention of using AIS by applying the UTAUT model combined with new factors. The results of this combined model found that performance expectancy and other factors are the factors as well as effort expectancy affecting the accountants’ behavioral intention of using AIS. These findings also argued that the higher the performance of new technology, the more convenient it can be, saving time and easy to operate, thereby making accountants more active in using AIS.

According to Lutfi (2022), accountants are the main users of AIS and the use of AIS contributes to the success of accounting work. The behavioral intention to use AIS of accountants was studied by applying the UTAUT model to examine the influencing factors and detect the factors of effort expectancy along with performance expectancy as well as favorable conditions and social influences that positively promote and play a decisive role in the behavioral intention to use AIS of accountants. These findings have contributed to the UTAUT theory, contributing to the impact on the behavioral intention to use AIS of accountants.

Pourghanbari et al. (2022b) argued that the successful implementation of AIS is due to the accountants’ behavioral intention using AIS. Therefore, applying the UTAUT model to study the factors affecting the accountants’ behavioral intention of using AIS. The study results revealed that performance expectancy along with effort expectancy promote a positive relationship with the accountants’ behavioral intention of using AIS. This result helps decision makers to choose a new AIS compatible with new technology to develop a new AIS in line with the trend of technological development.

Alquhaif and Al-Mamary (2025) appreciate the role of AIS in improving organizational efficiency and decision-making processes. Understanding the factors that influence accountants’ behavioral intention to use AIS is essential for organizations that want to optimize their technological capabilities. Applying the UTAUT model to study and argue that performance expectancy along with effort expectancy as well as social influence and facilitating conditions all have a direct and positive impact on behavioral intention of using AIS. In addition, behavioral intention also positively affects behavior of using AIS. The study affirms the importance of AIS usage, providing valuable insights for both theory and management practice.

Congcong (2025) based on three theories, including the UTAUT2 model, as the core foundation to explain the intention and behavior of accountants using AIS. The study found that social influence along with facilitating conditions significantly affected behavioral intention. In addition, behavioral intention significantly affected usage behavior. This result brings both theoretical and practical significance, providing insights for organizations to modernize their financial accounting operations.

In fact, the application of the UTAUT2 model is a model developed by Venkatesh et al. (2012) from the UTAUT model in many different fields, including the study of behavioral intentions and behavior of using AIS by accountants. As technology platforms develop and are increasingly applied in accounting, AIS is one of the indispensable systems for collecting, storing, processing data and financial reporting of organizations. Many research results have contributed to improving operational efficiency, supporting decision-making, ensuring tight internal control, and providing reliable financial information to relevant subjects, as well as providing valuable insights, significantly contributing to the development of theoretical and practical application, showing the important applications of the UTAUT2 model in examining the behavioral intentions and behavior of using AIS by accountants.

Hypothesis Development

Adding other elements such as Self-Efficacy and Seamless connectivity to the UTAUT2 model, because AIS has the role of collecting accurate, timely, complete, and reliable information; providing complete management information; supporting financial planning and implementation; facilitating financial reporting. The presentation of accounting and financial information must be clear, ensuring reliability, honesty, accuracy, and the ability to verify correctly, such as the comparison of information by a potential investor. Accounting and financial information must be useful, supporting appropriate decision-making. AIS provides information in a systematic, collected, and more effective order for use, confidentiality for accounting data, authentication, and access control during the use of AIS (Ha Van Duong, 2021). The use of AIS in accounting, self-efficacy and seamless connectivity to meet current accounting tasks, as well as changes in the process of using AIS for efficiency. Therefore, this research model is proposed as described in Figure 1.

Performance Expectancy (PE) refers to the perceptions and beliefs of users of a technology that will improve their performance and this factor reflects users' acceptance of using a technology system (Venkatesh et al., 2012). Ababio (2021) conducted an empirical analysis to examine the factors influencing the AIS adoption intention of accountants and found out the impact of performance expectancy on the AIS adoption intention of accountants. Al-Okaily et al. (2023) studied the factors that motivate the use of AIS and found that performance expectancy strongly influences the behavioral intention of accountants in using AIS. The empirical study by Amalia and Pratolo (2024) shows that the performance expectancy factor significantly influences the behavioral intention to use technology adoption in AIS. The use of AIS is increasingly considered a strategic trend and this is the goal of many organizations. Because organizations are more likely to use AIS and because this system has the necessary support in accounting and financial operations. Pourghanbari et al. (2022a) tested the factors affecting accountants’ behavioral intention of using AIS and found that performance expectancy and other factors are the factors as well as effort expectancy affecting the accountants’ behavioral intention of using AIS. Through empirical analysis to evaluate the factors affecting the use of AIS, the evaluation results have argued that performance expectancy is a factor that strongly affects the behavior of using AIS (Rabiu et al., 2025). Dewi and Juliarsa (2025) argued that technological change and development directly affected the performance of organizations. The adoption of AIS has become an organizational strategy. Therefore, research on AIS adoption has generated empirical evidence for the UTAUT model, which explains the effort expectancy factor contributing to the acceptance and use of AIS. From the previous research results, hypothesis H1 is presented as follows:

H1: Performance expectancy positively affects and plays a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

Effort Expectancy (EE) reflects the level of convenience, effort reduction, and ease an individual feels when performing a task through the application of technology (Venkatesh et al., 2012). Alamin et al. (2015) conducted an empirical study and analysis of the influences on the use of AIS by accountants and stated that the factor effort expectancy influences the behavioral intention of accountants to use AIS. Alamin et al., (2020) also indicated that the development of digitalization is increasing rapidly and bringing many applications to the accounting profession. Research on the application of AIS by accountants shows that it is influenced by many factors that determine the behavioral intention to use AIS, including effort expectancy. It helps to improve the accounting and financial management process better and better. The application of AIS helps to improve the accounting and financial management process better and identifying the factors that determine the behavioral intention to use AIS helps managers have supportive experiences that influence the increase in the behavioral intention to use AIS of accountants. Pourghanbari et al. (2022b) argued that effort expectancy promotes a positive relationship with the accountants’ behavioral intention of using AIS. The empirical study by Amalia and Pratolo (2024) shows that the outcome of the factor of effort expectancy significantly influences the behavioral intention to adopt technology in AIS. Alquhaif and Al-Mamary (2025) appreciate the role of AIS in improving organizational efficiency and decision-making processes and argue that effort expectancy has a direct and positive impact on the behavioral intention of using AIS. Dewi and Juliarsa (2025) argued that the adoption of AIS has become an organizational strategy, technological change and development directly affects the performance of organizations. Therefore, research on AIS adoption has generated empirical evidence for the UTAUT model, which demonstrates the effort expectancy factor contributing to the acceptance and use of AIS Inheriting the previous research results, hypothesis H2 is proposed as follows:

H2: Effort expectancy positively affects and plays a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

Social influence (SI) refers to the extent to which a user of a technology accepts the use of a technology through the transmission of opinions from others. Thereby, the user's behavioral intention towards using the new technology systems (Venkatesh et al., 2012). Ababio (2021) conducted an empirical analysis to examine the factors influencing the behavioral intention in using the AIS of accountants and made assertions about the impact of social influence on the AIS adoption behavioral intention of accountants. The empirical study by Amalia and Pratolo (2024) reflects the results of a social influence factor significantly influencing behavioral intention to adopt blockchain technology in AIS. Understanding the factors that influence accountants’ behavioral intention to use AIS is essential for organizations that want to optimize their technological capabilities. Hence, implementing the study and arguing that social influence has a direct and positive impact on the accountants’ behavioral intention of using AIS (Alquhaif and Al-Mamary, 2025). Organizations are more likely to use AIS because this system has the necessary support in accounting and financial operations. Hence, the use of AIS is increasingly considered a strategic trend and this is the goal of many organizations. Through empirical analysis to evaluate the factors affecting the use of AIS, the evaluation results have revealed that social influence is a factor that strongly affects the behavior of using AIS (Rabiu et al., 2025). Through the model that integrates other models with UAUT2 to explain the accountants' behavioral intention of using AIS, Congcong (2025) revealed that social influence significantly influenced the behavioral intention of auditors using AIS. Dewi and Juliarsa (2025) argued that technological developments directly affect the performance of organizations through AIS adoption. Hence, research on AIS adoption has generated empirical evidence for the UTAUT model, which demonstrates the social influence factor contributing to the acceptance and use of AIS. Through the previous research results, hypothesis H3 is presented as follows:

H3: Social influence positively affects and plays a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

Facilitation Conditions (FC) are factors that help or support the creation of technical and organizational infrastructure so that users have adequate resources to use a new technology system (Venkatesh et al., 2012). In their empirical study and analysis of the influences on the use of AIS by accountants, Alamin et al. (2015) stated that facilitating conditions influence the behavioral intention of accountants to use AIS. In an empirical analysis to examine the factors affecting the intention to use the AIS of accountants, Ababio (2021) revealed the impact of facilitating conditions on the behavioral intention to use the AIS of accountants. Aviyanti et al. (2021) assessed that platforms can be used to examine AIS usage behavior. Therefore, the empirical research results on the impact of facilitating conditions confirmed that this factor strongly influences actual AIS usage behavior. Technological change and development directly affect the performance of organizations. The adoption of AIS has become an organizational strategy. Through the study of AIS adoption, empirical evidence has been generated for the UTAUT model, adding valuable evidence on the factors of facilitating conditions contributing to the acceptance and use of AIS (Dewi and Juliarsa, 2025). Based on the opinion of Rabiu et al. (2025), the use of AIS is increasingly considered a strategic trend and this is the goal of many organizations. When analyzing the use of AIS in practice, it shows that organizations are more likely to use AIS because this system has the necessary support in accounting and financial operations. Through empirical analysis to evaluate the factors affecting the use of AIS, the evaluation results have demonstrated that facilitating conditions are one of the factors that strongly affect the behavior of using AIS. Hypothesis H4a and H4b are presented based on previous research results as follows:

H4a: Facilitating conditions positively affect and play a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

H4b: Facilitating conditions positively affect and play a decisive role in the accountants’ behavior of using AIS in Vietnam.

Figure 1. The Proposed model

Source: Venkatesh et al. (2012) and author's supplement

Hedonic motivation (HM) means the excitement, joy and enjoyment of users through the perceived usefulness of using the technology platforms (Venkatesh et al., 2012). Hedonic motivation is a strong indicator in considering technology acceptance. The results of empirical evidence have found a relationship between hedonic motivation and behavioral intention to use AIS in practice (Zaini et al., 2020). When studying the application of technology in the field of financial accounting, the results of the study by Handoko and Lantu (2021) showed that the use of information systems and data meets the activities in the field of financial accounting, thereby also showing that hedonic motivation affects the behavioral intention of using technology platforms, as well as data sources and information systems. Accounting activities that are enhanced with technology and AIS will create more trust in the usefulness and ease of use. Accounting and financial activities that use technology in the process will contribute to improving efficiency, as well as increasing behavioral intention to apply more technology in accounting and financial activities (Rikhardsson et al., 2022). By recognizing the significant changes in digital transformation in many industrial activities forcing organizations and individuals such as accountants to adapt to the development of technology, Saadah et al. (2022) evaluated the impact of variables positively affects and plays a decisive role in the accountants’ behavioral intention of using AIS. As a result, hedonic motivation has a positive and significant impact on the AIS usage behavior of users such as accountants. From the previous analysis results, the hypothesis H5 of the study is described as follows:

H5: Hedonic motivation positively affects and plays a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

Price value (PV represents the exchange and comparison between the benefits received, and the costs incurred when using a technology system by the user (Venkatesh et al., 2012). Saadah et al. (2022) identify significant changes in the technological revolution that have changed the technology usage behavior of many organizations and individuals, as well as assess the impact of variables that positively affect and play a decisive role in the behavioral intention to use AIS of accountants. The results show that price value has a positive and significant impact on the AIS usage behavior of users such as accountants. Chowdhury et al. (2023) studied the strategy of implementing new technology in accounting to explore the factors that accountants have behavioral intention to use technology in accounting. The findings indicate that high usefulness, reasonable cost perceived as price value will increase the use of technology in accounting to reduce workload and improve the quality of accounting data. Metrinesya et al. (2024) suggest that perceptions of the benefits and costs of using technology platforms influence accountants’ behavioral intentions and decisions to adopt these technology platforms. Al-Okaily (2025) stated that many organizations have used digital technologies to digitize accounting and financial functions. The study on the role of AIS usage has been empirically tested, and the results have emphasized the perception of benefits of AIS usage. This also reflects the comparison between the benefits received and the costs incurred when using AIS to digitize accounting and financial functions. Therefore, this study proposes hypothesis H6 along with the trend of previous research results as follows:

H6: Price value positively affects and plays a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

Habits (HA) refer to the behavior and extent to which users develop consistent preferences through the use of technology, and it is a factor that influences both the behavioral intentions and behavior of using technology platforms (Venkatesh et al., 2012). Saadah et al. (2022) commented on the waves of technological advancement that are impacting the operations of organizations. This rapid development forces organizations to turn to using advanced technology as a mandatory option to maintain adaptability and development. Through assessing the impact of variables, it positively affects the accountants’ behavioral intention of using AIS. The results show that habits positively affect and significantly determine the AIS usage behavior of users such as accountants. Zaini et al. (2020) investigated the factors influencing the use of AIS. The research results based on the UTAUT2 model found that all factors, including habits, were identified as positively and significantly promoting the behavioral intention to use AIS. Reza et al. (2023) argued that there are factors that influence the process of using AIS. Through research results, it has been proven that habits directly influence the behavioral variable of using AIS. Metrinesya et al. (2024) argue that behavior can influence future activities and habits can be used as a reflection in determining future behavior. The use of technology platforms has become routine and this is also true for accountants where financial reporting is tied to the use of technology platforms such as AIS. Hypotheses H7a and H7b are described based on the results of previous studies as follows:

H7a: Habits positively affect and play a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

H7b: Habits positively affect and play a decisive role in the accountants’ behavior of using AIS in Vietnam.

 

Self-efficacy (SE) refers to the confidence in one's ability to successfully solve specific problems or perform a task to accomplish a goal in specific domains (Zulkosky, 2009). It is an important concept related to social cognition, which is the idea that people feel, think, motivate themselves and act as well as exercise control over activities to complete tasks or succeed in activities or goals of a specific domain. In conducting an empirical study and analysis of the influences on the use of AIS by accountants, Alamin et al. (2015) stated that this study is very useful for accounting professional organizations and stakeholders in promoting the adoption of AIS and stated that self-efficacy factor influences the behavioral intention of accountants to use AIS. In the trend of adopting new technologies, self-efficacy is an important factor that helps users gain confidence in learning and using new technologies such as AIS. AIS users with high self-efficacy are more likely to adopt AIS, because they are confident in their ability to understand AIS-related knowledge and use them. Pourghanbari et al. (2022a) tested the factors affecting accountants’ behavioral intention of using AIS and found that self-efficacy affects the accountants’ behavioral intention of using AIS. The research results of Zainodin et al. (2024) assessed that self-efficacy is the strongest factor influencing AIS usage behavior. This shows that confidence in using AIS is important for decisions to use this technology. Hypothesis H8a and H8b are presented based on previous research results as follows:

H8a: Self-efficacy positively affects and plays a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

H8b: Self-efficacy positively affects and plays a decisive role in the accountants’ behavior of using AIS in Vietnam.

Seamless connectivity (SC) refers to digital devices and systems that maintain a stable and uninterrupted network connection when switching between different types of networks, as well as ensuring that users experience service continuity without significant interruptions or performance degradation, regardless of changes in the network environment (Bahr et al., 2024). Wiafe et al. (2020) identified convenience as a key factor influencing AIS usage behavior. This is because convenience plays an important role in shaping interactions during AIS usage. Ease of access to AIS, streamlined processes, and seamless connectivity are welcomed to seamlessly integrate AIS into workflows. Razak (2024) assessed the growing demand for new applications and software, especially new applications. Through the UTAUT model, it shows the relationship between the factors in the model with the behavioral intention in using the technology platforms. Therefore, through this assessment, it is shown that to meet the growing demand for digitalization, technology platforms with seamless integration features that enable application development and streamline database interactions for various types of applications, including AIS, work seamlessly together to contribute to promoting behavioral intention and behavior in using these platforms. Rahman et al. (2025) argue that previous empirical evidence confirms that any successful use of information systems depends on ensuring reliable internet connectivity, seamless connectivity to facilitate smooth transactions. Therefore, seamless connectivity is a driving factor and determinant of successful AIS adoption. Through research, Sultana et al. (2025) stated that AIS adoption is crucial due to the rapidly changing business environment. Successful AIS implementation depends on accounting knowledge, technical systems, mobile accessibility, seamless connectivity, etc. Therefore, seamless connectivity is considered a driver of the behavioral intention and behavior of using AIS. The previous research results are the basis for proposing two theories H9a and H9b as follows:

H8a: Seamless connectivity positively affects and plays a decisive role in the accountants’ behavioral intention of using AIS in Vietnam.

H8b: Seamless connectivity positively affects and plays a decisive role in the accountants’ behavior of using AIS in Vietnam.

Behavioral intention (BI refers to the desire to use technology or the attitude of a user of technology in a certain way to achieve the goal in the process of using technology (Venkatesh et al., 2012). The trend of digitalization is increasing rapidly and bringing many applications to the accounting profession, helping to improve the accounting and financial management process better and better. Research on the application of AIS by accountants shows that it is influenced by the determinant of behavioral intention to use AIS, which is self-confidence. The importance of this factor helps managers have supporting experiences to influence the increase in behavioral intention to use AIS by accountants (Alamin et al., 2020).  From the support of the literature on the relationship of factors of researchers based on the UTAUT model, the results of empirical evidence have produced the results of behavioral intention and behavior to use AIS in practice have a positive relationship (Zaini et al., 2020). Reza et al. (2023) argued that there are factors that influence the process of using AIS. The research results have demonstrated that behavioral intentions directly influence the behavioral variables of AIS use. The study by Congcong (2025) on a model that integrates other models with UAUT2, explains the intention and behavior of accountants using AIS, and demonstrates that behavioral intention significantly affects auditors’ AIS usage behavior. Alquhaif and Al-Mamary (2025) appreciate the role of AIS in improving organizational efficiency and decision-making processes and argue that behavioral intention positively affects behavior of using AIS. The study affirms the importance of AIS usage, providing valuable insights for both theory and management practice. The development of technology and the benefits of applying technology in AIS are the driving forces of studies related to behavioral intention to use AIS. Through research on accountants' behavioral intention to use AIS, it has been found that this factor positively affects and determines the behavior of using AIS. This result contributes to enhancing the implementation strategies in many organizations for using AIS (Sofyani et al., 2025). Hypothesis H10 is expressed according to the trend of previous studies as follows:

H10: Behavioral Intention positively affects and plays a decisive role in the accountants’ behavior of using AIS in Vietnam.

Research Methodology

Research design

Based on the conceptual framework and previous theoretical and empirical foundations through the use of library methods with textbooks, journals and websites to design the theoretical framework. The research subjects include accountants working in organizations, companies, who are AIS users and participated in the survey questionnaire of this study. The quantitative methods apply to test the hypotheses, examine the influence of factors on the accountants' behavioral intentions and behavior of using AIS.

Sample and data

The questionnaire collects information on the influence of factors on the accountants' behavioral intentions and behavior of using AIS. The latent variables are measured by items taken from previous literature and measured on a five-point Likert scale, from 1 to 5, representing completely nothing to completely full.

The sample size included 632 accountants, with the sampling technique used being non-probability sampling, and the subsection being convenience sampling. The survey data were focused on data analysis using SPSS 25.0 and AMOS 24.0 software.

This study applied structural equation modeling (SEM) to analyze the data and investigate the influence of factors on the accountants' behavioral intentions and behavior of using AIS through Cronbach's Alpha analysis to show that the internal consistency is satisfactory. Exploratory factor analysis (EFA) analyzes and forms more meaningful factors. Confirmatory factor analysis (CFA) assesses the model's suitability to the research data and SEM analysis of the model.

Estimation of regression coefficients is done based on fit indices. Hair et al. (2019) showed cut-off values for fit indices such as 0.08 or less for RMSEA (Root Mean Square Error of Approximation); 0.01 or more for P-value of Close Fit (PCLOSE); 5 or less for CMIN/df (Chi-square divided by degrees of freedom) and less than 2 are considered good; 0.9 is good and 0.95 or more is very good for indices such as CFI (Comparative Fit Index), GFI (Good Food Fit), TLI (Tucker-Lewis Index).

Research Results

Cronbach's alpha reliability analysis

The corrected item-total correlation coefficient meets the requirements according to the study of Hulin et al. (2001) when this coefficient is greater than 0.3. The results of measuring the internal consistency and reliability of a scale are all greater than 0.60 through the results of the Cronbach's Alpha coefficient, Hulin et al. (2001) and (Cresswell, 2010), this result has good stability and consistency of the observed variables as can be seen in Table 1.

Table 1. Independent, moderating and dependent variables in the research

No.	Code	Observed variables	Corrected Item-Total Correlation
	PE	Cronbach's alpha = 0.858
1	PE1	Using AIS makes it easy for accountants to prepare financial reports anywhere.	0.731
2	PE2	Using AIS makes it easier for accountants to understand and do their accounting work.	0.678
3	PE3	Using AIS meets the expectations of accountants in preparing financial reports.	0.603
4	PE4	Using AIS allows accountants to do their math work faster.	0.555
5	PE5	Using AIS is suitable for accountants' math work.	0.610
6	PE6	Using AIS makes accountants more comfortable when doing their math work.	0.746
	EE	Cronbach's alpha = 0.820
7	EE1	Using AIS helps accountants to enhance many accounting tasks.	0.549
8	EE2	Using AIS helps accountants to prepare more financial reports.	0.530
9	EE3	Using AIS helps accountants to perform accounting work more professionally.	0.667
10	EE4	Using AIS DA helps accountants to understand information clearly to perform accounting work.	0.595
11	EE5	Using AIS DA helps accountants to have enough information to perform accounting work.	0.731
	SI	Cronbach's alpha = 0.824
12	SI1	Accountants who use AIS in their accounting work are influenced by many influential people.	0.673
13	SI2	Many influential people recommend accountants to use AIS in their accounting work.	0.559
14	SI3	People familiar with accountants recommend them to use AIS in their accounting work.	0.619
15	SI4	Accountants who use AIS in their accounting work are influenced by their accounting colleagues.	0.558
16	SI5	Accountants receive support for using AIS in their accounting work from managers.	0.593
17	SI6	Many close friends support accountants to use AIS in their accounting work.	0.553
	FC	Cronbach's alpha = 0.835
18	FC1	Accountants are allowed to control accounting work when using AIS.	0.662
19	FC2	Accountants have sufficient professional capacity to apply AIS to perform accounting work.	0.603
20	FC3	Accountants are guaranteed the conditions for performing accounting work when using AIS.	0.618
23	FC4	Accountants have all the necessary resources to use AIS in the process of performing accounting work.	0.566
21	FC5	Accountants are guaranteed the safety and security of information and data when using AIS.	0.569
22	FC6	Accountants have smart devices and support in the process of using AIS to perform accounting work.	0.655
	HM	Cronbach's alpha = 0.767
23	HM1	Accountants feel comfortable doing math work through AIS.	0.558
24	HM2	Accountants feel lucky doing math work through AIS.	0.638
25	HM3	Accountants feel excited doing math work through AIS.	0.643
26	HM4	Accountants feel satisfied doing math work through AIS.	0.577
27	HM5	Accountants feel excited doing math work through AIS.	0.491
	PV	Cronbach's alpha = 0.680
28	PV1	Using AIS helps accountants save time on accounting work.	0.565
29	PV2	Using AIS helps accountants save significant costs in accounting work.	0.393
30	PV3	Using AIS helps accountants pay for internet services that are suitable for using AIS.	0.510
31	PV4	Using AIS helps accountants avoid having to pay for system checks in accounting work.	0.503
32	PV5	Using AIS helps accountants not to incur any costs in accounting work.	0.523
	HA	Cronbach's alpha = 0.807
33	HA1	Accountants can perform accounting work through AIS.	0.664
34	HA2	Accountants can adapt to preparing financial statements through AIS.	0.496
35	HA3	Accountants are familiar with accounting work through AIS.	0.645
36	HA4	Accountants are also guided on how to use AIS in accounting work.	0.552
37	HA5	When there is no simulation, accountants can still use AIS in accounting work.	0.608
	SE	Cronbach's alpha = 0.677
38	SE1	Accountants have strong confidence in performing accounting work through AIS.	0.434
39	SE2	Accountants believe that AIS is a reliable system when performing accounting work.	0.437
40	SE3	Accountants are self-efficient when performing accounting work through AIS.	0.389
41	SE4	Accountants feel secure when using AIS to perform accounting work.	0.465
42	SE5	Accountants are able to meet the requirements when using AIS to perform accounting work.	0.489
43	SE6	Accountants can collect, analyze and process data when performing accounting work through AIS.	0.464
	SC	Cronbach's alpha = 0.695
44	SC1	There is an efficient internet connection and fast data transmission when accountants perform accounting work through AIS.	0.494
45	SC2	There is a continuous, uninterrupted internet connection when accountants perform accounting work through AIS.	0.510
46	SC3	There is data and application synchronization when accountants perform accounting work through AIS.	0.528
	BI	Cronbach's alpha = 0.753
50	BI1	Accountants will continue to use AIS for accounting work.	0.578
51	BI2	When performing accounting work, auditors apply AIS.	0.572
52	BI3	The accountant will introduce other accountants to apply for AIS to perform accounting work.	0.596
	UB	Cronbach's alpha = 0.709
53	UB1	Accountants will be supported by technology platform service providers to overcome difficulties when using AIS.	0.495
54	UB2	Accountants may not need the help of technology platform service providers when performing accounting work through AIS.	0.541
55	UB3	Although accountants have never used AIS, they can use AIS to perform accounting work.	0.543

Source: Venkatesh et al. (2012) and the authors’ suggestions

 

Exploratory factor analysis

The results of EFA evaluation indicators for independent variables such as KMO (Kaiser-Meyer-Olkin) is 0.781, greater than 0.5, Sig = 0.000 less than 0.05, showing the suitability of EFA with the current data. Performing varimax rotation with absolute value smaller than 0.3, showing 46 observed variables with 11 groups and the lowest Eigenvalue criterion achieved are 1.119 greater than 1, as presented in Table 2.

Table 2. Exploratory factor analysis for independent variables

 

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	Cumulative %
1	4.958	10.550	10.550	4.958	10.550	10.550	3.570	7.596
2	3.601	7.662	18.211	3.601	7.662	18.211	3.384	14.797
3	3.039	6.465	24.676	3.039	6.465	24.676	3.255	21.723
4	2.765	5.883	30.559	2.765	5.883	30.559	2.985	28.074
5	2.617	5.567	36.126	2.617	5.567	36.126	2.874	34.189
6	2.406	5.118	41.244	2.406	5.118	41.244	2.652	39.831
7	2.326	4.949	46.193	2.326	4.949	46.193	2.298	44.720
8	2.203	4.688	50.881	2.203	4.688	50.881	2.157	49.309
9	1.739	3.701	54.582	1.739	3.701	54.582	1.908	53.368
10	1.336	2.843	57.425	1.336	2.843	57.425	1.516	56.593
11	1.119	2.381	59.806	1.119	2.381	59.806	1.510	59.806
12	.910	1.935	61.741
Extraction Method: Principal Component Analysis.

Source: The authors’ calculation from SPSS 25.0

The results of varimax rotation showed that the factor loadings were all greater than 0.5 after rotating the independent variables. Two new factors were generated from the variables PV1 and PV2 in the PV factor and from the variables SE5 and SE6 in the SE factor as shown in Table 3. Among them, PV1 and PV2 have similar characteristics with cost savings and are named Cost Savings (CS), as well as the hypothesis that CS has a positive impact on the behavioral intention and AIS usage behavior of accountants. SE5 and SE6 have similar characteristics with professional competence and are named Professional Competence (PC), as well as the hypothesis that PC has a positive impact on the behavioral intention and AIS usage behavior of accountants.

Table 3. Rotated component matrix for independent variables.

Variables			Component
	1	2	3	4	5	6	7	8	9	10	11
PE6	.823
PE1	.815
PE3	.747
PE2	.740
PE5	.726
PE4	.643
FC1		.777
FC6		.775
FC3		.732
FC2		.723
FC5		.677
FC4		.667
SI1			.802
SI3			.760
SI5			.724
SI2			.695
SI4			.694
SI6			.686
EE5				.850
EE3				.805
EE4				.749
EE1				.710
EE2				.678
HA1					.812
HA3					.794
HA5					.753
HA4					.712
HA2					.655
HM3						.805
HM2						.802
HM1						.734
HM5						.663
HM4						.559
SE2							.801
SE4							.738
SE3							.724
SE1							.651
PV5								.867
PV4								.806
PV3								.786
SC3									.804
SC2									.769
SC1									.765
PV1										.854
PV2										.818
SE6											.822
SE5											.797
Extraction Method: Principal Component Analysis. Rotation Method: Varimax with Kaiser Normalization.

Source: The authors’ calculation from SPSS 25.0

The results of EFA evaluation indicators for dependent variables such as KMO (Kaiser-Meyer-Olkin) is 0.689, greater than 0.5, Sig = 0.000 less than 0.05, showing the suitability of EFA with the current data. Performing varimax rotation with absolute value smaller than 0.3, showing 6 observed variables with 2 groups and the lowest Eigenvalue criterion achieved are 1.434 greater than 1, as presented in Table 4.

Table 4. Exploratory factor analysis for dependent variables

Component	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
	Total	% of Variance	Cumulative %	Total	% of Variance	Cumulative %	Total	Cumulative %
1	2.476	41.273	41.273	2.476	41.273	41.273	2.013	33.550
2	1.434	23.905	65.177	1.434	23.905	65.177	1.898	65.177
3	.675	11.254	76.431
Extraction Method: Principal Component Analysis.

Source: The authors’ calculation from SPSS 25.0

The results of varimax rotation showed that the factor loadings were all greater than 0.5 after rotating the independent variables. This result shows 2 factors as can be seen in Table 5.

Table 5. Rotated component matrix for dependent variables

Variable	Component
	1		2
BI3	.820
BI1	.809
BI2	.804
UB3		.815
UB2		.790
UB1		.757
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.

Source: The authors’ calculation from SPSS 25.0

 

Confirmatory factor analysis

The results of CFA evaluation indicators for the model fit of the model such as KMO is 0.769, greater than 0.5, Sig = 0.000 less than 0.05, showing the suitability of CFA with the current data.

Figure 2. Confirmatory factor analysis

Source: The authors’ calculation from AMOS 24.0

Performing varimax rotation with absolute value smaller than 0.3 with number of observations is 632 and links e3 and e4, e4 and e5, e9 and e10, e9 and e12, e13 and e14, e14 and e16, e15 and e16, e19 and e21, e19 and e23, e20 and e221, e21 and e23, e26 and e28, e32 and e33, e35 and e37, e361 and e37 determined the model that fits the market data. Because, the standardized and unstandardized coefficients are both greater than 0.5, Chi-square =1642. 108, with 1235 degrees of freedom (df); Chi-square/df = 1.330 < 3 with p-value = 0.000; CFI = 0.960; TLI = 0.955; GFI = 0.914; RMSEA = 0.023< 0.06; PCLOSE = 1.000 > 0.05 and the total variance is greater than 0.5, as presented in Figure 2.

Structural equation modeling

SEM results through factor analysis and regression analysis with evaluation indicators such as Chi-square = 1611.444; df = 241; p = 0.000; Chi-square/df = 1.339; CFI = 0.958; TLI = 0.954; GFI = 0.913; RMSEA = 0.023; PCLOSE = 1.000. This shows that the SEM results fit the market data, as presented in Figure 3.

Figure 3. Structural equation modeling

Source: The authors’ calculation from AMOS 24.0

With a significance level of 5%, the results of SEM analysis determined that EE affected BI, with p-value = 0.006 < 0.05; PV has an effect on BI, p-value = 0.046 < 0.05; CS has an effect on BI, p-value = 0.000 < 0.05. Besides, BI has an effect on UB, p-value = 0.000 < 0.05, HA has an effect on UB, p-value = 0.004 < 0.05, SC has an effect on UB, p-value = 0.012 < 0.05. The remaining variables are not significant, because p-value > 0.05 as can be seen in Table 6.

Table 6. Regression Weights and Standardized Regression Weights

			Unstandardized Coefficients				Standardized Coefficients
			Estimate	S.E.	C.R.	P	Estimate
BI	<---	PE	,041	,033	1,236	,216	,067
BI	<---	EE	,121	,044	2,722	,006	,134
BI	<---	SI	,030	,046	,664	,507	,033
BI	<---	FC	,001	,031	,032	,975	,002
BI	<---	HM	-,083	,057	-1,446	,148	-,074
BI	<---	PV	,103	,052	1,996	,046	,109
BI	<---	CS	,338	,067	5,016	***	,314
BI	<---	HA	-,024	,032	-,740	,460	-,037
BI	<---	SE	-,115	,081	-1,414	,157	-,092
BI	<---	IA	,007	,041	,168	,866	,012
BI	<---	SC	-,021	,063	-,329	,742	-,019
UB	<---	BI	,316	,052	6,053	***	,348
UB	<---	FC	-,041	,026	-1,585	,113	-,079
UB	<---	HA	,086	,030	2,877	,004	,150
UB	<---	SE	,078	,074	1,056	,291	,069
UB	<---	IA	-,051	,037	-1,381	,167	-,097
UB	<---	SC	,145	,058	2,508	,012	,143

Source: Authors‘calculation

With a significance level of 5%, this study performed the Bootstrap method with the number of replicate samples N=800 to increase the reliability and accuracy of SEM analysis results and the results achieved reliability with C.R < 1.96 as can be seen in Table 7.

Table 7. Bootstrap method on SEM

Parameter			SE	SE-SE	Mean	Bias	SE-Bias	C.R = Bias / SE-Bias
BI	<---	PE	,034	,001	,042	,001	,001	1.0
BI	<---	EE	,044	,001	,123	,002	,002	1.0
BI	<---	SI	,052	,001	,030	,000	,002	0
BI	<---	FC	,031	,001	,001	,000	,001	0
BI	<---	HM	,060	,002	-,081	,002	,002	1,0
BI	<---	PV	,054	,002	,102	-,001	,002	-0.5
BI	<---	CS	,075	,002	,344	,003	,003	1,0
BI	<---	HA	,035	,001	-,024	,000	,001	0.5
BI	<---	SE	,087	,003	-,114	,001	,004	-0.2
BI	<---	IA	,043	,001	,006	-,001	,002	-0.5
BI	<---	SC	,066	,002	-,018	,003	,003	1.0
UB	<---	BI	,056	,002	,318	,002	,002	1.0
UB	<---	FC	,028	,001	-,040	,000	,001	0
UB	<---	HA	,032	,001	,085	-,001	,001	-1.0
UB	<---	SE	,082	,002	,079	,001	,003	0.3
UB	<---	IA	,042	,001	-,052	-,001	,002	-0.5
UB	<---	SC	,062	,002	,144	-,001	,003	-0.3

Source: The authors’ calculation from AMOS 24.0

Discussions Results

Factors Accountants’ Behavioral Intention

The analysis results confirmed the acceptance of the hypothesis EE affected BI, not only providing valuable insights into the positive influence of EE on BI but also showing that it is consistent with the expected hypothesis according to the research results of Alamin et al. (2015), Alamin et al., (2020), Pourghanbari et al. (2022b), Amalia and Pratolo (2024), Alquhaif and Al-Mamary (2025), Dewi and Juliarsa (2025). This result indicates that when accountants apply AIS, it helps to improve the accounting and financial management process better, improve the decision-making process and help managers make appropriate decisions and promote the influence on increasing the behavioral intention of accountants to use AIS.

The analysis results confirmed the acceptance of the hypothesis that PV has an effect on BI. This result contributes to promoting the behavioral intention to use AIS of accountants, according to the research results of Saadah et al. (2022), Chowdhury et al. (2023), Metrinesya et al. (2024), Al-Okaily (2025). The results show that high benefits and reasonable costs will increase the use of AIS in accounting to reduce workload and improve the quality of accounting data. The benefits of using AIS to digitize accounting and financial functions in line with costs will promote the behavioral intention and decision to adopt AIS of accountants.

The analysis results confirmed the acceptance of the hypothesis that CS has an effect on BI. This result contributes to promoting the behavioral intention to use the AIS of accountants and shows that cost reduction, reasonable cost will increase the use of AIS in accounting to reduce workload and improve the quality of accounting data. The benefits from using AIS to digitize accounting and financial functions in accordance with low cost, cost reduction and cost will promote the behavioral intention and decision to adopt AIS of accountants.

Factors Affecting Accountants’ Use Behavioral

The test results confirmed the acceptance of the hypothesis that BI has an effect on UB. These results promote the AIS usage behavior of accountants and show that the trend of digitalization is increasing rapidly and bringing many applications to the accounting profession, helping to improve the accounting and financial management process better and better. The use of AIS by accountants found that it is influenced by the determination of behavioral intention to use AIS. The importance of this factor helps managers have supporting experiences to influence the increase in behavioral intention of using AIS by accountants according to research by (Alamin et al., 2020), (Zaini et al., 2020), Reza et al. (2023), Congcong (2025), Alquhaif and Al-Mamary (2025) (Sofyani et al., 2025). The researchers appreciate the role of AIS in improving organizational efficiency and decision-making processes and argue that behavioral intention positively affects behavior of using AIS. The studies affirm the importance of AIS usage, providing valuable insights for both theory and management practice, contributing to enhancing the implementation strategies in many organizations for using AIS.

The empirical results have argued that the HA hypothesis impacts on UB. This shows that rapid development forces organizations to turn to using advanced technology to maintain adaptability and development. By assessing the impact of variables, it positively affects the accountants’ behavioral intention of using AIS. The results show that habits positively affect and significantly determine the AIS usage behavior of accountants according to studies by Saadah et al. (2022), Reza et al. (2023), Metrinesya et al. (2024). Therefore, the use of technology platforms has become common and this is also true for accountants, where financial reporting is closely linked to the use of technology platforms such as AIS.

The empirical analysis results confirm the hypothesis that SC impacts UB. This is because convenience plays an important role in shaping interactions during AIS usage. Ease of access to AIS, streamlined processes, and seamless connectivity are welcomed for seamless integration of AIS into the workflow. The results show that seamless integration positively and significantly influences accountants’ AIS usage behavior, according to studies by Wiafe et al. (2020), Razak (2024), Rahman et al. (2025), Sultana et al. (2025). Therefore, seamless connectivity is a determinant of AIS adoption, which is a driving force behind successful AIS usage behavior.

Conclusions and Recommendations 

The empirical analysis focuses on studying the accountants’ behavioral intention and behavior of using AIS in Vietnam. This topic is in line with the trend of technology application in the field of accounting and finance, and our findings contribute to the existing body of knowledge in a meaningful way with the following propositions.

Effort expectancy is a factor that affects the behavioral intention of accountants to use AIS. Therefore, managers should invest in AIS development, develop technical infrastructure, transfer knowledge on AIS usage smoothly, and improve digital capacity so that accountants can use AIS more and more. Technology and software providers need to develop AIS so that it is easy to understand and easy to apply, so that accountants can apply it proficiently in accounting work. Accounting professional organizations need to coordinate with AIS providers to create AIS applications that are both advanced and convenient in the process of using AIS for accountants.

Price value influences behavioral intentions, or in other words, organizations benefit significantly from a well-functioning AIS. Therefore, managers need to invest in AIS that is characterized by high usability, performance, and reliability, along with appropriate investment and operating costs. Because technology adoption has become a trend in all fields, it is a challenge that organizations need to overcome to gain competitive advantage. Technology adoption has become a trend in all fields, and is a challenge that organizations need to overcome to gain competitive advantage. Organizations need successful AIS to help them achieve their strategic goals, so AIS technology, software and platform providers need to have appropriate AIS solutions and tools, ensuring that organizations benefit significantly from AIS while ensuring appropriate investment and operating costs.

Cost savings impact accountants’ behavioral intentions and perceived cost reduction and cost rationalization will increase accountants’ behavioral intentions. Therefore, managers need to attract accountants’ interest in using AIS, thereby reducing accounting workload and high-quality accounting reports. AIS technology, software and application providers need to start from the benefits of using AIS to digitize accounting and finance functions at low cost, reduce the cost of providing these technology products and services to increase accountants’ use of AIS.

Accountants’ behavioral intention is an important factor that positively influences accountants’ AIS usage behavior in Vietnam. To promote accountants’ AIS adoption, platform and software providers need to improve the usefulness of AIS, focus on advanced technology and develop AIS features that meet the requirements of accounting work. Organizations need to increase investment in adequate technical infrastructure to support smooth and seamless AIS usage, focus on strengthening synchronous technology systems, quickly processing audit data, bringing more convenience and efficiency to the increasing number of accountants using AIS in Vietnam.

The more frequently AIS will be applied, the accountant's habit will become an automatic habit and will be the driving force for the accountant's behavior to continue applying AIS in accounting work. Therefore, organizations and managers should strengthen the training and technical expertise of system operators, guide the skills of accountants in using AIS to help the accounting data processing process become proficient, form automatic habits, create accurate and reliable information. Managers need to have policies to encourage the increasing application of AIS in Vietnam and promote AIS application behavior. All accounting work is interacted with and processed through AIS to create timely, accurate and reliable reports. This automatic habit of accountants will create a higher adaptability to the increasingly advanced application of AIS, positively contribute to accounting work and effectively influence the increase of AIS usage behavior in accounting.

To increase the use of AIS by accountants, the need for support from managers means motivating accountants to continue and increase the use of AIS, meeting the needs of employees and work tasks, which means investing in new technologies and modern AIS, ensuring continuous use of AIS through seamless connectivity. Managers should apply appropriate methods according to the levels of readiness to implement new technology applications in accounting work. Therefore, these managers need to be aware of the importance of seamless connectivity when using AIS to create appropriate technology transitions to promote the intention of using AIS of accountants.

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