558 © 2025 Conscientia Beam. All Rights Reserved. The impact of AI-based accounting information systems on the performance of individuals and organizations in an emerging country Ha Viet Le1+ Manh Dung Tran2 1,2National Economics University, Vietnam. 1Email: halv.pd@neu.edu.vn 2Email: manhdung@ktpt.edu.vn (+ Corresponding author) ABSTRACT Article History Received: 3 January 2025 Revised: 25 March 2025 Accepted: 31 March 2025 Published: 17 April 2025 Keywords AI-based accounting information system Individual impact Information quality Service quality System quality. This study focuses on developing a model to investigate the impact of AI-based accounting information systems (AIS) in small and medium-sized enterprises (SMEs) in Vietnam. A quantitative research method was used to process data from 381 participants who utilized these systems, including managers (board of directors), accountants, chief accountants, and internal auditors. The list of organizations that have implemented this system was compiled from the websites of AI-based AIS software vendors. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to evaluate the model's fit and test the hypotheses. In addition, an independent samples t-test and one-way ANOVA were conducted to analyze the differences in users' perceptions of the impact of these systems among gender, position, and age groups of survey participants. The study's findings indicate that system quality, information quality and service quality directly influence individual user performance. The research also highlights the role of system quality and information quality in improving the quality of output information and enhancing organizational competitiveness. A key finding is that service quality only directly impacts individuals and does not affect organizational performance. This aligns with the current state of information systems implementation in Vietnam. The findings also illustrate that "providing online support services" is the most frequently implemented service by vendors to guide users in operating the software online. Contribution/Originality: The proposed model contributes to extending the theoretical literature on the impact of AI-based AIS in the context of SMEs in developing countries like Vietnam. In addition, the research provides crucial insights for managers in identifying the benefits and influence of various determinants to enhance individual and organizational performance. 1. INTRODUCTION An AI-based accounting information system (AI-based AIS) is an information system that applies artificial intelligence (AI) technologies to traditional accounting processes, enabling the automation of complex tasks, advanced data analysis, and intelligent decision-making. This system can learn from data, identify patterns, predict trends, and provide detailed information to support more accurate financial reporting while minimizing errors and fraud in accounting and auditing activities (Kindzeka, 2023; Marques et al., 2023). AI technology can detect accounting fraud and identify unusual signs in audits. AI-based AIS allows accountants to process large amounts of data, improve quality, and enhance information security within the organization (Adeyelu et al., 2024). Humanities and Social Sciences Letters 2025 Vol. 13, No. 2, pp. 558-573 ISSN(e): 2312-4318 ISSN(p): 2312-5659 DOI: 10.18488/73.v13i2.4180 © 2025 Conscientia Beam. All Rights Reserved. https://orcid.org/0009-0008-2085-4907 https://orcid.org/0000-0001-7883-4468 mailto:halv.pd@neu.edu.vn mailto:manhdung@ktpt.edu.vn https://www.doi.org/10.18488/73.v13i2.4180 Humanities and Social Sciences Letters, 2025, 13(2): 558-573 559 © 2025 Conscientia Beam. All Rights Reserved. Implementing AI-based AIS will strengthen competitiveness, automate financial management, support appropriate decision-making and promote successful organizational digital transformation (Iman, 2024; Kindzeka, 2023). Currently, there are two prominent models for researching information system deployment: the IS success model and the IS impact model. The IS Success model emphasized the indispensable role of service quality in successful system implementation (DeLone & McLean, 2002). On the other hand, the IS impact model primarily analyzes system quality's effects on users and output information quality without extensive consideration of service quality (Borgi & Alessa, 2023; Gable et al., 2008). Previous quantitative studies have often focused on evaluating the system's influence on user satisfaction and intention to use with less attention to its impact on the operational efficiency of small and medium-sized enterprises (SMEs). Notably, there is a lack of research in developing countries where organizations receive limited post-implementation support from vendors (Bunget & Lungu, 2023; Kindzeka, 2023). Previous research lacks a comprehensive model that integrates these determinants to explain the system's impact on both individuals and organizations (Lutfi et al., 2022; Marques et al., 2023). Moreover, previous findings may not apply to other countries due to economic, cultural and technological differences (Alshardan et al., 2016). Therefore, in the context of AI being encouraged for adoption by governments in emerging nations, it is imperative to investigate the impact of AI-based AIS in SMEs (Sandaruwandi et al., 2024). This research is conducted to scrutinize a comprehensive model for evaluating the impact of AI-based AIS in SMEs. The study draws upon the models of DeLone and McLean (2002) and Gable et al. (2008) to address two research questions: (i) How does AI-based AIS impact individual and organizational performance? (ii) Are there differences in the perceived influence of AI-based AIS across gender, position and age groups? The research conducted in-depth interviews to tailor the survey questionnaire to the actual context of implementing this system in Vietnam. A quantitative research method is employed to gather data from organizations that have implemented the system, allowing for testing research hypotheses. The findings of this study will provide managers with a deeper understanding of the system's influencing determinants to enhance individual and organizational performance. 2. THEORETICAL FRAMEWORK AND LITERATURE REVIEW 2.1. Artificial Intelligence-Based Accounting Information System AI-based AIS is viewed as an integration of human resources, network systems, accounting business processes, and AI technologies integrated into accounting hardware and software systems to collect, process, store and report financial data, supporting decision-making and economic control within business organizations (Najafi et al., 2022). With the advancement of technology, AI-based AIS has evolved from simple AIS systems to complex platforms, integrating multiple advanced technologies. Incorporating AI into AIS has created a breakthrough in the accounting field. AI-based AIS leverages AI technologies such as robotic process automation to automate tasks, machine learning, natural language processing, analyze complex data and support enhanced decision-making. This system thoroughly addresses the limitations of traditional AIS in handling large volumes of data or performing complex analyses (Lee & Tajudeen, 2020). The integration of AI allows AI-based AIS to surpass the limitations of traditional AIS by providing real-time analytics and insights, supporting fraud detection, risk assessment, and financial forecasting (Hasan, 2021). The fundamental difference between the two systems lies in the AI-based AIS's ability to learn from data, adapt to new situations and perform tasks that require human intelligence. This enables AI-based AIS to provide more timely and accurate financial analyses and forecasts, supporting strategic decision-making and improving organizational performance AI-based AIS are being increasingly adopted by businesses due to their positive impacts on individual and organizational performance. Implementing AI-based AIS in Malaysian organizations helps automate repetitive accounting operations and supports financial analysis and cash flow Humanities and Social Sciences Letters, 2025, 13(2): 558-573 560 © 2025 Conscientia Beam. All Rights Reserved. forecasting (Lee & Tajudeen, 2020). However, the adoption of AI-based AIS faces challenges related to data security and privacy (Borgi & Alessa, 2023). In addition, as AI automates routine tasks, accounting professionals need to cultivate novel competencies in data analysis, critical thinking and problem-solving (Stancu & Duţescu, 2021). There is limited research analyzing the impact of AI-based AIS adoption on individual performance and organizational effectiveness. In practice, some employees may resist change and hesitate to adopt new technologies due to a lack of understanding or fear of job loss which can hinder the successful deployment of AI-based AIS (Lee & Tajudeen, 2020). Furthermore, implementing AI-based AIS may require substantial investments in hardware, software, and training which can pose challenges for specific organizations. Organizations must assess and forecast the potential impacts and benefits of the system before proceeding with its adoption (Odonkor et al., 2024). In a nutshell, AI's utilization in AIS has sparked a technological revolution in the realm of accounting and auditing. However, existing research seems to focus heavily on the technical capabilities of AI-based AIS with less attention to their potential impacts on individual users and implementing organizations. 2.2. Benefits of AI-Based Accounting Information System for Individual and Organizational Performance AI-based AIS has a multi-faceted influence on both individual and organizational performance in the present and future (Gable et al., 2008). Integrating AI into AIS is considered a revolution in managing and utilizing financial accounting data for personal users. AI-based AIS increases user productivity by automating repetitive manual accounting tasks, reducing processing time and data entry (Kindzeka, 2023). The system digitizes invoices and receipts, automates accounting processes related to accounts payable and receivable, manages vendor payments and settlements, manages risk, and monitors user activity (Kindzeka, 2023; Lee & Tajudeen, 2020). Additionally, the system supports users in decision-making by automatically compiling accurate data from various financial reports (Borgi & Alessa, 2023). For firms, implementing AI-based AIS enhances the caliber of financial data information, transforms business data into valuable insights, and increases transparency and reliability in accounting practices (Li & Zheng, 2018; Rasit & Ibrahim, 2017). Studies by Alawaqleh (2021) and Yoon (2020) emphasize the importance of AI-based AIS in detecting anomalies in accounting activities and internal controls. The system can process large amounts of data to assess risks and predict potentially fraudulent activities, improving the overall security of accounting information and preventing financial fraud by assigning specific access rights to each user (Hamza et al., 2024; Kindzeka, 2023). Implementing the system contributes to increased profits, improved business processes, enhanced competitive advantage, and reduced operating costs (Davidson et al., 2020; Li & Zheng, 2018; Petter et al., 2008). AI-based AIS is vital in promoting firm growth in developing countries (Alshardan et al., 2016). 2.3. Theoretical Models of AI-Based Accounting Information System Impact As this study investigates the effects of an AI-based AIS, we employ the information system success model by DeLone and McLean (2002) and the information system impact model by Gable et al. (2008) to frame the study. 2.3.1. The Information System Success Model The IS success model is a framework for evaluating the success of an information system, emphasizing the relationships between different aspects of the system and the central role of users in determining the benefits derived from system use. The IS success model, proposed by DeLone and McLean (1992) is based on a comprehensive evaluation of research articles from 1981 to 1987 (DeLone & McLean, 1992). The DeLone and McLean IS success model has become the standard for measuring and evaluating the success of Information Systems (IS). The original model comprises six key dimensions: system quality, information quality, use, user satisfaction, individual impact, and organizational impact. In 2003, the model was refined to include aspects such as Humanities and Social Sciences Letters, 2025, 13(2): 558-573 561 © 2025 Conscientia Beam. All Rights Reserved. the quality of services provided and the overall net benefits derived from such systems while maintaining the multidimensional and interdependent nature of IS success (Alshardan et al., 2016; DeLone & McLean, 2002). However, some studies have pointed out limitations in the IS success model, including measurement deficiencies, lack of theoretical grounding, a narrow emphasis on fiscal performance metrics, shortcomings in survey tools, and inappropriate data collection methods (Davidson et al., 2020; Gable et al., 2008). Furthermore, there are concerns about the validity of relationships within the model due to a lack of comprehensive explanations of underlying theories and hypotheses leading to inconsistent findings regarding the causal relationship between determinants. Studies by Alshardan et al. (2016) and Lutfi et al. (2022) reveal inconsistent findings in previous research on the relationship between information quality and AIS implementation. DeLone and McLean (2002) themselves acknowledged the challenges in defining the measurement variable for the "benefits" factor and the extent of its impact when considering net benefits as a success variable. 2.3.2. Information System Impact Model The IS impact model was developed based on the foundation of the IS success model by DeLone and McLean (1992) and the empirical studies of Alshardan et al. (2016). The IS impact model encompasses four primary dimensions: individual impact, organizational impact, system quality, and information quality (Gable et al., 2008). The model is divided into the following two parts: the impact part assesses the benefits accumulated thus far while the quality part evaluates the potential future benefits. However, Gable et al. (2008) also acknowledged the lack of consensus on appropriate measures to assess IS success in the multidimensional IS impact model. Davidson et al. (2020) emphasized that their study was conducted on a single application necessitating validation across multiple applications and software implementations in various organizations of different sizes to assess the model's broader applicability. Therefore, further research and refinement of the model are necessary to address these limitations and expand its applicability to diverse contexts. In this study, we combine the IS success model and the IS impact model to comprehensively assess the impact of system quality, information quality, service quality, and individual effects on firms to promote successful AI-based AIS deployment within organizations. 2.4. Determinants Influencing Organizations in Implementing AI-Based Accounting Information Systems 2.4.1. System Quality System quality refers to the extent to which an information system meets desired technical and performance characteristics. System quality attributes include ease of use, reliability, response time, flexibility, and integration capabilities (DeLone & McLean, 2002). According to Petter et al. (2008), the quality of an AIS directly impacts user experience and performance. A high-quality system facilitates user interaction, saves time and minimizes errors during work processes (Lutfi et al., 2022). In addition, Alshardan et al. (2016) affirmed the positive influence of system quality on users' adoption of information technology. A system with intuitive design, sophistication, and fast response time enhances individual satisfaction and work efficiency (Petter et al., 2008). Therefore, we design the following hypothesis: H1: System quality has a positive impact on the individual performance of system users. The system quality of AIS profoundly impacts the performance and effectiveness of the entire organization. According to Gable et al. (2008) system quality evaluates IS performance emphasizing both technical and design aspects. A system considered to be of high quality will contribute to improving the organization's overall effectiveness (Petter et al., 2008). Negash et al. (2003) also pointed out the positive relationship between system quality and web-based customer support systems which influences organizational performance. Alshardan et al. (2016) developed a model for evaluating IS benefits in which system quality is one of five crucial aspects influencing the success of small and medium-sized enterprises. Therefore, this study proposes the following hypothesis: H2: System quality has a positive impact on organizational performance. Humanities and Social Sciences Letters, 2025, 13(2): 558-573 562 © 2025 Conscientia Beam. All Rights Reserved. 2.4.2. Information Quality Information quality is the degree to which the output of an information system meets the criteria of accuracy, completeness, timeliness, consistency, relevance and understandability for its users (Davidson et al., 2020; DeLone & McLean, 2002; Gable et al., 2008; Petter et al., 2008). For individual users, information quality directly impacts work efficiency and decision-making processes. Information quality represents the system's ability to provide users relevant, accurate, comprehensive and timely information during decision-making. High-quality information helps users minimize errors in operations, thereby enabling the creation of accurate and valuable information for decision- making (Elias, 2012). Therefore, we propose the following hypothesis: H3: Information quality has a positive impact on the individual performance of system users. Previous literature has yielded conflicting results concerning the influence of information quality on organizational performance leading to inconclusive hypotheses in some studies (Elias, 2012). Petter et al. (2008) emphasized the impact of information quality on organizations in managing accounting and finance. Some studies have indicated that this impact is not significant (Kautsar & Muslichah, 2022). Al-Okaily (2024) asserted that information quality does not affect organizational effectiveness. In the context of implementing AI-based AIS in SMEs, this study will examine the following hypothesis: H4: Information quality has a positive impact on organizational performance. 2.4.3. Service Quality Service quality is the level of support that users and firms receive from the information system's support team or provider (DeLone & McLean, 2002). Numerous studies have used the SERVQUAL model to measure service quality through specific activities such as providing readily available training and user guidance, offering prompt and timely troubleshooting support, delivering regular upgrades and maintenance services ensuring 24/7 online support and guaranteeing information security for the system (Alshardan et al., 2016; Davidson et al., 2020; Jiang, Klein, & Carr, 2002; Subiyakto & Ahlan, 2014). Service quality directly impacts individual users' experience and system usage effectiveness. According to Petter et al. (2008), determinants such as prompt and efficient support, reliability, technical competence, and empathy from support staff directly influence the user experience. When users' expectations regarding service quality are not met or the provider's response is delayed, it leads to dissatisfaction and negatively affects users' intention to use AIS, and vice versa (Alshardan et al., 2016; Davidson et al., 2020; Jiang et al., 2002; Subiyakto & Ahlan, 2014). Consequently, we posit the following subsequent hypothesis: H5: Service quality has a positive impact on the individual performance of system users. The impact of service quality on organizations varies depending on the specific context. Davidson et al. (2020) identified service quality as a key determinant of IS effectiveness supporting integration between business units and strengthening organizational performance. Good service quality promotes and enhances application integration between business units and influences organizational performance (Alshardan et al., 2016). Moreover, service quality is related to assisting businesses in selecting hardware and software, troubleshooting computer problems, and conducting regular maintenance. Therefore, good service quality is necessary for firms to implement AIS effectively (Jiang et al., 2002). Alshardan et al. (2016) underscored the significance of service quality in enhancing operational efficiency, performance and competitive advantage for SMEs in developing countries. In this study, a hypothesis is given below: H6: Service quality has a positive impact on organizational performance. 2.4.4. Individual Performance The concept of individual performance pertains to the extent to which an information system has influenced the capabilities and effectiveness of individuals within an organization. It encompasses aspects such as enhancing Humanities and Social Sciences Letters, 2025, 13(2): 558-573 563 © 2025 Conscientia Beam. All Rights Reserved. personal productivity, improving decision-making processes, increasing work efficiency and promoting the learning and application of new knowledge by users in their daily tasks (Gable et al., 2008). Users who perceive the system as reliable and valuable will utilize the AIS to improve work outcomes, benefiting the entire organization (Davidson et al., 2020; Gable et al., 2008). Conversely, if users encounter issues such as software malfunctions or lack of timely information, it can hinder user performance and negatively impact organizational productivity (Davidson et al., 2020). Meanwhile, organizational performance refers to the effects and benefits the information system brings to the entire organization at a broader level than individual performance. This concept includes determinants such as improving overall productivity, reducing operating costs, increasing revenue, enhancing business processes, and strengthening the organization's competitive advantage. In this study, we test the following hypothesis: H7: The individual performance of system users has a positive impact on the organizational performance. 2.4.5. Research Model The proposed research model is synthesized from the IS success model (DeLone & McLean, 2002) and the impact model (Gable et al., 2008) with the sample drawn from firms currently implementing AI-based AIS software. Previously, the research by Davidson et al. (2020) proposed combining the two models to investigate the reciprocal relationship between the determinants. However, their study focused solely on assessing the current impact of a public health application to explore and identify vulnerabilities in software solutions supporting healthcare agencies. This study evaluates how the deployment of AI-based AIS influences individuals and organizations aiming to propose solutions to enhance the successful adoption of AI-based AIS. The model comprises the following five determinants: system quality (SQ), information quality (IQ), service quality (SeQ), individual impact (II) and organizational impact (OI). This integration is done to inherit the theoretical foundation of IS success while aligning with the practicalities of AIS adoption in the context of small and medium-sized enterprises (SMEs) in the context of Vietnam, thus providing both coherence and generalizability to the measurement model. This extensive methodology facilitates a thorough assessment for a holistic evaluation of the effects of AI-based AIS on both individual users and the broader organizational context. Figure 1 illustrates the research model including dependent and independent variables. Figure 1. The proposed research model. 3. RESEARCH METHODOLOGY 3.1. Research Design This research employs a quantitative method to analyze and explore the determinants influencing individual and organizational performance when implementing AI-based AIS. Initially, we interviewed the following six AI- based AIS experts to adapt the survey questionnaire to the actual context of implementing this system in Vietnamese SMEs. The respondents were asked to review the wording of each item in the measurement scale for clarity and meaningfulness and to suggest any necessary revisions. The collected data was then processed to validate the model and research hypotheses, answer the two research questions and propose implications for enhancing this system's implementation level. The interview results clarified the survey questions and created a Humanities and Social Sciences Letters, 2025, 13(2): 558-573 564 © 2025 Conscientia Beam. All Rights Reserved. comprehensive and multi-faceted picture of how AI-based AIS has impacted individual and organizational performance. 3.2. Participants The survey participants were users of AI-based AIS, including managers (board of directors), accountants, chief accountants, and internal auditors. We searched the vendors' websites to obtain a list of organizations using AI- based AIS software. We sent out surveys in the following two ways: through email and directly to the organizations through personal connections. Participants were informed about the content and purpose of the research as well as the levels of agreement when answering questions. They were also assured that the information provided would only be used for research purposes and their identities would remain confidential. The survey was conducted with 381 individuals with 58.53% female and 41.47% male. 42.78% of participants were between 31 and 40 years old. They are a group with work experience, accounting knowledge, and adaptability to new technologies. 31.50% of participants were under 30 and 20.29% were between 40 and 50. The lowest percentage, 5.43% belonged to the group over 50 years old. They may face difficulties using new technologies or have fewer opportunities to interact with the system. Accountants accounted for 51.71% of the survey participants. Next were chief accountants (18.90%) and internal auditors (18.90%). Only 10.50% were managers. Regarding educational attainment, most respondents had bachelor's degrees (51.71%) followed by postgraduate degrees (41.47%). Only a small percentage (6.82%) had intermediate or college degrees. Implementing AI-based AIS was evenly distributed across organizations in various business lines. Among them, the service industry (25.20%), wholesale, retail, and trade (27.30%), finance, banking, and insurance (10.76%), and manufacturing (9.19%) were the most represented. Regarding firm size, 89.24% of participants worked in firms with 50 to 100 employees with the highest percentage (48.56%) in firms with 100-200 employees. This statistical ratio is consistent with the distribution of small and medium-sized enterprises in the context of Vietnam, a developing country. 3.3. Measurement The survey questionnaire was constructed based on the inherited and modified scales of Gable et al. (2008) and Petter et al. (2008). The questionnaire was written in Vietnamese and consisted of two parts which are as follows: Part 1 included information related to individuals and organizations implementing the system such as gender, age, education level, position, type, and organization size. Part 2 included questions about users' perceptions of the system's impact on individuals and organizations. Participants answered the questions using a Likert scale from 1 to 5 points regarding their subjective perceptions of system quality (6 questions), information quality (5 questions), service quality (5 questions), individual impact (5 questions), and organizational impact (8 questions). In part 2, some questions from the original questionnaire were removed based on Cronbach's alpha results from the pilot data. The study proposes an additional item, SeQ5 " provide online support services anytime" after interviewing experts and analyzing the actual post-implementation support services provided by software vendors. 3.4. Data Collection Data were collected from April to July 2024. We sent the questionnaire through links to 350 emails and distributed it directly to 83 users. The initial data was screened to ensure the validity of responses through the first question which confirmed whether the respondent had used the system. If the answer was invalid, subsequent responses were automatically discarded and not included in the data analysis. We decided to eliminate any reactions that appeared to be spam, such as selecting only a single option throughout the questionnaire due to potential concerns about the reliability of responses when using Google Forms. A total of 381 valid surveys were used in the data analysis process. Humanities and Social Sciences Letters, 2025, 13(2): 558-573 565 © 2025 Conscientia Beam. All Rights Reserved. 3.5. Data Analysis Data analysis was conducted using SPSS 27 and PLS-SEM 3 software to address research question number 1. Initially, we performed data cleaning to identify incomplete, inaccurate and duplicate responses through sample statistical analysis. Subsequently, the entire sample (n = 381) was subjected to exploratory factor analysis (EFA) and Cronbach's alpha reliability testing. PLS-SEM was utilized to evaluate the model's fit and test the hypotheses. Research question number 2 was elucidated through the results of an independent samples t-test for gender groups, and a one-way ANOVA for job title and age groups of survey participants. 4. RESEARCH RESULTS 4.1. The Impact of AI-Based Accounting Information Systems on Individual and Organizational Performance The analysis results indicated that all questions and observed variables in the model met the requirements for normal data distribution with the most significant absolute values of the skewness coefficient being 1.152 (< 3), and the most significant absolute values of kurtosis being 1.439 (< 10). Table 1 reveals that the proposed model achieved internal consistency reliability with Cronbach's alpha coefficients of the component scales all greater than 0.7 ranging from 0.879 to 0.930, and the composite reliability coefficients (CR) ranging from 0.912 to 0.945 (> 0.6). Regarding scale validation, the measurement items were suitable and accepted for use in the model because the single factor loading coefficients (outer loading) ranged from 0.732 to 0.916 (>0.6). In addition, the scales in the study met the requirements for convergent validity as the average variance extracted (AVE) values of the determinants ranged from 0.614 to 0.742 (> 0.5). This indicates that the measurement values reflecting the extent to which a latent variable explains the variance in its observed variables were all satisfactory. Table 1. Estimation results for selected indicators in the measurement model. Determinants Attributes Outer loading Cronbach’s alpha CR AVE Individual performance (IP) IP1. Improve work efficiency. 0.832 0.879 0.912 0.674 IP2. Reduce implementation time. 0.850 IP3. Improve system usage skills. 0.805 IP4. Enhance professional competence. 0.804 IP5. Improve personal decision-making effectiveness. 0.814 Organizational performance (OP) OP1. Increase profits. 0.782 0.910 0.927 0.614 OP2. Increase labor productivity. 0.778 OP3. Improve the quality of output information. 0.796 OP4. Automate business operations. 0.808 OP5. Improve business processes. 0.819 OP6. Enhance competitive advantage. 0.803 OP7. Reduce the risk of financial fraud. 0.748 OP8. Reduce management and storage costs. 0.732 Information quality (IQ) IQ1. Accurate information. 0.737 0.910 0.933 0.738 IQ2. Complete information. 0.877 IQ3. Consistent information. 0.856 IQ4. Understandable information. 0.901 IQ5. Timely information. 0.913 System quality (SQ) SQ1. Ease of use. 0.875 0.930 0.945 0.742 SQ2. Error reduction. 0.916 SQ3. Comprehensive support for accounting tasks. 0.911 SQ4. Easy interface customization. 0.870 SQ5. Quick feedback. 0.820 SQ6. Integration and expandability. 0.767 Service quality (SeQ) SeQ1. Ready to guide system usage. 0.776 0.897 0.924 0.709 SeQ2. Support to resolve incidents quickly and promptly. 0.862 SeQ3. Provide upgrade and periodic maintenance services. 0.844 SeQ4. Ensure system information security. 0.845 SeQ5. Provide online support services anytime. 0.880 Humanities and Social Sciences Letters, 2025, 13(2): 558-573 566 © 2025 Conscientia Beam. All Rights Reserved. The results in Table 2 confirmed that the proposed model met the requirements for discriminant validity. All latent constructs' Fornell and Larcker coefficients were higher than their corresponding row and column values (Hair, Risher, Sarstedt, & Ringle, 2019). Table 2. Assessment of discriminant validity. Factors IP IQ OP SQ SeQ IP 0.821 IQ 0.639 0.859 OP 0.689 0.637 0.784 SQ 0.601 0.374 0.581 0.861 SeQ 0.468 0.272 0.317 0.109 0.842 The results of the SEM structural model estimation in Table 3 indicate that system and information quality directly impact the performance of individual users and the organization (p < 0.05). Conversely, service quality only influences the individual performance of system users (p < 0.05) and does not have a direct impact on organizational performance (p = 0.137 > 0.05). In addition, the path coefficients (sample mean) of the three determinants evaluating the system quality, information, and services for individuals and organizations all yield positive values. The aforementioned relationships in the model have a positive effect. Additionally, no multicollinearity is present in this structural model as the highest inner variance inflation factor (VIF) values of the latent constructs are 2.875, less than 3 (Hair et al., 2019). Table 3. Estimation results of direct effects in the SEM structural model. Hypotheses Original sample (O) Sample mean (M) Standard deviation (STD) T Statistics (O/STDEV) P values Inner VIF values Hypotheses conclusions H1: SQ -> IP 0.419 0.416 0.042 10.083 0.000 1.163 Supported H2: SQ -> OP 0.285 0.285 0.051 5.613 0.000 1.666 Supported H3: IQ -> IP 0.396 0.394 0.041 9.554 0.000 1.241 Supported H4: IQ -> OP 0.340 0.343 0.054 6.303 0.000 1.693 Supported H5: SeQ -> IP 0.315 0.318 0.038 8.304 0.000 1.080 Supported H6: SeQ -> OP 0.067 0.072 0.045 1.490 0.137 1.365 Unsupported H7: IP -> OP 0.269 0.263 0.076 3.549 0.000 2.875 Supported The research model in Figure 2 demonstrated an acceptable level of overall fit. The standardized root mean square residual (SRMR) value in the saturated model which measures the discrepancy between the observed data and the predicted model is 0.044, less than the threshold of 0.05. The chi-square/df (χ2/df) value is 1.986 falling below 3 with a sample size of 381 which exceeds 200. This signifies the statistical significance of each relationship within the model. Therefore, the model satisfies the criteria for overall goodness-of-fit. Consequently, all research hypotheses are accepted except hypothesis H6 which is rejected (p-value = 0.137 > 0.05) (Hair et al., 2019). Figure 2 illustrates the research model depicting the relationships between system quality, information quality, and service quality with the operational effectiveness of individuals and organizations during the deployment of AI- based AIS. The coefficient of determination values, 0.675 and 0.589 are both statistically significant. This indicates that these three determinants account for 65.2% of the observed variance impacting individuals and explain 58.9% of the variance influencing organizations. Humanities and Social Sciences Letters, 2025, 13(2): 558-573 567 © 2025 Conscientia Beam. All Rights Reserved. Figure 2. Results of the AI-based AIS impact research model. 4.2. The Impact of AI-Based AIS on Individual and Organizational Performance by Genders Job Titles, and Ages We conducted an independent-sample t-test to examine the difference in the perceived impact of AI-based AIS between male and female accountants. The test results indicated a variance difference between the two groups (the corresponding sig. values for the two groups were 0.047 and 0.04, both less than 0.05). However, there was no significant difference in the mean perception of AIS impact on individuals and organizations between the male and female groups (the absolute values of the t-test results under the " equal variances not assumed" row were 0.878 and 1.069, both greater than 0.05). A one-way ANOVA test was done to examine the mean differences in the perceived impact of the system on individuals and organizations across different job positions and age groups of the respondents. For job positions, the analysis results revealed a variance difference among the groups regarding their perception of the system's impact on individuals and organizations (the sig. values in the Levene's test table were 0.02 and 0.039, respectively, less than 0.05). Similarly, there was a difference in the perceived impact of the system on individual performance ( sig. value in the Welch test was 0.02, less than 0.05), but no difference in the perceived impact on the organizational performance among different job positions ( sig. value in the Welch test was 0.08, greater than 0.05). Specifically, the mean values tended to decrease with the frequency of system use. The groups who used the system most frequently, namely chief accountants and accountants, had the highest mean values in assessing the system's impact, at 4.00 and 3.913, respectively. The analysis results for the age groups showed no variance in respondents' perception among different age groups (the sig. values in the Levene's test table were 0.360 and 0.509, respectively, greater than 0.05). However, there was a difference in the perceived impact of AI-based AIS on individual and organizational performance among different age groups (the sig. values in the ANOVA were 0.033 and 0.030, respectively, both less than 0.05). The mean values of the age groups under 50 did not differ significantly. In contrast, the group of respondents over 50 years old had the lowest mean perception of the system's impact (3.409 and 3.369, respectively). 5. DISCUSSION 5.1. Discussion of Research Model Validations This study employs a quantitative research approach to address two research questions: (i) How does an AI- based AIS impact individual and organizational performance, and (ii) Are there differences in the perceived impact of AI-based AIS across gender, position, and age groups? We select a diverse sample of 381 chief accountants, Humanities and Social Sciences Letters, 2025, 13(2): 558-573 568 © 2025 Conscientia Beam. All Rights Reserved. accountants, directors, and internal auditors. These individuals are system users who can objectively evaluate the system's quality, information, and services through daily accounting operations. The diversity in job positions among the respondents allows for assessing the system's impact from various perspectives, creating a comprehensive and multi-faceted picture of the system's implementation status. The combination of the DeLone and McLean (2002) and Gable et al.'s (2008) models to construct a new model for comprehensively studying the impact of AI-based AIS on individual and organizational performance has enriched the research on implementing new technologies like AI into traditional AIS. The validation results align with previous studies suggesting that system quality, information quality, and service quality all have a positive impact on the individual user's performance effectiveness (Alshardan et al., 2016; Elias, 2012; Lee & Tajudeen, 2020; Lutfi et al., 2022). Previous research has established the influence of system quality, information quality and service quality on organizational effectiveness (Alshardan et al., 2016; DeLone & McLean, 2002; Gable et al., 2008; Gorla et al., 2010). However, analysis conducted on a sample of 381 small and medium-sized enterprises indicates that service quality appears to have a limited impact on organizational outcomes. Hypothesis six (H6) which posits an effect of service quality on organizations is rejected (p = 0.137 > 0.05). This suggests a need to re-evaluate the role of service quality within the DeLone and McLean's (2002) model. Another novel finding is the difference in the perceived impact of system adoption across user groups with varying roles and ages. Those who frequently use the software tend to have a higher appreciation for its benefits compared to managers. Conversely, older individuals demonstrate a lower perception of system adoption's impact than younger users. 5.2. Discussion of the Impact of System Quality on the Performance of Individuals and Organizations System quality is identified as the determinant with the most substantial impact on the performance effectiveness of both individuals and organizations. The prominent role of system quality is evaluated through its ease of use, comprehensive support for accounting operations, and rapid information feedback to users (Davidson et al., 2020). The application of AI technology in accounting software enables organizations to automate the process of collecting and processing information, minimizing errors in user operations. Organizational performance is assessed by examining the extent to which the information system contributes to achieving strategic goals and improving the organization's overall operational efficiency (Gable et al., 2008). The benefits of implementing AIS, such as increased user productivity and better decision-making capabilities directly influence the improvement of operational efficiency and the organization's competitive capabilities (Alshardan et al., 2016). These findings are consistent with the research of Davidson et al. (2020) and Alshardan et al. (2016) which suggests that system quality is highly valued because it directly affects the ability to deploy the system to help the organization achieve business success. 5.3. Discussion of the Impact of Information Quality on the Performance of Individuals and Organizations Similar to previous studies, the findings indicate that information quality directly impacts the performance effectiveness of both individuals and organizations (Alshardan et al., 2016; DeLone & McLean, 2002; Gable et al., 2008). The quality of output information is a crucial factor in enabling users to make accurate business decisions, thereby enhancing the performance of both individuals and the organization (Alshardan et al., 2016; Gorla et al., 2010). If the information does not meet users' needs, it will lead to financial and accounting management difficulties and disrupt accounting operations within the organization. Conversely, complete, accurate, and timely information will support effective business planning and strategy execution, enhance user satisfaction, reduce waste, cut costs, and optimize the use of machinery (Al-Hashimi, 2019; Daoud & Triki, 2013). Humanities and Social Sciences Letters, 2025, 13(2): 558-573 569 © 2025 Conscientia Beam. All Rights Reserved. 5.4. Discussion of the Impact of Service Quality on the Performance of Individuals and Organizations We propose incorporating the service quality factor from the DeLone and McLean's (2002) model into the impact assessment model (Gable et al., 2008) to comprehensively study the impact of AI-based AIS. The findings indicate that service quality affects the effectiveness of individual users' performance. Service quality is measured through the provider's readiness to guide software usage, provide prompt troubleshooting support, and offer software maintenance and upgrade services. When the provider is willing to receive incident reports and resolve them quickly, provide reliable services and offer detailed error correction guidance, it helps users work more efficiently (Gorla et al., 2010). Service quality was expected to positively impact the organization while the research model yielded a surprising result. Service quality does not influence organizational performance (rejecting hypothesis H6: SeQ -> OI). This result contradicts the assertions of DeLone and McLean (2002) and Alshardan et al. (2016) which consider service quality to be a crucial aspect in evaluating system success. According to them, service quality is seen as impacting user satisfaction, thereby influencing the net benefits achieved by the organization (DeLone & McLean, 2002; Lutfi et al., 2022). This can be explained by the fact that user dissatisfaction with software errors or delays in vendor support only directly affects the user's work while the organization's accounting operations still need to be ensured stability. The accounting system is considered the lifeblood of a business's operations. All economic transactions that arise need to be recorded, processed and reflected accurately and continuously without being interrupted by any impact. The accounting system ensures that financial information is always transparent and timely, meeting the needs of internal management and business decision-making (Alshardan et al., 2016; Hendarmin & Sari, 2024). In the context of developing countries like Vietnam, users are often not instructed on how to use the software systematically. They find it challenging to master the functions and utilities of the software in a short time leading to inefficient use of the system in the early stages of deployment. At that time, firms will implement both traditional financial and accounting management methods and AI-based AIS in parallel. Therefore, in the early stages of system adoption, survey respondents acknowledged that the organization's accounting operations were largely unaffected even when the provider's service quality was not good. The research findings of Negash et al. (2003) and Subiyakto and Ahlan (2014) conclude that service quality has the most negligible impact on the net benefits of an organization. The study by Ghobakhloo and Tang (2015) did not establish a relationship between service quality and organizational effectiveness. Therefore, in specific contexts at small and medium enterprises in Vietnam, the impact of service quality may be weak or insignificant. In this paper, the rejection of hypothesis six (H6) reflects the reality that service quality needs significant improvement to impact the organization. Providers need to take measures such as organizing training courses, providing software user manuals, and offering online support services to improve software utilization efficiency. The next novel finding in the study is the variable SeQ5 " provide online support services anytime," which is newly proposed in the service quality factor. The test results for outer loading = 0.880, outer VIF values = 2.972 show that this is a crucial measurement variable of this factor. This factor correlates with the remaining variables in the service quality group and does not exhibit multicollinearity with other factors. The results of expert interviews also emphasized the role of the item " provide online support services anytime." This is an essential form of support that providers in Vietnam implement most often to advise and guide users on system operations through remote computer control tools such as UltraViewer and TeamViewer. 5.5. Discussion of the Impact of Individual Performance on Organizational Performance The findings confirm that individual performance effectiveness directly influences organizational performance effectiveness with hypothesis seven (H7) (II -> OI) being supported. When individuals feel satisfied and frequently use the software, it enhances their work efficiency and supports them in making accurate decisions (Alshardan et al., 2016; Petter et al., 2008). Individuals can contribute to achieving the organization's strategic goals by using Humanities and Social Sciences Letters, 2025, 13(2): 558-573 570 © 2025 Conscientia Beam. All Rights Reserved. systems that support their daily activities. When individuals work effectively, it brings many benefits to the organization, such as increased profits, enhanced competitiveness, reduced costs, and reduced financial fraud risks. The assessment of how individuals impact the organization needs to be conducted comprehensively, considering various stakeholders' perspectives and aligning with the organization's specific context (Gable et al., 2008; Li & Zheng, 2018). However, it is essential to note that the relationship between user performance and organizational performance indicators such as profit and revenue can vary. Some studies have found a strong correlation (Alshardan et al., 2016; Gable et al., 2008). 6. CONCLUSION AND IMPLICATIONS 6.1. Conclusion This research proposes an impact assessment model for AI-based AIS adapted from Gable et al.'s (2008) model in the context of unexplored research on implementing such systems in Vietnamese SMEs. The findings reveal a positive influence between the quality determinants of the system, information, and AI-based AIS services on the individual user's performance effectiveness. The research also emphasizes the role of system quality and information quality in improving the quality of output information and enhancing the organization's competitiveness. A notable finding in the study is that the service quality factor only directly impacts individuals and does not influence the organization's performance effectiveness. This is compatible with the realities of information system implementation in Vietnam because accountants are always held liable for all financial and accounting activities that occur inside the organisation regardless of whether an AIS system is used or not. Therefore, if the service quality of the software provider during implementation is suboptimal, the software has errors or the provider has not yet rectified issues, it has minimal impact on the organization's financial management activities. In addition, the newly introduced factor, SQ5, is consistent with the urgent need for online consulting support for users whenever software errors occur (Elias, 2012). This investigation contributes to theory development by providing a comprehensive model for evaluating the impact of the adoption of AI-based AIS in SMEs in nations characterized by emerging economies while the service quality factor has not been extensively addressed in studies on the impact of information systems (Gable et al., 2008; Gangwar, 2024).The study offers practical implications and valuable guidance for managers to understand how the benefits of applying new technologies such as AI to traditional AIS will influence the organization's financial and accounting management activities. 6.2. Implications The results from this investigation present numerous pragmatic ramifications for organizations concerning adopting and utilizing AI-based AIS. Firstly, organizations need to prioritize enhancing the overall quality of AI- based AIS. This includes investing in modern information technology (IT) infrastructure and selecting software that aligns with the scale and specific operational characteristics of the business. Specifically, firms can consider utilizing AI-integrated ERP solutions or highly customizable AI modules that cater to the unique needs of each department. Furthermore, the establishment and security of accounting databases are paramount requiring stringent data management policies, encompassing data encryption, access control, and regular backups. Firms also need to raise user awareness regarding the system's benefits through training sessions, specialized workshops, or detailed user manuals. Software providers need to actively collaborate with businesses to customize program modules, ensuring they are compatible with the specific accounting operations within each area of the organization's activities. Secondly, information quality can be improved by aligning IT strategy with the overall business strategy while simultaneously encouraging the adoption of new technologies such as AI, the Internet of Things (IoT), cloud computing, and Blockchain. Specifically, firms can apply AI to automate data entry processes, reconcile figures, or Humanities and Social Sciences Letters, 2025, 13(2): 558-573 571 © 2025 Conscientia Beam. All Rights Reserved. conduct financial risk analysis. IoT sensors can gather real-time data on inventory, production costs or revenue, providing accurate and timely information for decision-making. The application of cloud computing enables firms to store and access data flexibly while reducing infrastructure investment costs. Firms can leverage advanced data mining techniques to detect trends and correlations within accounting data, thereby improving financial management processes. In addition, AI-based AIS software should be able to automatically update changes in accounting laws, accounting regimes from the ministry of finance, and other regulations from government agencies. This ensures that the generated information is always accurate and legally compliant, and provides a solid foundation for decision-making. Thirdly, firms should emphasize the role of service quality in optimizing the effectiveness of AI-based AIS. Moreover, providing online services anytime, anywhere is essential. Providers might incorporate this service into their software packages and commit to supporting users throughout the system's operational lifespan. Consequently, providers might establish diverse support channels (telephone, email and online chat) with a professional technical team ready to address any arising issues promptly. Furthermore, providers should organize periodic training programs, software user guides, updates on new features, and equip users with the necessary knowledge to adapt to the changes brought about by AI. These programs can encompass both in-person formats (seminars, classes) and online formats (instructional videos and webinars). Finally, firms need to accelerate the deployment of AI applications to enhance accounting operational efficiency, optimize cash flow, minimize costs, strengthen internal controls, or conduct financial forecasting. The comprehensive and synchronized application of AI solutions will bring long-term benefits to firms (Hendarmin & Sari, 2024). The data is statistically significant with a sample size of 381 collected in the study. However, the research sample may not encompass all sectors of business lines within organizations. Future research could expand the research scope and incorporate several detailed case analyses on organizations successfully implementing AI-based AIS. In addition, the study primarily focuses on evaluating system impact from the perspective of software users without fully considering other external determinants such as government regulations or the organization's need for technological innovation. Expanding future studies by including these control variables will provide a more comprehensive picture of the current situations of AI-based AIS adoption. Funding: This study received no specific financial support. Institutional Review Board Statement: The Ethical Committee of the Banking Academy of Vietnam, Vietnam, approved this study on 17 February 2025 (Ref. No. 007/HDKH-KNH-HVNH). Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: Both authors contributed equally to the conception and design of the study. Both authors have read and agreed to the published version of the manuscript. REFERENCES Adeyelu, O. O., Ugochukwu, C. E., & Shonibare, M. A. (2024). The impact of artificial intelligence on accounting practices: Advancements, challenges, and opportunities. International Journal of Management & Entrepreneurship Research, 6(4), 1200-1210. Al-Hashimi, H. N. H. (2019). A review of impact of accounting and management information systems on the performance of organizations: Empirical study at Iraqi port. 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