Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6, 8915-8925 2024 Publisher: Learning Gate DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate © 2024 by the author; licensee Learning Gate * Correspondence: nguyenhoai@tmu.edu.vn Factors affecting the Audit expectation gap – A case study in Vietnam Nguyen Thu Hoai1* 1Thuongmai University, Vietnam; nguyenhoai@tmu.edu.vn (N.T.H.). Abstract: The concept of the audit expectation gap (AEG) is not novel in the field of auditing, having first been introduced approximately 50 years ago in Liggio's [1] study. Since then, AEG has garnered significant attention from both practitioners and academic researchers. However, there remains ongoing debate regarding the methods used to measure AEG and the factors that influence it. This study aims to address these gaps by proposing a new approach for measuring AEG and presenting a research model to identify the factors that impact AEG. Additionally, the study provides empirical evidence from Vietnam to validate the proposed measurement method and research model. The findings indicate the presence of AEG in Vietnam and highlight that factor such as users' understanding of audit, users' neess from audit, auditor’s independence, auditor’s competence, and the insufficient audit standards significantly influence AEG within the Vietnamese context. Keywords: Audit expectation gap, AEG, Factors affecting, Measurement method, Vietnam. 1. Introduction Previous studies have not only confirmed the existence of the audit expectation gap (AEG) at varying levels across different countries, but have also highlighted its negative impact on auditing practices and the broader economy. According to Porter and Hatherly [2], when the reputation of the audit is degraded to a certain extent, the audit profession will become meaningless. Further research by Noghondari and Foong [3], Ogbonna and Appah [4], and Farasangi and Noghondari [5] has demonstrated that AEG can adversely affect the investment decisions of information users. As such, determining the extent of AEG and identifying, measuring the factors influencing AEG are crucial for developing strategies to mitigate its impact. Due to the complex, multidimensional nature of AEG, although the number of studies on AEG is considerable, significant controversy remains regarding the methods of measurement and the consistency of research findings. Building on the strengths and limitations of existing AEG measurement methods, this study proposes a new approach based on Potter's [6] AEG structural model. This new AEG measurement method also allows the study to develop and propose a measurement model of factors affecting AEG. Especially, in the Vietnamese context, empirical studies addressing AEG, particularly those focusing on its influencing factors, remain limited. Therefore, this study aims not only to introduce a new method for measuring AEG and a research model for identifying its influencing factors but also to present empirical findings from Vietnam to validate these approaches. The structure of the paper is as follows: Section 2 reviews the relevant literature; Sections 3 and 4 outline the theoretical framework and research design, respectively; Section 5 presents the results and discussion; and the final section provides the conclusion and policy implications. 2. Literature Review 2.1. The Audit Expectation Gap Definitions Despite numerous attempts by researchers to provide a clear definition of AEG, the concept remains highly debated. He [7] suggests that previous studies on AEG can be analyzed from the perspective of the parties involved in the concept. The definitions of AEG can be categorized into two distinct groups: 8916 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate (1) The first group views AEG as the discrepancy between the expectations and perceptions of information users and auditors. Several studies adopt this approach, including those by Liggio [1], Guy and Sullivan [8], Monroe and Woodliff [9]. • Liggio [1] defined AEG as “the difference between the levels of expected performance as envisioned by the independent accountant and by the user of financial statements.” • Guy and Sullivan [8] stated that “a difference between what the bublic and financial statement users believe accountants and auditors are responsible for and what the accountants and auditors themselves believe they’re responsible for.” • Similarly, Monroe and Woodliff [9] argued that “An audit expectation gap exists when there are differences in beliefs between auditors and the public about the duties and responsibilities assumed by the auditors and the messages conveyed by audit reports.” (2) The second group conceptualizes AEG as the gap between the expectations and perceptions of a single party, such as the public, society, or the user of financial statements, as defined by the Cohen Commission [10] and Porter [6]. • The Cohen Commission [10] described AEG as “a gap that may exist between what the public expects or needs and what auditors can and should reasonably expect to accomplish.” • Porter [6] provided a more nuanced definition: “the gap between society's expectations of auditors and auditors' performance as perceived by society.” Among the two conceptual approaches outlined above, the second group is considered more relevant as it aligns with the nature of AEG and ensures the reliability and objectivity of the research, particularly in empirical studies. Therefore, this study adopts Porter’s [6] definition of AEG to design the model and research methodology. 2.2. Methods of Measuring Audit Expectation Gap Porter [6] is recognized as the first researcher to propose a method for measuring the Audit Expectation Gap (AEG) from the perspective of information users. This approach has been subsequently adopted and refined in later studies, such as those by Hassink et al. [11] and Litjens et al. [12] (2015). The research questionnaire includes a set of auditors' duties, both those currently specified in auditing standards and those not explicitly outlined. Respondents are asked to answer three questions for each duty: (1) Is the duty an existing duty of auditors? (2) If the duty is an existing duty of auditors, how well is it performed? (3) Should the duty be performed by auditors? • Duties that are part of the existing standards but are perceived by the public as inadequately performed contribute to the DP component of the AEG. • Duties not explicitly included in current standards but which are agreed upon by at least 20% of respondents or have a positive average agreement score are classified as duties auditors should perform. These duties, supported by both the auditee and the financial community, are considered reasonable and contribute to the DS. • In contrast, duties that are agreed upon by only one group (either the auditee or the financial community) are classified as unreasonable and contribute to the RG. Based on the responses and ratings from the participants, Porter [6] identified the duties contributing to the various components of the AEG and calculated the respective percentages for each gap. 2.3. Factors Affecting the Audit Expectation Gap Factors influencing the Audit Expectation Gap (AEG) can be identified by examining the various parties that both influence and are affected by the gap, including information users, auditors, and standard-setting bodies. Factors related to information users include their understanding of audit nature and needs of audit. According to reader-response theory, the user's knowledge about audits affects how they interpret information conveyed in audit reports, which can contribute to the AEG. For instance, a study by Bailey 8917 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate et al. [13] in the United States found that users with greater understanding about audits tend to place less responsibility on auditors. Similarly, Epstein and Geiger [14] suggested that investors with higher levels of education, particularly in financial analysis and investment, tend to have lower expectations for auditors' assurance levels. Research by Hussain et al. [15] on students before and after auditing courses, as well as studies by Pierce and Kilcommins [16] among economics and financial accounting students at different stages of their studies, revealed significant differences in the awareness of auditing between groups of respondents. Additionally, the needs of information users can greatly affect the divergence between auditors' and users' perceptions. As users' needs increase, so do their expectations of auditors (Alawi et al. [17]). Kamau [18] also demonstrated that the 'need hypothesis' significantly and positively influences the AEG. Auditor-related factors also play a critical role in the AEG. Akther and Xu [19] found that auditor independence significantly impacts the AEG. Auditors' competence, closely related to audit quality, also plays a key role in influencing the AEG, as suggested by Agyei et al. [20]. Macdonald [21] argued that professional knowledge and training for auditors can help reduce the AEG, and Kamau’s [18] empirical findings indicated that auditors’ skills statistically contribute to narrowing the AEG. In addition, standard-setting bodies are both influencers and affected parties in the AEG. Previous research has shown that factors related to audit reporting standards or inadequacies in auditing standards can significantly impact the AEG, as demonstrated by Ogbona and Appah [4], Sikka et al. [22], and Lee et al. [23]. Lee et al. [23] found that in Malaysia, professional standards do not clearly outline the auditor's responsibilities concerning fraud detection and illegal activities. Cosserat [24] argued that, following the collapses of Enron and Worldcom, auditing standards need revision to emphasize auditors' responsibilities for fraud detection. Furthermore, Porter and Gowthorpe [25] suggested that better control mechanisms are necessary to improve the performance of auditors' work. 3. Theoretical Framework 3.1. Inspired Confidence Theory The Inspired Confidence Theory, developed by Limperg in the late 1920s, posits that a discrepancy exists between the interests of management and stakeholders, leading to potential distortions in the information published by organizations. Limperg [26] argued that such discrepancies necessitate external audits to ensure the accuracy and reliability of financial disclosures. This theory underscores the vital role of auditors in fulfilling societal needs, which in turn informs factors influencing AEG, such as auditor independence and competence. Furthermore, Limperg [27] contended that a mechanism should be in place to ensure auditors adequately address societal demands, providing a framework for identifying the factor related to insufficient audit standards. 3.2. Stakeholder Theory Stakeholder Theory, as articulated by Hill and Jones [28], defines stakeholders as individuals or groups who can both influence and be influenced by the actions of a company. They categorize stakeholders into internal and external groups: internal stakeholders include managers, executives, employees, and shareholders, while external stakeholders encompass the local community, customers, creditors, suppliers, and government entities. Each group has distinct information needs regarding the company, and it is the responsibility of business managers to meet these needs. In the context of the AEG, Stakeholder Theory helps identify key factors influencing the gap, particularly those associated with the stakeholders involved in the audit process, such as users, auditors, and standard-setting bodies. 3.3. Reader – Response Theory Reader-Response Theory posits that there is no single "correct" interpretation of a text; instead, readers actively engage with and interpret texts based on their own psychological states, content knowledge, and personal motives (Wright [29]). A central assumption of this theory is that a reader's background knowledge and experiences significantly influence their interpretation of a text (Wright [29]). In the context of AEG, this theory provides a foundation for identifying user-related factors, such as the knowledge and information needs of audit report users, which may vary based on their individual 8918 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate backgrounds and perspectives. 3.4. Agency Theory Agency theory, initially developed by Alchian and Demsetz [30] and later expanded by Jensen and Meckling [31], focuses on the relationship between a principal and an agent, where the principal delegates tasks to the agent with the expectation of a favorable outcome. In the context of auditing, shareholders (principals) engage auditors (agents) to perform tasks on their behalf, creating potential conflicts of interest and risk-sharing issues, which contribute to the expectation gap. This theory underpins the thesis's approach to assessing the reasonableness of audit expectations, based on whether there is consensus between the principal and agent. It also distinguishes between two groups of information users: those who directly benefit from the audit results (principals) and the audit clients (agents). 4. Research Design 4.1. Research Model and Hypotheses Inherited from the previous studies and based on the theoretical framework, the research model is proposed as follows: Figure 1. Research model. The research hypotheses are stated as follows: H1: There is a relationship between the users’ understanding of the audit and AEG in Vietnam. H2: There is a relationship between the users’ needs from audit and AEG in Vietnam. H3: There is a relationship between the auditors’ independence and AEG in Vietnam. H4: There is a relationship between the auditors’ competence and AEG in Vietnam. H5: There is a relationship between the insufficient audit standards and AEG in Vietnam. The research model can be described as follow: AEG = β1 UU + β2 UN + β3 AI + β4 AC + β5IAS + εi Where: AEG: Dependent variable UU, UN, AI, AC, IAS: Independent variables βi: Coefficient εi: Random error 4.2. Method of Measuring Audit Expectation Gap 4.2.1. Sample This study inherited the Porter’s (1993) definition of AEG which approaches AEG from the users’ side. Therefore, two broad interest groups of users were identified as follows: 8919 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate (1) Auditees: board of directors, accountants and internal auditors. (2) Direct audit beneficiaries: investors, stockbrokers, bankers, financial analysts. 4.2.2. Questionnaire From the current regulations in the Vietnamese Law of Independent Audit, Vietnamese Standards on Auditing and previous studies, 25 auditor’s duties (9 duties are existing and 16 duties are not existing in the current regulations) were put into the questionnaire. For the question “How well is it performed?”, respondents were asked to select the appropriate response from “very poorly”, “poorly”, “fairly”, “well”, “very well”. These responses are coded 1, 2, 3, 4, and 5 respectively. Thus, AEG is measured as the difference between the highest level of expectations about audit results (5 points) and the assessment of information users about the quality of the implementation of these expectations in practice (on a 5-point Likert scale). Total audit expectation gap (∑AEG) = ∑ (5- User’s assessment of auditor’s performance of expectation i)n i=1 Audit Expectation Gap (AEG) = Total audit expectation gap Number of expectations 4.3. Method of Confirming the Influencing Factors A qualitative research approach was employed to identify the factors influencing AEG based on the findings of previous studies, with the aim of developing the research model, formulating hypotheses, and designing the survey questionnaire. The authors also assessed the validity of the research model and considered the inclusion of additional scales by conducting semi-structured expert interviews and pilot surveys. Following the qualitative research phase, the study developed a questionnaire consisting of 25 AEG scales and 21 scales related to factors influencing AEG. In addition, quantitative methods, including Cronbach’s Alpha coefficient, exploratory factor analysis (EFA), and multiple regression analysis, were utilized using SPSS 20.0 software. 5. Results and Discussion 5.1. Survey Sample – Respondent Groups The questionnaire was sent to 450 users including auditees and direct audit beneficiaries. The sample and response rates are shown in table 1. The results of the Table 1 indicate that overall response rates are of 77.69% from auditees and 63.75% from audit direct beneficiaries. Table 1. Sample and response rates. Group No. of survey sent Responses received Usage responses (%) N % Auditees Board of directors Accountants/Internal auditors 130 50 80 101 34 67 33.1 11.1 22 77.69 68 83.75 Audit direct beneficiaries Investors Bankers Stockbrokers Others 320 80 80 80 80 204 51 55 53 45 69.9 17.4 20 18 14.7 63.75 63.75 68.75 66.25 56.25 Total users 450 305 100 67.78 5.2. The Result of Audit Expectation Gap in Vietnam This study uses One Sample T-test to determine the existence of AEG in Vietnam. AEG is measured by the difference between the users’ assessment of auditors’ performance of the expectation and the test value. • Total audit expectation gap (∑AEG) = 28.988 8920 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate • Audit Expectation Gap (AEG) = Total audit expectation gap Number of expectations = 28.988 25 = 1.160 Table 2. Cronbach’s coefficients values and One sample T-test results of AEG. Codes of AEG Cronbach’s coefficients values One sample t-test with test value = 5 Corrected item- total correlation Cronbach's alpha if item deleted Sig. (2-tailed) Mean difference AEG1 0.415 0.691 0.000 -1.177 AEG2 0.387 0.703 0.000 -1.449 AEG3 0.322 0.718 0.000 -1.823 AEG4 0.337 0.794 0.000 -0.823 AEG 5 0.448 0.781 0.000 -1.148 AEG 6 0.492 0.776 0.000 -0.895 AEG7 0.523 0.670 0.000 -1.357 AEG8 0.417 0.693 0.000 -1.469 AEG9 0.380 0.699 0.000 -1.285 AEG10 0.376 0.705 0.000 -1.459 AEG11 0.437 0.783 0.000 -0.830 AEG12 0.465 0.780 0.000 -0.902 AEG13 0.419 0.692 0.000 -1.187 AEG14 0.456 0.780 0.000 -1.036 AEG15 0.520 0.773 0.000 -1.046 AEG16 0.367 0.701 0.000 -0.941 AEG17 0.353 0.702 0.000 -1.131 AEG18 0.384 0.697 0.000 -1.138 AEG19 0.513 0.774 0.000 -1.167 AEG20 0.304 0.711 0.000 -1.020 AEG21 0.479 0.777 0.000 -1.046 AEG22 0.554 00.684 0.000 -1.039 AEG23 0.447 0.697 0.000 -1.079 AEG24 0.559 0.768 0.000 -1.033 AEG25 0.328 0.712 0.000 -1.508 Total AEG -28.988 5.3. The Reliability of the Scales Study used Cronbach’s Alpha coefficient to test the reliability of the dependent and independent variable scales. According to Table 3, the Cronbach’s Alpha coefficients of all variables are in the range of 0.7-0.9. Therefore, the reliability of all scales is ensured for carrying out the empirical study. Table 3. The reliability of the scales. Cronbach’s alpha N The users’ understanding of audit (UU) 0.769 3 The users’ needs from audit (UN) 0.823 3 The auditors’ independence (AI) 0.825 4 The auditors’ competence (AC) 0.801 4 Insufficient audit standards (IAS) 0.800 4 8921 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate 5.4. Exploratory Factor Analysis Results The study used exploratory factor analysis (EFA) to determine whether the set of variables is eligible to participate in the next regression analysis or not. With the factor rotation method (Varimax), the results of EFA analysis according to Table 4 and 5 are as follows: The KMO coefficient is 0.666, it is higher than 0.5, so the discovery factor is appropriate for actual data. The Bartletts’s test has its significance equal 0.000, which is less than 0.05, this means that the observed variables have a linear correlation with the representative factors. Table 4. KMO and Bartlett’s test. Kaiser-Meyer-Olkin measure of sampling adequacy. 0.666 Bartlett's test of sphericity Approx. Chi-square 4523.138 df 153 Sig. 0.000 The results in the Table 5 indicate that the Eigenvalues is 1.136 higher than 1.0. It means that the only factors with Eigenvalue > 1 are kept in the analytical model. In addition, in the Cumulative column, the extracted variance is 71.151. This means that 71.151% of the change of the AEG is explained by observed variables. Table 5. Summary of explanatory variables. Component Initial eigenvalues Extraction sums of squared loadings Total % of variance Cumulative % Total % of variance Cumulative % 1 5.806 32.258 32.258 5.806 32.258 32.258 2 2.316 12.866 45.124 2.316 12.866 45.124 3 1.929 10.714 55.838 1.929 10.714 55.838 4 1.620 9.002 64.840 1.620 9.002 64.840 5 1.136 6.312 71.151 1.136 6.312 71.151 6 0.866 4.811 75.962 7 0.820 4.553 80.515 8 0.710 3.942 84.457 9 0.505 2.808 87.265 10 0.487 2.707 89.972 11 0.393 2.184 92.156 12 0.324 1.798 93.954 13 0.262 1.458 95.412 14 0.255 1.418 96.830 15 0.212 1.176 98.006 16 0.200 1.109 99.115 17 0.114 0.634 99.749 18 0.045 0.251 100.000 Table 6 shows that all the observed variables have loading coefficients greater than 0.5 and there is no cross-loading case. 8922 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate Table 6. Rotating factors. Component 1 2 3 4 5 AI2 0.934 AI4 0.802 AI1 0.735 AI3 0.683 AC3 0.833 AC2 0.816 AC4 0.699 AC1 0.540 IAS3 0.810 IAS2 0.770 IAS4 0.685 IAS1 UU2 0.856 UU3 0.783 UU1 0.725 UN2 0.869 UN3 0.830 UN1 0.786 5.3. Multiple Regression Analysis Results The results of multiple regression analysis are shown in the Tables 7, 8, 9. For testing the multicollinearity phenomenon: the results of the Variance Inflation Factor (VIF) index in Table 9 show that all the independent variables have VIF coefficients between 1.2 and 1.6, all of them are lower than 2.0, so the study concluded that there are no variables multicollinearity among independent variables. The Durbin-Watson residuals correlation test in Table 7 gives a value of 1.693, ranging from 1 to 3, so there is no correlation between the residuals. From the ANOVA test results in Table 8: the F statistic value calculated from the fully adjusted R2 coefficient has a very small significance level (sig.=0.000), so the multiple linear regression model is consistent with the available and usable data. The Coefficient R2 (R square) is 0.415 (according to Table 7), this means that 41.5% of the volatility of the AEG in Vietnam is explained by factors in the model. The regression results in Table 9 show that all 5 variables have significant influence on AEG (with sig. < 0.05). Specifically, Insufficient audit standards (IAS), Users’ needs from audit (UN) factors have positive influence on AEG (Beta coefficient > 0). Nevertheless, Auditors’ independence (AI), Auditors’ competence (AC), Users’ understanding of the audit (UU) factors have negative effects on AEG (Beta coefficient < 0). Table 7. Summary. Model R R square Adjusted R square Std. error of the estimate Durbin-Watson 1 0.644a 0.415 0.408 0.10872 1.693 8923 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate Table 8. ANOVA a test. Model Sum of squares df Mean square F Sig. 1 Regression 3.337 5 0.667 56.464 0.000b Residual 4.705 398 0.012 Total 8.042 403 Table 9. Regression analysis. Model Unstandardized coefficients Standardized coefficients t Sig. Collinearity statistics B Std. error Beta Tolerance VIF 1 (Constant) 1.114 0.054 20.546 0.000 UU -0.040 0.010 -0.172 -3.951 0.000 0.773 1.294 UN 0.093 0.010 0.397 9.317 0.000 0.808 1.238 AI -0.100 0.008 -0.551 -12.902 0.000 0.807 1.240 AC -0.072 0.013 -0.258 -5.479 0.000 0.662 1.511 IAS 0.120 0.012 0.494 9.889 0.000 0.589 1.698 Results from multiple regression analysis, the formalized regression equation for the factors affecting the AEG is as follows: AEG = 0.494 IAS - 0.551 AI – 0.258 AC + 0.397 UN – 0.172 UU+ εi The level of influence from strong too weak of the factors as follows: AI (0.551) > IAS (0.494) > UN (0.397) > AC (0.258) > UU (0.172) 6. Conclusions and Policy Implications The results of reliability of the scale, exploratory factor and multiple regression analysis show that all research hypotheses are accepted. Table 10. Summary of hypotheses testing results. Hypotheses Testing results Beta coefficient Correlation H1: There is a relationship between the users’ understanding of the audit and AEG in Vietnam. Accepted -0.172 Negative H2: There is a relationship between the users’ needs from audit and AEG in Vietnam. Accepted 0.397 Positive H3: There is a relationship between the auditors’ independence and the AEG in Vietnam. Accepted -0.551 Negative H4: There is a relationship between the auditors’ competence and AEG in Vietnam. Accepted -0.258 Negative H5: There is a relationship between the insufficient audit stadards and AEG in Vietnam. Accepted 0.494 Positive Based on the results of the model testing, several policy implications are proposed to narrow the AEG in Vietnam as follows: First, it is essential to revise and enhance certain aspects of current auditing standards. For instance, audit quality control standards could be strengthened by requiring peer reviews among audit firms or incorporating legal provisions that auditors must adhere to. Additionally, the establishment of an audit committee tasked with overseeing auditors' compliance with professional standards should be considered. Furthermore, auditing standards should expand auditors' responsibilities to include verifying and reporting on the effectiveness of the internal control systems. 8924 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 8, No. 6: 8915-8925, 2024 DOI: 10.55214/25768484.v8i6.3889 © 2024 by the author; licensee Learning Gate Second, professional bodies need to gain a deeper understanding of users' reasonable needs, such as assessing the future prospects of the auditee, financial forecasts, and any material misstatements, whether corrected or not. Moreover, an independent body should be established to oversee auditor and audit firm rotations in order to bolster auditors' independence. To enhance auditors’ competence, universities should play a more significant role by ensuring that their training programs bridge the gap between theory and practice, equipping students with essential skills. Audit firms should also organize professional training courses and facilitate experience exchanges among auditors. Additionally, narrowing the AEG may be partially achieved by increasing users' understanding of the audit's objectives and inherent limitations. The Vietnamese audit profession should consider implementing active educational programs to enhance users' knowledge of auditors' roles, responsibilities, and the significance of an unqualified audit report. Educational efforts should be intensified with clients, audit committees at shareholder meetings, and through professional and civic organizations, in order to communicate the merits and limitations of audits effectively. This study proposed a research model for factors influencing the AEG and conducted empirical research based on this model in Vietnam. However, due to the exploratory nature of this research, some limitations exist. For instance, the squared multiple correlation of the model is 41.5%, indicating that the five factors examined explain only 41.5% of the AEG. Therefore, future research could address these limitations by expanding the sample size or incorporating additional factors into the model. Copyright: © 2024 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] C. 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