594 American Journal of Economic and Management Business e-ISSN: 2835-5199 Vol. 4 No. 5 May 2025 THE NEW FACE OF AUDIT QUALITY: INTEGRATING ADVANCED ANALYTICS AND DIGITAL TOOLS Khairul Amri Hasibuan", F.X. Kurniawan Tjakrawala Universitas Tarumanagara Jakarta, Indonesia Email: khairul.126241087@stu.untar.ac.id, kurniawant@fe.untar.ac.id Abstract This study aims to analyze the influence of competence, independence, professional skepticism, and audit fees on audit quality with cloud computing as a moderation variable in Public Accounting Firms in Indonesia. This study uses a saturated sample method or census, involving 156 auditors from a total of 472 Public Accounting Firms in Indonesia as respondents. The data analysis technique used is SEM (Structural Equation Modelling) with the help of Smart PLS 3.0 software. Data analysis includes descriptive statistical tests, validity and reliability tests through outer model tests which include convergent validity, discriminatory validity, composite reliability, and inner model tests. The results showed that professional competence, independence, and skepticism had a positive and significant influence on audit quality, while audit fees did not show a significant influence on audit quality. In addition, cloud computing as a moderation variable strengthens the relationship between competence and independence on audit quality significantly, but does not have a moderation impact on the relationship between professional skepticism and audit fees on audit quality. This research makes a practical contribution to Public Accounting Firms in evaluating strategies and policies to prevent future audit quality declines. Keywords: competency, independency, professional skepticism, fee audit, quality audit, cloud computing INTRODUCTION Enhancing the expertise of auditors in information and technology is essential to meet global standards. It is crucial for auditors to focus on advancing their competencies, particularly in the realm of information technology. They must work collaboratively and synergize with IT professionals (Sitorus & Tambun, 2023). The rapid evolution of digital transformation introduces unique challenges to current auditing practices. The emergence of cloud computing has not only reshaped traditional auditing approaches but also brought forth numerous opportunities and complexities for auditors. Cloud computing systems increasingly enable auditors to access resources and data remotely through an internet connection (Kartika et al., 2019). This technology embodies an idea within the realm of information and communication technology, covering services, applications, data accessibility, and decentralized storage. Users are not required to store data in a single location or understand the detailed configuration of the system providing these services (Mihalache, 2011). The continuous evolution of technology, regulatory requirements, and dynamic business American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 595 environments have rendered traditional audit methods-relying on manual processes and sample- based testing-ineffective for handling complex digital data. Modern auditors must adapt their approaches to effectively manage digital transactions, maintain high audit quality, and apply a risk- based focus. The progress of digital technology has a substantial impact on the expansion of the business sector, with its influence also reaching the auditing processes for financial statements. These changes present new challenges and opportunities for Public Accountants (PAs) and Public Accounting Firms (PAFs). By leveraging digital technology, businesses can streamline operations, enhance efficiency, and capitalize on emerging opportunities, leading to shifts in auditing paradigms. Among the notable technological advancements, cloud computing has transformed traditional audit practices, introducing both complexities and prospects for auditors. Innovations like artificial intelligence, cloud computing, and big data are redefining and modernizing the role of the public accounting profession. Artificial Intelligence facilitates the analysis of big data by simplifying the process of storing, accessing, and backing up data, often utilizing internet-based platforms known as cloud computing. The rapid advancement of technology, coupled with evolving regulations and dynamic business environments, has rendered traditional auditing methods based on manual processes and sample testing insufficient to handle the complexity of digital data. Modern auditors must adapt their approaches to effectively manage digital transactions, prioritize risk-based audits, and ensure high-quality outcomes. This necessity has also driven the Indonesian Institute of Public Accountants (IAPI) to continuously update its Audit Standards (SA), which now comprise 37 standards (www.iapi.or.id). Audits are carried out to reduce information asymmetry between management and shareholders by engaging external parties to review and validate financial statements. Stakeholders depend on financial reports audited by public accountants, emphasizing the essential role public accountants play in upholding the credibility of financial reports prepared by management (De Angelo, 1981). The quality of audits gained significant attention in research following major corporate scandals such as Enron and WorldCom in recent decades (Almomani & Ayedh, 2017; El Dyasty & Elamer, 2021; James & Izien, 2014; Kim et al., 2015; Narayanaswamy & Raghunandan, 2019). Maintaining an objective perspective is crucial when performing audit tests, analyzing outcomes, and preparing financial statements. Auditors are expected to demonstrate both actual independence, ensuring complete neutrality throughout the audit process, and perceived independence, which reflects how their impartiality is viewed by external stakeholders (Aren et al., 2018). Research by Chen et al. (2019) and Leventis et al. (2011) reveals that intense competition in the product market significantly impacts audit fees. Higher competition serves as an external corporate governance mechanism, leading auditors to set lower fees. Similarly, Chou et al. (2011) discovered that market competition helps mitigate agency problems, resulting in reduced audit fees. Rahayu (2010) emphasizes the importance of applying professional prudence (professional skepticism) as a key element of professional competence. This approach highlights the obligation of independent audit professionals to adhere diligently to fieldwork and reporting standards. Audit fees represent the level of effort auditors dedicate to ensuring that financial statements are free from significant errors and misstatements (Wang & Chui, 2015). According to Arens et al. (2017), several factors influence the fees paid by companies to auditors, including risk levels, required expertise, audit complexity, the size of the client, and the nature of their business Khairul Amri Hasibuan", F.X. Kurniawan Tjakrawala 596 operations. Audit fees are often viewed as an indicator of audit quality (Ghafran & O'Sullivan, 2017), with higher fees being associated with improved audit quality (Jadiyappa et al., 2021). This relationship is also linked to competition among audit firms in the market (Ding & Jia, 2012). Nevertheless, Gunn et al. (2019) contend that higher audit fees do not always lead to improved audit quality. In the same vein, Jung et al. (2016) observed that elevated fees might actually contribute to a decrease in the quality of audit work conducted. Deis and Groux (1992) highlight that in situations involving power conflicts, clients may exert pressure on auditors to deviate from professional standards. A client's strong financial position can become a leverage tool, including the threat of replacing auditors, to compel compliance. This dynamic may undermine the auditor's ability to resist client demands, ultimately compromising their independence. Previous studies have highlighted various factors influencing audit quality, albeit with differing results. Biduri et al. (2021) found that time budget pressure and independence did not significantly impact audit quality, while due professional care and cloud computing had positive effects. Additionally, auditor experience was shown to moderate the relationships between these factors and audit quality in accounting firms in Sidoarjo and Surabaya. In contrast, Nursiam et al. (2021) reported that audit fees had no effect on audit quality, whereas audit rotation and audit reputation significantly influenced audit quality in manufacturing firms listed on the Indonesian Stock Exchange. Building upon these findings, this study seeks to explore audit quality under different environmental contexts and timeframes. Unlike prior research, this study incorporates time budget pressure, independence, due professional care, cloud computing, and auditor experience as variables influencing audit quality. It also introduces competence, independence, professional skepticism, and audit fees as factors, with cloud computing serving as a moderating variable. By examining cloud computing’s role, the research aims to determine whether it enhances or diminishes the effects of the studied variables on audit quality. The findings are expected to provide valuable insights for public accounting firms to assess and refine their audit policies and procedures, ultimately improving audit quality for the benefit of public stakeholders. Furthermore, while prior research focused on accounting firms (KAP) located in Sidoarjo and Surabaya, this study expands its scope to include accounting firms across Indonesia. The described circumstances indicate that numerous public accountants still exhibit professional attitudes that fall below established standards. Given the significant role Public Accounting Firms play in shaping public trust through their opinions on corporate financial statements, this study seeks to explore the impact of competence, independence, professional skepticism, and audit fees on audit quality in Public Accounting Firms in Indonesia, with cloud computing serving as a moderating variable. This study aims to analyze the influence of competence, independence, professional skepticism, and audit fees on audit quality in Public Accounting Firms across Indonesia, with cloud computing acting as a moderating variable. It seeks to determine how traditional audit quality determinants perform in a digitally transformed environment and whether cloud-based technology strengthens or weakens their effects. By doing so, the research intends to provide a deeper understanding of how modern technological innovations interact with core auditing principles to shape audit quality outcomes in the contemporary public accounting profession. The findings of this study are expected to contribute significantly to the strategic development of Public Accounting Firms by identifying which factors most affect audit quality in the digital era. Practically, the results offer valuable insights for audit practitioners and regulatory American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 597 bodies in improving audit standards, training frameworks, and technological infrastructure. The integration of cloud computing as a moderating variable highlights the importance of digital adaptability in sustaining high audit quality. Additionally, this study enriches academic literature by providing empirical evidence on the intersection between digital transformation and auditing practices in a developing country context. RESEARCH METHODS This study employs a quantitative research approach, emphasizing numerical data analysis. The research design is causal-associative, intended to examine potential cause-and-effect relationships between variables. This involves independent variables (influencing factors) and dependent variables (affected outcomes) as defined by Sugiyono (2011). The hypothesis testing evaluates whether audit quality, the dependent variable, is impacted by competence, independence, professional skepticism, and audit fees as independent variables, with cloud computing acting as a moderating factor. Primary data were collected directly from the field through a questionnaire comprising several structured statements. This study employs the Structural Equation Modelling (SEM) method. According to Singgih (2011), for SEM models with latent variables consisting of five or more constructs, each explained by three or more indicators, a sample size of 100-150 is deemed sufficient. SEM estimation relies on the Maximum Likelihood Estimation (MLE) method, which is most effective with a sample size of approximately 150-400 (Haryono and Wardoyo, 2012). The required sample size can also be determined by multiplying the number of parameters by 4-11. In this research, six constructs with 42 parameters were used, suggesting a minimum sample size of 6 x 42 = 252 respondents. The sample consisted of auditors working at public accounting firms, with questionnaires distributed over two weeks. Considering the importance of precision and confidence in determining the sample size, the Slovin formula (Slovin, 1960) was utilized with a confidence level of 90%. Using Slovin’s approach, a required sample size of 227 respondents was calculated, aligning with the desired confidence level for the study. Table 1. Instrument Variable Operations Variable Variable Definition Indicator Instruments Scale Audit Quality (Y) A situation where the auditor performs an audit in compliance with auditing standards, identifies and reports findings in line with relevant regulations, and takes appropriate actions based on the audit results (De Angelo, 1981). 1. The generous compensation does not affect reporting client errors; 2. Audits are conducted to minimize errors or irregularities that may arise; 3. The audit report is presented in a manner that is clear and comprehensible to the client; 4. Audit findings are reported honestly, without manipulation or alteration; Questionnaire Interval Khairul Amri Hasibuan", F.X. Kurniawan Tjakrawala 598 5. A firm commitment is maintained to complete the audit process within the agreed timeframe; 6. Decisions made during the audit process are approached with thorough and deliberate consideration; and 7. The audit report objectively presents the findings and conclusions, along with constructive recommendations for improvement. Competence (X1) Auditors have knowledge sourced from formal and informal education as well as sufficient experience in conducting Murtanto and Gudono audits (in Irawati, 2011: 19). 1. Possessing a thorough understanding of Financial Accounting Standards (SAK) and Auditing Standards (SA); 2. Executing audit procedures tailored to the specific industry of the client; 3. Demonstrating the capability to perform analytical assessments effectively. 4. Background in Accounting and following PPL; 5. Have analytical skills in auditing companies; 6. Having more than 2 years of audit experience will be much better at conducting audits; 7. The audit experience of large enterprise audits will be better; 8. The experience of doing a lot of audits would be much better; 9. Conducting a company audit going public audit will be better; 10. Conducting many audits of private companies does not yet have the Questionnaire Interval American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 599 ability to audit companies that go public; and 11. Doing a lot of audits and audits will be better than ever. Independence (X2) Auditors must have a neutral attitude and be free from anyone's interests and avoid conflicts of interest in planning, carrying out and reporting their audit work (Boyton et al, 2003: 66). 1. Remain independent in conducting audits of clients; 2. Not under pressure to provide an opinion on financial statements; 3. Remain independent despite being intimidated; 4. Not providing services other than audit services; 5. There were no audit restrictions; 6. Be careful in making decisions during audits; 7. Free from the obligations of others in the facts of the report; 8. Free from influence in audit considerations on the content of the report; and 9. Free from client misrepresentations due to client relationships. Questionnaire Interval Professional Skepticism (X3) A mindset that consistently involves critical thinking to carefully question and assess the validity of audit evidence in a constructive manner (Tuanakotta, 2010: 21). 1. Collecting and assessing evidence; 2. Questioning and critically evaluating evidence; 3. Collect and assess evidence objectively; 4. Not considering management dishonest but still questioning the honesty of management; and 5. Not satisfied with the evidence is less persuasive because management is considered honest. Questionnaire Interval Fee Audit (X4) Services provided by public accountants based 1. Receive benefits from professionals in addition Questionnaire Interval Khairul Amri Hasibuan", F.X. Kurniawan Tjakrawala 600 on the level of experience and regulations set by the association. (Agoes, 2017) to the fees specified in the audit contract; 2. Setting fees based on what the client will or has received; 3. Fees received from clients are mostly from KAP revenue; 4. Fees determined by the client with a high amount; and 5. Fees are given based on the rules of the association's management. Cloud Computing (Z) Cloud computing is part of artificial intelligence, where this is a development of the world of information technology Narayani dan Mahadewi (2023). 1. Cloud computing technology makes it easier to obtain information about all transactions in the client company; 2. Cloud computing technology helps improve the quality of audit work carried out; 3. Cloud computing technology makes it easier to supervise audit work better; 4. Cloud computing technology makes audit work more productive; and 5. Cloud computing technology helps speed up the audit process. Questionnaire Interval RESULT AND DISCUSSION Based on the gathered research data, respondent characteristics were categorized based on: 1) gender, 2) years of service, 3) position within the Public Accounting Firm (KAP), and 4) frequency of participation in Continuing Professional Education (CPE). Table 2. Characteristics of Respondents Variable Characteristic Total Percentage Gender Man Woman 89 67 57,14% 42,86% Working Period 1 – 3 Years 3 – 5 Years 39 45 25,00% 28,57% American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 601 5 – 8 Years > 8 Years 11 61 7,14% 39,29% Position in KAP Junior Auditor Senior Auditor Supervisor Manager Partner 45 56 17 17 22 28,57% 35,71% 10,71% 10,71% 14,29% Frequency of Participating in PPL 1 – 5 times/year 5 – 10 times/year > 10 times/year 111 33 11 71,43% 21,43% 7,14% Source: Researcher's Processed Data (2025) From the table above, the respondent demographics consist of 156 auditors employed at Public Accounting Firms (KAP) across Indonesia. Regarding gender, 89 auditors (57.14%) are male, while 67 auditors (42.86%) are female. The descriptive statistics for the research variables, based on responses obtained from the completed questionnaires, are summarized in Table 3 below. Table 3. Descriptive Statistics N Minimum Maximum Mean Std. Deviation Competence Independence Professional Skepticism Fee Audit Audit Quality Cloud Computing Valid N (listwise) 156 156 156 156 156 156 156 2,000 2,000 2,000 2,000 2,000 2,000 2,000 5,000 5,000 5,000 5,000 5,000 5,000 5,000 4,053 4,041 3,803 3,832 4,047 4,053 0,938 0,940 0,900 0,901 0,939 0,936 Source: Researcher's Processed Data (2025) The data in the table above shows that the competency variable has a minimum value of 2, a maximum value of 5, and an average (mean) of 4.053, indicating general agreement among the responses. The standard deviation of 0.938 reflects the level of variation in the responses. For the independence variable, the minimum value is 2, the maximum is 5, and the mean is 4.041, also indicating agreement, with a standard deviation of 0.940. The professional skepticism variable reveals a minimum score of 2, a maximum of 5, and a mean of 3.803, suggesting a neutral stance among respondents, with a standard deviation of 0.900. The audit fee variable has a minimum score of 2, a maximum of 5, and a mean of 3.832, signifying agreement on the response scale, with a standard deviation of 0.901. For the audit quality variable, the minimum value recorded is 2, the maximum is 5, and the mean is 4.047, indicating agreement among the responses, with a standard deviation of 0.939. Lastly, the cloud computing variable has a minimum score of 2, a maximum score of 5, and a mean of 4.053, reflecting agreement, with a standard deviation of 0.936. The research data was analyzed using the SmartPLS 3.0 software, as depicted in the diagram below: Khairul Amri Hasibuan", F.X. Kurniawan Tjakrawala 602 Figure 1. Path Diagram with Loading Factor Value The initial data processing results are presented below, encompassing six variables and a total of 42 statements. Table 4. Convergent Validity Test Results Variable Item Outer Loading Value Outer Loading Value Limitation Results Competence (X1) Item1 0.8914 0,6 Valid Item2 0.9027 0,6 Valid Item3 0.9288 0,6 Valid Item4 0.8999 0,6 Valid Item5 0.9031 0,6 Valid Item6 0.8730 0,6 Valid Item7 0.8968 0,6 Valid Item8 0.9087 0,6 Valid Item9 0.9111 0,6 Valid Item10 0.9095 0,6 Valid Item11 0.8743 0,6 Valid Independence (X2) Item1 0.9102 0,6 Valid Item2 0.9167 0,6 Valid Item3 0.9118 0,6 Valid Item4 0.8962 0,6 Valid Item5 0.9167 0,6 Valid Item6 0.8981 0,6 Valid Item7 0.8965 0,6 Valid Item8 0.9274 0,6 Valid Item9 0.8956 0,6 Valid American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 603 Professional Skepticism (X3) Item1 0.9081 0,6 Valid Item2 0.9136 0,6 Valid Item3 0.9040 0,6 Valid Item4 0.9172 0,6 Valid Item5 0.8856 0,6 Valid Fee Audit (X4) Item1 0.9105 0,6 Valid Item2 0.9139 0,6 Valid Item3 0.8980 0,6 Valid Item4 0.9105 0,6 Valid Item5 0.9103 0,6 Valid Audit Quality (Y) Item1 0.9071 0,6 Valid Item2 0.9090 0,6 Valid Item3 0.9203 0,6 Valid Item4 0.9512 0,6 Valid Item5 0.9383 0,6 Valid Item6 0.9188 0,6 Valid Item7 0.8711 0,6 Valid Cloud Computing (Z) Item1 0.9013 0,6 Valid Item2 0.8976 0,6 Valid Item3 0.9273 0,6 Valid Item4 0.9118 0,6 Valid Item5 0.8782 0,6 Valid Source: Researcher's Processed Data (2025) Convergent validity within the measurement model is determined by analyzing the relationship between the score of an item (indicator) and the score of its corresponding construct (loading factor), with a required loading factor value for each instrument of > 0.6. Based on data analysis, all instruments for the competency variable were valid (>0.6). Similarly, all instruments for the independence variable, professional skepticism variable, audit fee variable, audit quality variable, and cloud computing variable were valid (>0.6). As a result, no loading factors needed to be eliminated or removed from the model. Discriminant validity examines the degree to which an indicator uniquely assesses its target construct, distinguishing it from other constructs. This validity can be evaluated using Cross Loading, which examines the correlation coefficient of an indicator with its own construct (cross- loading) relative to its correlation with other constructs. An indicator's correlation value with its associated construct must be higher than its correlation with any other construct. A higher value confirms the indicator's alignment with its intended construct rather than with others (Jorg Henseler et al., 2014). Khairul Amri Hasibuan", F.X. Kurniawan Tjakrawala 604 Table 5. Fornell-Larcker Criterion Discriminant Validity Cloud Comp uting Fee Audi t Inde pen- denc e Com pe- tenc e Audit Qualit y CC Mode ration : Audit Fees on Audit Qualit y CC Moder ation: Indepe ndence to Audit Qualit y CC Mode ration : Comp etence to Audit Qualit y CC Mode ration : Profes sional Scepti cism on Audit Qualit y Profes sional Skepti cism Cloud Coumput ing 0.9034 Fee Audit 0.9730 0.908 7 Independ ence 0.9926 0.977 5 0.907 7 Compete nce 0.9934 0.979 6 0.996 8 0.90 01 Audit Quality 0.9936 0.975 0 0.996 9 0.99 74 0.916 9 CC Moderati on: Audit Fees on Audit Quality - 0.6755 - 0.666 9 - 0.684 5 - 0.68 46 - 0.669 8 10.000 CC Moderati on: Independ ence to Audit Quality - 0.6664 - 0.664 0 - 0.677 0 - 0.67 48 - 0.661 6 0.9854 10.000 CC Moderati on: Compete nce to Audit Quality - 0.6689 - 0.667 8 - 0.678 6 - 0.67 56 - 0.661 7 0.9860 0.9978 10.00 0 CC Moderati on: Professio nal - 0.6869 - 0.685 3 - 0.697 4 - 0.69 55 - 0.682 5 0.9804 0.9916 0.990 6 10.000 American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 605 Scepticis m on Audit Quality Professio nal Skepticis m 0.9788 0.972 7 0.985 0 0.98 52 0.980 5 - 0.7144 - 0.7052 - 0.707 2 - 0.7206 0.9058 Source: Researcher's Processed Data (2025) The table demonstrates that each indicator's loading value on its associated construct surpasses its cross-loading values with other constructs. This result verifies that all latent variables possess robust discriminant validity, as the indicators within each construct are better suited to measuring their respective constructs than those of others. The reliability test results in PLS were evaluated using Composite Reliability and Cronbach's Alpha, as detailed below: Table 6. Composite Reliability and Cronbach Alpha Variabel Laten Cronbach's Alpha Composite Reliability Cloud Coumputing 0.9436 0.9569 Fee Audit 0.9472 0.9595 Independence 0.9733 0.9768 Competence 0.9765 0.9791 Audit Quality 0.9683 0.9736 CC Moderation: Audit Fees on Audit Quality 10.000 10.000 CC Moderation: Independence to Audit Quality 10.000 10.000 CC Moderation: Competence to Audit Quality 10.000 10.000 CC Moderation: Professional Scepticism on Audit Quality 10.000 10.000 Professional Skepticism 0.9452 0.9580 Source: Researcher's Processed Data (2025) Referring to the table above, the composite reliability value for each latent variable exceeds 0.7, indicating that the model demonstrates high reliability. The Cronbach’s Alpha values are as follows: competence (X1) at 0.9765, independence (X2) at 0.9733, professional skepticism (X3) at 0.9452, audit fee (X4) at 0.9472, and audit quality (Y) at 0.9683. The moderation variables X1*Z, X2*Z, X3*Z, and X4*Z have a Cronbach’s Alpha of 1.000, all surpassing the threshold of 0.7, signifying that the indicators of these variables are reliable. Additionally, the cloud computing variable (Z) shows a Cronbach’s Alpha value of 0.9436, also above 0.7, confirming the reliability of its indicators. The reliability test results in PLS are evaluated using Composite Reliability and Cronbach’s Alpha, as summarized below: Khairul Amri Hasibuan", F.X. Kurniawan Tjakrawala 606 Table 7. R-Square Value Results R Square R Square Adjusted Audit Quality 0.9971 0.9969 Source: Researcher's Processed Data (2025) The table above shows that the R-Square value for the audit quality variable is 0.9971, signifying that 99.71% of the variance in audit quality is explained by the constructs of competence, independence, professional skepticism, audit fees, and cloud computing. The remaining portion is influenced by factors not covered in this research. This evaluation focuses on determining the degree to which endogenous latent variables are impacted by exogenous latent variables. The outcomes of this analysis are detailed below: Table 8. Results of Effect Size Analysis (f2) Audit Quality Cloud Coumputing 0.0999 Fee Audit 0.0088 Independence 0.3615 Competence 0.3546 Audit Quality CC Moderation: Audit Fees on Audit Quality 0.0013 CC Moderation: Independence to Audit Quality 0.0837 CC Moderation: Competence to Audit Quality 0.0842 CC Moderation: Professional Scepticism on Audit Quality 0.0001 Professional Skepticism 0.0420 Source: Researcher's Processed Data (2025) From the table above, the following can be summarized: 1) The competency variable significantly impacts audit quality, with an f-square value of 0.3546. 2) The independence variable also has a substantial impact on audit quality, with an f-square value of 0.3615. 3) The professional skepticism variable has a minor influence on audit quality, reflected by an f-square value of 0.0420. 4) The audit fee variable has a small impact on audit quality, with an f-square value of 0.088. 5) The cloud computing variable demonstrates a limited effect on audit quality, with an f-square value of 0.0999. Regarding cloud computing moderation: the effects on competence, independence, professional skepticism, and audit fees on audit quality are minimal, with f-square values of 0.0842, 0.0837, 0.0001, and 0.0013, respectively. To test the hypotheses, statistical values are analyzed using an alpha level of 5% or 0.05 (p-values < 0.05). The critical t-statistic threshold for this analysis is 1.96. A hypothesis is considered supported if the t-statistic > 1.96. The hypothesis test results for this study are as follows: American Journal of Economic and Management Business Vol. 4 No. 5 May 2025 607 Table 9. Results of Hypothesis Tests with Path Coefficients Original Sample (O) Sample Mean (M) Standard Deviation (STDEV) T Statistics (|O/STDEV|) P Values Competence -> Audit Quality 0.5112 0.5286 0.0845 60.526 0.0000 Independence -> Audit Quality 0.4435 0.4330 0.0672 66.023 0.0000 Professional Skepticism -> on Audit Quality -0.0736 -0.0767 0.0303 24.251 0.0157 Fee Audit -> Audit Quality -0.0277 -0.0311 0.0244 11.388 0.2553 Cloud Coumputing -> Audit Quality 0.1586 0.1580 0.0445 35.605 0.0004 CC Moderation: Competence on Audit Quality -> Audit Quality 0.2184 0.2078 0.0698 31.317 0.0018 CC Moderation: Independence on Audit Quality -> Audit Quality -0.2154 -0.2078 0.0683 31.545 0.0017 CC Moderation: Professional Skepticism on Audit Quality -> Audit Quality 0.0037 0.0028 0.0328 0.1133 0.9099 CC Moderation: Fee Audit on Audit Quality -> Audit Quality 0.0108 0.0149 0.0237 0.4569 0.6479 Source: Researcher's Processed Data (2025) Based on the table above, the results of the path coefficients can be interpreted as follows: 1. Hypothesis 1: Competence positively influences audit quality with a coefficient of 0.0000. The P-value is 0.0000 < 0.05, indicating a significant impact. This confirms the first hypothesis that competence affects audit quality. It suggests that improved competence leads to better audit quality. 2. Hypothesis 2: Independence positively impacts audit quality with a coefficient of 0.0000. The P-value is 0.0000 < 0.05, showing a significant effect. This supports the second hypothesis, confirming that independence influences audit quality. An increase in independence corresponds to enhanced audit quality. 3. Hypothesis 3: Professional skepticism positively affects audit quality with a coefficient of 0.0157. The P-value is 0.0157 < 0.05, indicating a significant relationship. This validates the third hypothesis, suggesting that higher professional skepticism results in improved audit quality. 4. Hypothesis 4: Audit fees have no significant influence on audit quality, as evidenced by a coefficient value of 0.2553 and a P-value of 0.2553, which exceeds the threshold of 0.05. This contradicts the fourth hypothesis, demonstrating that changes in audit fees do not improve audit quality. 5. Hypothesis 5: Cloud computing positively influences audit quality with a coefficient of 0.0004. The P-value is 0.0004 < 0.05, indicating a significant impact. This supports the fifth hypothesis, suggesting that advancements in cloud computing improve audit quality. 6. Hypothesis 6: Cloud computing moderates the relationship between competence and audit quality positively, with a coefficient of 0.0018. The P-value is 0.0018 < 0.05, indicating significant moderation. This confirms the sixth hypothesis, showing that better cloud computing strengthens the link between competence and audit quality. Khairul Amri Hasibuan", F.X. Kurniawan Tjakrawala 608 7. Hypothesis 7: Cloud computing positively moderates the relationship between independence and audit quality, with a coefficient of 0.0017. The P-value is 0.0017 < 0.05, indicating significant moderation. This supports the seventh hypothesis, showing that improvements in cloud computing enhance the influence of independence on audit quality. 8. Hypothesis 8: Cloud computing does not moderate the relationship between professional skepticism and audit quality, with a coefficient of 0.9099. The P-value is 0.9099 > 0.05, indicating no significant moderation. This refutes the eighth hypothesis, suggesting that cloud computing has no effect on the relationship between professional skepticism and audit quality. 9. Hypothesis 9: Cloud computing does not moderate the relationship between audit fees and audit quality, with a coefficient of 0.6479. The P-value is 0.6479 > 0.05, indicating no significant moderation. This rejects the ninth hypothesis, showing that improvements in cloud computing do not impact the relationship between audit fees and audit quality. CONCLUSION This research investigates the influence of Competence, Independence, Professional Skepticism, and Audit Fees on Audit Quality within Public Accounting Firms in Indonesia. The findings indicate that Competence, Independence, and Professional Skepticism significantly affect Audit Quality, while Audit Fees show no meaningful impact. Furthermore, Cloud Computing positively moderates the relationships between Competence and Independence with Audit Quality but does not serve as an effective moderator for the relationships involving Professional Skepticism and Audit Fees. These results underscore the importance of enhancing auditor competencies and maintaining independence, particularly in the context of digital transformation through cloud-based systems. 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