change the way of writing for this paper keeping the same stats and meaning Executive Summary This study primarily focuses on determining the determinants of Audit fees using multivariate analysis. For analysis, data are compiled from 269 companies using eight variables. The identified variables for the study are Big 4 Auditor, Audit size, BETA, Return on Asset, Non-audit fee, inventory, current liabilities, and sales. The study employed descriptive statistics, correlation, regression, and factor analysis. The finding concluded that the non-audit fee is strongly significant in determining Audit fees. Introduction The history of the auditing job can be traced back to an early time in world history, for example, auditing activities during Babylonian times (around 3,000 BC) and from ancient China (Mohammed & Barwari, 2018). The available literature presents that audit quality is influenced by different factors, including audit fees and audit quality is one of the most concerning factors in the field of auditing (Owusu & Bekoe, 2019). Audit fees are defined as the amounts of money charged by the auditor for an audit performed for the company. The determinants of audit fees as a topic have been researched for decades, but still, it is a subject of interest for many researchers. The amount of audit fees is often determined before conducting the audit process and further, the determination of audit fees is based on the agreement between the auditor, and the audit firms depending upon the various factors. Therefore, the primary objective of the study is to identify the factors determining the audit fees with various independent variables. The study uses multivariate analysis like descriptive, correlation, regression, and factor analysis on various independent variables to identify the significant factors in determining audit fees. Literature Review Mohammed and Barwari, (2018) examine audit fee determinants in AIM, focusing on 23 machinery equipment firms from 2007 to 2011. Auditee size and complexity appear as significant factors affecting audit fees, with audit timing also showing a positive correlation. On the other hand, auditee risk does not significantly impact fees. Furthermore, client size and provision of non-audit services do not affect fee variation. The findings suggest unique market regulatory dynamics influencing fee determinants compared to previous research. ElGammal and Gharzeddine, (2020) examine the perceived importance of 28 audit fee determinants in Egypt, surveyed among 63 external auditors and client representatives. Findings suggest importance across attributes, with top determinants including audit firm reputation and complexity level. The study also showed no significant difference between auditor and client perceptions, with auditor-related factors rated higher. Hassan and Naser, (2013) examine factors influencing audit fees paid by non-financial companies listed on Abu Dhabi Stock Exchange (ADX). Backward regression analysis was conducted to assess the relation between audit fees and certain company attributes. The result showed a direct relationship between audit fees and corporate size, business complexity, and audit report lag variables. The findings further revealed that audit fees are not significantly influenced by a company's profitability, risk, and status of the audit firm. Kikhia, (2015) examines the factors influencing the level of external audit fees paid by firms to their auditors in Jordan. The study uses variables like auditee size, the complexity of the client, profitability, client risk, auditor size, and auditor tenure to identify the determinants of audit fees paid. The study revealed that auditee size played a key determinant of external audit fees. On the other hand, audit tenure has no significant relationship with audit fees. Owusu and Bekoe, (2019) examine the perception of external auditors on the leading factors that influence audit fees determination. Researchers used Exploratory Factor Analysis (EFA) to explore the determination of audit fees on some identified factors. The EFA results suggest that audit fee determinants are grouped into five distinct factors Audit firm reputation, experience & expertise; Nature and scope of the audit; Market-wide factor; Client size; and Client risk. The findings of this study show that client risk was rated the most important followed by the nature and scope of the audit determinant of audit fees. On the other hand, the market-wide factor was rated the least important factor in the determination of audit fees. Hypothesis H1: The audit fee has a significant relationship with BETA, Big4Auditor, Auditee size, Return on Assets, Non-Audit Fees, Current Liabilities, Sales, and Inventory. Table 1 Definition of variables Variables Sign Inferences Source Dependent variable Audit Fees AUDIT FEE Amount of fee paid by an organisation to the auditor (Mohammad Hassan & Naser, 2013) Independent Variable Big 4 Auditor BIG4AUDITOR Dummy variable: the value of 1 if the auditor belongs to the Big Four and 0 otherwise. (ElGammal & Gharzeddine, 2020) Auditee size LnTOTASSETS Natural log of total assets (Kikhia, 2014) BETA BETA Risks arising from the market (Mulyadi & Narsa, 2020) Return on Assets ROA Explains the profitability of the organisation (Owusu & Amoah Bekoe, 2019) Non-Audit Fees NAS Services provided by the auditor such as advice (Mohammed et al., 2018) Inventory INVENTORY Physical inventory and records (Amba & Al-Hajeri, 2013) Current Liabilities CURLIAB Debts a firm must pay within an operating cycle, (ulHaq & Leghari, 2015) Sales SALES all activities which involved in selling a product and service (ElGammal & Gharzeddine, 2020) Source: Author's Design Analysis One: Data Exploration Table 2 Descriptive Statistic Mean Std. Deviation Minimum Maximum AUDIT_FEES 355.21 941.19 11 8,400.00 BIG4AUDITOR 0.75 0.43 0 1 INVENTORY 64,410.51 202,028.55 0 1,878,000.00 BETA 0.69 2.11 -25 5.62 SALES 536,399.88 1,691,959.50 2071 15,177,800.00 CURLIAB 153,537.82 475,922.48 46 3,487,873.00 ROA 0.03 0.25 -1.85 1.46 NAS 240.51 591.21 0 5072 LnTOTASSETS 11.73 1.67 8.34 17.08 Source: Author's calculation Table 2 presents a descriptive analysis of the variables. The mean audit fees paid by the companies in this study was 355.21 and the highest audit fees paid was 8,400. There were companies in this study which did not have any inventories. The minimum value of BETA and ROA was negative, which shows that there were companies that faced no risk arising from the market and there were also companies that ran on loss for the period of study. Table 3 Missing value analysis Frequency Percent Valid Percent Cumulative Percent .00 201 74.7 74.7 74.7 1.00 51 19.0 19.0 93.7 2.00 15 5.6 5.6 99.3 3.00 2 .7 .7 100.0 Total 269 100.0 100.0 Source: Author's calculation Table 3 presents the missing values from the data collected and compiled. Out of 269 data collected, 201 have responded completely and 68 (51, 15, and 2) of them have not responded to the various questions like 1, 2, and 3. Figure 1 Outliers Figure 1 presents the outliers from the data. Of the eight variables used to identify the determinants of the audit fees, the sales variable has an extreme outlier followed by current liabilities. 1. Scatter plot Figure 2 Audit Fees by Total asset Figure 2 illustrates the relationship between the natural logarithm of total assets (LnTOTASSETS) and audit fees. The R square value is 0.349 signifying that approximately 34.9% of the variation in audit fees can be explained by changes in LnTOTASSETS. Figure 3 Audit Fees by Big4 Auditor Figure 3 presents the relationship between Audit fees and Big4 Auditors. The R square value is 0.020, which suggests that only about 2% of the variation in audit fees can be attributed to the Big4 Auditor. Therefore, the analysis presents a weak positive relation between the two variables. Figure 4 Audit Fees by BETA Figure 4 show the relationship between audit fees and BETA. The calculated R square value is 0.011 which explain only 1.1% of the variation in audit fees. Therefore, scatter plot suggests a weak positive relation between beta coefficients and audit fees. Figure 5 Audit Fees by ROA Figure 5 presents the relationship between Audit fees and Return on Asset. The calculated R square value is 0.002 which explain only 0.2% of the variation in audit fees. Therefore, the scatter plot depicts a very minimal relationship. Figure 6 Audit Fees by Non-Audit Service Figure 6 presents the relationship between Audit fees and Non-Audit Service/Fees. The calculated R square value is 0.792 which explain only 79.2% of the variation in audit fees. Therefore, the scatter plot depicts a strong relationship and a significant factor in determining audit fees. Figure 7 Audit Fees by Inventory Figure 7 presents the relationship between Audit fees and Inventory. The calculated R square value is 0.526 which explain only 52.6% of the variation in audit fees. Therefore, the scatter plot depicts a strong relationship and a significant factor in determining audit fees. Figure 8 Audit Fees by Current Liabilities Figure 8 presents the relationship between Audit fees and current liabilities. The calculated R square value is 0.611 which explain only 61.1% of the variation in audit fees. Therefore, the scatter plot depicts a strong relationship and a significant factor in determining audit fees. Figure 9 Audit Fees by Sales Figure 9 presents the relationship between Audit fees and sales. The calculated R square value is 0.467 which explain only 46.7% of the variation in audit fees. Therefore, the scatter plot depicts a moderate relationship in determining audit fees. Table 4 Correlation Analysis AUDIT_FEES BIG4AUDITOR LnTOTASSETS BETA ROA NAS INVENTORY SALES CURLIAB AUDIT_FEES Pearson Correlation 1 Sig. (2-tailed) BIG4AUDITOR Pearson Correlation 0.142 1 Sig. (2-tailed) 0.055 LnTOTASSETS Pearson Correlation .591** .274** 1 Sig. (2-tailed) 0.000 0.000 BETA Pearson Correlation 0.106 0.126 .146* 1 Sig. (2-tailed) 0.152 0.088 0.049 ROA Pearson Correlation 0.045 -0.027 0.080 -0.091 1 Sig. (2-tailed) 0.548 0.718 0.282 0.217 NAS Pearson Correlation .890** .148* .605** 0.107 0.040 1 Sig. (2-tailed) 0.000 0.044 0.000 0.147 0.589 INVENTORY Pearson Correlation .726** .149* .561** 0.069 0.064 .569** 1 Sig. (2-tailed) 0.000 0.044 0.000 0.355 0.392 0.000 SALES Pearson Correlation .683** 0.144 .486** 0.082 0.039 .555** .866** 1 Sig. (2-tailed) 0.000 0.054 0.000 0.275 0.600 0.000 0.000 CURLIAB Pearson Correlation .781** .150* .460** 0.092 0.003 .641** .713** .863** 1 Sig. (2-tailed) 0.000 0.042 0.000 0.215 0.964 0.000 0.000 0.000 ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). Table 4 above shows the correlation between variables used in this study. The independent variables were Big 4 Auditor, Natural log of Total Assets (Size), BETA, Return on Assets, Non-audit Fees, inventory, sales, and current liabilities. The test was conducted at 5% level of significance. A total of 183 samples were used in this study. From the table, Big 4 auditor was positively significant with size of the auditee, non-audit fees, inventory, and current liabilities. The size of the auditee was also significant with BETA at 5% level of significance, and with NAS, inventory, sales, and current liabilities at 1% level of significance. NAS also shared a positive significance with inventory, sales, and current liabilities at 1% level of significance. Audit fees shared the highest positive relation with NAS (correlation: 0.890, sig: 0.000) at 5% level of significance. On the other hand, audit fees shared its lowest relation with LnTOTASSETS (correlation: 0.591, sig: 0.000). Table 5 Regression Analysis Unstandardized Coefficients Standardized Coefficients t Sig. Collinearity Statistics B Std. Error Beta Tolerance VIF (Constant) 125.330 209.088 0.599 0.550 BIG4AUDITOR -34.559 55.179 -0.016 -0.626 0.532 0.907 1.103 BETA 4.764 11.003 0.011 0.433 0.666 0.961 1.041 ROA 34.009 90.479 0.009 0.376 0.707 0.976 1.024 NAS 0.951 0.057 0.597 16.672 0.000 0.450 2.225 LnTOTASSETS -10.457 18.929 -0.018 -0.552 0.581 0.519 1.928 INVENTORY 0.002 0.000 0.394 7.533 0.000 0.211 4.741 CURLIAB 0.001 0.000 0.428 8.064 0.000 0.205 4.889 SALES 0.000 0.000 -0.348 -5.073 0.000 0.123 8.162 Model summary R Square Adjusted R Square F Sig. Durbin-Watson 0.901 0.897 195.516 0.000 1.827 Source: Author's calculation Table 5 above presents the regression analysis of the variables used in this study. For the analysis, audit fee was kept as a dependent variable, and big 4 auditor, BETA, return on assets (ROA), non-audit fees (NAS), natural log of total assets (LnTOTASSETS), inventory (INVENTORY), current liabilities (CURLIAB), and SALES were kept as independent variables. The analysis was conducted at 5% level of significance. The significance value of NAS, INVENTORY, CURLIAB, and SALES was less than 0.05 (i.e. 0.000). Therefore, it can be stated that NAS, INVENTORY, CURLIAB, and SALES had a significant impact on the audit fees of the companies considered in this study. The adjusted R square from the model summary shows that 89.7% of the times the independent variables are explaining the change in dependent variables. The F value (195.516) from ANOVA table was also significant at 5% level of significance. Table 6 Factor Analysis Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.737 Bartlett's Test of Sphericity Approx. Chi-Square 700.265 df 10 Sig. 0.000 Source: Author's calculation Table 7 Component Matrix Component INVENTORY .892 SALES .911 CURLIAB .886 NAS .790 LnTOTASSETS .719 Total variance explained 71.016% Extraction Method: Principal Component Analysis. 1 component extracted. Source: Authors calculation Table 6 and Table 7 above show the factor analysis of the variables used in the study. The KMO Bartlett's test presents that the test was significant at a 5% level of significance. A total of one component was identified from the component matrix. The component can be named a COMPANY component as all the variables in this component are internal to any organisation. Findings The findings are as follows: From the descriptive analysis, it is found that, amongst the eight variables, the most significant determinants influencing the audit fees are Non-Audit Service, Current Liabilities, Inventory and sales with 79.2%, 61.1%, 52.6% and 46.7% respectively. On the other hand, variables like Return on Asset, BETA, and Big4 Auditor have a very minimal effect on determining audit fees. The correlation analysis revealed several significant relationships among the variables. Particularly, the Big 4 auditor, total asset, and NAS showed positively with the other variables. Most importantly, the audit fees demonstrated the strongest positive correlation with NAS and the weakest with LnTOTASSETS. These findings suggest that factors such as the Big 4 auditor, Total Asset, and NAS are influential in determining audit fees. Regression analysis results showed that NAS, INVENTORY, CURLIAB, and SALES had a significant impact on audit fees, with values below 0.05. The adjusted R square value collectively explains 89.7% of the variability in audit fees by independent variables. Conclusion The study concludes that, from the eight identified variables, only four variables have a significant impact on determining the Audit fees in the organization. The most significant determinant of audit fees is Non-Audit Service as per the study. However, there are other analyses and considerations of factors to determine the determinants of Audit Fees