Research Article: 2020 Vol: 24 Issue: 2
Raed Kanakriyah, Al-Balqa Applied University
Purpose - This study examines the factors that have an impact on external audit fees on manufacturing companies listed on Amman Stock Exchange (ASE). Also, it tries to builds a model to determined and measuring external audit fees. Design/methodology/approach - A multiple regressions model was developed for the factors which expect to have possible impact on audit fees ( such as: client size, profitability, client’s risk, client’s complexity, client’s industry type, status of audit firm, external audit report lag, audit rotation and audit committee independence). The data were collected from the annual reports and information available on ASE website covering the period from 2014 to 2018. Findings - The most important factors that have significant effect on audit fees are: Audit Report Lag, risk, client size, status of the audit firm, and corporate complexity. Also audit fees are negatively and significantly associated with industry type and profitability. Moreover, no relations were detected between audit committee independence and audit rotation with the audit fees. Originality/ value – To the best of the researchers’ knowledge, there are little studies that have been performed to examine the factors influencing external audit fees of manufacturing companies listed on Amman Stock Exchange (ASE) for 5 years period. In addition, this study is considered among the first studies to build a model which help companies to determine auditing fees.
Audit Fees, Factors Effect on Audit Fees, Manufacturing Companies Listed on ASE, Client Size, Profitability, Client’s Risk, Client’s Complexity, Client’s Industry Type, Status of Audit Firm, External Audit Report Lag, Audit Rotation and Audit Committee Independence.
Corruption control is a subject that captures lots of attention recently. Many discussions had been made within both private and public sectors on what methods for ''anti-corruption'' can be applied. Since Enron and Arthur Andersen case, there was an increasing need for new regulations to improve corporate governance. As a result, Sarbanes Oxley Act (SOX) was issued in 2002, to secure investors from the possibility of fraudulent accounting activities by corporations and to mandate strict reforms to improve financial disclosures and prevent accounting fraud.
Audit profession plays a salient role in preserving investors' interest among other parties that use financial information. Thus, the financial statements must be prepared accordance to International Accounting Standards (IAS) and authorized by an external auditor who ensures that financial information reflects the company’s true position. Therefore, audit fees consider essential factor that reflect the degree of sincerity of published financial reports.
In essence, this study aims to explore all factors that may impacting on external audit fees of manufacturing companies listed on Amman Stock Exchange (ASE) and to generate a model to help the related parties.
Previous literature conducted and discussed the factors influencing external audit fees only covering developed countries. In addition, most previous studies used four to five variables that may effect on audit fees such as (size, profitability, risk, type of industry and complexity), (Apadore, & Letchumanan, 2016; AL-Mutairi, et al., 2017; Januarti & Wiryaningrum, 2018; Ghadhab et al., 2019). Hence, the importance of the current study arises from the fact that it considers more comprehensive factors that may effect on audit fees. Moreover, previous literature had considered the audit fees as a factor which has an influence in external audit. However, most of these studies were conducted in developed countries.
Therefore, this study generates a more comprehensive view over audit fees as it considers nine factors which have possible impact on audit fees. Moreover, this study aims to build a model to define the main factors that have an effect on external audit fees, as well as, it is expected to enrich the abilities of both the auditor and the auditee to approximate the audit fees. Finally, some recommendations for developing audit career and regulators in Jordan.
The next section presents the literature review that is related to the current study in order to determine the main factors affecting the audit fees. In addition, the next sections include the theoretical framework for the current study.
Over the past three decades or so, a large body of literature has tackled the issue of audit fees (AL-Mutairi et al., 2017; Januarti & Wiryaningrum 2018; Ghadhab et al., 2019). The main goal of these studies is to find the factors which can determine the audit fees. Since the 1980s, Simunic (1980) argued that business size and company’s complexity are two important factors which determine audit fees. Later on, Zhang & Myrteza, (1996) suggests that audit workload, complexity, risks, auditee's inputs and the size of accounting firm are all among the factors which influences the audit fees determination. This view was consisted in literature with some exception. For example, (Toto & Stephanus, 2015; Rusmanto & Waworuntu, 2015) find a perplexing result which suggest that profit, business complexity and the number of subsidiaries are not significant variables in determining audit fee. Thus, to inform this debate, this study reviews a plethora of prior studies to extract the most possible factors which are expected to influence Jordanian audit fees.
Recently, Naser & Hassan (2016) studied the determination criteria for audit fees among non-profit companies which are active in United Arab Emirates by measuring the following factors; business size, independence of the Audit Committee, complexity, profitability, risk, industry type and audit report lag. Their finding suggests a strong relationship between the company size, the independence of the Audit Committee and audit fees. In other hand, they find a negative correlation between the business complexity and external audit fees indicating that audit fees are not significantly correlated with the company's profitability, risk, industry type, audit report lag and status of audit firm. In their study, Alzeban & Sawan (2016) concluded that external audit fees are affected by compliance with International Accounting Standards (IAS) and the levels of internal audit, which indicate that compliance with these standards requires more auditing process and increase the audit fees.
In the other spectrum, Nelson & Muhammad Rushdie (2015) tested the normal relationship between corporate ownership structures and audit fees paid to external auditors. Their results suggest a positive relationship between audit fees and foreign ownership firms. Shan & Troshani, (2016) study the relation between extensible Business Reporting Language (XBRL) and audit fees for a number of companies operating in an emerging economy. The study indicates that (XBRL) has a negative effect on audit fees. Additionally, Lin & Yen (2016) find that companies which applied IFRS in their financial reports paid incremental fees. Musa (2017) foreshadowed that Ghanaian companies’ business size, international firms, affiliation of audit firms and profitability are significant when determining the audit. A more recent study conducted by Tran, et al., (2019) argued about a number of factors that positively affect audit fees, namely; the characteristics of the audit company, auditors, auditors’ characteristics and the relationship between the audit company and clients. The study also revealed that the auditor’s age and qualifications have an impact on the audit fees. while Ghadhab et al. (2019) detected a significant correlation between the size and reputation of the audit firms and the audit quality, also they discovered a strong association between audit fees charged and the audit quality.
Another stream of the literature investigates the relationship between the industry specialization and audit fees. Interestingly, results were inclusive regarding their effect as Fleming et al. (2014) results, which indicate a negatively related between auditor industry and audit fees, while Nagy (2014), showed a significant positive relation for both audit specialization and audit fees.
A broad strand of literature studied the relation between audit fees and auditing firms’ classification. Findings suggest a positive relation between the audit fees and the classification of auditing firms, as well as, auditors take higher audit fees for higher risks firms (Fafatas & Sun, 2010; Campa, 2013). Similar results were captured when investigating the relationship between audit fees and companies’ corporate governance. Lenard et al. (2012) examined the extent of which the corporate governance variables such as (audit independence, audit committee experience and fraud litigation) can affect audit fees. The result indicates a positive correlation between fraud and auditing fees.
In Jordanian context, Suwaidan & Qasim, (2010) found that Jordanian managers prioritize the objectivity, competence and work performance when choosing their audit firm.
Overall, prior studies have shown that the factors affecting the auditor's fees are not always consisted. One explanation of this differentiation may refer to the companies’ financial position statement, negotiations with the client on the commercial budget, the additional work stage during the audit process (Maarse, 2018), study environment and the different legislation and regulations from one country to another. Moreover, prior studies (Dou et al., 2019; Tran et al., 2019) suggest that customer characteristics have a strong impact on audit fees, whereas, other studies suggest that the audit characteristics have the strongest impact on audit fees (Tran et al., 2019). Furthermore, there are a general consensus in literature about the impact of business size, the complexity of the audit process, the risk of audit, the size of the audit office, the time required by the audit, and the integrity of the internal control system.
In that respect, this study argues about an inconsistent result found in prior literature regarding the factors that may effect of the external audit fees. To inform this debate, this study focuses on how to identify the factors needed to be taken into consideration when determining the audit fees, and how to build a new model to help companies to determining audit fees. The next section furnished the adopted independent variables and how builds the study’s hypotheses.
Client Size
The Prior literature showed a consensus that the main factor influences the external audit fees is the client size (Scott 7 Gist, 2013; Simunic, 1980; Apadore & Letchumanan, 2016; Musah, 2017; Abdullah et al., 2017; Wu, 2018; Januarti & Wiryaningrum 2018; Ghadhab et al., 2019). These studies among others had determined three factors to impact the audit fees, namely; company size, degree of business complexity and risk. They also utilized the company’s total assets to measure client size, and found a significant positive relationship between audit fees and business size. Thus, similar method will be adopted in this study to generate the following hypothesis:
H1: There is a strong association between the client size and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
Client’s Profitability
Profitability is considered as an essential indicator for evaluating management performance in using their assets effectively. Profitability can be defined as an effective used of company’s resources and a high return on assets. Highly profitable firms are usually pay higher audit fees due to the extra time and effort needed for collecting and testing the audit evidences. This indicates a positive relationship between profitability and audit fees (Simunic, 1980). However, Hossain, & Sobhan, (2019), in their study did not find any relation between profitability and audit fees. Hence, this study used the companies’ net income divided by the total assets at the year-end to measure the profitability, such as (Simunic,1980).
H2: There is a strong association between the client’s profitability and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
Client’s Risk
Audit risk is considered as another major element in determining the audit fees. Risk can be defined as the opportunity of a loss. Auditors are expected to be cautious when expressing their opinion in order to avoid future litigations. This requires extra working time and effort for completing the auditing process, hence, increasing the audit fees. This argument is supported by Habib et al., (2013) who argue that auditors charge higher audit fees from firms characterized by higher risks. Consequently, external audit fees are significantly associated with clients’ risk (Gonthier-Besacier & Schatt, 2007). Moreover, other evidences suggest a strong relation between firm leverage levels and company risk (Simunic, 1980; Suryani & Sitorus, 2018). Therefore, the client risk can be measured by the leverage ratio as total debt to total assets.
H3: There is a strong association between the client’s risk and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
Client’s Complexity
It is well established in accounting context that a greater number of subsidiaries lead to a greater operations and activities. This resulted in more auditing activities that will increase the audit fees. In other words, increasing diversified or foreign operations of the client or number of products require more audit work. Therefore, audit firms charge higher audit fees (Hassan & Naser, 2013). Furthermore, Simon (1985) expresses that the number of branches and the percentage of foreign investment in a company are the major aspects of complexity. Hence, the company’s complexity is expected to be positively related to the remuneration of the external auditor.
H4: There is a strong association between the client’s complexity and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
Client’s Industry Type
Industrial companies are expected to involve certain type of expertise by the audit company along with an extra working hour compared to other sectors. Fleming et al., (2014) argue that a negative relation exists between the company’s sector and the audit fees. However, (Nagy 2014; Tran et al., 2019) illustrate a positive relation between company’s sector and audit fees. Hence, this study did not detect a measurement base for this factor. Nonetheless, previous studies stated that manufacturing firms tend to disclose more voluntary information which in turn requires more audit fees. Thus, the current study used dummy variable to measure this variable.
H5: There is a strong relation between the industry type of client and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
Status of Audit Firm
The classification of an audit firm is a significant element influencing audit fees. Lennox (1999) noted that large audit firms can offers a greater audit quality and reliability to clients' financial statements compared to the small one. This is expected to lead to a higher audit fees charged by those companies (Francis, 1984; Simunic and Stein, 1987; Fafatas & Sun, 2010; Campa, 2013; Tran et al., 2019; Super, & Shil, 2019). The findings suggest a positive relation between the audit fees and the classification of auditing firms. Consistent with Simunic, (1980), this research used a dummy variable for the big four audit companies and suggests the following hypothesis:
H6: There is a strong association between the audit firm status and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
External Audit Report Lag
In Jordan as in many countries, the financial year ended on December 31. Jordanian companies are subjected to the audit process during the “Peak Season” which begins in December 31 till March 31. As a result, auditors usually charge more prices for this peak season. Hassan, (2018) noted that preparing audit report through the period lag between year-end and 31 April does not show a significant correlation with the audit fees. while Dao (2014) discover a negative relationship between audit report lag and audit fees.
H7: There is a strong association between the lag of audit report and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
Audit Committee Independence
Audit committee independence considered as another influential factor that may impact the audit fees in a positive or a negative manner (Abbott et al., 2003). Lenard et al., (2012) indicate a positive correlation between the audit committee independence and the audit fees. This implies that the number of independent members in the audit committee (from company external) require more auditing fees and vice versa. The Jordanian Companies’ Act (No. 197), require that the audit committee must be independent from the board of directors. Given this fact, the current study examines this factor by measuring the number of independent members on the audit committee from the total.
H8: There is a strong association between the independence of audit committee and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
Auditor Rotation
Accounting literature showed that the audit companies charge higher at the beginning of the engagement. These fees are gradually decreasing by coming years due to the repetitive procedures and the familiarities gained by the work. Depending on Malagila et al. (2020), they found that audit rotation boosts confidence in the audit process and negatively impacts audit costs. The current study used a dummy variable to measure this variable.
H9: There is a strong association between ''auditor time with same company'' and audit fees within listed manufacturing companies in Amman Stock Exchange (ASE).
Audit fees (Dependent Variable)
Audit fee is the sum of the money paid to the auditor for an audit process that had been performed. There are several factors which impact the audit fees. For example: time spending in auditing process, service required, and the number of employees involved in the audit process (Hassan, 2018). In the other hand, there are other important non-quantitative factors affecting the audit fees such as internal audit costs, the extent of using accounting standards consistently, the extent of multiple client activities and the extent of competition between audit companies.
Overall, there are few studies examined the factors influencing external audit fees of manufacturing companies listed on Amman Stock Exchange (ASE) for a period of 5 years. None of these studies have investigated the audit fees as a main study problem except in the context of other financial and accounting topics or with less coverage. Hence, this study intends to provide a more comprehensive view over this topic. The next section discusses the adopted research methodology to answer the studies questions.
As in 2018, the number of listed companies in Amman Stock Exchange (ASE) was 234. According to Jordanian Companies’ Act (No. 142), public companies are required to prepare an audited financial statement by an external auditor in order boost investor’s confidence toward companies. Hence, a number of analytical techniques were used in this study to ensure that the expected results could be achieved and to measure the effectiveness of the model. Therefore, the study applied regression analysis and correlation (correlation matrix), descriptive statistics and linear regression, to test the ability of this model in determining the audit fees. The researcher also applied a multivariate analysis for each year (5 years) as well as for all five years combined.
Population and Sample
The study population consists of all manufacturing companies listed on Amman Stock Exchange (ASE) between the years 2014 to 2018. Thus, a total of 64 of manufacturing companies were included, as this sector plays a significant role in Jordanian economic development. Therefore, creating a model that is able understand the factors used to determine the auditing fees is expected to be extremely beneficial. In addition, a number of companies were excluded from the current study due to the lack of some needed data or undeclared audit fees. Hence, the final sample is 58 with 290 firm-years observation for generalizing the study’s result and overcoming the small sample size of Jordanian market.
Study Variables
Dependent Variable
The dependent variable in this study is the audit fees which will be measured by the normal amount paid as the auditor’s fees.
Independent Variables and their Measurement
Table 1 furnishes the main independent variables adopted in the current study.
| Table 1 Variables and Their Measurements | |||
| Variable | Type | Code | Proxies |
| Audit fees | Dependent | ADFEES | Audit fees measured by the natural logarithm of the auditor’s fees |
| Client Size | Independent | CSIZE | The natural logarithm of the total assets. |
| Profitability | Independent | PROF | Profitability measured by net income/sales. |
| Client’s Risk | Independent | CRISK | Risk measured by total liabilities/total assets. |
| Client’s complexity | Independent | COMP | Measured by number of subsidiaries. |
| Industry | Independent | INDS | Manufacturing companies |
| Auditor Rotation | Independent | TPA | If company changed the auditor in the first three years = 0; if not change = 1 |
| Status of the audit firm | Independent | AUST | 1 for big four companies, 0 if otherwise |
| Audit report Lag | Independent | ARL | The lag between the audit report and the end of the accounting year. |
| Audit committee independence | Independent | ADCOM | The proportion of independent members on the audit committee. |
The Regression Model
A multiple regression model including the adopted variables was developed in order to answer the study’s hypotheses:
ADFEES = α0 + α1 CSZIE + α2 PROF + α3 CRISK + α4 COM + α5 INDS + α6 AUST + α7 ARL + α8INDCOM + α9TPA + e.
Where:
ADFEES: Audit fees
CSIZE: Company size.
PROF: Profitability
CRISK: Company Risk.
COM: Corporate complexity.
INDS: Industry.
AUST: Status of the audit firm.
ARL: Audit report Lag.
ADCOM: Audit committee independence.
TPA: time period in the same company
α0: Intercept.
Α1-8: Coefficients.
e: Error term.
Descriptive Statistics of The Study Variables
Table 2 presents the findings of the descriptive statistics conducted for the study’s variables in order to identify the general indicators of the independent and the dependent variables.
| Table 2 Results of Descriptive Statistics | |||||
| N | Minimum | Maximum | Mean | Std. Deviation | |
| Audit Fees | 290 | 0.00000 | 212930.00000 | 32127.0131250 | 34378.22426022 |
| Risk | 290 | 0.08 | 227.53 | 36.6224 | 29.42676 |
| Profitability | 290 | -937.70- | 362.16 | -5.2358- | 93.37671 |
| (The logarithm of the asset size) | 290 | 3.83 | 9.25 | 7.2353 | 0.91567 |
| complexity | 290 | 0.00 | 16.00 | 1.4236 | 1.94911 |
| Audit report Lag | 290 | 0.00 | 253.00 | 61.5269 | 30.49894 |
| Status of The Audit | 290 | 0.00 | 1.00 | 0.3611 | 0.48116 |
| Firm Industry | 290 | 0.00 | 1.00 | 0.7222222 | 0.289387 |
| Audit Committee Independence | 290 | 0.01 | 4.00 | 0.02172 | 0.4950738 |
| Time Period in The Same Company | 290 | 0.00 | 1.00 | 0.1918487 | 0.2475685 |
| Valid N (list wise) | 290 | ||||
The results of the descriptive analysis indicate that audit fees paid by the companies vary from zero to 212930. The corporation size is measured by the natural logarithm of the company’s total assets with mean equal 7.2353. Profitability values ranges from -937 to 352.16, with a mean of -5.235 indicating some losses in some companies. Additionally, risk as a variable show a value spread from 0.08 to 227.53, with a mean 0.36 indicating that most companies depends on debt to finance their activities. As a matter of fact, 36% of companies’ asset was found to be financed by debts.
The complexity of the companies begins by 0 to 16, indicating that some companies don't have any subsidiaries, while others has 16 subsidiaries. Furthermore, the audit report lag showed an expected average of 61 days to complete the report. In addition, 31% of the sample was audited by one of the big four, and 19% changed their auditor during the first three years of engagement. Finally, the number of independent audit committee members ranges between 1- 4 with average of 2.17 member.
Empirical Test and Analysis
This section discusses the analysis test and the main findings. The correlation matrix for the main variables is illustrated in Table 3. Further, Table 4 depicts the multi regression analysis for the study’s variables that is aimed to predict the audit fees.
| Table 3 Correlations Matrix | |||||||||
| Pearson correlation | Audit Fees | Risk | profitability | Assets size | complexity | ARL | AUST | TPA | ADCOM |
| audit fees | 1 | 0.108 | 0.017 | 0.335** | 0.371** | 0.023 | 0.219** | 0.187** | 0 .093 |
| Risk | 0.108 | 1 | -0.097- | -0.064- | 0.081 | 0.245** | -0.017- | -0.070 | 0.064 |
| Profitability | 0.017 | 0.097 | 1 | 0.061 | -0.077- | -0.074- | 0.013 | 0.314 | 0.401 |
| asset size | 0.335** | 0.064 | 0.061 | 1 | 0.257** | 0.047 | 0.133* | 0.381** | 0.378 |
| complexity | 0.371** | 0.081 | -0.077- | 0.257** | 1 | 0.189** | 0.208** | 0.023 | 0.0312 |
| ARL | 0.023 | 0.245** | -0.074- | 0.047 | 0.189** | 1 | 0.260** | 0.165 | 0.452 |
| AUST | 0.219** | 0.017 | 0.013 | 0.133* | 0.208** | 0.260** | 1 | 00.051 | 0.0189 |
| TAP | 0.187** | -0.070 | 0.314 | 0.381** | 0.023 | 0.165 | 00.051 | 1 | 0.213 |
| ADCOM | 0.093 | 0.064 | 0.401 | 0.378 | 0.0312 | 0.452 | 0.0189 | 0.213 | 1 |
| Table 4 Illustrate Multiple Regression Findings for 2014, 2015, 2016, 2017, 2018 | |||||||||||||
| variable | 2014 | 2015 | 2016 | 2017 | 2018 | For all years | |||||||
| coff | p-value | coff | p-value | coff | p-value | coff | p-value | coff | p-value | coff | p-value | VIF | |
| Afees | 5.37 | 0.00 | 4.84 | 0.000 | 5.61 | 0.00 | 6.17 | 0.000 | 7.61 | 0.000 | 5.99 | 0.000 | 1.15 |
| Size | 0.642 | 0.00 | 0.516 | 0.000 | 0.489 | 0.00 | 0.551 | 0.000 | 0.494 | 0.006 | 0.538 | 0.000 | 1.25 |
| Profi | 0.016 | 0.03 | 0.171 | 0.401 | 0.031 | 0.20 | 0.025 | 0.047 | 0.041 | 0.042 | 0.081 | 0.031 | 1.05 |
| Risk | -0.088 | 0.01 | -0.079 | 0.593 | -0.085 | 0.03 | -0.082 | 0.031 | -0.020 | 0.011 | -0.641 | -0.034 | 1.28 |
| comp | 0.089 | 0.02 | 0.122 | 0.388 | 0.109 | 0.02 | 0.198 | 0.011 | 0.113 | 0.061 | 0.027 | 0.015 | 1.56 |
| Indust | 0.109 | 0.00 | 0.109 | 0.189 | 0.275 | 0.05 | 0.012 | 0.276 | 0.218 | 0.003 | 0.148 | 0.041 | 1.44 |
| Status | 0.048 | 0.04 | 0.122 | 0.223 | 0.141 | 0.08 | 0.009 | 0.118 | 0.320 | 0.091 | 0.019 | 0.065 | 1.31 |
| Lag | 0.248 | 0.12 | 0.109 | 0.182 | 0.281 | 0.31 | 0.014 | 0.267 | 0.198 | 0.011 | 0.171 | 0.041 | 1.29 |
| Aci | 0.201 | 0.23 | 0.087 | 0.005 | 0.265 | 0.00 | 0.176 | 0.116 | 0.026 | 0.021 | 0.014 | 0.028 | 1.81 |
| Tap | 0.198 | 0.16 | 0.245 | 0.221 | 0.036 | 0.14 | 0.043 | 0.137 | 0.243 | 0.172 | 0.124 | 0.052 | 1.27 |
| R | 0.765 | 0.773 | 0.793 | 0.778 | 0.781 | 77,9 | |||||||
| R2 | 0.749 | 0.753 | 0.771 | 0.75.9 | 0.76 | 75.8 | |||||||
| modelF test | 14.556 | 16.984 | 17.421 | 16.449 | 15.769 | 40.824 P=0.000 |
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Table 4 presents the multiple regressions for estimation model. The test will ensure the suitability of data (Robustness test), as well as, the study adopted the multi regression test for all variables to detect its effect on audit fees. The values of all variables of VIF was less than 10 indicating an absent of multicollinearity problem. The adjusted R2 is equal to 75% indicating the suitability of model to interpreting the deviation with audit fees. This result is also supported by the F- test for the estimation model (β = 40.84; p- value = 0.000). Therefore, the results indicate that out data has the ability to explain 75% of its deviation. On the other hand, the independent variable shows the following; client size has positive significant effect on audit fees, as elaborated through its (p-value < 0.05). Hence, our result is consistent with prior studies such as (Kikhia, 2015; Apadore, & Letchumanan, 2016; Musah, 2017; Wu, 2018; Januarti and Wiryaningrum, 2018; Hossain & Sobhan, 2019). In addition, the results indicate that client size is attributed to company assets, tangible and intangible assets, operation magnitude, production lines, activities, event and transaction financial events, requiring more time and effort in recording and preparing financial reports, which expected to reflected in audit fees.
Table 4 also shows that the company’s profitability has an effect on audit fees. The F- test for the estimation model is (β = 0.03; p- value < 0.05). Companies are seeking to achieve accuracy, completeness, deducting cost, and speedy operation in order to attain its goals, and the important goal is profitability. Profitability defined as the efficiency of company's management in using its resources. Thus, companies with high profitability permanently disclose more information to motivate potential investors toward investing in their company. Our results show a weak relation between profitability and audit fees. This indicates that an audit fees are not impacted by companies’ profitability which leads to reject the second hypothesis.
The third hypothesis speculates a relation between client's risk and audit fees. The test notices a negative and significant effect (β = -0.03; p- value < 0.05) which consistent with previous result (Suryani, & Sitorus, 2018). This suggest that companies depend on debts to finance its operation which may expose themselves to financial crisis (the debt settlement). Hence, corporate indebtedness is vulnerable to financial problems (possible future litigation) and the audit firm need more time to fulfill the auditing process which lead to higher audit fees. The fourth hypothesis speculate a relationship between companies’ complexity and audit fees. The result detects a positive effect of complexity on audit fees, (β = 0.027; p- value < 0.05). In other words, companies which have more subsidiaries need extra effort which resulted in higher audit fees.
The fifth hypothesis expected a positive relation between the industry type of client and audit fees. Our finding confirms such relationship (β = 0.148; p-value= 0.041 < 0.05) as companies which characterized by high technology need extra effort to complete the auditing process leading to higher audit fees than those of other sectors. Prior literature consistently agreed that manufacturing sector companies need to disclose more compulsory or voluntary information than those in other sectors (Kikhia, 2015; Apadore & Letchumanan, 2016; Musah, 2017; Wu, 2018; Januarti & Wiryaningrum 2018; Hossain & Sobhan, 2019). Therefore, the involved complexity in the preparation of financial statements for manufacturing companies requires higher audit fees. Moreover, manufacturing companies have extra burden regarding their social responsibility toward environment. Thus, manufacturing companies seek to attract more stakeholders by publishing more information to satisfy the social and public need.
The sixth hypothesis speculates a strong relation between audit firm status and audit fees. A large body of literature adopted the status of audit firm as significant factors affecting the amount of audit fees (Lennox, 1999; Naser & Hassan, 2016; Tran et al., 2019; Super & Shil, 2019). Large audit firms such as the big four provide higher quality and reliable reports to their customers compared to small audit firms. In addition, large audit firms accrue higher fees than small audit firms (Simunic & Stein, 1987).
The seventh hypothesis speculate a strong relation between audit report lag and audit fees, the regression analysis confirms this hypothesis (p- value = 0.041 > 0.05). This countered our expectation, as a strong effect with audit fees was detected. This could be explained by the fact that auditing companies tend to be very busy during this period leading to more auditing fees.
The eighth hypothesis predicts a strong relationship between audit committee independence and audit fees. The audit committee is considered as key element in improving, development programs, decreasing risk process, enhancing transparency, governance and improving internal control practices. The regression analysis detects a weak relation which consisted with accounting literature (β =0.014; p- value < 0.05), (Kikhia, 2015; Musah, 2017; Wu, 2018; Januarti & Wiryaningrum, 2018).
The final hypothesis expected a strong relationship between audit rotation and audit fees. Prior literature detect that auditing fees will be higher in the first year and tend to decline in the following years of engagement. However, our results contradict this believe as the regression analysis shows no statistically significant relationship between the audit fees and the auditor's rotations. This may explain by the extended contract signed between the auditor and the auditee (Kikhia, 2015).
Numerous studies have been shown that audit fees can be determined by many factors. Hence, this study examined the impact of several factors on audit fees using the rich literature covering this topic. More important, this study detected some gaps and inconsistency in the prior findings and built a model with the intention of mitigating these gaps. As a result, the study finds that the most important factors to have a significant effect on audit fees are: Audit Report Lag, risk, client size, status of the audit firm, and corporate complexity. In addition, the study finds that audit fees are negatively and significantly associated with the industry type and profitability. On the other hand, no relations were detected between the audit committee independence and audit rotation with the audit fees. Therefore, our finding is in line with prior research (Naser and Nuseibeh 2008; Kikhia, 2015; Musah, 2017; Wu, 2018; Januarti & Wiryaningrum, 2018; Ghadhab, 2019; Hossain, & Sobhan, 2019; Malagila et al., 2020). This indicates that manufacturing companies are subjected to lower audit fees than those of service and financial sectors. This result could be attributed to the fact that Jordanian manufacturing companies are characterized as small or medium companies compared to other manufacturing companies in developed countries. Therefore, these companies do not require high quality of audit which in turn decreases the audit fees.
Additionally, our results present a strong relation between all examined variables in explaining the audit fees. The R for entire model equal 77.9 % that mean independent variables employed in this model have ability to interpreting more than 75.8 % of the variation of audit fees. One of the important consequences refer to the powerful relationship amongst audit fees and company's size, client size, status of the audit firm, and corporate complexity that plays a significant role in this model.
One important implication of our findings is the usefulness of this model for both companies and audit firms to determine the suitable amount of auditing fees accurately. Although this model is more suitability for developing countries, but, some of the examined factors should also be useful for some developed countries. Furthermore, this research is considered an important source and reference for researchers who interested in the auditing profession and its development, in particular professional associations that regulate the profession and seek to upgrade them, as well as for both audit offices and companies to help them get a model to be used in determining the auditing fees.
Finally, future research should focus on other factors which affect the audit fees. For example, non-quantitative factors affecting the audit fees such as internal audit costs, the extent of using accounting standards consistently, the extent of multiple client activities and the extent of competition between audit companies.