1 Introduction
2 Conceptional and Operational Framework
2.1 Information Asymmetries and Internal Auditing
2.2 Company Characteristics and the Size of the IAF
Study | Sample and methodology | Dependent variables | Independent variables | Main results |
---|---|---|---|---|
Carcello et al. (2005a) | 271 publicly listed U.S. companies Data from 2002–2003 OLS regression | Percentage change in IAF budget /IAF staff | Financial information: LN (total assets), assets-to-liabilities, return on assets, cash flow-to-assets, debt-to-assets Company: Industry by SIC code | The IAF budget is larger in smaller companies and the IAF budget and IAF staff are larger in companies with greater financial resources. Industry differences exist |
Carcello et al. (2005b) | 217 publicly listed U.S. companies Data from 2002–2003 OLS regression | LN (IAF budget) | Financial information: LN (total assets), leverage stock issue, debt issue, accounts receivable-to-total assets, inventory-to-total assets, restatements, assets-to-liabilities, return on assets, cash flow-to-assets, sales growth Governance: IAF budget from audit committee, IAF outsourcing, LN (audit fees) Company: Industry (financial, service, or utility), number of segments, number of subsidiaries, number of foreign subsidiaries | The IAF budget is positively related to company size, leverage, specific industry types, the amount of inventory operating cash flow, and audit committee oversight of the IAF budget |
Goodwin-Stewart and Kent (2006) | 115 Australian companies Data from 2010 OLS regression | IAF staff | Financial information: Receivables, total assets, non-current liabilities, PPE-to-market value Governance: Audit committee meetings, directors’ shareholding; Big Five auditor Company: Number of segments | Company size drives IAF staff. IAF staff is negatively related to the number of segments and the use of Big Five auditor, and debt. IAF staff is positively related to the number of audit committee meetings |
Barua et al. (2010) | 181 publicly listed U.S. companies Reused data from Carcello et al. (2005a, b) OLS regression | LN (IAF budget) | Financial information: Total assets, debt-to-assets, inventory-to-total assets, cash flow-to-total assets Governance: IAF budget from audit committee, restatement, audit committee size, audit committee independence, audit expert, accounting expert, audit committee tenure, audit committee meeting, IAF outsourcing Company: Industry (financial, service), LN (total assets), leverage, inventory intensiveness, cash flow ratio | IAF budget is negatively associated with audit committee expertise and positively associated with the number of audit committee meetings. The results suggest a complementary and a substitution effect of the audit committee on the IAF budget |
Anderson et al. (2012) | 173 publicly listed and private companies from North America Data from 2007–2008 OLS regression | IAF staff LN (IAF staff) | Governance: Audit committee size, audit committee meetings, private meetings of the CAE, approval budget, management training ground; % of CIA exams, CAE experience, focus on technology, IAF mission, outsourcing, IAF activities Company: LN (assets), industry, listing status, number of foreign subsidiaries | The results show positive effects of audit committee governance, CAE experience, a mission involving an IT audit, management training ground arrangement, company size, and the number of foreign subsidiaries on the IAF staff |
3 Sample, Variables, and Methodology
3.1 Sample Based On Survey Data
3.2 Dependent and Independent Variables
Variable | Scale | Content and categories |
---|---|---|
IAF_Size | Metric | Total number of employees in the IAF, including administrative staff and supervisors |
Employ | Metric | Total number of full-time equivalent employees |
AudObj | Metric | Number of objects that should be covered by the IAF |
Sub | Metric | Number of subsidiaries |
ForSales | Metric | Percentage share of foreign sales in total sales |
UnplAud | Metric | Percentage share of unplanned audits in total audits |
Assur | Metric | Percentage share of working time for assurance tasks in total working time |
StakeIntens | Metric | Total value that measures the intensity with which six different stakeholders use the IAF |
Industry | Categorial | Company’s industry (1) finance or insurance sectors, (0) all other industries |
Listing | Categorial | Company’s listing status (1) listed, (0) not listed |
AudPlanSig | Categorial | Signing of the company’s audit plan (2) Audit committee and supervisory board sign, (1) either audit committee or supervisory board signs, (0) neither audit committee nor supervisory board signs |
ACMeet | Categorial | Meetings between the CAE and the audit committee (1) CAE has private meetings with the audit committee, (0) CAE does not have any private meetings with the audit committee |
3.3 Methodology
4 Results
4.1 Descriptive Statistics and Correlations
Metric variables | Min | Median | Max | Mean | Std. Dev |
IAF_Size | 1 | 5 | 110 | 8.11 | 11.24 |
Employ | 30 | 1,800 | 17,200 | 3400.68 | 3852.91 |
AudObj | 1 | 70 | 650 | 106.78 | 109.96 |
Sub | 0 | 4 | 150 | 17.89 | 27.76 |
ForSales | 0 | 1 | 99 | 19.36 | 28.98 |
UnplAud | 0 | 10 | 60 | 12.25 | 9.05 |
Assur | 10 | 80 | 100 | 79.83 | 14.40 |
StakeIntens | 6 | 16 | 30 | 16.72 | 4.94 |
Categorial variables | 0 | 1 | 2 | Mean | Std. Dev |
Industry | 174 | 109 | – | 0.39 | 0.49 |
Listing | 176 | 107 | – | 0.38 | 0.49 |
AudPlanSig | 198 | 71 | 14 | 0.35 | 0.57 |
ACMeet | 214 | 69 | – | 0.24 | 0.43 |
Bravais-Pearson correlation | |||||||
Employ | AudObj | Sub | ForSales | UnplAud | Assur | StakeIntens | |
Employ | 1.000 | – | – | – | – | – | – |
AudObj | 0.234*** | 1.000 | – | – | – | – | – |
Sub | 0.514*** | 0.104* | 1.000 | – | – | – | – |
ForSales | 0.390*** | 0.078 | 0.569*** | 1.000 | – | – | – |
UnplAud | 0.229*** | 0.030 | 0.142** | 0.085 | 1.000 | – | – |
Assur | −0.024 | 0.158*** | 0.023 | −0.154*** | −0.203*** | 1.000 | – |
StakeIntens | −0.024 | 0.167*** | 0.061 | 0.100* | −0.182*** | 0.121** | 1.000 |
Cramér’s V | |||||||
Industry | Listing | AudPlanSig | ACMeet | – | – | – | |
Employ a | 0.325*** | 0.098 | 0.089 | 0.125 | – | – | – |
AudObj a | 0.132 | 0.248*** | 0.145 | 0.120 | – | – | – |
Sub a | 0.276*** | 0.218** | 0.129 | 0.151 | – | – | – |
ForSales a | 0.234*** | 0.260*** | 0.166* | 0.151 | – | – | – |
UnplAud a | 0.328*** | 0.138 | 0.111 | 0.091 | – | – | – |
Assur a | 0.242*** | 0.160 | 0.080 | 0.166 | – | – | – |
StakeIntens a | 0.537*** | 0.393*** | 0.263*** | 0.399*** | – | – | – |
Industry | 1.000 | – | – | – | – | – | – |
Listing | 0.258*** | 1.000 | – | – | – | – | – |
AudPlanSig | 0.087 | 0.194*** | 1.000 | – | – | – | – |
ACMeet | 0.287*** | 0.270*** | 0.296*** | 1.000 | – | – | – |
4.2 Regression Results
Independent variables | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 |
---|---|---|---|---|---|
Metric | |||||
Employ | 4.575*** | – | – | 4.748*** | 0.00113*** |
AudObj | 1.544 | 1.941*** | 1.974*** | 1.319 | 0.00693 |
Sub | – | 1.002 | – | 2.664** | −0.04875* |
ForSales | – | – | 1.492 | 2.032 | −0.01121 |
UnplAud | 1.000 | 1.000 | 1.000 | 1.000 | −0.00839 |
Assur | 1.000 | 1.000 | 1.000 | 1.000 | 0.05059 |
StakeIntens | 2.178** | 2.246*** | 2.263*** | 1.785** | 0.37255** |
Categorial | |||||
Industry | 8.851*** | 4.346*** | 4.384*** | 8.300*** | 6.939*** |
Listing | −0.895 | 0.072 | −0.152 | −0.040 | 0.567 |
AudPlanSig | −2.667** | −2.271* | −2.379** | −2.788*** | −2.469** |
ACMeet | 2.136 | 3.116* | 3.127** | 2.063 | 2.415 |
Constant | 5.452*** | 6.443*** | 6.548*** | 5.400*** | −8.172* |
N | 283 | 283 | 283 | 283 | 283 |
Adjusted R2 | 0.343 | 0.209 | 0.213 | 0.365 | 0.292 |