The Moderating Effect of Gender on the Relationship Between Intellectual Capital and Audit Quality
DOI:
https://doi.org/10.14738/assrj.69.6964Abstract
In our globe today, the auditing profession is under scrutiny. This is because of how audit quality can be ensured, obtained, and maintained. The problem of audit quality is discussed from the viewpoint of intellectual capital and the interaction of the gender of the auditor. Audit quality consists of auditors’ competence and independent, while intellectual capital is made up of human capital, rational capital, and structural capital. This research was a correlational design which applied self-constructed questionnaires with Cronbach alpha of 0.82 and 0.75 for intellectual capital and audit quality respectively. Using a convenience sampling technique, 354(8%) out of 4,390 registered chartered accountants with Chartered Institute of Accountants, Ghana (ICA, GH) who were practicing auditors were used in this study. The study respondents were made up of 191 (54%) males and 163 females, of which 210 (59.3%) had attained degrees, and 144 (40.7%) had an MBA. The study used PROCESS v3.2 and Andrew Haye model 1 to establish the regression and the moderating effect. The results of the study disclosed that there is a high positive significant relationship between intellectual capital and audit quality, and intellectual capital can predict audit quality by 53.68%. Also, there is an enhancing moderating effect of gender on the relationship between intellectual capital and audit quality but not statistically significant. The study recommends that the role of the auditor on audit quality and their intellectual capital must not be differentiated. Therefore, attention must not be placed on the gender of the auditor to give a quality audit.
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Peprah, W. K. (2019). The Moderating Effect of Gender on the Relationship Between Intellectual Capital and Audit Quality. Advances in Social Run MATRIX procedure: ************** PROCESS Procedure for SPSS Version 3.2.01 ***************** Written by Andrew F. Hayes, Ph.D. www.afhayes.com
Documentation available in Hayes (2018). www.guilford.com/p/hayes3 ************************************************************************** Model : 1
Y : AuditQua X : Intellec W : Gender Sample Size: 354 ************************************************************************** OUTCOME VARIABLE: AuditQua Model Summary R R-sq MSE F df1 df2 p .7327 .5368 .1252 135.2133 3.0000 350.0000 .0000 Model coeff se t p LLCI ULCI constant .9131 .4290 2.1285 .0340 .0694 1.7568 Intellec .8232 .1057 7.7888 .0000 .6154 1.0311 Gender .3528 .2893 1.2194 .2235 -.2163 .9219 Int_1 -.0890 .0720 -1.2352 .2176 -.2307 .0527 Product terms key: Int_1 : Intellec x Gender Covariance matrix of regression parameter estimates: constant Intellec Gender Int_1 constant .1840 -.0449 -.1172 .0287
Intellec -.0449 .0112 .0287 -.0072 Gender -.1172 .0287 .0837 -.0207 Int_1 .0287 -.0072 -.0207 .0052 Test(s) of highest order unconditional interaction(s): R2-chng F df1 df2 p X*W .0020 1.5256 1.0000 350.0000 .2176 ----------Focal predict: Intellec (X) Mod var: Gender (W) Data for visualizing the conditional effect of the focal predictor: Paste text below into a SPSS syntax window and execute to produce plot. DATA LIST FREE/Intellec Gender AuditQua . BEGIN DATA. END DATA. GRAPH/SCATTERPLOT= Intellec WITH AuditQua BY Gender . *********************** ANALYSIS NOTES AND ERRORS ************************ Level of confidence for all confidence intervals in output: 95.0000 NOTE: Standardized coefficients not available for models with moderators. NOTE: Variables names longer than eight characters can produce incorrect output. Shorter variable names are recommended. ------ END MATRIX -----
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