The influence of E-auditing adoption on internal audit department performance amid COVID-19 in Saudi Arabia

Figures & data

Figure 1. Research Framework.

Table 1. Constructs, measurements and their source.

Constructs	Code	Indication	Source
The independent variables
Information Quality	IQ1	‘Information from the E-Auditing is always timely’.	Lin et al. (2006)
	IQ2	‘Information from the E-Auditing is useful’.
	IQ3	‘Information provided by the E-Auditing is accurate’.
	IQ4	‘Information from the E-Auditing is easy to understand and related to decision-making’.
Service Quality	SRVQ1	‘The information I receive from the IS department is accurate’.	Alzoubi (2011)
	SRVQ2	‘Training provided by the IS department improves the quality of my work’.
	SRVQ3	‘The IS department solves my problems and provides me with prompt service’.
System Quality	SYSQ1	‘E-Auditing user interface can be easily adapted to one’s personal approach’.	Lin et al. (2006)
	SYSQ2	‘E- Auditing is easy to use’.
	SYSQ3	‘E-Auditing responds quickly enough’.
	SYSQ4	‘E-Auditing is always up and running as necessary’.
Mediating variables
Electronic Auditing	EA1	‘E-Auditing is used frequently’.	Rajan and Baral (2015)
	EA2	‘I spend most of my time each day using E-auditing for work purposes’.
	EA3	‘I depend highly on E-auditing use’.
User satisfaction	USAT1	‘I am satisfied with the SysQ’.	Hsu et al. (2015)
	USAT2	‘I am satisfied with the InfQ’.
	USAT3	‘I am satisfied with the SrvQ’
The dependent variable
Internal Audit Department Performance	IADP1	‘I believe that E-auditing can improve the internal audit department’s operational performance’.	Alzeban and Gwilliam (2014; Laitinen (2014)
	IADP2	‘I believe that E-auditing can increase the productivity of the internal audit department’.
	IADP3	‘I believe that E-auditing can improve the accuracy and reliability of financial reports’.
	IADP4	‘I believe that E-auditing can provide us with more accurate data’.

Table 2. Questionnaire delivery and return details.

Information	Value
Number of surveys distributed	670
Respondents who did not answer the survey	437
Number of respondents who replied	213
The percentage of returned answers	32.76%

Table 3. Participants’ characteristics.

Demographic Items	Frequency		Percentage
Gender
Male	157		73.7%
Female	56		26.3%
Age
Less than 29 years	18		8.5%
30–39 years	140		65.7%
40–49 years	50		23.5%
50 and more	5		2.3%
Job title
Manager of internal audit department	22	10.3%
Assistant manager of internal audit department	19	8.9%
Internal auditor	172	80.8%
Education level/qualification
Diploma or below	43		20.2%
Bachelor’s degree	155		72.8%
Master’s degree	10		4.7%
PhD	5		2.3%
Employment history
Less than 5 years	37		17.4%
5–9 years	142		66.7%
10–14 years	31		14.6%
15 or above	3		1.4%
Number of auditors in internal audit department
1–4 employees	15		7.0%
From 5 to 10	120		56.3%
From 11 to 15	68		31.9%
More than 16	10		4.7%

Figure 2. Measurement model.

Table 4. Reliability, validity and quality of the measurement model.

Constructs	Items	Factors lodgings (FL)	CA	rho_A	CR	AVE	H^2
Information Quality (IQ)	IQ1	0.828	0.901	0.903	0.931	0.773	0.606
	IQ2	0.897
	IQ3	0.909
	IQ4	0.880
Service Quality (SRVQ)	SRVQ1	0.929	0.928	0.939	0.954	0.874	0.697
	SRVQ2	0.939
	SRVQ3	0.936
System Quality (SYSQ)	SYSQ1	0.628	0.840	0.871	0.894	0.682	0.478
	SYSQ2	0.894
	SYSQ3	0.876
	SYSQ4	0.876
Electronic Auditing	EA1	0.858	0.836	0.840	0.901	0.753	0.482
	EA2	0.872
	EA3	0.873
User satisfaction	USAT1	0.880	0.818	0.822	0.892	0.733	0.450
	USAT2	0.835
	USAT3	0.853
Internal Audit Department Performance	IADP1	0.693	0.816	0.818	0.880	0.648	0.412
	IADP2	0.836
	IADP3	0.818
	IADP4	0.863

Table 5. Cross-loadings.

Factor	IQ	SRVQ	SYSQ	EA	USAT	IADP
IQ1	0.828	−0.412	0.331	0.549	0.384	0.395
IQ2	0.897	−0.331	0.374	0.604	0.489	0.512
IQ3	0.909	−0.381	0.347	0.584	0.418	0.460
IQ4	0.880	−0.405	0.379	0.569	0.443	0.487
SRVQ1	−0.376	0.929	−0.305	−0.242	−0.285	−0.247
SRVQ2	−0.402	0.939	−0.309	−0.284	−0.296	−0.320
SRVQ3	−0.432	0.936	−0.321	−0.304	−0.304	−0.323
SYSQ1	0.088	0.092	0.628	0.236	−0.036	0.126
SYSQ2	0.347	−0.295	0.894	0.367	0.130	0.278
SYSQ3	0.425	−0.360	0.876	0.363	0.202	0.339
SYSQ4	0.412	−0.419	0.876	0.382	0.223	0.356
EA1	0.505	−0.211	0.263	0.858	0.452	0.556
EA2	0.567	−0.281	0.339	0.872	0.457	0.547
EA3	0.629	−0.281	0.464	0.873	0.445	0.536
USAT1	0.419	−0.296	0.164	0.427	0.880	0.435
USAT2	0.398	−0.238	0.191	0.429	0.835	0.384
USAT3	0.449	−0.276	0.102	0.476	0.853	0.468
IADP1	0.290	−0.224	0.152	0.485	0.444	0.693
IADP2	0.458	−0.241	0.305	0.493	0.406	0.836
IADP3	0.434	−0.289	0.316	0.470	0.361	0.818
IADP4	0.512	−0.276	0.339	0.565	0.401	0.863

Table 6. Discriminant validity – Fornell–Larcker criteria.

Scale	IQ	SRVQ	SYSQ	EA	USAT	IADP
IQ	0.879
SRVQ	−0.433	0.935
SYSQ	0.407	−0.334	0.826
EA	0.657	−0.299	0.415	0.868
USAT	0.494	−0.316	0.175	0.520	0.856
IADP	0.529	−0.320	0.346	0.629	0.503	0.805

Table 7. Discriminant validity – HTMT ratios.

Scale	IQ	SRVQ	SYSQ	EA	USAT	IADP
IQ	–
SRVQ	0.473	–
SYSQ	0.445	0.401	–
EA	0.752	0.334	0.483	–
USAT	0.614	0.365	0.404	0.760	–
IADP	0.573	0.361	0.229	0.627	0.611	–

Figure 3. Distribution of prediction errors – EA.

Figure 4. Distribution of prediction errors – USAT.

Figure 5. Distribution of prediction errors – IADP.

Table 8. PLS predict assessment.

Indicator	PLS			LM			PLS–LM
	RMSE	MAE	Q²	RMSE	MAE	Q²	RMSE	MAE	Q^2
EA1	0.803	0.618	0.230	0.822	0.627	0.194	−0.019	−0.009	0.036
EA2	0.785	0.643	0.316	0.807	0.664	0.277	−0.022	−0.021	0.039
EA3	0.771	0.615	0.420	0.783	0.628	0.402	−0.012	−0.013	0.018
USAT1	0.937	0.739	0.077	0.953	0.742	0.045	−0.016	−0.003	0.032
USAT2	0.859	0.666	0.203	0.880	0.679	0.164	−0.021	−0.013	0.039
USAT3	0.911	0.705	0.191	0.932	0.716	0.154	−0.021	−0.011	0.037
IADP1	0.933	0.738	0.247	0.947	0.729	0.225	−0.014	0.009	0.022
IADP2	0.967	0.771	0.146	0.979	0.772	0.124	−0.012	−0.001	0.022
IADP3	0.960	0.750	0.137	0.976	0.758	0.108	−0.016	−0.008	0.029
IADP4	0.888	0.730	0.150	0.892	0.736	0.141	−0.004	−0.006	0.009
EA	–	–	0.435	–	–	–	–	–	–
USAT	–	–	0.197	–	–	–	–	–	–
IADP	–	–	0.274	–	–	–	–	–	–

Figure 6. Structural model.

Table 9. Hypotheses testing results.

Path	Estimate	t	p	Status
IQ → EA	0.590	11.091	0.000	H1: Supported
SYSQ → EA	0.178	2.347	0.019	H2: Supported
SRVQ → EA	0.017	0.286	0.775	H3: Not Supported
EA → USAT	0.520	10.274	0.000	H4: Supported
EA → IADP	0.504	8.365	0.000	H5: Supported
USAT → IADP	0.241	3.637	0.000	H6: Supported

Table 10. R-squared index.

Outcome variable	R square	R square adjusted
EA	0.457	0.449
USAT	0.270	0.267
IADP	0.438	0.433

Table 11. Effect sizes of the latent variables.

Predictor/Outcome	EA	IADP	USAT
EA		0.329	0.370
IQ	0.472
SRVQ	0.000
SYSQ	0.048
USAT		0.076

Table 12. Predictive relevance.

Outcome variable	Q square
EA	0.331
USAT	0.192
IADP	0.273

Table 13. Collinearity diagnostics.

Predictor/Outcome	EA	IADP	USAT
EA		1.370	1.000
IQ	1.361
SRVQ	1.278
SYSQ	1.244
USAT		1.370

Figure 7. Structural model.

Table 14. Assessment of non-linear/quadratic effects.

Non-linear relationship	Coefficient	t	p	F^2	CI
IQ x IQ → EA	−0.013	0.271	0.786	0.000	−0.119, 0.077
SRVQ x SRVQ → EA	0.012	0.209	0.835	0.000	−0.097, 0.124
SYSQ x SYSQ → EA	0.044	1.106	0.269	0.009	−0.039, 0.130
EA x EA → USAT	−0.044	1.174	0.241	0.004	−0.116, 0.034
EA x EA → IADP	0.031	0.771	0.441	0.003	−0.051, 0.111
USAT x USAT → IADP	−0.022	0.515	0.607	0.001	−0.109, 0.063

Figure 8. IPMA map for EA.

Figure 9. IMPA map for USAT.

Figure 10. IMPA map for IADP.

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Data availability statement

The datasets used during the current study are available from the corresponding author on reasonable request.