The relationship of internal auditors’ characteristics with external auditors’ reliance and its impact on audit efficiency: Empirical evidence from Indonesian government institutions

Figures & data

Figure 1. Research model.

Table 1. Questionnaire delivery and return details

Information	Value
Number of target respondents from all regencies and cities throughout Indonesia	508
Respondents who do not respond/give answers	324
Respondents who respond/give answers	184
Response rate	36.22%

Table 2. Respondent demography profiles

Variable	Category	Frequency	Percentage
Gender	Male	107	65.2%
	Female	57	34.8%
Age	25–35 years	7	4.3%
	35–45 years	103	62.8%
	45–50 years	46	28%
	>50 years	8	4.9%
Education	Bachelor	137	83.5%
	Graduate	27	16.5%
Years of service	5–10 years	3	1.8%
	10–15 years	41	25%
	>15 years	120	73.2%
Team Role	Leader	133	81%
	Technical controller	31	19%

Table 3. The results of reliability and convergent validity tests

Variables	Number of indicators	Composite reliability	Cronbach Alpha	AVE	Loading range
Objectivity	8	0.963	0.956	0,875	0.841–0.925
Competence	9	0.943	0.932	0.805	0.757–0.807
Work performed	5	0.961	0.950	0.912	0.878–0.934
Reliance	1	1.000	1.000	1.000	1.000
Audit efficiency	1	1.000	1.000	1.000	1.000
Institutional environment	1	1.000	1.000	1.000	1.000
IE*REL	1	1.000	1.000	1.000	1.000

Table 4. Constructs of discriminant validity

Variable	Objectivity	Competence	Work performed	Reliance	Audit efficiency	Institutional environment	IE*REL
Objectivity	(0.875)	0.556	0.551	0.461	0.130	0.240	−0.281
Competence	0.556	(0.805)	0.668	0.597	0.212	0.280	−0.279
Work performed	0.551	0.668	(0.912)	0.653	0.359	0.187	−0.153
Reliance	0.461	0.597	0.653	(1.000)	0.342	0.258	−0.348
Audit efficiency	0.130	0.212	0.359	0.342	(1.000)	−0.148	0.445
Institutional environment	0.240	0.280	0.187	0.258	−0.148	(1.000)	−0.336
IE*Reliance	−0.281	−0.279	−0.153	−0.348	0.445	−0.336	(1.000)

Figure 2. The test results of the research model.

Table 5. Model fit and quality indices

Model fit and quality indices	Value	Sign	Rule of thumb	Conclusion
Average path coefficient (APC)	0.331	P<0.001	P-value<0.05	Fit
Average R-squared (ARS)	0.474	P<0.001	P-value<0.05	Fit
Average adjusted R-Squared (AARS)	0.466	P<0.001	P-value<0.05	Fit
Average block VIF (AVIF)	2.979		<=5	Fit
Average full colinearity VIF (AFVIF)	2.003		<=5	Fit
Tenenhaus GoF	0.650		small >0.1 medium >=0.25 large >=0.36	Fit

Table 6. Hypothesis testing

Hypothesis	Parameter (β)	Ρ-value	Conclusion
Objectivity → Reliance	0.198	0.004	Confirmed
Competence → Reliance	0.132	0.042	Confirmed
Work Performed → Reliance	0.446	<0.001	Confirmed
Reliance → Audit Efficiency	0.409	<0.001	Confirmed
Reliance*Intitutional Environment → Audit Efficiency	0.468	<0.001	Confirmed

Table 7. Analysis of multigroup

Inputs
N1	128	(District Government size)
N2	36	(City Government size)
Beta1	0.2730	(District Government beta, or path, coefficient)
Beta2	0.6630	(City Government beta, or path, coefficient)
SE1	0.0830	(District Government standard error)
SE2	0.1050	(City Government standard error)
Outputs: Pooled standard error methd
Tm	2.7322	(T value for multi-group difference effect)
Pm	0.0035	(P value for multi-group difference effect, one-tailed)
Pm’	0.0069	(P value for multi-group difference effect, two-tailed)
Outputs: Satterthwaite method
Tm	2.9139	(T value for multi-group difference effect)
Pm	0.0020	(P value for multi-group difference effect, one-tailed)
Pm’	0.0040	(P value for multi-group difference effect, two-tailed)