Investigating the factors of SMEs’ business resilience in the post-pandemic crisis of COVID-19 with technology adoption as a quasi-moderator: a multigroup analysis of Indonesian and Malaysian SMEs

Figures & data

Figure 1. Research framework.

Table 1. Data collection process.

	Indonesia	Malaysia
Total incoming responses	500 (100%)	640 (100%)
Total sample that meets sampling criteria (SME’s owner that still running the business during covid-19, and already adopt technology to support their business)	339 (67.8%)	215 (33.60%)
Total final data after bad data reduction	335 (67.0%)	204 (31.88%)

Table 2. Research variables and measures.

Variable	Label	Measurement items
Dependent Variable Business Resilience	BUS_RES_1	We still run our business as usual during the Covid-19 pandemic.
	BUS_RES_3	Our business remains resilient because we think that the Covid-19 pandemic is a challenge that must be faced positively rather than feared
	BUS_RES_8	We believe that building networks is important for business resilience.
	BUS_RES_9	The provision of complete business accounting records also contributes to business resilience.
Independent Variables Financial Literacy	FIN_LIT_1	I have the ability and finance knowledge to manage the business income.
	FIN_LIT_2	I can make a right financial decision.
	FIN_LIT_3	I can use financial knowledge to survive in difficult economic conditions.
	FIN_LIT_4	I can use financial knowledge to increase business growth.
	FIN_LIT_5	I join the financial program organized by the government
	FIN_LIT_6	I am willing to spend money to learn financial management and accounting.
Independent Variables Compliance Cost	COMP_COST_3	I am willing to invest in new technology to ensure business sustainability.
	COMP_COST_4	I need to invest in risk management to decrease the risk of threat.
	COMP_COST_5	I need to look for opportunities and quickly adapt to keep running the business and survive through the crisis phase.
	COMP_COST_6	Online selling requires employee training.
	COMP_COST_7	The business needs to bear training costs since starting the online business
Independent Variables Financial Resource	FIN_RES_1	It is easy for entrepreneurs to obtain financial assistance.
	FIN_RES_2	It is easy for entrepreneurs to obtain bank loans.
	FIN_RES_3	It is easy for entrepreneurs to obtain loans from Amanah Ikhtiar/government
	FIN_RES_4	The tax incentives provided by the government helped ease the financial burden of my business.
	FIN_RES_5	Interest subsidy for micro-enterprise loans helped ease the financial burden of my business.
	FIN_RES_6	Delayed payment of the principal for micro-enterprise loans helped ease the financial burden of my business.
Independent Variables Government Support	GOV_SUPT1	During the Covid-19 crisis period, the government always ensures that micro-businesses and Small and Medium Enterprises (SMEs) can run well.
	GOV_SUPT2	Government assistance during the pandemic has greatly helped cash flow management during the Covid-19 crisis.
	GOV_SUPT3	The government’s regulation and policies in handling the Covid-19 pandemic are still within reasonable limits and acceptable to the business world.
	GOV_SUPT4	The government always introduces new incentives to help the business during the Covid-19 crisis.
	GOV_SUPT5	The government always provides skills education such as courses, seminars, and entrepreneurial motivation in enhancing the competitiveness of micro-businesses and SMEs.
	GOV_SUPT6	The government always provides business exposure such as information, activities, and entrepreneurial opportunities.
	GOV_SUPT7	The government always provides business support such as networking access, mentor services, and business consultant services.
Quasi Moderator Variable Technology Adoption	PEOU_2	Promotion is conducted easily through digital platforms
	PEOU_3	My interaction with the customers in promoting the products or services would be clear and understandable if using digital platforms.
	PU_1	Using the digital platforms to promote products or services would enable the business to accomplish growth more quickly.
	PU_2	The use of digital platforms in my business helps day -to -day affairs become more effective
	PU_3	The use of digital platforms in my business helps to accomplish tasks more quickly
	PU_4	The use of digital platforms in my business gives me more opportunities to take control of my daily activities
	PU_5	Using digital platforms is much easier for the business to gain access to the potential customers
	PU_6	Using digital platforms is much easier to make or receive payments.
	PU_7	Using the digital platforms would increase the number of sales

Table 3. Descriptive statistics.

	Indonesia		Malaysia
Demography	Qty	%	Qty	%
Gender
Male	104	31.04%	96	47.06%
Female	231	68.96%	108	52.94%
Age
18–29 years old	296	88.36%	100	49.02%
30–39 years old	20	5.97%	54	26.47%
40–49 years old	12	3.58%	31	15.20%
50–59 years old	7	2.09%	18	8.82%
>59 years old	0	0%	1	0.49%
Marital Status
Married	37	11.04%	107	52.45%
Single	296	88.36%	91	44.61%
Widow/Widower	2	0.60%	6	2.94%
The number of dependents
1–2 Persons	47	14.03%	61	29.90%
3–4 Persons	34	10.15%	34	16.67%
5–6 Persons	5	1.49%	23	11.27%
7–8 Persons			3	1.47%
9 persons and more			1	0.49%
None	249	74.33%	82	40.20%
Education Level
Elementary School	0	0%	4	1.96%
Secondary School	3	0.90%	31	15.20%
High School	206	61.49%	7	3.43%
Diploma	10	2.99%	82	40.20%
Bachelor	115	34.33%	62	30.39%
Master/Doctoral	1	0.30%	11	5.39%
Professional	0	0%	7	3.43%
Business Sector
Food and Beverage	164	48.96%	89	43.63%
Service	59	17.61%	53	25.98%
Fashion	46	13.73%	8	3.92%
Retail Sector	24	7.16%	16	7.84%
Engineering	4	1.19%	10	4.90%
Others	38	11.35%	28	13.73%
Number of Employee
Sole proprietor	186	55.52%	45	22.06%
1–2 Persons	84	25.07%	51	25.00%
3–4 Persons	34	10.15%	23	11.27%
5–6 Persons	14	4.18%	22	10.78%
7–8 Persons	5	1.49%	8	3.92%
9 persons and more	12	3.58%	55	26.96%
Primary Source of Capital
Own savings	298	88.96%	144	70.59%
Borrowing from family	23	6.87%	13	6.37%
Loan institution	4	1.19%	32	15.69%
Borrowing from friends	2	0.60%	3	1.47%
Inheritance property	1	0.30%	6	2.94%
Others	7	2.09%	6	2.94%
Annual revenue
<300 million IDR	299	89.25%
300 million to 2.5 billion IDR	33	9.85%
>2.5–50 billion IDR	3	0.90%
RM10,000 and below			57	27.94%
RM10,001–RM20,000			25	12.25%
RM20,001–RM50,000			25	12.25%
RM50,001–RM70,000			17	8.33%
RM70,001–RM100,000			22	10.78%
RM100,001 and above			58	28.43%

Table 4. Reliability and convergent validity.

	Indonesia				Malaysia
Construct/Item	Loadings	Cronbach Alpha	CR	AVE	Loadings	Cronbach Alpha	CR	AVE
Business Resilience		0.706	0.725	0.527		0.735	0.754	0.555
BUS_RES_1	0.650				0.658
BUS_RES_3	0.669				0.707
BUS_RES_8	0.783				0.806
BUS_RES_9	0.790				0.797
Compliance Cost		0.823	0.832	0.583		0.823	0.850	0.584
COMP_COST_3	0.781				0.762
COMP_COST_4	0.797				0.846
COMP_COST_5	0.753				0.835
COMP_COST_6	0.728				0.730
COMP_COST_7	0.757				0.627
Financial Literacy		0.903	0.904	0.674		0.823	0.837	0.530
FIN_LIT_1	0.826				0.619
FIN_LIT_2	0.834				0.631
FIN_LIT_3	0.851				0.703
FIN_LIT_4	0.866				0.779
FIN_LIT_5	0.795				0.802
FIN_LIT_6	0.750				0.808
Financial Resource		0.882	0.897	0.626		0.920	0.923	0.715
FIN_RES_1	0.734				0.762
FIN_RES_2	0.762				0.860
FIN_RES_3	0.754				0.831
FIN_RES_4	0.859				0.874
FIN_RES_5	0.851				0.865
FIN_RES_6	0.776				0.876
Government Support		0.908	0.919	0.644		0.930	0.931	0.707
GOV_SUPT1	0.827				0.770
GOV_SUPT2	0.799				0.852
GOV_SUPT3	0.758				0.806
GOV_SUPT4	0.789				0.902
GOV_SUPT5	0.807				0.790
GOV_SUPT6	0.807				0.877
GOV_SUPT7	0.827				0.880
Technology Acceptance		0.921	0.928	0.617		0.959	0.963	0.757
PEOU_2	0.688				0.793
PEOU_3	0.614				0.746
PU_1	0.832				0.898
PU_2	0.838				0.927
PU_3	0.796				0.924
PU_4	0.842				0.868
PU_5	0.798				0.896
PU_6	0.785				0.866
PU_7	0.842				0.897

Table 5. Discriminant validity-fornell larcker criterion.

Country	1. Compliance Cost	2. Financial Literacy	3. Financial Resource	4. Government Support	5. SME’s Business Resilience	6. Technology Acceptance
Indonesia
1. Compliance Cost	0.764
2. Financial Literacy	0.615	0.821
3. Financial Resource	0.297	0.350	0.791
4. Government Support	0.310	0.329	0.556	0.802
5. SME’s Business Resilience	0.494	0.490	0.211	0.356	0.726
6. Technology Acceptance	0.512	0.569	0.102	0.329	0.554	0.785
Malaysia
1. Compliance Cost	0.764
2. Financial Literacy	0.548	0.728
3. Financial Resource	0.336	0.608	0.846
4. Government Support	0.272	0.486	0.519	0.841
5. SME’s Business Resilience	0.490	0.458	0.367	0.490	0.745
6. Technology Acceptance	0.549	0.533	0.317	0.520	0.654	0.870

Table 6. Discriminant validity-HTMT.

Country	1. Compliance Cost	2. Financial Literacy	3. Financial Resource	4. Government Support	5. SME’s Business Resilience	6. Technology Acceptance
Indonesia
1. Compliance Cost
2. Financial Literacy	0.706
3. Financial Resource	0.340	0.374
4. Government Support	0.361	0.361	0.611
5. SME’s Business Resilience	0.608	0.594	0.277	0.456
6. Technology Acceptance	0.568	0.623	0.134	0.357	0.655
Malaysia
1. Compliance Cost
2. Financial Literacy	0.650
3. Financial Resource	0.382	0.753
4. Government Support	0.305	0.578	0.563
5. SME’s Business Resilience	0.588	0.576	0.465	0.593
6. Technology Acceptance	0.614	0.581	0.341	0.549	0.754

Figure 2. PLS algorithm Indonesia model.

Figure 3. PLS algorithm Malaysia model.

Table 7. The measurement of invariance assessment (Step1 and 2).

	Step 1	Step 2
	Configural Invariance	Compositional Invariance
Construct	Result	C = 1	CIs	P Value	Result
Compliance Cost	Yes	0.997	(0.992, 0.997)	0.344	Yes
Financial Literacy	Yes	0.999	(0.994, 0.998)	0.502	Yes
Financial Resource	Yes	0.997	(0.987, 0.996)	0.500	Yes
Government Support	Yes	0.999	(0.997, 0.999)	0.512	Yes
SME’s Business Resilience	Yes	0.999	(0.985, 0.996)	0.812	Yes
Technology Acceptance	Yes	1.000	(0.999, 1.000)	0.395	Yes

Table 8. The measurement of invariance assessment (Step 3a and 3b).

	Step 3a. Equal Mean Invariance				Step 3b. Equal Variance Invariance
Construct	Different	CIs	p value	Result	Different	CIs	p value	Result
Compliance Cost	0.057	(−0.146, 0.140)	.239	Yes	0.073	(−0.226, 0.241)	.330	Yes
Financial Literacy	0.833	(−0.158, 0.150)	.000	No	0.143	(−0.213, 0.219)	.148	Yes
Financial Resource	0.171	(−0.147,) 0.141	.020	No	−0.084	(−0.214, 0.230)	.251	Yes
Government Support	−0.147	(−0.138, 0.139)	.042	No	0.070	(−0.198, 0.212)	.314	Yes
SME’s Business Resilience	0.024	(−0.145, 0.142)	.383	Yes	−0.063	(−0.204, 0.207)	.290	Yes
Technology Acceptance	0.256	(−0.144, 0.147)	.002	No	−0.353	(−0.217, 0.219)	.005	No

Table 9. Analysis of structural model and MGA.

		Path coefficients		T-values		p-values		Confidence intervals Bias Corrected (95%)		Differences of	p-values (1 tailed)
Hypotheses		INA	MAL	INA	MAL	INA	MAL	INA	MAL	Path Coefficient	Henseler MGA	permutation	MGA Result
H1	COMP_COST→BUS_RES	0.207	0.169	3.462	2.232	0.000	0.013	(0.110, 0.309)	(0.027, 0.279)	0.038	0.347	0.346	No different
H2	FIN_LIT→BUS_RES	0.107	−0.019	1.402	0.235	0.080	0.407	(-0.016, 0.236)	(-0.154, 0.107)	0.126	0.127	0.120	No different
H3	FIN_RES→BUS_RES	0.006	0.104	0.086	1.212	0.466	0.113	(-0.107, 0.114)	(-0.031, 0.255)	−0.098	0.816	0.161	No different
H4	GOV_SUPT→BUS_RES	0.157	0.193	2.288	2.865	0.011	0.002	(0.046, 0.272)	(0.085, 0.302)	−0.035	0.644	0.347	No different
H5	TECH→BUS_RES	0.340	0.397	4.809	5.458	0.000	0.000	(0.216, 0.449)	(0.286, 0.526)	−0.057	0.712	0.296	No different
H6	TECH x COMP_COST → BUS_RES	−0.063	−0.090	1.134	1.296	0.128	0.097	(-0.159, 0.024)	(-0.209, 0.015)	0.027	0.387	0.396	No different
H7	TECH x FIN_LIT→BUS_RES	0.064	0.117	0.979	1.442	0.164	0.075	(-0.047, 0.168)	(-0.003, 0.266)	−0.054	0.701	0.020	No Difference
H8	TECH x FIN_RES→BUS_RES	−0.106	0.097	1.683	1.154	0.046	0.124	(-0.204, 0.006)	(-0.038, 0.239)	−0.204	0.973	0.303	Different
H9	TECH x GOV_SUPT→BUS_RES	0.017	−0.176	0.301	2.948	0.382	0.002	(-0.071, 0.106)	(-0.258, −0.071)	0.194	0.018	0.024	Different

Table 10. Robustness Test-Nonlinearity Test.

Nonlinearity test	Coefficient	p value
QE (FIN_LITERACY) -> BUS_RESILIENCE	−0.007	0.835
QE (COMPLIANCE_COST) -> BUS_RESILIENCE	0.031	0.383
QE (FIN_RESOURCE) -> BUS_RESILIENCE	−0.024	0.464
QE (GOV_SUPPORT) -> BUS_RESILIENCE	−0.004	0.908

Table 11. Evaluation of endogeneity test with the Gaussian copula procedure.

	Latent Variable	Coefficient	p value
Model 1 Gaussian copula in endogenous variables (FIN_LITERACY).	GC (FIN_LITERACY) -> BUS_RESILIENCE	0.101	0.531
Model 2 Gaussian copula in endogenous variables (COMPLIANCE_COST).	GC (COMPLIANCE_COST) -> BUS_RESILIENCE	−0.049	0.730
Model 3 Gaussian copula in endogenous variables (FIN_RESOURCE).	GC (FIN_RESOURCE) -> BUS_RESILIENCE	−0.111	0.745
Model 4 Gaussian copula in endogenous variables (GOV_SUPPORT).	GC (GOV_SUPPORT) -> BUS_RESILIENCE	0.159	0.475
Model 5 Gaussian copula in endogenous variables (FIN_LITERACY, COMPLIANCE_COST).	GC (FIN_LITERACY) -> BUS_RESILIENCE	0.133	0.428
	GC (COMPLIANCE_COST) -> BUS_RESILIENCE	−0.079	0.596
Model 6 Gaussian copula in endogenous variables (FIN_LITERACY, FIN_RESOURCE).	GC (FIN_LITERACY) -> BUS_RESILIENCE	0.123	0.478
	GC (FIN_RESOURCE) -> BUS_RESILIENCE	−0.176	0.626
Model 7 Gaussian copula in endogenous variables (FIN_LITERACY, GOV_SUPPORT).	GC (FIN_LITERACY) -> BUS_RESILIENCE	0.082	0.621
	GC (GOV_SUPPORT) -> BUS_RESILIENCE	0.142	0.533
Model 8 Gaussian copula in endogenous variables (COMPLIANCE_COST, BUS_RESILIENCE).	GC (COMPLIANCE_COST) -> BUS_RESILIENCE	−0.040	0.791
	GC (FIN_RESOURCE) -> BUS_RESILIENCE	−0.081	0.828
Model 8 Gaussian copula in endogenous variables (COMPLIANCE_COST, GOV_SUPPORT).	GC (COMPLIANCE_COST) -> BUS_RESILIENCE	−0.080	0.566
	GC (GOV_SUPPORT) -> BUS_RESILIENCE	0.196	0.388
Model 9 Gaussian copula in endogenous variables (GOV_SUPPORT, FIN_RESOURCE).	GC (GOV_SUPPORT) -> BUS_RESILIENCE	0.184	0.427
	GC (FIN_RESOURCE) -> BUS_RESILIENCE	−0.178	0.616
Model 10 Gaussian copula in endogenous variables (FIN_LITERACY, COMPLIANCE_COST, FIN_RESOURCE).	GC (FIN_LITERACY) -> BUS_RESILIENCE	0.146	0.404
	GC (COMPLIANCE_COST) -> BUS_RESILIENCE	−0.068	0.664
	GC (FIN_RESOURCE) -> BUS_RESILIENCE	−0.137	0.719
Model 11 Gaussian copula in endogenous variables (FIN_LITERACY, FIN_RESOURCE, GOV_SUPPORT).	GC (FIN_LITERACY) -> BUS_RESILIENCE	0.108	0.541
	GC (FIN_RESOURCE) -> BUS_RESILIENCE	−0.230	0.536
	GC (GOV_SUPPORT) -> BUS_RESILIENCE	0.169	0.471
Model 12 Gaussian copula in endogenous variables (GOV_SUPPORT, FIN_RESOURCE, COMPLIANCE_COST).	GC (GOV_SUPPORT) -> BUS_RESILIENCE	0.209	0.366
	GC (FIN_RESOURCE) -> BUS_RESILIENCE	−0.136	0.721
	GC (COMPLIANCE_COST) -> BUS_RESILIENCE	−0.069	0.645
Model 13 Gaussian copula in endogenous variables (GOV_SUPPORT, COMPLIANCE_COST, FIN_RESOURCE, FIN_LITERACY).	GC (GOV_SUPPORT) -> BUS_RESILIENCE	0.199	0.396
	GC (COMPLIANCE_COST) -> BUS_RESILIENCE	−0.093	0.541
	GC (FIN_RESOURCE) -> BUS_RESILIENCE	−0.186	0.632
	GC (FIN_LITERACY) -> BUS_RESILIENCE	0.137	0.440

Table 12. Unobserved heterogeneity assessment.

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
AIC	1.248.711	1.191.824	1.136.843	1.091.360	1.072.826	1.061.604
AIC3	1.258.711	1.212.824	1.168.843	1.134.360	1.126.826	1.126.604
AIC4	1.268.711	1.233.824	1.200.843	1.177.360	1.180.826	1.191.604
BIC	1.291.608	1.281.908	1.274.114	1.275.818	1.304.471	1.340.435
CAIC	1.301.608	1.302.908	1.306.114	1.318.818	1.358.471	1.405.435
HQ	1.265.490	1.227.058	1.190.534	1.163.507	1.163.429	1.170.663
MDL5	1.543.197	1.810.244	2.079.198	2.357.649	2.663.049	2.975.761
LnL	−614.356	−574.912	−536.422	−502.680	−482.413	−465.802
EN	0.000	0.341	0.726	0.600	0.731	0.846
NFI	0.000	0.374	0.701	0.517	0.648	0.788
NEC	0.000	355.210	147.596	215.344	145.013	82.814

Data availability statement

The corresponding author’s data supporting the study’s findings are available upon reasonable request.