Cushioning the Covid-19 Economic Consequences on Entrepreneurial Ecosystems: The Role of Stakeholders` Engagement, Collaboration, and Support

Figures & data

Figure 1. SEM Model Results.

Table 1. Summary of Construct Measurement(operationalization)and Reliability Results.

	Constructs and Measurement Items	Loadings	Remarks
Entrepreneurial Ecosystems Performance (EEP) during COVID-19 pandemic (Nicotra et al., 2018; Kansheba, 2020)
CA = 0.906 CR = 0.88 AVE = 0.606
EEP1	The rate of new startups joining early-stage entrepreneurial activities	0.85***
EEP2	The rate of high growth startups	0.83***
EEP3	The rate of innovation of startups	0.70***
EEP4	The survival rate of startups	0.80***
EEP5	The level of productive entrepreneurship	0.70***
Entrepreneurial Ecosystem Quality (EEQ) during COVID-19 pandemic (Isenberg, 2010; Ratten, 2020; Maritz et al., 2020)
CA = 0.728 CR = 0.74 AVE = 0.490
EEQ1	Access to financial resources	0.68*
EEQ2	Presence of entrepreneurship supporting culture	0.72***
EEQ3	Availability of Market	0.69***
EEQ4	Government support eg good policies and programs	0.43	Removed
EEQ5	The level of knowledge creation and transfer eg availability of universities and R & D centers	0.4	Removed
Entrepreneurial Ecosystem Stakeholder Collaboration (EESC) during COVID-19 pandemic (Denning & Dunham, 2010; Ndlela, 2019;Bischoff & Volkmann, 2018; Maritz et al., 2020)
CA = 0.931 CR = 0.93 AVE = 0.82
EESC1	The extent of key information sharing among EE stakeholders	0.86***
EESC2	The extent of interaction and networking among EE stakeholders	0.94***
EESC3	The extent of partnering among EE stakeholders	0.92***
Entrepreneurial Ecosystem Stakeholder Support (EESS) during COVID-19 pandemic (Tehseen et al., 2019; Al-Shammari et al. 2018; Bischoff et al., 2017)
CA = 0.761 CR = 0.76 AVE = 0.518
EESS1	Support from financial providers eg. Good financial terms	0.70***
EESS2	Support from customers and other business partners	0.75***
EESS3	support from other EE stakeholders eg universities, government agents, and accelerators	0.46	Removed
EESS4	Support from the community eg. family members and friends	0.71***
EESS5	Support from talented and innovative employees (human capital)	0.39	Removed
Entrepreneurial Ecosystem Stakeholder Engagement (EESE) during COVID-19 pandemic (Shams & Khojastehpour, 2019; Chandler & Werther, 2013; Bischoff & Volkmann, 2018)
CA = 0.726 CR = 0.77 AVE = 0.524
EESE1	The extent that entrepreneurs and startups involve and work with financial providers in daily operations	0.73***
EESE2	The extent that entrepreneurs and startups involve and work with their business partners such as customers and suppliers in their daily operations	0.74***
EESE3	The extent that entrepreneurs and startups involve and work with government agents in their daily operations	0.44	Removed
EESE4	The extent that entrepreneurs and startups involve and work with other entrepreneurial enablers such as incubators, accelerators, large companies, professionals	0.37	Removed
EESE5	The extent that entrepreneurs and startups involve and work with community in daily operations	0.70***
The effect of Government COVID-19 measures on EE (GCM) (Ratten, 2020; Maritz et al., 2020)
CA = 0.70 CR = 0.73 AVE = 0.579
GCM1	The effect of travel restrictions on the quality and functioning of EE	0.59*	The item is retained for practical purpose as it hovers around .5 cutoff (Hair et al., 2006)
GCM2	The effect of social distancing and closure of social events on the quality and functioning of EE	0.90**
Entrepreneurial Ecosystem Vulnerability to COVID-19economic consequences (Kuckertz et al., 2020; Ligouri & Winkler, 2020; Ratten, 2020)
CA = 0.70 CR = 0.76 AVE = 0.631
EEVC1	The extent that COVID-19 has affected the functioning of the EE	0.53*	The item is retained for practical purpose as it hovers around .5 cutoff (Hair et al., 2006)
EEVC2	The Extent that COVID-19 has weakened the quality of the EE	0.99***

CFA Model fit indices: Chi-square = 289.74, df = 168, CFI = 0.925, TLI = 0.907, RMSEA = 0.057, SRMR = 0.071. CA stands for Cronbach Alpha, CR stands for Composite Reliability, and AVE stands for Average Variance Extracted. In parentheses are standard errors. *, **, and *** = Statistical Significance at 10%, 5%, and 1% respectively.

Table 2. Non-response Bias Test: Mean Comparison between Two (First 119 Responses and Last 118 Responses) sub-samples.

Variable	Test value	df	p-value
Gender	Chi = 2.256	1	0.133
Age	F = 0.413	1	0.521
Education	F = 0.283	1	0.595
EE stakeholders` type	F = 0.027	1	0.641
Experience	F = 0.215	1	0.526
Sector	F = 0.034	1	0.854

Table 3. Discriminant Validity Results.

	EP	EEQ	EESC	EESS	EESE	GCM	EEVC
EEP	1
EEQ	0.002	1
EESC	0.018	0.052	1
EESS	0.06	0.101	0.013	1
EESE	0.136	0.321	0.095	0.198	1
GCM	0.054	0.058	0.017	0.327	0.059	1
EEVC	0.000	0.000	0.002	0.083	0.012	0.011	1
AVE	0.606	0.490	0.823	0.518	0.524	0.579	0.631

Table 4. Descriptive Statistics.

Variables	Obs	% of total Obs	Mean	SD	Min	Max
General characteristics of the respondents
Gender
Female	123	0.52	0.481	0.501	0	1
Male	114	0.48
Age
Below 31 yrs	102	0.43	1.717	0.707	1	3
31 yrs-45 yrs	100	0.42
46 yrs and above	35	0.15
Education
Basic Education	144	0.61	0.392	0.489	0	1
Higher Education	93	0.39
Experience
Below 6 yrs	34	0.14	2.422	0.943	1	4
6 yrs-10 yrs	161	0.68
10 yrs and above	42	0.18
Entrepreneurial Ecosystem Stakeholders
Entrepreneurs	113	0.48	0.477	0.501	0	1
Other stakeholders	124	0.52
Sectors
Local and retail trade	31	0.42	2.236	0.984	1	4
International trade	14	0.19
Services	48	0.24
Manufacturing	20	0.15
Constructs
Entrepreneurial Ecosystem Performance (EEP)	237		2.763	1.493	1	4
Entrepreneurial Ecosystem Quality (EEQ)	237		3.318	2.174	1	5
EE Stakeholder Collaboration	237		3.273	1.979	1	4
EE Stakeholder Support	237		2.127	1.255	1	5
EE Stakeholder Engagement	237		3.034	1.977	1	5
EE Vulnerability to COVID-19 (COVID-19 Impact to EEQ) (EEVC)	237		4.450	2.539	1	5
Government COVID-19 Measures (GCM)	237		4.154	2.054	1	5

Table 5. Correlation Results among Constructs.

	EEP	EEQ	EESC	EESS	EESE	GCM	EEVC
EEP	1
EEQ	0.05	1
EESC	−0.133	−0.229	1
EESS	0.246	0.318	−0.114	1
EESE	0.368	0.567	−0.308	0.445	1
GCM	0.233	0.24	−0.132	0.572	0.243	1
EEVC	−0.006	−0.016	0.046	0.288	0.11	0.106	1

Table 6. SEM Estimation Results.

	EEQ	EEVC	EESS	EESC	EEP
	Estimate	Estimate	Estimate	Estimate	Estimate
EEQ					0.463** (0.191)
EEVC	−0.1504* (0.077)				−0.107 (0.158)
EESE	0.174** (0.067)	0.087 (0.163)	0.217* (0.126)	0.388** (0.139)
EESC	0.131** 0.026)	−0.0581* (0.032)	0.336** (0.152)
EESS	0.039 (0.077)	0.196 (0.28)
GCM	−0.369** (0.097)	0.201** (0.043)
Gender Dummy: Male					0.089 (0.078)
Age Dummy 1: 31 yrs to 45 yrs					0.211 (0.222)
Age Dummy 2: 46 yrs and above					0.47 (0.332)
Education Dummy: Higher Education					−0.292* (0.158)
Stakeholder Dummy: Entrepreneurs (startups)					0.535** (0.269)
Experience Dummy 1: 6 yrs to 10 yrs					0.334** (0.157)
Experience Dummy 2: Above 10 yrs					0.447** (0.224)
Sector Dummy 1: Service					−1.5*** (0.212)
Sector Dummy 2: International trade					−1.42*** (0.169)
Sector Dummy 3: Manufacturing					−0.939** (0.292)

Model fit: Chi-square = 638.034, df = 355, CFI = 0.896, TLI = 0.871, RMSEA = 0.08, SRMR = 0.79. Observations = 237. In parentheses are standard errors. *, **, and *** = Statistical Significance at 10%, 5%, and 1% respectively.

Table

Hypothesis	Relationship	Remarks
H1a	EEQ ~ GCM	Confirmed
H1b	EEVC ~ GCM	Confirmed
H1c	EEQ ~ EEVC	Confirmed
H1d	EEP ~ EEVC	Confirmed
H2a	EEQ ~ EESE	Confirmed
H2b	EEVC ~ EESE	Rejected
H3a	EESC ~ EESE	Confirmed
H3b	EEQ ~ EESC	Confirmed
H3c	EEVC ~ EESC	Confirmed
H4a	EESS ~ EESE	Confirmed
H4b	EESS ~ EESC	Confirmed
H4c	EEQ ~ EESS	Rejected
H4d	EEVC ~ EESS	Rejected

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