Regional determinants of access to entrepreneurial finance: a conceptualisation and empirical study in Norwegian startup ecosystems

Figures & data

Figure 1. Specified model of access to entrepreneurial finance.

Table 1. Measurements.

Constructs	Measurements	Theoretical justification
INFV	UK Community Innovation Survey
PFP	UK Community Innovation Survey
RSC	GEM (2016b)
PAFS	Bastesen and Vatne (2014)	Frimanslund (2022)
EENC		Frimanslund (2022), Spigel and Harrison (2018)
EEA		Frimanslund (2022), Spigel and Harrison (2018)
RIH		Frimanslund (2022), Spigel and Harrison (2018)

Table 2. Self-reported geographic distribution.

Urban/rural
		Frequency	Percentage	Valid percentage	Cumulative percentage
Valid	Rural	24	17.6	18.3	18.3
	Urban	107	78.7	81.7	100.0
	Total	131	96.3	100.0
Missing	System	5	3.7
Total		136	100.0

Table 3. Inter-item correlations.

Inter item correlations
	PAEC	PABL	PAPI	PAPE	EA1	EA2	EA3	EA4	EA5	INFVM	INFVK	INFVN	INFVL	EENC1	EENC2	EENC3	RIA1	RIW1	RIW2	PISLG1	PISLG2	PISLG4
PAEC	1
PABL	,456**	1
PAPI	,659**	,534**	1
PAPE	,428**	,332**	,387**	1
EA1	0,006	0,029	0,041	0,006	1
EA2	−0,117	−0,082	−0,08	−0,072	,531**	1
EA3	−0,027	−0,131	−0,053	−0,068	,349**	,479**	1
EA4	−0,02	−0,156	−0,122	−0,092	,386**	,556**	,679**	1
EA5	0,032	−0,09	−0,052	−0,138	,477**	,528**	,594**	,685**	1
INFVM	0,132	0,015	0,164	0,098	0,075	−0,067	−,177*	−,170*	−0,134	1
INFVK	0,162	0,125	,233**	,209*	−0,011	−0,056	−0,099	−0,09	−0,115	,791**	1
INFVN	,247**	0,12	,313**	,227**	0,058	−0,104	−0,122	−0,066	−0,041	,708**	,719**	1
INFVL	,224**	0,109	,309**	,234**	−0,053	−0,155	−,184*	−0,131	−0,09	,591**	,561**	,716**	1
EENC1	−0,103	−0,045	−0,047	0,053	−0,018	0,087	0,043	−0,057	0,014	0,094	,171*	,196*	0,119	1
EENC2	0,026	−0,027	0,038	0,079	0,025	−0,029	0,045	0,069	0,004	0,103	0,167	,169*	0,075	,470**	1
EENC3	0,009	−0,049	−0,011	0,15	0,053	,169*	−0,032	0,015	0,155	0,121	,184*	0,161	0,097	,558**	,439**	1
RIA1	,284**	,185*	,302**	,202*	−0,06	−0,074	0,042	−0,085	−0,12	−0,043	−0,042	0,041	0,11	0,103	0,038	0,07	1
RIW1	,363**	,338**	,484**	,415**	−0,035	−0,072	−0,003	−0,097	−0,11	−0,082	−0,036	0,091	0,102	0,095	0,065	,178*	,521**	1
RIW2	,214*	,206*	,298**	0,163	−0,134	−0,137	−0,105	−,208*	−0,136	−0,043	0,077	0,163	0,094	0,131	0,065	0,154	,422**	,518**	1
PISLG1	−,510**	−,293**	−,460**	−,265**	−0,033	−0,028	−0,07	−0,024	−0,069	−0,06	−0,078	−0,078	−0,13	0,051	−0,027	0,026	−,171*	−,333**	−0,121	1
PISLG2	−,521**	−,196*	−,431**	−,185*	−0,028	−0,026	−0,116	−0,054	−0,084	0,012	0,018	−0,049	−0,14	0,096	0,039	0,04	−,174*	−,287**	−,192*	,857**	1
PISLG4	−,416**	−0,139	−,292**	−,208*	0,008	−0,028	−0,054	−0,091	−0,056	0,045	0,078	−0,007	−0,02	0,052	0,079	0,007	−0,136	−,264**	−0,125	,608**	,683**	1

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

Table 4. Inter-construct correlations with the square root of AVE.

	PAFS	EA	INFV	PFP	EENC	RIH	RSC	Mean	SD
PAFS	0.697							2.59	0.92
EA	−0.098	0.731						2.39	0.81
INFV	0.267**	−0.131	0.828					3.77	0.93
PFP	−0.478**	−0.073	−0.045	0.859				3.95	1.02
EENC	0.002	0.051	0.194*	0.056	0.704			3.82	0.94
RIH	0.457**	−0.136	0.049	−0.274**	0.145	0.705		3.14	0.95
RSC	0.298**	−0.138	0.103	−0.14	−0.123	0.378**	0.637	3.13	0.79

** Correlation is significant at the 0.01 level (2-tailed).

* Correlation is significant at the 0.05 level (2-tailed).

Table 5. Construct reliability and AVE.

		STD. Regr.	SQRD. Mult.	1 – SQRD	Construct	Average variance	Square root of
		Weight	Correlation	Mult. correl.	Reliability	Extracted	AVE
EENC	EENC1	0.77	0.59	0.41	0.74	0.50	0.704
	EENC2	0.60	0.36	0.64
	EENC3	0.74	0.54	0.46
EA	EA1	0.53	0.28	0.72	0.85	0.54	0.731
	EA2	0.67	0.45	0.55
	EA3	0.76	0.57	0.43
	EA4	0.85	0.72	0.28
	EA5	0.81	0.65	0.35
INFV	INFVM	0.86	0.74	0.26	0.90	0.69	0.828
	INFVK	0.87	0.75	0.25
	INFVN	0.85	0.73	0.27
	INFVL	0.72	0.52	0.48
PFP	PISLG1	0.90	0.80	0.20	0.89	0.74	0.859
	PISLG2	0.96	0.92	0.08
	PISLG4	0.71	0.50	0.50
RIH	RIW1	0.87	0.75	0.25	0.74	0.50	0.705
	RIW2	0.61	0.37	0.63
	RIA1	0.61	0.37	0.63
RSC	RSC1	0.65	0.42	0.58	0.66	0.41	0.637
	RSC2	0.48	0.23	0.77
	RSC3	0.75	0.57	0.43
PAFS	PAEC	0.79	0.62	0.38	0.78	0.49	0.697
	PABL	0.59	0.35	0.65
	PAPI	0.85	0.72	0.28
	PAPE	0.51	0.26	0.74

Table 6. Difference between rural and urban groups.

Variable	Levene’s test sig value	Equal variances assumed	T values	Sig. (2 tailed)
PAFS	0.967	Yes	2.307	0.023
EA	0.055	Yes	1.742	0.084
INFV	0.051	Yes	−.289	0.773
PFP	0.616	Yes	−1.679	0.095
EENC	0.661	Yes	1.046	0.298
RIH	0.911	Yes	−.688	0.492
RSC	0.396	Yes	−1.640	0.103

Figure 2. Common method bias test introducing Common latent factor (CLF).

Figure 3. Common method bias test using common marker variable.

Figure 4. Measurement model with correlations.

Figure 5. Structural model and path values.

Table 7. Goodness of fit metrics.

Goodness of fit	Criterion	Value
χ² test
χ²	p > 0.05	271.56	(p < 0.01)
χ²/df	<5	1.34
Fit indices
CFI	>0.9	0.948
NFI	>0.9	0.828
RMSEA	<0.08	0.051
Hoelter at 0.05	>100	118
Hoelter at 0.01	>100	126

Table 8. Hypothesis test results.

Hypothesis test results			Standardised path	Standard error	Critical ratio
			coefficient (β)	(S.E.)	(t value)	Result
H1 Exit attitude	→	Investor non-finance value	0.163	0.189	1.673	Not supported at p < 0.05
H2 Entrepreneurial ecosystem network collaboration	→	Investor non-finance value	0.248	0.106	2.306	Supported at p < 0.05
H3 Investor non-finance value	→	Perceived access to financial sources	0.326	0.087	4.029	Supported at p < 0.001
H4 Entrepreneurial ecosystem network collaboration	→	Perceived access to financial sources	0.155	0.093	1.814	Not supported at p < 0.05
H5 Perceived firm performance	→	Perceived access to financial sources	0.383	0.086	4.294	Supported at p < 0.001
H6 Regional investor heterogeneity	→	Perceived access to financial sources	0.495	0.099	4.988	Supported at p < 0.001
H7 Regional investor heterogeneity	→	Perceived firm performance	0.352	0.103	3.535	Supported at p < 0.001

Table

EA1	Public support agencies in my region consider an exit as something negative
EA2	The banks consider an exit as something negative
EA3	My co-founder(s) consider(s) an exit as something negative
EA4	Local investors consider an exit as something negative
EA5	Remote investors consider an exit as something negative

Table

INFVM	Governance (e.g. board member, mentor or similar)
INFVK	Knowledge
INFVN	Network
INFVL	Legitimacy for the firm

Table

EENC1	We cooperate with nearby firms
EENC2	We cooperate with firms elsewhere in the country or abroad
EENC3	Firms in our network supply valuable knowledge
EENC4	Firms in our network supply valuable investments
EENC5	I would like to re-invest capital and knowledge in nearby firms
EENC6	To be part of an environment with other startups and founders is important for me
EENC7	To be part of an environment with other startups and founders is important for my firm
EENC8	I can influence the environment in which I am a part

Table

PISLG1	I feel that the lack of access to capital hinders my firm’s profitability
PISLG2	I feel that the lack of access to capital hinders my firm’s growth
PISLG3	I spend unreasonable time and effort in acquiring capital
PISLG4	Lack of access to capital reduces my ability to employ the people I need
PISLG5	Lack of access to capital reduces my ability to achieve sufficient sales volumes

Table

PAEC	We have (had) sufficient access to external capital
PABL	We have (had) sufficient access to bank loans
PAPI	We have (had) sufficient access to private investors
PAVC	We have (had) sufficient access to venture capital funds or similar
PAPE	We have (had) sufficient access to private equity funds or similar
PAGR	We have (had) sufficient access to public grants, loans or investments
PACR	We have (had) sufficient access to crowdfunding or crowdlending
PAOF	Our own funds are sufficient

Table

RSC1	There is a culture for creativity and innovation in my region
RSC2	People prefer safe and stable employment in my region
RSC3	People like to seek new opportunities in my region

GEM. 2016b. “GEM – Entrepreneurial Finance Special Report 2015–2016.” http://gemconsortium.org/report

 Google Scholar

Bastesen, J., and E. Vatne. 2014. “Rapid-Growth Firms: Exploring the Role and Location of Entrepreneurial Ventures.” In Agglomeration, Clusters and Entrepreneurship: Studies in Regional Economic Development, 159–198. Edward Elgar Publishing.

 Google Scholar

Frimanslund, T. 2022. “Financial Entrepreneurial Ecosystems: An Analysis of Urban and Rural Regions in Norway.” International Journal of Global Business and Competitiveness. doi:10.1080/08276331.2022.2035171.

 Google Scholar

Spigel, B., and R. Harrison. 2018. “Toward a Process Theory of Entrepreneurial Ecosystems.” Strategic Entrepreneurship Journal 12 (1): 151–168.

 Web of Science ®Google Scholar