Identifying necessary conditions to deep-tech entrepreneurship

Dionisio, Eduardo Avancci; Inacio Junior, Edmundo; Morini, Cristiano; Carvalho, Ruy de Quadros

doi:10.1108/RAUSP-09-2022-0203

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Sumário

Research Paper • RAUSP 58 (2) • Apr-Jun 2023 • https://doi.org/10.1108/RAUSP-09-2022-0203 copy

Identifying necessary conditions to deep-tech entrepreneurship

Authorship SCIMAGO INSTITUTIONS RANKINGS

Abstract

Purpose

This paper aims to address which resources provided by an entrepreneurial ecosystem (EE) are necessary for deep technology entrepreneurship.

Design/methodology/approach

The authors used a novel approach known as necessary condition analysis (NCA) to data on EEs and deep-tech startups from 132 countries, collected in a global innovation index and Crunchbase data sets. The NCA makes it possible to identify whether an EEs resource is a necessary condition that enables entrepreneurship.

Findings

Necessary conditions are related to political and business environment; education, research and development; general infrastructure; credit; trade; diversification and market size; and knowledge absorption capacity.

Research limitations/implications

The results show that business and political environments are the most necessary conditions to drive deep-tech entrepreneurship.

Practical implications

Policymakers could prioritize conditions that maximize entrepreneurial output levels rather than focusing on less necessary elements.

Social implications

Some resources require less performance than others. So, policymakers should consider allocating policy efforts to strengthen resources that maximize output levels.

Originality/value

Studies on deep-tech entrepreneurship are scarce. This study provides a bottleneck analysis that can guide the formulation of policies to support deep-tech entrepreneurship, as it allows to identify priority areas for resource allocation.

Keywords
Entrepreneurial ecosystems; Emerging technology

Type, variable, code and brief description	Source
Outcome
Deep-tech startup, DTS	Crunchbase
Number of active deep tech-based companies founded between 2016–2021	Crunchbase
Conditions
Institutions	Global Innovation Index
Political environment, PE
It measures the countries’ political stability, the quality of public services and the capacity to implement public policies
Regulatory environment, RE
It measures the ability of governments to implement policies and regulations that promote private-sector development
Business environment, BE
It captures the procedures, time and cost required to start a business
Human capital and research
Education, E
It measures the quality of secondary education by the total government spending on education, average spending per student, PISA performance and the pupil–teacher ratio
Tertiary education, TE
It captures the quality of and access to higher education through the proportion of tertiary education enrollment, and the proportion of graduates in science and engineering
Research and development, RD
It measures the number of full-time researchers involved in R&D activities. The total domestic R&D expenditure (% GDP) and largest companies’ expenditure on R&D in a country
Infrastructure
Information and communication technologies, ICT
It measures the access and use of ICTs by both the population and governments
General infrastructure, GI
It measures energy production, logistics performance, and gross capital formation (as % GDP)
Ecological sustainability, ES
It measures how close countries are to meeting environmental policy goals
Market sophistication
Credit, C
It captures the ease of getting credit, as well as the proportion of microfinance loans and credit available to the private sector
Investment, I
It measures the regulation and extent of the financial market
Trade, diversification and market size, TDMS
Originally named “Trade, Diversification, and Market Scale”, it measures domestic industry diversification and internal market size
Business sophistication
Knowledge workers, KW
It captures the proportion of employment in knowledge-intensive services and business expenditures on R&D
Innovation linkages, IL
It measures business-university cooperation, the level of diffusion and development of clusters, foreign-funded R&D expenditures, the number of joint ventures and strategic alliances, as well as the number of patents filed
Knowledge absorption, KA
It measures knowledge absorption, considering intellectual property charges, imports of high technology, flows of foreign direct investment and the number of full-time researchers in the business sector

Dimensions	Variables	Effect size (d)	p-value
Institutions	PE Political environment	0.377^l	0.028^ Notes: ns = not significant, Effect size (d): small = (0 < d < 0.1); medium = (0.1 ≤ d < 0.3); large = (0.3 ≤ d < 0.5); very large = (d ≥ 0.5), p-value: = significant at 5% (p < 0.05); *** = significant at 1% (p < 0.01); ns = nonsignificant. Shaded cells are the ultimate necessary conditions to be used in the next step
	RE Regulatory environment	0.219^m	0.081 ^ns
	BE Business environment	0.458^l	0.000^* Notes: ns = not significant, Effect size (d): small = (0 < d < 0.1); medium = (0.1 ≤ d < 0.3); large = (0.3 ≤ d < 0.5); very large = (d ≥ 0.5), p-value: = significant at 5% (p < 0.05); *** = significant at 1% (p < 0.01); ns = nonsignificant. Shaded cells are the ultimate necessary conditions to be used in the next step
Human capital and research	E Education (secondary)	0.312^l	0.019^ Notes: ns = not significant, Effect size (d): small = (0 < d < 0.1); medium = (0.1 ≤ d < 0.3); large = (0.3 ≤ d < 0.5); very large = (d ≥ 0.5), p-value: = significant at 5% (p < 0.05); *** = significant at 1% (p < 0.01); ns = nonsignificant. Shaded cells are the ultimate necessary conditions to be used in the next step
	TE Tertiary education	0.076^s	0.095 ^ns
	RD Research and development	0.013^m	0.000^* Notes: ns = not significant, Effect size (d): small = (0 < d < 0.1); medium = (0.1 ≤ d < 0.3); large = (0.3 ≤ d < 0.5); very large = (d ≥ 0.5), p-value: = significant at 5% (p < 0.05); *** = significant at 1% (p < 0.01); ns = nonsignificant. Shaded cells are the ultimate necessary conditions to be used in the next step
Infrastructure	ICT Information and communication technology	0.109^m	0.139 ^ns
	GI General infrastructure	0.184^m	0.019^ Notes: ns = not significant, Effect size (d): small = (0 < d < 0.1); medium = (0.1 ≤ d < 0.3); large = (0.3 ≤ d < 0.5); very large = (d ≥ 0.5), p-value: = significant at 5% (p < 0.05); *** = significant at 1% (p < 0.01); ns = nonsignificant. Shaded cells are the ultimate necessary conditions to be used in the next step
	ES Ecological sustainability	0.047^s	0.152 ^ns
Market sophistication	C Credit	0.278^m	0.001^* Notes: ns = not significant, Effect size (d): small = (0 < d < 0.1); medium = (0.1 ≤ d < 0.3); large = (0.3 ≤ d < 0.5); very large = (d ≥ 0.5), p-value: = significant at 5% (p < 0.05); *** = significant at 1% (p < 0.01); ns = nonsignificant. Shaded cells are the ultimate necessary conditions to be used in the next step
	I Investment	0.127^m	0.270 ^ns
	TDMS Trade, diversification and market size	0.302^l	0.004^* Notes: ns = not significant, Effect size (d): small = (0 < d < 0.1); medium = (0.1 ≤ d < 0.3); large = (0.3 ≤ d < 0.5); very large = (d ≥ 0.5), p-value: = significant at 5% (p < 0.05); *** = significant at 1% (p < 0.01); ns = nonsignificant. Shaded cells are the ultimate necessary conditions to be used in the next step
Business sophistication	KW Knowledge workers	0.093^s	0.068 ^ns
	IL Innovation linkages	0.119^m	0.054 ^ns
	KA Knowledge absorption	0.113^m	0.000^* Notes: ns = not significant, Effect size (d): small = (0 < d < 0.1); medium = (0.1 ≤ d < 0.3); large = (0.3 ≤ d < 0.5); very large = (d ≥ 0.5), p-value: = significant at 5% (p < 0.05); *** = significant at 1% (p < 0.01); ns = nonsignificant. Shaded cells are the ultimate necessary conditions to be used in the next step

…different desired levels (%) of outcome (DTS) Y = deep-tech startup	Required minimum levels of the necessary condition (%) for …
	Institutions		Human capital and research		Infrastructure	Market sophistication		Business sophistication
	PE	BE	E	RD	GI	C	TDMS	KA
0	NN	NN	NN	NN	NN	NN	NN	NN
10	37.9	41.1	13.9	0.1	13.0	24.5	18.8	3.5
20	37.9	41.1	13.9	0.1	13.0	24.5	18.8	3.5
30	37.9	42.4	13.9	0.1	13.0	26.3	18.8	3.5
40	37.9	48.4	21.9	0.1	13.0	26.3	18.8	6.6
50	37.9	48.4	43.5	2.2	13.0	26.3	42.5	7.9
60	37.9	49.7	43.5	2.2	13.0	26.3	42.5	7.9
70	37.9	49.7	43.5	2.2	27.7	34.4	42.5	21.4
80	37.9	49.7	43.5	2.2	27.7	34.4	42.5	21.4
90	37.9	49.7	43.5	2.2	27.7	34.4	42.5	21.4
100	37.9	49.7	43.5	2.2	27.7	34.4	42.5	21.4

Raw data	Code	SD	Minimum	Mean	Maximum
Outcome
Deep-tech startup	DTS	103.5	0.0	77.3	301.0
Conditions
Institutions
Political environment	PE	17.5	0.00	60.0	100.0
Regulatory environment	RE	18.0	17.4	64.6	99.1
Business environment	BE	12.3	31.3	70.2	93.1
Human capital and research
Education	E	15.3	0.00	48.0	82.5
Tertiary education	TE	15.8	0.00	30.2	63.4
Research and development	RD	23.7	1.60	19.4	89.8
Infrastructure
Information and communication technologies	ICT	19.9	21.3	63.4	94.8
General infrastructure	GI	12.5	2.60	30.2	67.3
Ecological sustainability	ES	12.6	12.7	30.8	60.4
Market sophistication
Credit	C	15.9	0.30	41.4	88.0
Investment	I	16.9	4.00	34.4	88.4
Trade, diversification and market size	TDMS	14.7	26.7	67.0	96.9
Business sophistication
Knowledge workers	KW	18.2	3.3	34.1	44.7
Innovation linkages	IL	15.3	1.20	25.8	82.1
Knowledge absorption	KA	13.5	11.4	29.4	70.7
Normalized data
Outcome
Deep-tech startup	DTS	0.34	0	0.26	1
Conditions
Institutions
Political environment	PE	0.17	0	0.60	1
Regulatory environment	RE	0.22	0	0.58	1
Business environment	BE	0.20	0	0.63	1
Human capital and research
Education	E	0.18	0	0.58	1
Tertiary education	TE	0.25	0	0.48	1
Research and development	RD	0.26	0	0.22	1
Infrastructure
Information and communication technologies	ICT	0.27	0	0.57	1
General infrastructure	GI	0.19	0	0.43	1
Ecological sustainability	ES	0.26	0	0.38	1
Market sophistication
Credit	C	0.18	0	0.47	1
Investment	I	0.20	0	0.36	1
Trade, diversification and market size	TDMS	0.21	0	0.57	1
Business sophistication
Knowledge workers	KW	0.24	0	0.41	1
Innovation linkages	IL	0.19	0	0.30	1
Knowledge absorption	KA	0.23	0	0.30	1

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E-mail: rausp@usp.br

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[1] Eduardo Avancci Dionisio can be contacted at: avancci.eduardo@gmail.com