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Exploring the differences in early-stage incubator and
accelerator startups across developed and developing
countries: Evidence from the U.S., Israel, and
Thailand
24/12/2021
Denis Solan1, Izack Cohen2*, Fernando Gómez-Baquero3, Avraham Shtub4,5,
Sabin Srivannaboon6
1Faculty of Industrial Engineering and Management
Technion – Israel Institute of Technology, Haifa, Israel 3200003
denis.solan@campus.technion.ac.il
2Faculty of Engineering
Bar-Ilan University, Ramat-Gan, Israel 5290002
Izack.cohen@biu.ac.il
3Jacobs Technion-Cornell Institute, Cornell University,
2 West Loop Road, New York, NY 10044
fernando@cornell.edu
4Dean of Engineering
Ruppin Academic Center
Emek Hefer, 4025000, ISRAEL
5Professor Emeritus
Faculty of Industrial Engineering and Management
Technion – Israel Institute of Technology, Haifa, Israel 3200003
shtub@technion.ac.il
6Sasin School of Management
Chulalongkorn University
254 Phayathai Road, Pathumwan, Bangkok 10330 Thailand
sabin.srivannaboon@sasin.edu
*Corresponding author: Izack Cohen <Izack.cohen@biu.co.il>
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Exploring the differences in early-stage incubator and
accelerator startups across developed and developing
countries: Evidence from the U.S., Israel, and
Thailand
Keywords:
Early-stage startup
Incubator/accelerator
Entrepreneurial ecosystem
Developed/developing countries
Fundraising
Venture capital
Patents
Abstract
We investigate differences in the characteristics of early-stage startups associated
with incubators or accelerators across three developing (Thailand) and developed (the
U.S. and Israel) countries. Based on analyses of 90 startups, we found significant
differences in fundraising and patent activity patterns—two characteristics that are
fundamental for startups. The U.S. and Israeli startups patent their innovations to a
greater extent compared to those in Thailand. While U.S. startups raise funds from local
venture capital firms and Thailand from foreign ones, Israel uses both sources. We
explain how these results are associated with entrepreneurial ecosystems.
1. Introduction
We are motivated by the increasing contribution of startups to local and global
economies (Global Startup Ecosystem Report, 2019) and by the trend of fostering
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startups within technology business incubators and accelerators (Crișan et al., 2021,
Mian et al., 2016). Despite decades of research, scientists still strive to explain
differences between startup characteristics (Stenholm et al., 2013) such as culture,
market, and financial performance (Cumming and Zhang, 2019), and understand the
impact of different incubators and accelerators on the development of entrepreneurial
ventures (Mian et al., 2016), especially in developing countries (Crișan et al., 2021).
Startups operate within different entrepreneurial ecosystems around the world
(World Economic Forum, 2014). According to the Global Startup Ecosystem Report
(2020), a startup and its ecosystem are typically located within a 100-kilometer radius
and include policymakers, accelerators, incubators, coworking spaces, educational
institutions, and funding groups. Our focus is on early-stage startups, within the time
interval between opportunity identification to roughly five years of venture launching,
associated with incubators or accelerators. Incubators and accelerators play a critical
role in supporting and growing startups, offering them the services needed to increase
their survival chances during their formative years (Mian et al., 2016). Incubators have
a long history since the first incubator was founded in 1959 at Batavia Industrial Centre
in New York. Currently, there are more than seven thousand incubators globally
(Galbraith et al., 2021). An accelerator is a relatively new incubator model (by 2016,
over three thousand accelerators existed globally; Hochberg, 2016) that provides
intense, targeted assistance over a limited period (Pauwels et al., 2016). The typical
assistance includes education, mentoring, networking, monitoring, and connecting with
investors (Crișan et al., 2021; Hausberg and Korreck, 2018).
Fundraising from investors is considered one of the main obstacles facing startup
development (Patton et al., 2009). Venture capital (VC) represents a dominant source
of funds for high-potential startups commercializing risky inventions and technologies
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(Lerner and Nanda, 2020). Research has shown that VC-funded startups grow faster
than non-VC-funded startups (Dutta and Folta, 2016) and that VC firms tend to invest
differently depending on national culture (Li and Zahra, 2012). Zacharakis et al. (2007)
argued that VC firms in developed countries rely upon market information, such as
market size, market growth, competitor strength, to a greater extent than VC firms in
developing countries, which frequently consider human capital information. Prior
studies indicated that granted patents and patent applications might contribute to
fundraising from VC investors, private investors (Hoenen et al., 2014; Zhou et al.,
2016), and corporate investors (Cockburn and MacGarvie, 2009). Hereafter, we will
use the term patent activity when a startup owns a granted patent or a patent application
and investigate whether there is a difference in patent activity between startups in
developing and developed countries.
The research gap on which we focus is the fundraising and patent activity of early-
stage startups associated with incubators or accelerators in representative developed
and developing countries, issues that are central to startups (Noelia and Rosalia, 2020).
We adopt the World Bank classification of developed countries as high income
economies (Israel and the U.S. in this research) and developing countries as low and
middle income economies (Thailand in the context of this research) 1. Thailand was
selected to represent the developing countries by virtue of its emerging ecosystem (e.g.,
Bangkok was ranked among the top 100 startup emerging ecosystems; Global Startup
Ecosystem Report, 2020).
This study is unique as most databases do not include early-stage startups
(Bjornskov and Foss, 2016). Additionally, entrepreneurship scholars tend to focus on
1 The U.S. population was over 328 million in 2019, with GNI per capita of 65,850 U.S. dollars. Thailand
had more than 69 million with GNI per capita of 7,260 U.S. dollars; the population of Israel in 2019 was
around 9 million people with GNI per capita of 43,110 U.S. dollars (https://www.worldbank.org).
5
activities in advanced economies (Cao and Shi, 2021), especially American and
Western European countries (Aldrich and Ruef, 2018). The study conducts a
comparative analysis using a sample of 90 startups that are operating (or operated) in
an incubator or accelerator in one of the three countries named above during 2019 and
2020. This study offers new contributions to the literature about early-stage startup
ecosystems in the context of fundraising and patent activity.
The paper is structured as follows. In Section 2, we develop three research
hypotheses on fundraising and patent activity of startups across developing and
developed countries. In Section 3, we describe the research methods, and in Section 4,
we report the research results. In Section 5, we discuss the research results, propose
practical implications, present research limitations and directions for future study, and
in Section 6, we present the research conclusions.
2. Hypotheses development
2.1. Entrepreneurial ecosystems
The entrepreneurial ecosystem is a recent concept in entrepreneurship research
(Brown and Mason, 2017), emerging from the cluster concept that refers to a
geographic concentration of interconnected companies and institutions in a particular
field such as Silicon Valley in microelectronics (Porter, 1998). Research revealed that
the flow of knowledge and investment funding, and the existence of networks and
partnerships between regional organizations, such as universities, public and private
funding groups, and large and small firms, lead to innovative products, services, and
technologies (Cooke, 1997; Granstrand and Holgersson, 2020).
Drawing an analogy from natural ecosystems, James Moore described how
business ecosystems emerge from the capital, customer interest, and talent generated
by innovations, similar to how plants develop when given sunlight, water, and soil
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nutrients (Moore, 1993). The fundamental ideas of entrepreneurial ecosystems have,
since the 1980s, moved from a focus on individual entrepreneurs towards an
interdependent community perspective (Stam and Van de Ven, 2021), incorporating the
social, political, economic, and cultural elements (Spigel, 2015) that have a critical role
in reducing startups’ innovation obstacles (Noelia and Rosalia, 2020). An
entrepreneurial ecosystem includes eight pillars, two of which are essential for the
growth of early-stage startups—access to funding and markets (World Economic
Forum, 2014).
Cao and Shi (2021, p. 75) presented the prevalent definition of the
entrepreneurial ecosystem as “a community of multiple coevolving stakeholders that
provides a supportive environment for new venture creations within a region”. Many
governments are trying to support entrepreneurship since entrepreneurial ventures are
considered an essential source of innovation, productivity growth, and employment
(World Economic Forum, 2014). To build strong entrepreneurial ecosystems,
governments need to engage the private sector early, modify cultural norms, remove
regulatory barriers, and provide financing programs, among other steps (Isenberg,
2010).
Economic achievements support the reputation of entrepreneurial ecosystems,
which, in turn, contributes to attracting investors into the ecosystem (Audretsch et al.,
2019). According to Audretsch et al. (2019), the commercialization of knowledge is a
crucial measure of success that could facilitate the conversion from invention to
innovation in entrepreneurial ecosystems. The knowledge spillover process can be
disclosed and protected by intellectual property rights (Jarchow and Röhm, 2019).
Silicon Valley is considered the “gold standard” entrepreneurial ecosystem
(Isenberg, 2010, p. 43), facilitating conditions for long-term entrepreneurial success and
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the development of advanced technologies (Lerner, 2020). Silicon Valley
entrepreneurship revolves around high technology firms based on knowledge spillovers
from university research, according to the United States Congress mandate for
promoting innovation and economic growth through the Small Business Innovation
Research (SBIR) program, and the emergence of financial entities, such as angel and
VC investors (Audretsch, 2021). Isenberg (2010) argued that the Silicon Valley
ecosystem evolved in an environment that includes a local aerospace industry,
invention spillover from Fairchild Semiconductor Inc., Stanford University’s
supportive relationships with industry, and a culture that encourages collaborative
innovation and tolerates failures.
The Israeli ecosystem emerged out of military research and development (R&D),
which has been generating innovative technologies such as memory USBs since the
1970s (Isenberg, 2010). Since the mid-1980s, Israeli high tech has grown remarkably
fast, probably due to government policies and extensive support (Trajtenberg, 2002).
This government support has taken the form of a series of innovative programs beyond
R&D grants: the Yozma government-sponsored VC fund; the "Incubators" program,
which was a government response to the potential for innovative products presented by
the mass immigration of scientists and skilled professionals from the Soviet Union; and
the Magnet program, designed to support the development of generic technologies by
groups of industrial firms and academic institutions (Trajtenberg, 2002). Capital
markets were extremely limited in the early stages of Israeli high-tech development
(Trajtenberg, 2002). Indeed, Athena Venture Partners was the only VC fund operating
in Israel (Lerner, 2020).
The Israeli government launched Yozma (meaning initiative in Hebrew) in 1992,
a fund of $100 million, funded by the Israeli government and local and foreign private
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investors with proven funds management expertise (Isenberg, 2010), that created ten
VC funds within three years. The goal of Yozma was to bring foreign VC investment
expertise and contact networks to Israel. In fact, the ten VC funds were founded by
groups from the United States, Western Europe, and Japan (Lerner, 2020). After five
years, the private investors bought Yozma’s shares in these funds at a predetermined
price (Trajtenberg, 2002).
In parallel, the Israeli government signed bilateral R&D cooperation agreements
with foreign governments to support Israeli technology and products in accessing
global markets (Trajtenberg, 2002). The goal was to develop science-based, export-
oriented industries, mainly to increase employment (Trajtenberg, 2002).
Since then, the Israeli VC industry has achieved self-sustainability and
tremendous growth (Isenberg, 2010). It is interesting to note that the ratio of venture
investment to GDP in Israel is consistently higher than in any other nation (Lerner,
2020). Additionally, Israeli incubators are considered essential for providing initial
funding for startups (Conti et al., 2013).
The Kingdom of Thailand has a history of responsible and inclusive innovation
pioneered by His Majesty King Bhumibol Adulyadej in water aeration and rainmaking,
impacting the lives of millions (Thawesaengskulthai et al., 2020). Thailand’s strategy
to become an innovation-driven economy, highlighting investment opportunities and
trends in targeted industries to address fundamental social challenges, as pioneered by
the late King Rama IX, began in 2018 (Thawesaengskulthai et al., 2020). Compared to
its neighbors, e.g., Singapore, the Thai startup ecosystem is relatively new and small
and concentrated in Bangkok (Leung and Cossu, 2019). The promise of digital
entrepreneurship has encouraged Thailand’s younger generations to take risks (Leung
and Cossu, 2019), resulting in Thailand’s entrepreneurs being typically under 40 years
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old; they also come from relatively wealthy backgrounds (Leung and Cossu, 2019).
Thailand’s entrepreneurial ecosystem benefited from a decade of public investment in
innovation infrastructures such as science parks and accelerators (Thawesaengskulthai
et al., 2020), and since the 1980s, substantial capital investment from Japan and the
United States (Leung and Cossu, 2019). The most active VC funds in Thailand are 500
Startups from the United States and Cyberventure from Japan (Leung and Cossu, 2019).
Thailand launched its business incubation process in 2002 to support startups with
innovative technology-driven products, and nearly all Thai business incubators operate
in universities and are publicly funded (Munkongsujarit, 2016). Nevertheless, Thai
incubators struggle to support their entrepreneurs adequately due to insufficient funding
and a lack of business incubation experience among staff and entrepreneurial skills of
the startup companies (Munkongsujarit, 2016).
2.2. The number of investors in early-stage startups
Startups encounter severe barriers in their development because of a scarcity of
financial resources (Noelia and Rosalia, 2020). For example, four out of every ten
startups have only three months of capital runway (Global Startup Ecosystem Report,
2020). VC represents a dominant source of funds for high-potential startups
commercializing risky inventions and technologies (Lerner and Nanda, 2020). Previous
studies have investigated VC investment across developed and developing countries
but have not examined, to the best of our knowledge, the number of investors that
invested in an early-stage startup across developed and developing countries.
Moreover, previous studies measured the number of investors that spread the risk of
their investments in a specific VC round, given that the VC industry uses risk-sharing
partnerships (Lerner and Nanda, 2020). However, previous studies have not examined
these investments across countries (Hoenen et al., 2014; Zhou et al., 2016). Over the
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last decade, the amount of funds deployed worldwide by VC investors has grown
substantially (Lerner and Nanda, 2020). The U.S., which is considered the most
advanced VC market globally (Li and Zahra, 2012), had more than 1,000 VC funds in
2019 (Lerner and Nanda, 2020). Both the U.S. and Israel, which represent the
developed countries in this research, rank among the top 10 countries worldwide in the
number of investment deals, including VC deals from 1977 to 2012 (Cumming and
Zhang, 2019). The Israeli government’s support for civilian R&D began in 1969
(Trajtenberg, 2000); nowadays, Israel ranks third in the number of artificial intelligence
startups globally (Global Startup Ecosystem Report, 2020). Currently, the Thai high-
tech industry specializes in a few products (https://www.worldbank.org). One reason
for limited high-technology products derives from the evidence that the Thai
entrepreneurial ecosystem experiences scarce resources (Cao and Shi, 2021), especially
the limited number of angel and VC investors (Munkongsujarit, 2016; Scheela and
Jittrapanun, 2012). From the above, we can see that startups in the U.S. and Israel have
a higher number of potential investors interested in advanced technology compared to
Thailand. Since Thai startups in our sample had a single VC round, we focus on the
first VC round across countries (this applies to all hypotheses), given that we are
interested in determining the number of investors that spread the risk of their
investments in a specific VC round in developed and developing countries. This
discussion leads to our first hypothesis.
Hypothesis 1. Startups in developed countries (the U.S. and Israel) are more likely to
raise funds from a higher number of investors in their first VC round compared to
startups in developing countries (Thailand).
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2.3. The geographic proximity of VC firms to early-stage startups
Typically, VC firms reside in areas where VC investments have a high success
rate (Chen et al., 2010). The geographic proximity between VC firms and ventures
reduces information asymmetry and increases the likelihood of VC funding.
Conversely, geographical distance and institutional differences affect the costs and
risks of foreign VC firms (Tykvová et al., 2014). Therefore, geographically outlying
VC firms should have experience investing from a distance (Shafi et al., 2020).
Foreign VC firms dominate the Asian VC industry and have a relative advantage
over local VC firms in size and experience; however, in general, foreign VC firms have
difficulties gathering information and monitoring due to geographical and cultural
distances (Dai et al., 2012). Therefore, foreign VC firms often establish local offices
(Devigne, 2016) and form partnerships with local VC firms to address the information
asymmetry and monitoring complexity (Dai et al., 2012).
The U.S. is considered the most advanced VC market (Li and Zahra, 2012). The
Israeli VC is self-sustainable and growing remarkably (Isenberg, 2010). The small size
of Israel makes it a unique geographical cluster where information about new
technologies is spread from one district to another within a short period of time (Conti
et al., 2013). Both in the U.S. and Israel, startups have access to a higher number of
geographically close VC firms than their counterparts in Thailand. Specifically, we are
interested in determining the geographic proximity of VC firms to early-stage startups
in developed and developing countries. We concentrate on the first VC round across
countries and hypothesize that:
Hypothesis 2. Startups in developed countries (the U.S. and Israel) are more likely to
raise funds from geographically close VC firms in their first VC round compared to
startups in developing countries (Thailand).
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2.4. Patent activity of early-stage startups
Patent activity, such as granted patents or patent applications, which is
considered an innovation activity, is positively associated with survival (Zhang et al.,
2020) and may signal quality in the early stages when technological uncertainty is high
(Cockburn and MacGarvie, 2009; Hsu and Ziedonis, 2013). Indeed, patents contribute
to fundraising and are typically required to enter foreign markets by blocking
competitors (Neuhausler, 2012). Domestic patents are granted by national patent offices
and international patents are granted by the World Intellectual Property Organization
(WIPO). The applications are filed in those countries that are targeted for export (Grupp
and Schmoch, 1999). For example, foreign companies that target the U.S. market file
patent applications with the United States Patent and Trademark Office (USPTO).
Previous research has shown the differences in patent filing across developed
and developing countries and found that developing countries’ weak protection of
intellectual property rights discourages innovation in those countries compared to
developed countries (Allred and Park, 2007). The latter research, however, did not
explore the linkage between patent activity and fundraising. In addition, research has
shown that companies from developing countries are less likely to target foreign
markets and, therefore, not apply for foreign patents or may be hindered by the high
cost of patenting (de Rassenfosse et al., 2013) or low returns to R&D expenditure
(Minniti and Lévesque, 2010).
The typically limited market size of developing countries that rely on
incremental, adaptive, and imitative development (Allred and Park, 2007), accordingly,
may offer little incentive for startups to protect their products from competitors.
Research about critical success factors of Thai startups did not mention any patent
activity (Nalintippayawong et al., 2018), although some Thai startups develop high-
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technology products. On the other hand, the annual number of U.S. patents granted
(both the total and those assigned to U.S. inventors) doubled during the 1990s
(Trajtenberg, 2002). The number of U.S. patents granted to Israeli inventors has more
than tripled since 1985 (the U.S. market is the primary target for Israeli startups),
making Israel one of the leading countries holding patents per capita (Trajtenberg,
2002).
Studies have measured whether a startup filed at least one patent application
before a specific VC funding round but not in the context of different countries (Hoenen
et al., 2014; Zhou et al., 2016). In the context of this research, we are interested in
determining the patent activity of early-stage startups before a VC round across
developed and developing countries. We concentrate on the timing of patent activity in
connection with fundraising in the first VC round across countries. Thus, we
hypothesize that:
Hypothesis 3. Startups in developed countries (the U.S. and Israel) are more likely to
possess patent activity before their first VC round compared to startups in developing
countries (Thailand).
3. Methods
This section details the research setting and methods. We conducted a preliminary
study in Israel between September 2018 and March 2019. We used this stage to collect
data and feedback from startup founders, incubator and accelerator directors, VC
directors, innovation authority directors, and for fine-tuning the research methods. In
the next stage, we conducted the research in the U.S., Israel, and Thailand between May
2019 and August 2020.
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3.1. Data collection
For data collection purposes, we established a collaboration with several
international incubators and accelerators for early-stage startups. We collected the data
through an online questionnaire,2 databases, and interviews (see also Cohen et al.,
2000). The use of a questionnaire is a common data collection method for startup
research; for example, Davila and Foster (2007) developed a questionnaire to collect
data about control systems for managing early-stage startups. Sullivan et al. (2021)
employed the same approach for collecting data about learning activities and network
ties of early-stage startups in incubators. The purpose of our questionnaire was to
collect data about a variety of startup characteristics (e.g., founders’ experience,
fundraising, and product development stages). We contacted the startups’ founders via
email, provided them with the study overview, and included a web link to the
questionnaire (when necessary, we sent a reminder via email). In addition, we collected
complementary financial and patent data from databases such as CB Insights
(https://www.cbinsights.com), IVC data and insights (https://www.ivc-online.com/)
and PatBase (https://www.patbase.com). Collecting data through secondary sources is
a common approach; for example, Devigne et al. (2016) and Greenberg (2013)
collected data from commercial databases on VC firms and startups; Greenberg (2013)
and Lerner and Nanda (2020) used complementary data from the U.S. patent and
trademark office. Finally, we conducted a 30-minute interview with the founders to
clarify the collected data.
3.2. Data analyses
2 A literature review and the preliminary study facilitated the development of the questionnaire. The
questionnaire was reviewed by an incubator director (Ph.D. and entrepreneur) and fine-tuned in
selected topics following feedback from the first startups.
15
We employed zero-order Pearson correlation analyses for identifying correlations
between variables, and Wilcoxon and Kruskal-Wallis nonparametric tests for assessing
the differences between sample groups at a significance level of 5%. In addition, we
performed a Qualitative Comparative Analysis (QCA) to explore similarities and
differences across comparable cases by pooling similar cases and comparing them as
configurations of causal conditions producing a particular outcome.
We verified the Pearson correlation assumptions by analyzing the distribution of
each variable. The continuous variables—startup age, founders’ experience, number of
employees, and amount of funds raised—conform with the log-normal distribution;
therefore, we used the natural logarithm of these variables. Additionally, we checked
for normality by conducting a Shapiro-Wilk test, which supported the univariate
normality assumption for all variables other than startup age. We considered one startup
as an outlier since its founders were undergraduate students who had just started their
venture. In addition, we used bivariate analyses to examine a linear distribution of
observations. The continuous variables—annual revenue, startup experience, number
of patents, and number of VC rounds—were non-linearly distributed with other
variables; therefore, we used dummy variables for these variables as for countries and
industry sectors. Further, we employed QCA, designed for small samples, as an
alternative for linear regression analysis to explain the outcome of interest (Fainshmidt
et al., 2020; Ragin, 2014). In QCA, causal conditions and outcomes are coded either as
present (1) or absent (0). For QCA, we used categorical variables found to be significant
in quantitative analyses—patent activity (PA), local VC firm (LVC), foreign VC firm
(FVC), and VC firm proximity to startup (VCP)—as causal conditions to identify
whether the presence or absence of causal conditions is consistent with the presence or
16
absence of the outcome, developed country (DC) in the analysis. We used JMP software
for the quantitative analyses and QCA software 3 for the qualitative analyses.
3.3. Research sample
We contacted 150 early-stage startups. One hundred startups from the U.S., Israel,
and Thailand responded to our questionnaire (66.6%). Of those, 90 were interviewed,
resulting in a sample size of 90 startups (60%). We benchmarked our sample with that
of the Sullivan et al. (2021) study about early-stage ventures in U.S. incubators and
found it comparable (e.g., for startup age and size, industry sector, founder gender and
education). Sullivan et al. (2021), however, did not concentrate on fundraising and
patent activity, which are the focus of the current study.
Table 1 presents descriptive statistics of the sample. About half of the startups
were from the U.S. (Panel A). Information technology and health care sectors accounted
for 60% of the startups (Panel B). More than 25% of the startups were backed by VC
firms (Panel C). The difference between the mean number of VC-backed startups across
countries was insignificant. In the first VC round, the mean startup age was 2.34 years.
The typical founder in the sample was male (86.8%), 38.5 years old (s.d.=10.4), had a
Master’s degree (most likely in engineering), had founded a company in the past, and
had 13 years of entrepreneurial experience. The typical startup age was 3.5 years
(s.d.=1.9), had raised $2 million (s.d.=$3.6 million), had an annual revenue of $0.5
million (s.d.=$2.2 million), and managed nine employees (s.d.=10.9). An insignificant
difference existed in the mean number of employees and mean age of startups across
countries.
3 https://www.qca-addin.net.
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Half of the startups were operating in an incubator or accelerator, and the other
half has operated within at least one in the past (Panel D). An insignificant difference
existed in the mean number of startups that were operating in an incubator or accelerator
across countries.
Stam (2015) argued that entrepreneurs are important players in creating the
ecosystem and keeping it healthy. The mean number of startup founders was 2.25. In
about half of the U.S. and Israeli startups, at least one of the founders held a Ph.D.
degree (23% in the Thai startups). Additionally, in about two thirds of the U.S. and Thai
startups, at least one of the founders had an engineering education (88% in the Israeli
startups). Generally, the Israeli founders had diverse experience. The mean number of
Thai founders who had worked together previously was higher compared to the other
countries. Additionally, the mean number of Thai startups that conducted internal
monitoring and control (M&C) was relatively high; yet, low for external M&C than in
other countries.
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Table 1
Descriptive statistics on sample of startups.
Panel A: Country statistics
Country
U.S.
Israel
Thailand
Total
Number of startups
47
26
17
90
Panel B: Industry statistics
Industry sectora
Information technology
Health care
Industrials
Consumer discretionary
Other
Total
Number of startups
28
26
19
7
10
90
Panel C: Financing statistics
Primary financing type
Angel
VC
Grant
University
Corporate investor
Founder
Other
Total
Number of startups
26
25
12
10
7
4
6
90
Panel D: Research variables statistics
All U.S. Israel Thailand
Variable Mean SD Mean SD Mean SD Mean SD
1. Startup ageb
3.28
1.97
3.06
2.08
2.96
1.28
4.40
2.24
2. Number of founders
2.25
0.85
2.12
0.87
2.42
0.80
2.35
0.86
3. Single founder
0.18
0.39
0.25
0.44
0.07
0.27
0.17
0.39
4. Technological experiencec
16.12
16.12
14.56
17.34
20.90
16.12
13.11
11.01
5. Industrial experiencec
16.20
16.51
13.40
15.30
22.50
19.88
14.29
11.35
6. Business experiencec
19.90
18.37
16.79
16.64
23.80
20.86
22.52
18.49
7. Managerial experiencec
17.03
17.18
14.97
15.69
23.48
21.59
12.88
10.39
8. Entrepreneurial experiencec
13.32
12.25
11.71
10.43
17.53
16.29
11.29
8.20
9. Startup experienced
1.56
1.81
1.55
1.77
1.80
2.07
1.23
1.52
10. Founders worked together
0.36
0.48
0.34
0.47
0.34
0.48
0.47
0.51
11. PhD
0.48
0.50
0.59
0.49
0.46
0.50
0.23
0.43
12. MBA
0.26
0.44
0.21
0.41
0.34
0.48
0.29
0.46
13. Engineer
0.71
0.45
0.61
0.49
0.88
0.32
0.70
0.46
14. Formal internal M&C
0.57
0.49
0.53
0.50
0.53
0.50
0.76
0.43
15. Formal external M&C
0.32
0.46
0.34
0.47
0.34
0.48
0.23
0.43
16. Number of granted patents
0.63
1.58
0.72
1.93
0.69
1.28
0.29
0.77
17. Number of pending patents
1.40
2.12
1.46
2.56
2.00
1.57
0.29
0.68
18. Incubator as patent applicant
0.21
0.41
0.42
0.50
0.04
0.21
0.00
0.00
19
All U.S. Israel Thailand
Variable Mean SD Mean SD Mean SD Mean SD
19. Incubator as patent assignee
0.21
0.41
0.42
0.50
0.04
0.21
0.00
0.00
20. Number of employees
8.94
10.92
9.82
13.84
6.26
4.37
10.58
8.10
21. Attending incubator/accelerator
0.50
0.50
0.59
0.49
0.38
0.49
0.41
0.50
22. Several incubators/accelerators
0.38
0.49
0.36
0.48
0.50
0.50
0.29
0.46
23. VC finance
0.27
0.45
0.25
0.44
0.26
0.45
0.35
0.49
24. Number of VC rounds
0.38
0.74
0.40
0.82
0.38
0.75
0.35
0.49
25. Total funds raisede
1.89
3.68
2.37
4.87
1.89
1.60
0.55
0.73
26. Annual revenuee
0.48
2.19
0.65
2.93
0.08
0.20
0.64
1.21
27. Revenuef
0.48
0.50
0.36
0.48
0.46
0.50
0.88
0.33
28. Health care
0.28
0.45
0.29
0.46
0.23
0.42
0.35
0.49
29. Information technology
0.31
0.46
0.36
0.48
0.19
0.40
0.35
0.49
30. Industrials
0.21
0.41
0.10
0.31
0.50
0.50
0.05
0.24
31. Artificial intelligence
0.30
0.46
0.29
0.46
0.42
0.50
0.11
0.33
32. Time to product deliveryb
2.24
1.99
2.25
2.05
2.53
1.33
1.80
2.59
33. Patent activity in 1st VC round
0.56
0.50
0.58
0.51
0.85
0.37
0.16
0.40
34. Funds raised in 1st VC rounde
1.89
1.47
2.77
1.55
1.27
0.61
0.84
0.97
35. Time to 1st VC roundb
2.43
1.44
2.44
1.29
1.57
1.05
3.41
1.66
36. Num of investors in 1st VC round
3.48
1.96
4.16
2.28
3.85
0.89
1.66
0.81
37. Geographic proximity of VC
0.64
0.48
0.75
0.45
0.85
0.37
0.16
0.40
38. Productg
0.64
0.48
0.57
0.49
0.57
0.50
0.94
0.24
39. Local VC in 1st VC round
0.76
0.43
1.00
0.00
0.85
0.37
0.16
0.40
40. Foreign/faraway VC in 1st VC
d
0.60
0.50
0.25
0.45
0.85
0.37
1.00
0.00
Legend:
a Industry sector follows the global industry classification standard (https://www.msci.com/gics).
b Age in years.
c Experience of founders in years refers to the current founders.
d Startup experience refers to previous companies established by the current founders.
e Funds raised in millions of dollars.
f Revenue refers to a startup that produces annual revenue.
g Product refers to a startup that owns a product.
4. Results
Table 2 presents the means, standard deviations, and Pearson correlations of
startup variables. Table 3 shows possible configurations for the conditions PA, LVC,
FVC, VCP, and outcome DC. Table 4 presents the QCA of causal conditions sufficient
for DC (High DC) and ~DC (Low DC) outcomes since, unlike correlational
20
relationships, the relationships between causal conditions are asymmetric (Ragin,
2014).
4.1. General findings
The results concerning patents were consistent with previous research (Hoenen
et al., 2014; Zhou et al., 2016), which added to our confidence when analyzing the
differences across countries (see Section 4.2). There was a positive and significant
correlation between startups that held a patent and the amounts of funds raised. In
addition, startups that owned both a product and a patent had a significantly higher
mean amount of funding and mean annual revenue compared to the other startups. In
line with the findings of Dutta and Folta (2016), we see that VC investments contributed
to startup growth. For example, the VC-backed startups had significantly higher mean
amounts of funding, mean annual revenue, and mean number of employees compared
to non-VC-backed startups. Further, the mean number of VC-backed startups that
produced at least $1 million annual revenue was significantly higher than the non-VC-
backed startups. Before the VC investment, half the startups had at least a minimum
viable product (MVP), and the other half at least a proof of concept.
4.2. Differences and similarities across countries
The results indicate three basic types of startups in developed countries: those
combining patent activity and local VC funding (the majority of U.S. and Israeli
startups); those combining local VC funding and lack of VC geographical proximity (a
minority of U.S. startups); and those lacking foreign VC funding (the majority of U.S.
startups and a minority of Israeli startups). Additionally, the results indicate one type
of developing country startups: those that combine a lack of both patent activity and
local VC funding (the majority of Thai startups).
21
The study revealed that the U.S. and Thai startups target their domestic market,
while Israeli startups target the global market, mainly the U.S. and the European Union.
Among startups with granted patents, the U.S. startups and all but one of the Israeli
startups held patents granted in the U.S., whereas the Thai startups held patents granted
in Thailand and a neighboring country. The results regarding Israeli startups are similar
to those in Conti et al. (2013), who examined a dataset of 787 Israeli startups, 16%
operated in incubators, and showed the percentage of startups granted a patent. Among
startups with patent applications, more than 45% of the Israeli startups, 25% of the Thai
startups, and less than 19% of the U.S. startups held international patent applications.
According to World Economic Forum (2014), there is a difference in the initial
revenues of an early-stage startup with regards to timing and magnitude. To gain insight
into the role of incubators and accelerators across countries, we analyzed the proportion
of startups that generated revenue while operating in an incubator or accelerator and
found that all Thai startups produced revenue compared to 28% and 10% of the U.S.
and Israeli startups, respectively. Accordingly, the mean number of Thai startups that
owned a product was significantly higher compared to U.S. and Israeli startups.
Additionally, the results revealed that more than 69% and 46% of Israeli and U.S.
startups, respectively, received initial funds from an incubator or accelerator compared
to less than 24% of Thai startups. The results may indicate that Thai startups perceive
incubators and accelerators as an opportunity to achieve networking support (Crișan et
al., 2021; Hausberg and Korreck, 2018) and improve their chances to grow, while U.S.
and Israeli startups see them as places to learn and validate their products.
The U.S. incubators and accelerators were more involved in patent applications
(in more than 24% of the U.S. startups that showed patent activity) compared to less
than 7% of Israeli startups and none of Thai startups. That is an important finding due
22
to the positive contribution of patents to fundraising and revenue. The results suggest
that the startup population in the U.S. and Israel are aware of the positive impact of
patents with more than 85% and 58% of Israeli and U.S. startups, respectively,
demonstrating patent activity before their first VC funding round compared to less than
17% of Thai startups. Moreover, all but one of the U.S. and Israeli startups that owned
patents had at least one granted patent before the first VC round.
We also found that 100% of U.S. startups and more than 85% of Israeli startups
received funding from local VC firms in the first VC round compared to less than 17%
of Thai startups. We confirmed these findings by the QCA that indicated the presence
of a causal combination of patent activity and local VC firms for the majority of the
U.S. and Israeli startups and the absence of both for the majority of the Thai startups in
the first VC round.
In the context of the first VC round, the mean amount of funds raised was
significantly higher for U.S. startups compared to Israeli and Thai startups ($2.7
million, $1.2 million, $0.8 million in U.S., Israeli, and Thai startups, respectively).
Additionally, the mean time to the first VC round was significantly shorter for Israeli
startups compared to Thai startups (1.57 months, 2.44 months, 3.41 months for Israeli,
U.S., and Thai startups, respectively). Also, in the first VC round, the mean number of
investors in U.S. and Israeli startups was significantly higher compared to Thai startups
(4.1, 3.8, 1.6 per U.S., Israeli, and Thai startups, respectively). It is reasonable to assume
that U.S. startups require more time to raise a higher amount of funds from a higher
number of investors than Israeli and Thai startups. The results regarding Israeli startups
are similar to those in Conti et al. (2013), who examined a dataset of 787 Israeli startups
with external funding from 1994 to 2011 and showed the average number of investors
participating in each round and the average amount invested in the first round.
23
Additionally, we analyzed the location of startups and their investors in the first
VC round. We found that most local investors resided in the approximate area of their
startups (100% of Israeli and Thai startups, and 75% of U.S. startups, were financed by
local VC firms whose headquarter reside within a 100-km radius from the startups).
Our results are consistent with those of Lerner (2020) and Li and Zahra (2012). We also
found that in the first VC round, 100% of Thai startups and more than 85% of Israeli
startups received funding from foreign VC firms, versus 25% of U.S. startups by
faraway VC firms residing outside a 100-km radius from the startups. Our results are
in line with Cumming and Zhang (2019), Dai et al. (2012), and Lerner and Nanda
(2020). Notably, we found that more than 83% of Thai startups were financed by a
single foreign VC firm operating in Bangkok and less than 17% by both local and
foreign VC firms. Since foreign VC firms are less likely to invest in early-stage
companies, it may indicate that this foreign firm invests in information-transparent
startups (Dai et al., 2012) residing close to its local office in Bangkok (Devigne, 2016).
Interestingly, in the first VC round, more than 33% of the U.S. startups and 28%
of Israeli startups were supported by government grants versus none of the Thai
startups. The results are in line with the World Economic Forum (2014), which showed
differences across countries in how government policy promotes the development of
early-stage startups. Results regarding Israeli startups are similar to Conti et al. (2013),
who measured the percentage of startups awarded government grants. Moreover, the
results indicate a significantly higher number of Israeli startups that operated in several
incubators or accelerators before the first VC round compared to the other countries.
The result may further indicate that Israeli startups perceive incubators and accelerators
as an opportunity to achieve knowledge, product validation, and networking support
(Crișan et al., 2021; Hausberg and Korreck, 2018).
24
For validation purposes, we reanalyzed the results using a subsample of 79
startups more than a year old (we assumed here that very young startups are less likely
to have had substantial financial and patent activity). The results of these analyses were
similar with regard to the findings obtained using the entire sample.
Overall, consistent with Hypothesis 1, we found that startups in developed
countries (the U.S. and Israel) raise funds from a significantly higher number of
investors in their first VC round compared to startups in developing countries
(Thailand). Regarding Hypothesis 2, we found that startups in both developed (the U.S.
and Israel) and developing countries (Thailand) raise funds from geographically close
VC firms in their first VC round. Consistent with Hypothesis 3, we found that a
significantly higher number of startups from developed countries (the U.S. and Israel)
possess patent activity before their first VC round compared to startups in developing
countries (Thailand).
25
Table 2
The means, standard deviations, and Pearson correlations of startup variables.
Variable Mean SD 1 2 3 4 5 6 7 8 9 10 11 12
1. Startup agea
1.33
0.50
1
2. Single founderb
0.18
0.39
-0.01
1
3. Technological experiencec
2.50
0.82
0.35***
-0.23*
1
4. Industrial experiencec
2.47
0.89
0.42***
-0.33**
0.74***
1
5. Business experiencec
2.65
0.92
0.39***
-0.37***
0.49***
0.69***
1
6. Managerial experiencec
2.50
0.88
0.43***
-0.33**
0.54***
0.73***
0.80***
1
7. Entrepreneurial experiencec
2.35
0.78
0.50***
-0.27**
0.58***
0.76***
0.76***
0.78***
1
8. Startup experienced
0.62
0.48
0.11
-0.09
0.36***
0.43***
0.37***
0.40***
0.54***
1
9. PhDe
0.48
0.50
-0.00
0.09
0.18
0.08
-0.09
-0.10
-0.10
-0.06
1
10. MBAf
0.26
0.44
-0.02
-0.17
0.01
0.13
0.34***
0.20
0.16
-0.04
-0.00
1
11. Engineerg
0.71
0.45
-0.10
-0.19
-0.02
0.15
0.02
0.11
0.08
0.05
0.03
-0.02
1
12. Patenth
0.24
0.43
0.32**
-0.07
0.27**
0.24*
0.18
0.18
0.22*
-0.09
0.06
0.04
-0.20*
1
13. Number of employees
1.90
0.85
0.42***
-0.28**
0.34***
0.34***
0.35***
0.31**
0.39***
0.17
-0.08
0.05
-0.06
0.26*
14. Incubator/acceleratori
0.50
0.50
-0.31**
0.02
-0.16
-0.15
-0.27**
-0.21*
-0.28**
-0.04
0.04
-0.05
0.09
-0.25*
15. Formal internal M&Cj
0.57
0.49
0.24*
-0.21*
0.10
0.27**
0.23*
0.25*
0.21*
0.16
-0.06
-0.06
-0.04
0.06
16. Formal external M&Ck
0.32
0.46
0.14
-0.15
0.20
0.27**
0.21*
0.33**
0.14
-0.00
0.03
-0.09
-0.03
0.21*
17. VC financel
0.27
0.45
0.25*
-0.17
0.30**
0.26*
0.17
0.20*
0.24*
0.12
-0.11
-0.00
-0.04
0.28**
18. Total funds raisedm
13.22
1.74
0.28**
-0.23*
0.54***
0.50***
0.33**
0.41***
0.40***
0.15
0.13
0.20*
-0.03
0.36***
19. Revenuen
0.48
0.50
0.42***
-0.01
0.14
0.27**
0.35***
0.24*
0.34***
0.02
-0.06
0.03
-0.11
0.32**
20. Health careo
0.28
0.45
0.06
0.06
-0.06
-0.01
0.00
-0.04
-0.06
-0.21*
0.16
0.11
-0.18
0.09
21. Information technologyp
0.31
0.46
-0.25*
0.04
-0.15
-0.10
-0.08
-0.16
-0.12
0.12
0.11
0.04
0.05
-0.21*
22. Industrialsq
0.21
0.41
-0.05
-0.18
0.10
0.06
-0.04
0.00
0.02
-0.10
-0.07
-0.14
0.20*
-0.04
23. Artificial intelligencer
0.30
0.46
-0.08
-0.13
-0.13
0.04
0.04
0.18
0.02
0.06
0.08
-0.02
0.14
0.02
24. U.S.s
0.52
0.50
-0.18
0.17
-0.14
-0.23*
-0.16
-0.15
-0.15
0.08
0.22*
-0.19
-0.21*
-0.02
26
Legend:
a Startup age in years.
b Single founder – dummy variable that takes the value of 1 if the startup has a single founder (n=17), and 0 otherwise.
c Experience of founders in years refers to the current founders.
d Startup experience – dummy variable that takes the value of 1 if at least one of the current founders established a company in the past (n=56), and 0 otherwise.
e PhD – dummy variable that takes the value of 1 if at least one of the current founders has a PhD degree (n=44), and 0 otherwise.
f MBA – dummy variable that takes the value of 1 if at least one of the current founders has an MBA degree (n=24), and 0 otherwise.
g Engineer – dummy variable that takes the value of 1 if at least one of the current founders has engineering education (n=64), and 0 otherwise.
h Patent – dummy variable that takes the value of 1 if a startup has at least one granted patent (n=24), and 0 otherwise.
25. Israelt
0.28
0.45
-0.01
-0.18
0.22*
0.27**
0.10
0.23*
0.18
-0.00
-0.03
0.24*
0.24*
0.09
26. Thailandu
0.18
0.39
0.26*
-0.01
-0.07
-0.01
0.08
-0.07
-0.02
-0.09
-0.24*
-0.02
-0.00
-0.07
Variable 13 14 15 16 17 18 19 20 21 22 23 24 25 26
13. Number of employees
1
14. Incubator/acceleratori
-0.22*
1
15. Formal internal M&Cj
0.30**
-0.00
1
16. Formal external M&Ck
0.19
-0.26*
0.34***
1
17. VC financel
0.53***
-0.37***
0.22*
0.36***
1
18. Total funds raisedm
0.63***
-0.26*
0.15
0.29**
0.55***
1
19. Revenuen
0.37***
-0.26*
0.20
0.03
0.13
0.12
1
20. Health careo
-0.22*
0.09
-0.05
-0.01
-0.12
-0.16
0.01
1
21. Information technologyp
0.04
-0.04
-0.05
0.00
0.01
-0.02
-0.03
-0.42***
1
22. Industrialsq
0.06
-0.02
0.16
0.05
0.04
0.13
-0.01
-0.32**
-0.34***
1
23. Artificial intelligencer
0.08
-0.02
0.16
0.22*
0.08
0.13
-0.00
-0.09
0.13
0.13
1
24. U.S.s
-0.08
0.20
-0.09
0.04
-0.05
-0.00
-0.26*
0.02
0.11
-0.26*
-0.00
1
25. Israelt
-0.04
-0.14
-0.05
0.03
-0.01
0.23*
-0.03
-0.08
-0.16
0.45***
0.17
-0.66***
1
26. Thailandu
0.16
-0.08
0.18
-0.08
0.08
-0.27**
0.37***
0.06
0.04
-0.18
-0.19
-0.50***
-0.30** 1
27
i Incubator/accelerator – dummy variable that takes the value of 1 if a startup is attending an incubator or accelerator (n=45); and 0 otherwise.
j Formal internal M&C – dummy variable that takes the value of 1 if a startup has formal internal monitoring and control (n=52); and 0 otherwise.
k Formal external M&C – dummy variable that takes the value of 1 if a startup has formal external monitoring and control (n=29); and 0 otherwise.
l VC finance – dummy variable that takes the value of 1 if a startup raised funds from venture capital (n=25); and 0 otherwise.
m Funds raised in total in millions of dollars.
n Revenue – dummy variable that takes the value of 1 if a startup has annual revenue (n=44); and 0 otherwise.
o Health care – dummy variable that takes the value of 1 for those startups in the health care sector (n=26).
p Information technology – dummy variable that takes the value of 1 for those startups in the information technology sector (n=28).
q Industrials – dummy variable that takes the value of 1 for those startups in the industrials sector (n=19).
r Artificial intelligence – dummy variable that takes the value of 1 for those startups applying artificial intelligence (n=34).
s U.S. – dummy variable that takes the value of 1 for U.S. startups (n=47).
t Israel – dummy variable that takes the value of 1 for Israeli startups (n=26).
u Thailand – dummy variable that takes the value of 1 for Thai startups (n=17).
N = 90.
*p < .05
**p < .01
***p < .001
All continuous variables are natural log transformed.
The table presents correlations for dummy variables with at least ten startups.
Note:
The correlations results are similar when dummy variables are replaced with the natural logarithm of the variables: startup experience, number of patents, number of VC
rounds, and annual revenue.
28
Table 3
The truth table of configurations for the conditions PA, LVC, FVC, VCP, and the outcome DC.
Configuration Condition Outcome Number of startups
PA LVC FVC VCP DC
1 0 0 1 0 0 1
2
0
0
1
1
0
3
3
0
1
0
0
1
1
4
0
1
0
1
1
3
5
0
1
1
0
1
1
6
0
1
1
1
C
2
7
1
0
1
1
C
2
8
1
1
0
0
1
1
9
1
1
0
1
1
5
10
1
1
1
0
1
1
11
1
1
1
1
1
5
Legend:
PA – patent activity, LVC – local VC firm, FVC – foreign VC firm, VCP – geographic proximity
of VC firm to startup, DC – developed country.
1 = present, 0 = absent, C = contradiction (an identical configuration of conditions is linked to both the
presence and absence of the outcome, treated as does not exist).
N = 25.
Table 4
The qualitative comparative analysis of causal conditions sufficient for DC and ~DC outcomes.
Condition Outcome
DC ~DC
PA ϴ
LVC
ϴ
FVC
ϴ
VCP
ϴ
Solution
SO1
SO2
SO3
SN1
Configurations
8,9,10,11
3,5,8,10
3,4,8,9
1,2
Legend:
PA – patent activity, LVC – local VC firm, FVC – foreign VC firm, VCP – geographic proximity
of VC firm to startup, DC – developed country, ~DC – developing country.
= present, ϴ = absent.
N = 25.
29
5. Discussion
This study analyzes the differences and similarities of two important early-stage
startup characteristics—fundraising and patent activity—across three countries (the
U.S. and Israel, which are developed countries; and Thailand, a developing country).
Table 5 summarizes the main findings.
Notably, we found that U.S. and Thai startups target their domestic markets while
Israeli startups target the international market. One explanation may be that U.S. and
Thai startups have access to a broad domestic market; in opposition, Israeli startups
have a limited local market and enjoy free trade agreements with the U.S. and the
European Union (Trajtenberg, 2000).
Concerning the funding, the study highlights the tendency of U.S. and Israeli
startups to raise funds from a higher number of investors in their first VC round
compared to Thai startups. Cumming and Zhang (2019) showed that legal, economic,
and cultural differences could explain the number of investments. In the context of this
research, these differences between developed and developing countries may be
attributed to the advanced U.S. and Israeli VC markets (e.g., Li and Zahra, 2012) and
the Israeli and U.S. government funding incentives. Our results are in line with Feldman
and Kelley (2006), Guerini and Quas (2016), and Islam et al. (2018), who noted that
the U.S. and Israeli governments support startups through grants, which may signal to
investors the startups’ technological innovation and business viability. In contrast, the
Thai entrepreneurial ecosystem suffers from scarce resources (Scheela and Jittrapanun,
2012), especially having a limited number of angel and VC investors (Munkongsujarit,
2016). Consequently, Thai startups we examined raised funds only from a few investors
in their first VC round and underwent insufficient external monitoring and control,
which may impact their future growth. The results suggest that Thailand has a different
30
entrepreneurial ecosystem in the sense that Thai early-stage startups have revenue and
are less dependent on a high amount of funding at this early stage.
Regarding the first VC round pattern, the study indicates that U.S. startups
typically raise funds from geographically close VC firms, Israeli startups from both
geographically close and foreign VC firms, and Thai startups from a single foreign VC
firm operating in Bangkok. Possible explanations for this finding are that the U.S.
startups have an abundance of local VC funds (Lerner and Nanda, 2020) and Israel is
one of the worldwide leaders in the number of VC deals (Cumming and Zhang, 2019),
attracting both local and international investors. Since local VC firms regularly make
the first startup investments (Berger and Köhn, 2020), most likely Israeli startups get
funds from local and foreign VC firms in their first VC round due to the interest of
international VC firms in Israeli startups. We note that Israeli startups operated in more
incubators or accelerators before their first VC round compared to startups in the U.S.
and Thailand, which may improve their chances of surviving and acquiring access to
additional investors.
Since Thai startups suffer from an insufficient number of local VC investors
(Munkongsujarit, 2016), and it is known that local VC investors in developing countries
are typically less experienced (Chemmanur et al., 2016), these startups are encouraged
to reach out to international investors (Cao and Shi, 2021). Indeed, there are specific
international VC firms that target Asian startups and often open local offices. Another
explanation may be related to the risk-averse nature of Thai local VC investors who
require sustainable revenues from Thai startups before investing. Therefore, incubators
and accelerators in Thailand may serve as an opportunity for early-stage startups to
achieve global networking (Crișan et al., 2021) and acquire access to local and
international investors (a pillar of entrepreneurial ecosystems) and consequently
31
improving their chances of growing (versus Israeli and U.S. startups that see them
places to learn and validate their products).
Concerning patent activity, U.S. and Israeli startups patent their innovations,
protect them and signal quality significantly more than Thai startups. These results can
explain findings from prior studies about the contribution of patent activity to
fundraising from VC firms (Hoenen et al., 2014; Zhou et al., 2016). Signaling quality
through patent activity may serve startups in the early stages when technological
uncertainty is high (Cockburn and MacGarvie, 2009; Hsu and Ziedonis, 2013). In line
with Zhang et al. (2020), who argued that patent activity impacts the chances of
surviving, our study points out that U.S. and Israeli startups patent their advanced and
high-risk technologies, which, in turn, may help them to reach more investors. As
discussed earlier, Thai startups that specialize in a few high-technology products may
not withstand the high cost of patenting or encounter difficulty demonstrating novelty
in their invention. Similar to Minniti and Lévesque (2010), who argued that startups in
developing countries tend to engage in imitative entrepreneurship rather than research-
based entrepreneurship associated with patenting, our results demonstrate that Thai
startups lack patent activity.
5.1. Practical implications
This research offers practical implications for startups, incubators, and
accelerators, regardless of a specific country. Entrepreneurs should consider the value
of patented technology and assess the potential of a patent application. Further,
entrepreneurs should realize the significance of government grants as a source of
finance and quality signaling and assess the potential and required conditions to attract
VC investors. Incubators and accelerators are encouraged to understand the impact of
early-stage startup characteristics on the startup outcome and adjust their management
32
processes accordingly, for example, by promoting patent applications, applying for
grants, and facilitating VC access.
5.2. Limitations and future research
This study has several limitations. As mentioned previously, there is a lack of
databases and studies on early-stage startups across countries, and specifically in
developing countries. Thus, the sample size is a limiting factor towards drawing
additional insights. A larger sample, covering multiple entities that differ in culture, life
cycle, technology, or institutional context would enable discovering how particular
conditions influence startup characteristics and facilitate a generalization of the results.
Future research could benefit from extending the current study by 1) examining early-
stage startups from developing countries, and 2) investigating incubators’ and
accelerators’ support in promoting patent applications and facilitating VC access to
their startups.
Table 5
The main findings on early-stage startups across countries.
Early-stage startup characteristics U.S. Israel Thailand
Target market Domestic market International market Domestic market
Patent activity in 1st VC round
Common
Common
Uncommon
Investors in 1st VC round
Several investors
Several investors
A few investors
Funded by local VC in 1st VC round
Common
Common
Uncommon
Funded by foreign VC in 1st VC round
Uncommon
Common
Common
6. Conclusions
The findings indicate different types of startups in developed and developing
countries; therefore, one solution does not fit all. The results highlight the importance
of the entrepreneurial ecosystem on fundraising and patent activity, both of which are
key to startups’ survival and growth. Specifically, early-stage startups in developed
33
countries require geographically close VC firms, and incubators and accelerators and
early-stage startups in developing countries need foreign VC firms with local offices.
The results reveal that incubators and accelerators play different roles in supporting
early-stage startups across countries. It appears that incubators and accelerators in the
U.S. and Israel provide a wide range of services beyond the initial funding, including
training, product validation, networking to access investors, while in Thailand, the last
characteristic is the most important, especially networking to access international
investors with local offices. It turns out that more than 33% of the U.S. and 28% of
Israeli startups received governmental support before the first VC round. Policymakers,
especially government authorities in developing countries, may consider enhancing
startup support using government grants that may provide an incentive for future
support from local VC firms. Developing countries may also consider partnering with
the private sector for startup capital, similar to Israel’s Yozma fund, since funding
enables entrepreneurial ecosystems to grow.
The research sheds light on two unique ecosystems: Israeli startups operating in
a relatively small developed country within a developed entrepreneurial ecosystem and
Thai startups acting in a broad developing economy within an emerging entrepreneurial
ecosystem. Specifically, the Israeli startup funding, most likely derived from a
combination of government policies, patented high-technology products, incubator and
accelerator support, local VC funding, and access to the international market and
foreign VC investors, is particularly noticeable. The Thai startup product development,
although relying on scarce resources, is exceptional and most stems from domestic
economic conditions and a focus on national customer needs.
34
Acknowledgments
The authors gratefully acknowledge financial support from the Julia and Joshua Ruch
Exchange Program 2019 and Cornell Tech Runway Program. The authors are also
grateful for the valuable comments and suggestions of the editor and two anonymous
referees, particularly the advice to integrate QCA. The authors would like to extend
their gratitude to startup founders, incubator and accelerator directors, VC directors,
and innovation authority directors who supported this research by providing data and
feedback during interviews, and incubator and accelerator startups that supported this
research by participating in the survey.
35
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