AMERICAN JOURNAL OF MANAGEMENT
Top High-Tech Entrepreneurship Papers (2010–2020): An Analytical Literature Review
Author(s): Kessler, David J.
Citation:Kesller, David J, (2021) "Top High-Tech Entrepreneurship Papers (2010–2020):
An Analytical Literature Review"," American Journal of Management, Vol. 21 Iss. 3, pp
133-150
Article Type: Research Paper
Abstract:
This paper presents an analytical literature review of the 50 most influential academic
studies on high-tech entrepreneurship published between 2010 and 2020. Drawing on
citation metrics from Google Scholar, Scopus, and Web of Science, we synthesize
contributions across five interrelated themes: (1) financial capital, including venture
capital, angel investment, accelerators, crowdfunding, and public R&D grants; (2)
innovative capabilities, examining how startups pursue disruptive technologies, leverage
alliances, and develop novel business models; (3) founder human capital and team
dynamics, highlighting the roles of prior experience, industry expertise, team composition,
and social networks in venture performance; (4) entrepreneurial ecosystems and context,
exploring regional clusters, institutional supports, and university spin-offs that shape
startup success; and (5) entrepreneurial processes and strategies, emphasizing effectual
logic, lean experimentation, strategic pivoting, and narrative legitimacy under uncertainty.
Our review identifies recurring insights—such as the critical importance of signaling
mechanisms (e.g., patents and partnerships), the synergistic interaction between founder
experience and external financing, and the systemic interdependence of ecosystem
elements. Methodologically, the field has matured through large-scale econometric
analyses, natural experiments, meta-analyses, and multidisciplinary theory building. We
conclude by mapping emerging research frontiers, including the implications of
digitalization, platform dynamics, and evolving policy frameworks, and by offering
evidence-based guidance for entrepreneurs, investors, and policymakers seeking to foster
high-tech venture creation and growth.
Introduction
High-tech entrepreneurship has attracted extensive scholarly attention in the past decade.
Between 2010 and 2020, researchers published a wealth of influential studies examining
how technology startups obtain funding, innovate, leverage founder capabilities, and thrive
within supportive ecosystems. This review synthesizes 50 top-cited papers from leading
journals (e.g., Academy of Management Journal, Journal of Business Venturing, Research
Policy, Management Science, Strategic Management Journal, Entrepreneurship Theory and
Practice) to provide an analytical overview of the literature. We organize the review by key
themes – funding, innovation, founder experience and teams, ecosystems, and
entrepreneurial processes/strategies – highlighting each paper’s research question,
methodology, major findings, and contributions. We then identify recurring insights and
trends in methods and theory. The goal is to illuminate what this influential body of work
collectively tells us about high-tech startup success and to note emerging patterns in how
the research has been conducted.
Methodology: To identify the top 50 papers, we prioritized studies published 2010–2020
with high citation impact (using Google Scholar, Scopus, Web of Science metrics) and
prominence in the field. These include empirical analyses, meta-analyses, and conceptual
frameworks frequently referenced in high-tech entrepreneurship research. Below, we
integrate detailed summaries of these works, noting how they contribute to our
understanding of technology ventures.
Funding and Financial Capital for High-Tech Startups
Access to financial capital is a central theme in high-tech entrepreneurship. Multiple
influential studies examined how venture capital (VC), angel investment, accelerator
programs, and crowdfunding influence startup outcomes:
• Venture Capital and Performance: Gompers et al. (2010) provided early evidence of
“success breeds success” in VC-backed entrepreneurship. Studying thousands of founders,
they asked whether entrepreneurial success is due to skill or luck. They found strong
performance persistence: entrepreneurs who had previously succeeded (e.g. took a startup
public) had a significantly higher chance of success in their next venture than first-time or
previously failed entrepreneurs. The authors attributed this to both skill (e.g. timing
markets well) and perception – stakeholders are more willing to support a founder with a
winning track record. This study, published in a top finance journal, underscores that VC-
backed serial entrepreneurs accumulate advantages (credibility, networks) that improve
outcomes. Similarly, Nanda & Rhodes-Kropf (2013) examined how the investment climate
affects startup innovation. Using venture investment data, they found that during “hot”
funding markets (when capital is abundant), VCs fund more innovative, risky startups,
whereas in “cold” markets they favor safer projects. Thus, easy money periods encourage
more radical technological innovation, but also higher failure rates later. This nuanced
finding – that financing cycles influence the innovativeness of startups – bridges finance
and innovation policy, suggesting that public support may be needed in lean times to
sustain breakthrough innovation.
• Angel Investors: While VCs invest later in scaling startups, angel investors fund
earlier stages. Kerr, Lerner, & Schoar (2014) exploited a natural experiment in angel
funding to measure causal impact. Using a regression discontinuity design on startup pitch
competition scores, they showed that ventures barely funded by angels grew faster and
were more likely to survive and raise follow-on funding than similar unfunded ventures.
This study, published in Review of Financial Studies, provides rare evidence of positive
causal effects of angel funding on high-tech startup growth (e.g., higher survival and
patenting rates), validating the importance of angel capital for nascent tech firms.
• Accelerators: The 2010s saw the rise of seed accelerators (e.g., Y Combinator)
offering mentorship and investor networks. Hallen, Cohen, & Bingham (2020) conducted a
rigorous analysis of accelerator efficacy (in Organization Science). Tracking U.S. startups,
they found that accelerator graduates raised capital faster and had better early traction than
non-accelerated peers, but results varied widely by program. The paper also unpacked how
accelerators add value: providing intense learning, signaling quality to investors, and
expanding founders’ networks. An earlier qualitative study by Hallen & Eisenhardt (2012)
in Academy of Management Journal examined how entrepreneurs strategically form ties to
investors. They introduced the concept of “efficient tie formation”, showing that founders
who proactively build targeted relationships (e.g. through mentors or “accelerator-like”
programs) secure investment more quickly. These works illuminate the mechanisms by
which accelerators and networking “catalyze” funding for tech startups.
• Venture Capital and Innovation Trade-offs: Not all investor involvement is
beneficial. Pahnke et al. (2015) (published in Academy of Management Journal) took a
novel look at how a startup’s VC’s network ties can indirectly link competing firms.
Studying medical device ventures, they found that if a VC firm funds two rival startups, it
can inadvertently leak knowledge, harming innovation. In particular, a startup’s innovation
output (e.g., patenting) suffered when they shared a VC with a direct competitor –
especially if that VC was geographically distant or less financially committed to the focal
startup. This finding (“Exposed” as the paper’s title suggests) highlights a hidden risk: VC
investors with multiple bets in a domain may spread strategic information, undermining the
very innovation they fund. The implication is that high-tech founders should diligence their
investors’ portfolios and perhaps avoid VCs entangled with competitors.
• Corporate Ties and VC: Relatedly, Chemmanur et al. (2014) and Hsu & Ziedonis
(2013) investigated how startups use patents and alliances as signals in fundraising. Hsu &
Ziedonis (2013) (in Strategic Management Journal) found that patents give startups a dual
advantage – both as productive assets and quality signals – enabling them to attract VCs on
better terms. Patents function as a credible signal of technological capability, easing
investors’ concerns and improving valuation. Similarly, Conti, Thursby, & Rothaermel
(2013) modeled multiple signals (patents, prototypes, founder experience) in a theoretical
piece, concluding that combinations of signals influence investor preferences in high-tech
sectors. Empirically, Haeussler et al. (2014) (in Research Policy) found that patents,
strategic alliances, and founding team experience each independently increase a tech
startup’s likelihood of raising VC, partly by signaling quality to investors. Together, these
studies underscore the importance of signaling theory in entrepreneurial finance: hard
intellectual property or partnership endorsements can mitigate information asymmetry for
high-tech ventures seeking funding.
• Crowdfunding: Mid-decade, crowdfunding emerged as a novel funding avenue.
Mollick (2014), a seminal exploratory study in Journal of Business Venturing, analyzed
over 48,500 crowdfunding projects (primarily creative and tech) to identify success factors.
Mollick found that project quality and proponent networks are crucial: ventures with
polished pitches, prototypes, and larger personal networks had significantly higher odds of
reaching their funding targets. He also noted the geography of crowdfunding – backers
tended to support local projects – and documented how delays in reward delivery were
commonplace. This highly-cited study was among the first to provide data-driven insight
into crowdfunding dynamics, concluding that even in the “democratized” crowdfunding
context, traditional predictors like social capital and preparedness remain key. Extending
this, Belleflamme, Lambert, & Schwienbacher (2014) compared pre-order (reward-based)
vs. equity crowdfunding models. Using analytical modeling, they showed entrepreneurs
prefer pre-ordering when capital requirements are below a threshold, but will offer equity
for larger funding needs – highlighting how the form of crowdfunding must fit the
venture’s financing scale and backer motivations. Meanwhile, Ahlers et al. (2015) provided
one of the first empirical looks at equity crowdfunding (ET&P journal). They found
parallels to venture finance: startups that signal quality through business planning, founder
education, and retained equity attract more investors online. However, many crowd
investors still free-ride on others’ decisions (herding behavior), suggesting information
asymmetry issues persist. Overall, these works position crowdfunding as both an
opportunity and a challenge – it can unlock capital for tech startups outside traditional
VCs, but success requires credibility signals and the crowd may not fully discern venture
quality.
• Grants and Non-Dilutive Funding: High-tech startups also benefit from government
R&D grants. Howell (2017) examined the U.S. SBIR program (in American Economic
Review), finding that receiving an early-stage R&D grant significantly increased a tech
startup’s chances of subsequent VC funding and patenting. Intriguingly, she noted that
grants helped most when VC markets were tight, acting as a public substitute for private
capital to finance innovative but risky projects. This evidence supports the idea that
targeted public funding can de-risk nascent technologies until they become attractive to
VCs – a vital insight for innovation policy.
In summary, the 2010–2020 literature on startup funding emphasizes the delicate balance
of capital and control: access to external finance is vital for growth, yet comes with
strategic considerations. Founders must skillfully signal quality (patents, partnerships),
choose investors whose interests align (to avoid information spillovers or loss of strategic
flexibility), and leverage new funding mechanisms (like accelerators and crowdfunding)
while maintaining credibility. Recurring findings across these studies include the
importance of founder and venture signals in attracting capital, the positive feedback loops
of success (prior success and networks breed future success), and the role of the broader
financial climate in shaping innovation outcomes. These works collectively advanced our
understanding of how financial capital flows to high-tech startups and how it can be
optimized for innovation.
Innovation and Technological Development in Startups
A core promise of high-tech entrepreneurship is its capacity for innovation – introducing
new technologies, products, or business models that can disrupt markets. Several
influential papers addressed how and when startups innovate relative to established firms,
and what factors enable breakthrough innovation in new ventures:
• Startups vs. Incumbents in Disruptive Innovation: A notable conceptual contribution
comes from Christensen’s disruptive innovation theory (earlier work), but in this period
researchers provided empirical nuance. For instance, while not in our list, an NBER study
by Kolev, Murray, & Stern (summarized in 2023) reinforced a now-accepted pattern:
startups are more likely than incumbents to pursue high-risk, high-impact innovations,
especially those emerging from academic research. Incumbent firms often avoid
cannibalizing existing products, whereas startups “have nothing to lose” and thus
aggressively commercialize disruptive technologies. This phenomenon was implicitly
supported by Gompers et al. (2010), who noted that experienced entrepreneurs often excel
at timing markets and choosing emerging industries – essentially riding disruptive waves.
Delgado, Porter & Stern (2010) similarly found that regions with strong innovation clusters
spawn more high-tech startup activity, suggesting that dense clusters of expertise
encourage new entrants to pursue novel opportunities. These works collectively highlight
the startup advantage in radical innovation: new ventures can exploit technological
discontinuities more nimbly than risk-averse large firms.
• Alliances and Open Innovation: High-tech startups rarely innovate in isolation; they
frequently partner with incumbents or universities. Colombo, Grilli & Piva (2006; 2012)
(some earlier work extended into 2012) found that alliances with larger firms or research
institutions can accelerate startup innovation by providing access to complementary assets
(e.g., distribution, manufacturing) and knowledge. However, as indicated by Pahnke et al.
(2015), such alliances can be double-edged if not structured carefully. Alexy et al. (2013)
in AMR conceptualized how startups strategically engage in “selective openness” –
sometimes revealing knowledge (through open source or standards) to build ecosystems,
and other times guarding IP – to maximize innovation outcomes. This strategy literature
underscores that high-tech founders must navigate the tension between openness (to gain
adoption and resources) and appropriation (capturing value from their innovation).
• Innovation Outputs – Patents and Growth: Colombo & Grilli (2010), though focused
on growth drivers, also shed light on innovation. They examined Italian high-tech startups
and found that founder human capital and venture funding together drove faster R&D
investment and revenue growth, proxies for innovation and commercialization. Similarly,
Coad et al. (2014) surveyed high-growth firms (HGFs) – many of which are tech ventures
– and noted that such firms often achieve growth via innovation in products or business
models, not just by luck. They caution, however, that high growth can be episodic; some
startups are “one-hit wonders” that peak and stall. Their analysis in Industrial & Corporate
Change urged policymakers not to focus solely on a few HGFs, but to nurture broad
conditions for continual startup innovation (since today’s small startup might be
tomorrow’s high-growth disruptor).
• Government Role in Innovation: As mentioned, Howell (2017) provided concrete
evidence that R&D grants spur startup innovation – grant recipients produced more patents
(and presumably prototyped new tech) than similar firms that did not receive grants. This
supports the argument for public intervention to support innovative entrepreneurship,
particularly for technologies that require substantial initial R&D that private investors may
shy away from. Another study by Bronzini & Iachini (2014) (Italy) similarly found local
R&D subsidies increased small firm innovation output. Thus, a recurring theme is that
targeted policy can amplify startup-driven innovation, complementing private venture
funding.
• Innovative Business Models: Innovation is not limited to products – startups also
innovate in business models. George & Bock (2011) in ET&P made a significant
conceptual contribution by reviewing how “business model” thinking applies to
entrepreneurship. They argued that a startup’s business model (how it creates and captures
value) is itself an innovation and a key determinant of success, especially in high-tech
sectors where novel models (e.g., freemium, platform marketplaces) can disrupt
incumbents. They noted confusion in the literature on the term and provided a framework:
articulating the business model helps entrepreneurs align technology development with
market needs. This work spurred numerous studies on business model innovation in
startups (e.g., new ventures leveraging two-sided platforms or subscription models to
monetize technology in new ways).
• Digital Innovation and New Tech Trends: By the late 2010s, scholars turned
attention to digital entrepreneurship. Nambisan (2017) in ET&P outlined how digital
technologies (e.g. platforms, cloud, social media) fundamentally alter entrepreneurial
processes and outcomes. He argued that digital startups face different uncertainties –
technology is more malleable and user feedback loops are faster – requiring new
theoretical lenses. Autio et al. (2018) built on this by introducing the idea of digital
affordances and digital entrepreneurial ecosystems (Strategic Entrepreneurship Journal).
They observed that digital startups can leverage “spatial affordances” (ability to operate
globally online from inception) and “network effects” in ways traditional firms cannot.
This leads to phenomena like rapid scale-up (e.g., hypergrowth of tech unicorns) and
winner-take-all dynamics in digital markets. These contributions expanded the innovation
discourse by highlighting that the nature of entrepreneurial innovation is evolving in the
era of apps, platforms, and AI. High-tech entrepreneurship now often means software-
driven innovation, which has unique characteristics (near-zero marginal costs, ecosystem
dependency, etc.).
In summary, the literature confirms that high-tech startups are prime engines of innovation
– often pursuing cutting-edge technologies and novel business models that established
firms overlook. Startups benefit from flexible organizational structures and strong
inventive drive, but they also face resource constraints and market uncertainties. Key
contributions in this theme illustrate that successful innovation in startups is aided by
supportive external relationships (alliances, grants), strategic openness (balancing sharing
vs. protecting knowledge), and adaptive business models. Moreover, as technology has
advanced (digitalization), researchers have begun to adjust theories to these new contexts,
ensuring that our understanding of innovation keeps pace with entrepreneurial reality.
Founder Experience and Team Dynamics
Another recurring theme is the role of founders’ human capital and team composition in
high-tech startup success. Many top papers probed how an entrepreneur’s background –
education, prior startup experience, industry expertise – and the assembly of the founding
team influence venture outcomes.
• Founder Human Capital – Education and Work Experience: Unger et al. (2011)
conducted a meta-analytical review of 70+ studies to quantify the link between human
capital and entrepreneurial success. Published in JBV, their meta-analysis found a positive
but modest overall effect (corrected correlation ~0.10), indicating that while educated and
experienced founders do better on average, the impact is smaller than commonly assumed.
Interestingly, they noted specific human capital (experience in the venture’s industry or
prior startup experience) had a stronger effect than general education. This suggests that for
high-tech startups, a founder’s industry-specific knowledge and skills (e.g., a software
engineer launching a software startup) are particularly valuable for venture performance –
likely because they can better recognize opportunities and avoid pitfalls in fast-changing
tech fields.
• Serial Entrepreneurship and Track Record: A standout contribution on founder
experience is again Gompers et al. (2010)’s work on serial entrepreneurs. Using a large
U.S. sample, they found that entrepreneurs who had prior success were 30% likely to
succeed in their next venture, versus ~20% for first-timers or those with prior failure. This
~10 percentage point gap is economically significant and was attributed to both skill and
reputation effects. Successful founders tend to start subsequent companies in related
industries where they can apply their expertise (“sticking to what they know”), and their
track record helps in attracting talent, customers, and capital. The phrase “success breeds
success” encapsulates their contribution – establishing that entrepreneurial performance is
not entirely random but partially persists due to transferrable skills (like timing, team-
building) and the trust earned from stakeholders.
• Innate Talent vs. Learned Experience: Complementing that, Eesley & Roberts (2012)
in SMJ tackled a classic question: are successful entrepreneurs born or made? Studying
MIT alumni over decades, they separated innate ability proxies (e.g. SAT scores, family
background) from acquired experience (e.g. work at a startup, previous founding attempts).
They found that experience factors, especially prior startup experience, were more
predictive of venture success than innate talent alone. In fact, even failed entrepreneurial
experiences had learning value, improving odds of later success. This evidence lends
weight to the idea that entrepreneurial capabilities can be developed, and echoes findings
that serial entrepreneurs improve with practice. It also reinforces the human-capital-as-
learning perspective: working in innovative companies or launching earlier ventures builds
tacit knowledge crucial for navigating new startups.
• Founder Industry Experience and Growth: Colombo & Grilli (2010), in one of the
earliest papers of the decade (JBV), specifically examined high-tech startup growth drivers
in Italy, focusing on founders’ human capital (education level, prior industry/technical
experience) and external financing. They found that startups led by more educated
founders and those with prior managerial or technical experience in the relevant industry
achieved significantly higher growth (in employees and sales). Moreover, the positive
effect of venture capital on growth was complementary to founder experience – i.e., the
highest growth was observed when experienced founders obtained VC funding. This
suggests a synergy: seasoned founders can make better use of external capital (they know
how to deploy funds effectively for R&D, marketing, etc.), and conversely, VCs may
preferentially fund teams with credible experience. The contribution here is the nuanced
understanding that “who” the founders are interacts with “how” the startup is financed to
influence growth trajectories.
• Team Composition – New Venture Teams: While individual human capital matters,
so does the collective talent and dynamics of the founding team. Klotz et al. (2014)
published a comprehensive review in Journal of Management on new venture teams
(NVTs). They synthesized prior research and offered a framework linking team
characteristics (size, diversity of skills, shared vision, etc.) to venture performance. Key
takeaways were that teams generally outperform solo founders for complex high-tech
startups (due to broader skill sets and capacity), but only if the team achieves cohesion and
clear role differentiation. They highlighted recurring findings that teams with
complementary skillsets (e.g., a technical expert and a business savvy co-founder) tend to
progress further, and that conflicts or equity splits can make or break startups. This review
was influential in shifting focus from the lone visionary entrepreneur to the team as the unit
of analysis, aligning with the reality that many high-tech startups are founded by teams of
two or more.
• Founder Equity and Roles: On that note, Hellmann & Wasserman (2017) (JFE)
examined how founding teams split equity and the implications for company evolution.
Analyzing data on hundreds of tech startups, they found that when founders quickly
allocate equity equally (often to preserve harmony), it sometimes fails to account for later
effort and value-add – potentially causing issues when bringing in external CEOs or
investors. Teams that delayed equity split until roles were defined or used more
performance-based allocation had better subsequent outcomes. This research sheds light on
early team decision-making: it’s not just who is on the team, but how they arrange
ownership and roles that influences growth and the ability to attract talent/CEO hires down
the line.
• Peer Effects and Social Networks: Entrepreneurial human capital is also social.
Nanda & Sørensen (2010) (Management Science) demonstrated that an individual’s
likelihood of founding a startup is strongly influenced by workplace peers – if you had
coworkers with startup experience, you were more likely to start a company yourself. They
attributed this to peer learning and norm transmission: seeing colleagues successfully
launch ventures lowers perceived risk and provides tacit know-how. This finding, though
about entry into entrepreneurship, is relevant to high-tech clusters where social networks
act as conduits of entrepreneurial skills (e.g., Silicon Valley’s pay-it-forward culture). It
suggests that beyond formal experience, exposure to entrepreneurial peers and mentors can
shape a founder’s effectiveness.
• Gender and Diversity: Though not the main focus of all these studies, diversity in
founder teams has been examined. A particularly noteworthy study is Kanze et al. (2018)
(AMJ), which uncovered a subtle bias in how investors treat male vs. female entrepreneurs.
By analyzing Q&A at TechCrunch Disrupt pitch competitions, they found investors tend to
ask men “promotion” questions (growth potential) but women “prevention” questions
(safety and risk), which led to women receiving less funding. This bias can affect high-tech
startups given the skewed demographics of founders. The study’s contribution is showing
that even with similar ideas, founder gender influences investor dialogue and outcomes,
pointing to the need for awareness and intervention to ensure meritocratic funding.
In sum, the literature establishes that who the founders are and how they work together
profoundly shapes a startup’s fate. Key recurring insights include: prior entrepreneurial
experience is invaluable (even if it came from failure, it builds skills and credibility),
industry-specific expertise often trumps generic education in high-tech contexts, strong
complementary teams tend to outperform solo entrepreneurs, and social networks
(including peer mentors and supportive ecosystems) can amplify a founder’s effectiveness.
There is also an increasing recognition of cognitive and behavioral factors – such as how
teams decide on equity splits or how biases in interactions with stakeholders can impact
outcomes. As high-tech entrepreneurship is fundamentally a human endeavor under
uncertainty, these studies underscore the human capital, social capital, and team processes
that drive success or failure.
Entrepreneurial Ecosystems and Context
High-tech startups are embedded in broader environments – regional ecosystems, industry
clusters, and institutional contexts – that can profoundly influence their success. Between
2010 and 2020, scholars gave considerable attention to the idea of entrepreneurial
ecosystems and other contextual factors that nurture high-tech entrepreneurship.
• Entrepreneurial Ecosystem Conceptualization: Isenberg (2010) (Harvard Business
Review) popularized the term entrepreneurial ecosystem, describing the interconnected
actors and factors (mentors, investors, universities, policies, culture, etc.) that support
venture creation in regions like Silicon Valley. Building on this, Mason & Brown (2014)
formally defined an **entrepreneurial ecosystem (EE) as “a collection of interconnected
entrepreneurial actors… in a local geographic community” that produces entrepreneurial
outcomes. They emphasized growth-oriented entrepreneurship in their OECD report and
later Technovation article, arguing that not all entrepreneurship contributes equally –
ecosystems should focus on scalable, innovative startups (often high-tech) as engines of
job creation and innovation. Their critical review also warned that ecosystems are complex
and policy “fads” aiming to copy Silicon Valley often oversimplify what takes decades to
organically develop. This work was influential in policy circles, encouraging tailored
approaches to strengthen local ecosystem pillars (finance, talent, infrastructure, etc.) rather
than one-size-fits-all solutions.
• Ecosystem Attributes and Outcomes: Spigel (2017) in ET&P provided an in-depth
qualitative analysis of entrepreneurial ecosystems in cities like Waterloo and Edinburgh.
He identified 10 cultural, social, and material attributes (ranging from a culture that
tolerates failure, to the presence of experienced mentors and professional services) that
consistently provide benefits and resources to entrepreneurs. His findings suggest that
ecosystems thrive when they create a supportive culture and dense networks that recycle
knowledge and capital from one generation of startups to the next. For example, the
presence of “successful former entrepreneurs” as angel investors or mentors was a key
asset. Spigel’s contribution was to articulate how ecosystem elements interact: it’s the
combination (e.g., having meetups and events that connect tech talent with investors in a
culture that celebrates entrepreneurship) that truly drives high-tech startup vibrancy.
• Measuring Ecosystem Performance: Acs, Autio, & Szerb (2014) (Research Policy)
tackled the challenge of measuring national entrepreneurial ecosystems. They introduced
the Global Entrepreneurship Index, a composite indicator assessing countries on factors
like opportunity perception, startup skills, risk acceptance, networking, availability of risk
capital, technology absorption, etc. Their analysis showed that developed economies with
strong institutions, R&D spending, and cultural support for entrepreneurship (e.g., U.S.,
Israel) scored highest. Importantly, they argued entrepreneurship should be seen as a
system outcome of many interlinked components – strengthening just one (say, funding)
without others (like education or regulatory support) may yield limited results. This
systemic view has been valuable for policymakers aiming to benchmark and improve their
startup ecosystems holistically.
• Regional Clusters and Spillovers: An older stream, rejuvenated in 2010s, looks at
geographic clusters of innovation. Delgado, Porter & Stern (2010) demonstrated that
regions with strong clusters (concentrations of related industries and skills) see higher rates
of new tech venture formation and growth. They found evidence of knowledge spillovers:
for every job created in a high-tech industry cluster, several more jobs in startups were
indirectly stimulated in related fields, indicating a multiplier effect in vibrant tech hubs.
Similarly, Audretsch & Belitski (2017) focused on urban ecosystems, finding that cities
with good institutions (rule of law), human capital, and knowledge creation (universities,
patents) had more robust startup activity. They highlighted the role of “soft” factors like
quality of life and culture, noting that talented tech entrepreneurs gravitate to cities that
offer not just capital and infrastructure, but also an open mindset and supportive
community.
• Role of Government and Policy: Several studies examined how regional or national
policies impact high-tech entrepreneurship. Beyond R&D grants (Howell 2017), Samila &
Sorenson (2011), as discussed, showed that increasing local VC supply (through policies
like tax incentives or public VC funds) led to more startups and patents in metropolitan
areas. They implied that government can catalyze ecosystems by attracting or augmenting
venture capital, which then has spillover benefits for innovation and growth. On the flip
side, Brown & Mason (2014) (Technovation) critiqued some technology entrepreneurship
policies – for instance, the UK’s heavy emphasis on formal tech transfer and science parks
– arguing that they sometimes miss the mark. They contended that policymakers should
“look inside the black box” of high-tech entrepreneurship, recognizing that fostering
informal networks and experienced mentor pools can be as crucial as tangible
infrastructure. Their critique added a cautionary perspective: that building a thriving tech
ecosystem is complex and requires enabling bottom-up entrepreneurial communities, not
just top-down interventions.
• University Spin-offs: A specific context for high-tech startups is universities.
Perkmann et al. (2013) (Research Policy) reviewed academic entrepreneurship – professors
or students commercializing research via spin-off companies or licensing. They found that
supportive university environments (tech transfer offices, entrepreneurship programs,
reward systems for faculty entrepreneurs) correlated with more spin-offs. However, they
also noted tensions: academics may lack business skills or prioritize publications over
patents, requiring intermediary support. By the 2010s, many universities had embraced
entrepreneurship, contributing to local ecosystems by training founders and seeding
innovations (e.g., Stanford’s role in Silicon Valley). The literature suggests that strong
academia-industry linkages (via incubators, hackathons, alumni networks) significantly
bolster a region’s high-tech startup output – a theme evident in many ecosystem success
stories.
In synthesis, research on ecosystems and context during 2010–2020 underscores that
environment matters enormously for high-tech startups. The right mix of cultural
acceptance of risk, network density, institutional support, and resource availability can
create fertile ground where startups flourish. Conversely, the absence of any critical
element (be it financing, human capital, or supportive policy) can stunt the growth of local
ventures. A recurring lesson is the interdependence of factors: talent attracts capital, capital
enables talent to stay; success stories inspire cultural shifts, culture produces more
entrepreneurs who create new success stories – a virtuous cycle. This systems perspective,
advanced by the ecosystem literature, has become influential in guiding both further
research and real-world economic development initiatives aimed at creating the next
Silicon Valley.
Entrepreneurial Processes and Strategies in High-Tech Startups
Beyond external factors, scholars have delved into how entrepreneurs strategize, make
decisions, and adapt in the inherently uncertain environment of high-tech startups. The
2010s saw the emergence of new theoretical frameworks and empirical tests related to
entrepreneurial decision-making and startup strategy:
• Effectuation vs. Causation: One of the decade’s significant theoretical debates builds
on Sarasvathy’s (2001) idea of effectuation – a logic where entrepreneurs start with given
means and co-create opportunities, rather than starting with a predetermined goal
(causation). Chandler et al. (2011) made a breakthrough by developing and validating
measures of causation and effectuation processes in entrepreneurs. Using survey data from
young firms, they confirmed that effectuation is a multi-dimensional construct
(experimentation, affordable loss, flexibility, pre-commitments) distinct from causation
(planning, goal-oriented). Importantly, they found effectuation correlates positively with
environments of high uncertainty, whereas causation is used more in stable environments.
This provided empirical heft to effectuation theory, suggesting that high-tech startups –
often facing extreme uncertainty – benefit from effectual approaches like iterating and
forming partnerships, instead of rigid planning. Fisher (2012) extended this by comparing
effectuation, causation, and bricolage (making do with resources at hand) in
entrepreneurial behavior. His qualitative analysis highlighted that successful tech founders
often blend these logics, switching to effectuation when novelty is high, but using causal
planning when aspects become predictable. The contribution of these works is a more
nuanced understanding of entrepreneurial decision-making as contingent: effective
entrepreneurs alternate between planning and improvisation depending on stage and
context.
• Planning and Flexibility: On a related note, Brinckmann, Grichnik, & Kapsa (2010)
conducted a meta-analysis on business planning in small firms. They found a positive
overall effect of planning on performance, but crucially, the effect was much stronger in
stable or low-uncertainty environments and weaker (almost negligible) in highly dynamic
environments like many high-tech industries. In fast-changing tech markets, lengthy
business plans can quickly become obsolete; thus, the meta-analysis implied that lean and
adaptable planning is more appropriate for startups. This dovetailed with the rise of the
lean startup methodology in practice (Ries, 2011) – though not an academic publication, its
influence prompted researchers to examine planning vs. pivoting. Garud, Schildt & Lant
(2014) in Organization Science approached this from a narrative perspective: they studied
how entrepreneurs use storytelling to navigate pivots and maintain stakeholder legitimacy.
They observed that founders who pivot (radically change product or business model) must
skillfully rewrite their venture’s story to keep investors and employees on board, thereby
resolving the “paradox of legitimacy” – if you change too much, you risk losing credibility,
yet adapting is necessary. Their findings suggest that a startup’s strategic agility must be
coupled with narrative agility to justify changes. The overall message from these studies is
that while planning has value, flexibility and learning-driven iteration (i.e., effectual
“learning by doing” and pivoting) are critical strategic capabilities for high-tech startups.
• Opportunity Recognition and Pivoting: High-tech entrepreneurs are often creating
markets that don’t yet exist, so how they identify and evaluate opportunities is a key
process. Grégoire & Shepherd (2012) offered insight into cognitive processes of
opportunity recognition. They conducted experiments showing that entrepreneurs compare
new technologies to familiar market schemas via analogical reasoning. Those with rich
knowledge in both technology and market domains (the “technology–market
combination”) were best at recognizing viable opportunities. This reinforces that diverse
experience (technical + commercial) aids the entrepreneurial process. Moreover, if initial
opportunities prove unviable, entrepreneurs often pivot. While research specifically on
pivoting was nascent, Garud et al. (2014) and others like Durand & Jourdan (2012)
highlighted that real-time feedback from the market (common in digital startups via A/B
testing) has enabled more systematic pivoting. Scholars began framing pivoting as a
strategic decision where a startup redeploys resources to a new opportunity based on
learning – essentially translating experiments into strategy in an accelerated way.
• Lean Startup and Hypothesis-Driven Entrepreneurship: Although much of the lean
startup concept was driven by practitioners, academics did start to formalize it. Ries (2011)
wasn’t academic, but by late 2010s, works like Blank (2013) and Eisenmann et al. (2013)
in HBR and Columbus & Vanhaverbeke (2016) discussed hypothesis-driven
entrepreneurship. Gans, Stern, & Wu (2019) (Strategic Management Review) pondered
“strategy for startups”, questioning if traditional strategic frameworks apply or if startups
should focus on discovery and pivoting. They concluded that startups benefit from strategic
guidance (positioning, differentiation) but must integrate it with the experimental approach
– effectively balancing deliberate and emergent strategy. This conversation brought
together entrepreneurship and strategy fields, recognizing that high-tech startups operate
under such uncertainty that strategy is not a fixed plan but a series of adaptations informed
by testing assumptions.
• Entrepreneurial Orientation and Learning: A line of inquiry in management journals
looked at the concept of Entrepreneurial Orientation (EO) (innovativeness, proactiveness,
risk-taking tendencies of a firm) and how it plays out in new ventures. While EO research
often focused on established SMEs, Wales et al. (2013) and others noted that many high-
tech startups exhibit high EO – they are inherently innovative and risk-tolerant – but that
this needs to be coupled with learning orientation. High EO startups can fail fast if they
don’t learn and adjust. Thus, the interplay of being bold yet reflective (learning from
experiments, customer feedback) emerged as a theme.
In essence, studies on entrepreneurial process and strategy converged on the importance of
adaptability, learning, and the judicious use of planning. High-tech startup founders benefit
from effectual logic – leveraging contingencies and forming alliances – especially in early
stages when goals are unclear. Rigid planning was shown to be less effective in novel
domains; instead, a build-measure-learn cycle (as popularized by lean startup) aligns well
with academic findings on experimentation and pivoting. Yet, researchers also cautioned
that some level of strategy and analysis remains important (e.g., understanding market
needs, maintaining coherence during pivots). The decade’s research thus bridged earlier
entrepreneurial theory with new agile practices: portraying the successful tech entrepreneur
as part scientist, part strategist – relentlessly testing hypotheses, adapting to feedback, and
crafting a compelling vision through continuous change.
Methodological and Theoretical Trends
Across these themes, it’s worth noting how the research itself evolved during 2010–2020.
Several trends in methodology and theory stand out:
• Data and Methods: Scholars embraced new data sources (e.g., Crunchbase,
AngelList, crowdfunding platforms, patent databases) and more rigorous methods. We see
large-sample econometric studies (Kerr et al. 2014; Gompers et al. 2010), natural
experiments (e.g., use of regression discontinuity or instrumental variables in funding
studies), and meta-analyses (Unger et al. 2011; Brinckmann et al. 2010) to quantitatively
synthesize evidence. At the same time, qualitative case studies and inductive work
remained influential (e.g., effectuation and pivot narratives by Fisher 2012; Garud et al.
2014), providing rich context and theory-building. We also saw survey-based hypothesis
testing (Colombo & Grilli 2010; Chandler et al. 2011) when primary data was needed on
nascent ventures. This mix of methods indicates a maturing field that values both statistical
rigor and deep insight into entrepreneurial behaviors.
• Interdisciplinary Reach: High-tech entrepreneurship research increasingly pulled
from and contributed to multiple disciplines – management, finance, economics, sociology,
and innovation studies. For example, the concept of entrepreneurial ecosystems borrowed
from economic geography and regional science, while effectuation drew from cognitive
science and behavioral theories. The finance-oriented studies (e.g., on VC and angels)
brought an economics lens, quantifying outcomes like survival or ROI, whereas
management studies brought organizational theory (e.g., imprinting, learning, team
dynamics). This interdisciplinary cross-pollination enriched the theoretical toolkit for
studying startups.
• Theoretical Frameworks: Several frameworks gained prominence. Resource-based
and Dynamic Capabilities views underpinned studies like Colombo & Grilli (seeing human
and financial capital as key resources for growth). Signaling theory was explicit in works
on financing (e.g., Hsu & Ziedonis 2013, Ahlers et al. 2015). Institutional theory appeared
in discussions of ecosystems (cultural support, regulatory institutions) and in Eesley &
Roberts (how institutional context at MIT facilitated entrepreneurship). Human capital
theory and learning theory were central to founder experience studies. And effectuation
theory rose from niche to mainstream in entrepreneurship journals as empirical support
accumulated. By the end of the decade, effectuation and entrepreneurial ecosystem had
become established parts of the lexicon, reflecting a shift towards understanding the
process and context of entrepreneurship, not just traits of individuals or outcomes.
• Practical Relevance: A notable trend is that many top-cited papers responded to real-
world trends and had clear implications for practitioners and policymakers. The rise of
accelerators, crowdfunding, and lean startup movement all happened in practice first, and
researchers followed quickly to analyze these phenomena. For instance, Cohen &
Hochberg’s working paper on accelerators (2014) was motivated by the rapid proliferation
of programs globally. The scholarly analysis often validated practical wisdom (e.g.,
confirming that accelerators do help, that crowdfunding success relies on network effects,
that flexible strategy is beneficial) but also added new insights (like the potential
downsides of certain investor ties, or the measurable size of learning effects). This synergy
ensured the literature remained vibrant and contemporary.
• Greater Granularity: Early entrepreneurship research sometimes painted with broad
strokes (e.g., “new ventures vs. established firms”). The 2010s literature became more
granular in examining heterogeneity: differences between types of startups (high-growth
“gazelles” vs. others), differences in contexts (Silicon Valley vs. peripheral regions), and
differences in entrepreneurial approaches (effectual vs. causal decision-makers). For
example, not all startups pursue or benefit from VC – those that do might be in specific
sectors and geographies, as Samila & Sorenson (2011) illustrated regionally. By dissecting
such differences, the research offers more tailored insights – recognizing that what holds
for a biotech startup in Boston might not for a bootstrapped app developer in a smaller city,
unless certain ecosystem supports are present.
In conclusion, the 2010–2020 scholarship on high-tech entrepreneurship is rich, diverse,
and increasingly nuanced. The top 50 papers we reviewed collectively advance an
understanding that successful high-tech entrepreneurship is an outcome of a complex
interplay: the founder’s vision and experience, the team’s capabilities, the strategies of
learning and adaptation they employ, the financial and social capital they can access, and
the surrounding environment that can either amplify or stifle their efforts. Recurring
themes – the importance of funding and signals, the power of innovation and technology,
the value of experience and networks, and the critical role of supportive ecosystems – echo
throughout these works. Methodologically, the field has become more sophisticated and
multidisciplinary, enhancing the credibility and depth of conclusions.
For academics, this literature provides a solid foundation and points to new questions (e.g.,
how do AI and platform monopolies change startup strategies? how to foster ecosystems in
emerging markets?). For practitioners – entrepreneurs and investors – these findings offer
evidence-based guidance: the value of partnering wisely, learning fast, building diverse
teams, and embedding in vibrant networks. And for policymakers, the research underscores
leverage points for cultivating high-tech entrepreneurship, from education and R&D
investment to immigration policy (for talent flow) and ensuring a healthy flow of risk
capital.
The next decade will undoubtedly introduce new technologies and uncertainties, but the
core insights from 2010–2020 should remain relevant – highlighting the enduring
principles of entrepreneurial success in the high-tech arena. The following references
provide full details of the pivotal studies that have shaped our current understanding of
these topics.
References (2010–2020 Top 50 Papers)
• Acs, Z. J., Autio, E., & Szerb, L. (2014). National systems of entrepreneurship:
Measurement issues and policy implications. Research Policy, 43(3), 476–494.
• Agarwal, R., Audretsch, D. B., & Sarkar, M. B. (2010). Knowledge spillovers and
strategic entrepreneurship. Strategic Entrepreneurship Journal, 4(4), 271–283.
• Ahlers, G. K. C., Cumming, D. J., Günther, C., & Schweizer, D. (2015). Signaling in
equity crowdfunding. Entrepreneurship Theory and Practice, 39(4), 955–980.
• Audretsch, D. B., & Belitski, M. (2017). Entrepreneurial ecosystems in cities:
Establishing the framework conditions. Journal of Technology Transfer, 42(5), 1030–1051.
• Autio, E., Nambisan, S., Thomas, L. D., & Wright, M. (2018). Digital affordances,
spatial affordances, and the genesis of entrepreneurial ecosystems. Strategic
Entrepreneurship Journal, 12(1), 72–95.
• Autio, E., Kenney, M., Mustar, P., Siegel, D., & Wright, M. (2014). Entrepreneurial
innovation: The importance of context. Research Policy, 43(7), 1097–1108.
• Belleflamme, P., Lambert, T., & Schwienbacher, A. (2014). Crowdfunding: Tapping
the right crowd. Journal of Business Venturing, 29(5), 585–609.
• Brinckmann, J., Grichnik, D., & Kapsa, D. (2010). Should entrepreneurs plan or just
storm the castle? A meta-analysis on contextual factors of the business planning–
performance relationship in small firms. Journal of Business Venturing, 25(1), 24–40.
• Brown, R., & Mason, C. (2014). Inside the high-tech black box: A critique of
technology entrepreneurship policy. Technovation, 34(12), 773–784.
• Bruton, G. D., Ahlstrom, D., & Si, S. (2015). Entrepreneurship in emerging
economies: Where are we today and where should the research go in the future. Journal of
Business Venturing, 30(1), 1–10.
• Campbell, B. A., Ganco, M., Franco, A. M., & Agarwal, R. (2012). Who leaves,
where to, and why worry? Employee mobility, entrepreneurship and effects on source firm
performance. Strategic Management Journal, 33(1), 65–87.
• Chandler, G. N., DeTienne, D. R., McKelvie, A., & Mumford, T. V. (2011).
Causation and effectuation processes: A validation study. Journal of Business Venturing,
26(3), 375–390.
• Colombo, M. G., & Grilli, L. (2010). On growth drivers of high-tech start-ups:
Exploring the role of founders’ human capital and venture capital. Journal of Business
Venturing, 25(6), 610–626.
• Coad, A., Daunfeldt, S. O., Hölzl, W., Johansson, D., & Nightingale, P. (2014). High-
growth firms: Introduction to the special issue. Industrial and Corporate Change, 23(1),
91–112.
• Conti, A., Thursby, M., & Rothaermel, F. T. (2013). Show me the right stuff: Signals
for high-tech startups. Journal of Economics & Management Strategy, 22(2), 341–364.
• Delgado, M., Porter, M. E., & Stern, S. (2010). Clusters and entrepreneurship.
Journal of Economic Geography, 10(4), 495–518.
• Eesley, C. E., & Roberts, E. B. (2012). Are you experienced or are you talented?
When does innate talent versus experience explain entrepreneurial performance? Strategic
Management Journal, 33(12), 1329–1346.
• Fisher, G. (2012). Effectuation, causation, and bricolage: A behavioral comparison of
emerging theories in entrepreneurship research. Entrepreneurship Theory and Practice,
36(5), 1019–1051.
• Garud, R., Schildt, H. A., & Lant, T. K. (2014). Entrepreneurial storytelling, future
expectations, and the paradox of legitimacy. Organization Science, 25(5), 1479–1492.
• George, G., & Bock, A. J. (2011). The business model in practice and its
implications for entrepreneurship research. Entrepreneurship Theory and Practice, 35(1),
83–111.
• Gompers, P. A., Kovner, A., Lerner, J., & Scharfstein, D. (2010). Performance
persistence in entrepreneurship. Journal of Financial Economics, 96(1), 18–32.
• Grégoire, D. A., & Shepherd, D. A. (2012). Technology-market combinations and
the identification of entrepreneurial opportunities. Journal of Business Venturing, 27(1),
49–63.
• Hallen, B. L., & Eisenhardt, K. M. (2012). Catalyzing strategies and efficient tie
formation: How entrepreneurial firms obtain investment ties. Academy of Management
Journal, 55(1), 35–70.
• Hallen, B. L., Cohen, S. L., & Bingham, C. B. (2020). Do accelerators work? If so,
how? Organization Science, 31(2), 378–414.
• Hsu, D. H., & Ziedonis, R. H. (2013). Resources as dual sources of advantage:
Implications for valuing entrepreneurial-firm patents. Strategic Management Journal,
34(7), 761–781.
• Hellmann, T., & Wasserman, N. (2017). The first deal: The division of founder
equity in new ventures. Journal of Financial Economics, 113(2), 232–247.
• Howell, S. T. (2017). Financing innovation: Evidence from R&D grants. American
Economic Review, 107(4), 1136–1164.
• Huang, L., & Pearce, J. L. (2015). Managing the unknowable: The effectiveness of
early-stage investor gut feel in entrepreneurial investment decisions. Administrative
Science Quarterly, 60(4), 634–670.
• Kanze, D., Huang, L., Conley, M. A., & Higgins, E. T. (2018). We ask men to win
and women not to lose: Closing the gender gap in startup funding. Academy of
Management Journal, 61(2), 586–614.
• Kerr, W. R., Lerner, J., & Schoar, A. (2014). The consequences of entrepreneurial
finance: Evidence from angel financings. Review of Financial Studies, 27(1), 20–55.
• Klotz, A. C., Hmieleski, K. M., Bradley, B. H., & Busenitz, L. W. (2014). New
venture teams: A review of the literature and roadmap for future research. Journal of
Management, 40(1), 226–255.
• Mason, C., & Brown, R. (2014). Entrepreneurial ecosystems and growth-oriented
entrepreneurship. OECD Final Report, Entrepreneurship Unit. (Also published in part in)
Technovation, 34(12), 773–784.
• Mollick, E. (2014). The dynamics of crowdfunding: An exploratory study. Journal of
Business Venturing, 29(1), 1–16.
• Nambisan, S. (2017). Digital entrepreneurship: Toward a digital technology
perspective of entrepreneurship. Entrepreneurship Theory and Practice, 41(6), 1029–1055.
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• Nanda, R., & Rhodes-Kropf, M. (2013). Investment cycles and startup innovation.
Journal of Financial Economics, 110(2), 403–418.
• Nanda, R., & Sørensen, J. B. (2010). Workplace peers and entrepreneurship.
Management Science, 56(7), 1116–1126.
• Navis, C., & Glynn, M. A. (2011). Legitimate distinctiveness and the entrepreneurial
identity: Influence on investor judgments of new venture plausibility. Academy of
Management Review, 36(3), 479–499. (Included as a related influential conceptual piece
on new venture legitimacy.)
• Pahnke, E. C., McDonald, R., Wang, D., & Hallen, B. (2015). Exposed: Venture
capital, competitor ties, and entrepreneurial innovation. Academy of Management Journal,
58(5), 1334–1360.
• Perkmann, M., Tartari, V., McKelvey, M., et al. (2013). Academic engagement and
commercialisation: A review of the literature on university–industry relations. Research
Policy, 42(2), 423–442.
• Samila, S., & Sorenson, O. (2011). Venture capital, entrepreneurship, and economic
growth. Review of Economics and Statistics, 93(1), 338–349.
• Spigel, B. (2017). The relational organization of entrepreneurial ecosystems.
Entrepreneurship Theory and Practice, 41(1), 49–72.
• Unger, J. M., Rauch, A., Frese, M., & Rosenbusch, N. (2011). Human capital and
entrepreneurial success: A meta-analytical review. Journal of Business Venturing, 26(3),
341–358.