probabilities, suggesting the need for future research
on the operational dynamics of startups and accelerators across
different country groups. This research opens avenues for further studies on
the mediating and moderating effects of IC components on performance,
particularly in the context of human capital and financial constraints, thereby
addressing gaps in the literature and expanding our understanding of startup
survival determinants in emerging economies.
Keywords: Accelerator Programs, For-Profit New Venture in
emerging markets, Intellectual Capital Dimensions
JEL CODE: L26, O34, C250
Introduction
The Object of Study
The importance of
new businesses for regional and economic growth has been recognized since
Joseph Schumpeter’s 1934 work, “The Theory of Economic Development” (Del Sarto
et al., 2020). Scholars, practitioners, and policymakers acknowledge that
startups significantly contribute to economic expansion, job creation, and
societal prosperity (Audretsch et al., 2006; Pradhan
et al., 2020).
These nascent
ventures face operational, competitive, resource, and planning challenges that
threaten their long-term survival. Startups are especially prone to failure in
their early years, with a 30% failure rate within the first two years
(Santisteban & Mauricio, 2017; Picken, 2017). This risk diminishes over
time (Yang & Aldrich, 2017), but factors like inexperienced management, low
trust and legitimacy, and inconsistent strategy make them vulnerable in the
early stages (Noboa, 2022).
The survival of
new firms can be examined through three main factors: personal attributes,
firm-specific characteristics, and the external environment (Brüderl et al., 1992). Various elements often challenge
survival, including limited financial resources (Smilor,
1997) and an inexperienced founding team (Gruber et al., 2008).
While the existing
literature on startup survival is ample, it is often limited to a single
theoretical perspective, region, or industry (Andreeva & Garanina, 2016; Cressy, 1996; Gimmon
& Levie, 2010; Baum & Silverman, 2004). Most studies have been
conducted in advanced economies like Europe, North America, and Asia, focusing
less on emerging economies (Azeem & Khanna, 2023). Empirical research
primarily uses regression-based models, especially Logistic Regression Models
(LR), to examine the relationship between various antecedents and outcomes,
often considering internal resources and non-financial performance measures.
Given the limited
scope of past research, future studies should prioritize cross-country analyses
and the impact of initial conditions entrepreneurs face in emerging markets.
The rise of startups in these economies and the growing body of research on
startup survival present numerous opportunities for new investigations. These
studies can explore the interplay between internal and external factors
affecting startup survival in emerging markets. Therefore, it is essential to
include emerging countries in such research endeavors.
The Impact of
Initial Conditions on the Companies’ Performance
Predicting new venture performance based on initial
observable factors is a key interest for entrepreneurship researchers, as it
can optimize resource allocation and benefit both entrepreneurs and society
(Dahlqvist et al., 2000).
Startup survival has been extensively studied from
various theoretical perspectives. Azeem & Khanna (2023) highlight the
Resource-Based View (RBV) as the most frequently cited perspective, which
serves as the basis for our study. RBV posits that a company’s unique,
valuable, difficult-to-replicate, and irreplaceable resources and capabilities
confer a competitive edge and enhance performance (Wernerfelt, 1984; Barney,
1991). Among these resources, Intellectual Capital (IC) is particularly
crucial, as it is an intangible asset closely linked to a company’s strategy
and longevity (Rossi et al., 2016).
Our research, grounded in Barney et al., (2001) RBV
and Human Capital Theory (Becker, 1964; Mincer, 1974), emphasizes the pivotal
role of individual expertise and abilities in driving economic productivity.
Consistent with Cooper et al. (1994), we argue that a company’s initial
financial resources and human capital are fundamental to its survival,
especially for nascent enterprises (Van Praag, 2003).
This perspective underscores the critical contribution of both tangible and
intangible resources possessed by founding teams to the success of a startup (Strotmann, 2007).
Research Purpose
Our study focuses
on startups, which are catalysts of innovation and economic growth. We aim to
explore the impact of initial financial resources and Intellectual Capital (IC)
on startup development during the crucial pre-seed phase. This phase involves setting
business goals, identifying challenges, establishing market positioning, and
formulating strategic plans (Wong et al., 2005).
Nascent
enterprises often turn to accelerator programs for financial backing and expert
advice at this stage (Radojevich-K & Hoffman,
2012). Our study examines the correlation between the IC contributed by the
founding team—including their skills, knowledge,
experience, relationships, and abilities—and the survival rate of budding
for-profit ventures (Beckman & Burton, 2008).
In this context,
“startups,” also known as “nascent for-profit ventures,” are organizations
created in unstable environments to exploit new market opportunities (Davidsson
& Honig, 2003). Our research aims to determine how a founder’s initial IC
affects these ventures’ survival chances in emerging countries.
We seek to answer:
In the context of emerging countries, what is the impact of a startup’s IC on
its economic and financial performance? This question is vital as it
underscores the role of IC in determining a startup’s future. This study
focuses on startups in global accelerator programs, using data from the
Entrepreneurship Database Program (EDP) at Emory University, part of the Global
Accelerator Learning Initiative (GALI, 2020). The EDP, launched in 2013,
studies the causal effects of accelerating impact-oriented ventures. It uses
standardized questions that all participating accelerators incorporate in their
application processes, enhancing responsiveness and allowing observation of
nearly the entire pool of serious applicants. Program managers also track which
applicants join their programs (Lall et al., 2020). These programs value the IC
of the founders according to their selection criteria (GALI, 2021).
Most studies on
startup survival focus on developed countries (Azeem & Khanna, 2023), while
startups in emerging countries have not been widely analyzed. Our research
addresses this gap by considering geographic and socio-economic diversity. We
recognize the lack of comprehensive studies on startups in emerging economies
(Andreeva & Garanina, 2016) and aim to rectify
this. Our study broadens the scope by investigating the impact of IC—comprising
both initial financial resources and intangible knowledge—on the growth of
startups worldwide, with a particular emphasis on the pre-seed stage of
accelerator programs.
This study uses a
Logistic Regression Model to evaluate how different factors influence the
survival probabilities of startups in our sample. This model also predicts the
likelihood of a startup’s success in its early stage. We aim to enhance the
understanding of how IC affects the success of pre-seed startups, especially in
accelerator programs. This study has implications for various stakeholders.
Policymakers could use this knowledge to design policies that promote startup
growth by highlighting the value of IC, creating a dynamic and prosperous
startup ecosystem. Accelerator programs and venture capitalists could use this
knowledge to improve their startup selection and support processes, increasing
the success rate of the startups they back. Practitioners, such as startup
founders and employees, could use this knowledge to focus on building and
improving their IC.
The structure of
the remaining sections of the study is as follows: The second section
establishes the relevant literature that supports the conceptual framework and
hypotheses under study. The third section discusses materials and methods,
followed by the estimation results and their discussion. The validation of the
proposed hypotheses substantiates the next section, and the last section
addresses the practical and academic implications of the study and directions
for further research.
Literature review and hypotheses statement
Our analysis
investigates how a firm’s founding conditions affect its performance,
particularly its longevity. Various perspectives have examined this topic.
Organizational Ecology suggests that firms with superior initial resources are
more likely to survive through natural selection (Hannan & Freeman, 1977;
Fuertes-Callén et al., 2022; Romanelli, 1989). Other
research highlights the enduring impact of strategic choices made at the
outset. For instance, Eisenhardt and Schoonhoven (1990) demonstrated that founding
teams have a lasting influence on firm performance. Similarly, Cooper et al.
(1994) found that initial financial and human capital are strong predictors of
firm performance and survival. Kimberly (1979) also argued that environmental
conditions, the founder’s personality, and initial strategic choices
significantly shape organizational behavior.
The Entrepreneurial Context
Entrepreneurship
is a dynamic process involving team formation and adaptation to meet customer
demands. Salamzadeh and Kesim (2015) liken business
development to a life cycle, encompassing idea conception, prototype
development, market entry, product sales, and job creation. Wong et al. (2005)
identify the preparation for the start-up stage as the initial phase of defining
emerging ventures. Bruderl and Schussler (1990)
suggest that early survival signifies success, while sustained endurance
indicates adaptability in later stages.
The impact of initial conditions on
companies´ performance
The Resource-Based
View (RBV) posits that a founder’s attributes and circumstances significantly
impact venture performance. Dencker et al. (2009) found that knowledge
positively influences startup survival in Germany, with founders’ cumulative
experience enhancing knowledge integration. Research shows a varied positive
correlation between talent and performance, depending on a country’s
macroeconomic development (Furlan, 2019; Mayer-Haug et al., 2013). Studies also
demonstrate a differentiated positive impact of talent across entrepreneurial
stages and economic contexts (Kerrin et al., 2017). However, these effects can
be non-linear, contingent on factors like business development stages, survival
duration, administrative maturity, technological orientation, funding sources,
performance measures, and specific country and sector conditions (Delmar &
Shane, 2006).
Human Capital
Theory suggests that an individual’s human capital—education, work experience,
and job training—is vital for achieving organizational goals, securing a
competitive edge, and enhancing financial performance (Becker, 1994; Unger et
al., 2011). Gimmon and Levie (2010) applied this
theory to examine how founder qualities affect the ability to attract external
investment and ensure the survival of new high-tech firms. Their research
underscores that a founder’s managerial experience and academic credentials are
more critical in attracting external investment than technological expertise.
Cooper et al.
(1994) distinguished four types of initial capital: general human capital,
management expertise, industry-specific knowledge, and financial capital.
General human capital includes knowledge that increases productivity and access
to network resources. Management expertise is tacit knowledge from previous
general management experience. Industry-specific expertise, also tacit, is
essential for understanding the context of suppliers, competitors, and
customers. Financial capital serves as a cushion and allows strategic
flexibility.
This study
leverages RBV and Human Capital Theory, building on Cooper et al.'s findings,
to propose that initial allowances of financial and human capital are reliable
predictors of new venture survival (Van Praag, 2003).
Intellectual Capital
Intellectual Capital (IC), a concept widely
acknowledged despite its lack of a precise definition (Bontis,
1998), gained prominence with the rise of knowledge-based assets. Initially, IC
was quantified as the difference between market and accounting values (Brennan
& Connell, 2000). The modernization of the Human Capital (HC) concept by
Gary Becker in 1993 reignited academic interest in HC (Nyberg & Wright,
2015). The RBV paradigm signals a shift from physical and financial resources
to intangible assets (Spender, 1996; Abeysekera, 2021), positioning IC as a
strategic resource for emerging technological ventures (Juma & McGee,
2006).
This study defines IC as an
organization’s intangible assets, based on Stewart’s (1997) concept of IC as
“packaged useful knowledge”. These assets include employee knowledge,
adaptability, customer and supplier relationships, brands, intellectual
property, product trade names, internal processes, and R&D capabilities.
These assets are not in traditional financial statements, but they create
future value and competitive advantage. IC, as intangibles in financial
statements, often shows values three to four times higher than their book
values (Edvinsson & Malone, 1997).
The Three Dimensions of Intellectual
Capital in Entrepreneurship
Bontis (1998) suggests a research framework with three dimensions of
Intellectual Capital (IC): Human, Social, and Structural. This framework, which
views IC as valuable interconnected elements, explains IC’s dimensions (Marr
& Moustaghfir, 2005). It also enables the
empirical examination of IC components’ impact on performance (Felício, et al.,
2014).
Human capital (HC)
attributes, such as education and experience, are associated with small
business success (Baptista et al., 2014). Financial intermediaries and venture
capital firms value entrepreneurial experience highly when assessing startups,
using managerial skills and experience as primary selection criteria (Piva
& Rossi-Lamastra, 2018). HC is crucial in knowledge-based companies (Bosma
et al., 2004), and the value of specific human capital is evident in new
business founders’ entrepreneurial experience, especially among habitual
entrepreneurs who have previously founded at least one business (Baptista et
al., 2014).
Relational Capital (RC), or
Social Capital (SC), is an intangible asset that values relationships. It
involves cultivating, preserving, and enhancing quality relationships with
entities such as individuals, organizations, or groups that can impact business
performance (Welbourne & Pardo-del-Val, 2009).
RC encompasses the
knowledge derived from relationships with stakeholders like customers,
suppliers, and industry associations. This knowledge influences the
organization, adds value, and strengthens its operations. These relational
networks serve as crucial business resources, enabling entrepreneurs to tap
into resources otherwise unavailable within their venture (Bandera &
Thomas, 2018; Burt, 2017).
A significant aspect of SC
is the reputation, experience, and contacts facilitating entrepreneurs'
financing access (Baum & Silverman, 2004). New ventures can improve their
financing conditions through effective communication with investors and customers
(Gardner & Avolio, 1998).
Structural Capital (STC) is
recognized in the literature as the company’s internalized knowledge. It
pertains to the organizational structure and systems that bolster employee
productivity (Edvinsson & Malone, 1997). It encompasses all non-human intangible
assets of an organization. This includes culture, philosophy, internal
processes, information systems, databases, organizational charts, process
manuals, software, planning, strategies, routines, technology, and intellectual
property rights such as patents, trademarks, and copyrights. The value of these
assets to the company surpasses their material worth (Abdulaali,
2018). Intellectual property is often the only source of competitive advantage
for knowledge-based companies McGee & Dowling, 1994). Structural capital
focuses on organizational efficiency and its value derives from internal
infrastructure, processes, and culture on the one hand and from the adaptative
and development strategies adopted by the company on the other (Brennan &
Connell, 2000).
Impact of intellectual capital on the
performance of start-ups. Empirical evidence.
The Resource-Based View
(RBV) theory highlights Human Capital (HC) as a crucial determinant of firm
performance (Barney et al., 2001). HC, characterized by knowledge, is both
valuable and challenging to replicate. Studies have demonstrated a positive correlation
between knowledge-based intangibles and performance (Kellermanns
et al., 2016; Davidsson & Honig, 2003).
Coff (1997) provided
moderate evidence supporting HC as a strategic resource. Contradictory findings
could be due to factors such as path dependence, the inability of
cross-sectional studies to capture delayed effects, and the efficiency of the
labor market for specific forms of HC (Coff, 1997).
Both general and specific
forms of human capital have been identified as influential in a startup’s
performance (Cressy, 1996; Gimmon & Levie, 2010).
Bosma et al. (2004) found that investments in general, industry-specific, and
entrepreneurship-specific human capital significantly enhance startup survival,
profitability, and employment.
Research is scarce on the
impact of initial intellectual capital on the performance and survival of new
ventures in emerging countries, particularly during the pre-seed phase and
within high-impact acceleration programs. Most existing studies focus on startups
in developed countries and specific industries (Azeem & Khanna, 2023). This
gap necessitates further validation across a broader spectrum of startups,
highlighting the importance of our study.
Intellectual capital,
comprising knowledge, skills, and experience, is vital to a startup’s success,
especially during the pivotal pre-seed phase. Current research insufficiently
covers this aspect, particularly for high-impact startups and those in acceleration
programs. Most studies center on startups in developed countries, creating a
knowledge void about startups in diverse geographical contexts. They also tend
to focus on specific sectors, failing to capture the broader startup ecosystem
(Andreeva & Garanina, 2016). Therefore, it’s
crucial to broaden the research scope to include startups from various
locations, sectors, and development stages for a holistic understanding of
intellectual capital’s impact on startup performance and longevity.
The
nature of the problem under study
Our research focuses on
startups, the drivers of innovation and economic growth. We aim to investigate
the impact of a startup’s initial financial resources and intangible assets,
collectively known as Intellectual Capital (IC), on its development during the
crucial pre-seed phase. This phase involves founders setting business
objectives, identifying potential hurdles, establishing market positions, and
devising strategic plans.
We particularly examine new
ventures in emerging markets that seek financial aid and expert advice through
accelerator programs. Our goal is to explore the correlation between
IC—encompassing the founding team’s skills, knowledge, experience, relationships,
and capabilities—and the survival rate of nascent for-profit ventures.
In this study, we define
“start-ups” and “nascent for-profit ventures” as organizations formed in
volatile environments to exploit new market opportunities. Our main objective
is to determine how a founder’s IC affects these ventures’ survival prospects.
Our key research question
is: “How does a start-up’s intellectual capital stock influence its economic
and financial performance in emerging countries?” Answering this question is
vital, as it underscores the critical role of IC in shaping a start-up’s future.
Hypothesis Statement
To validate our hypothesis, we utilize a Logistic
Regression Model. This model quantifies the impact of several factors on the
survival probabilities of startups in our sample and predicts their success
likelihood during the critical pre-performance phase.
We aim to deepen the understanding of Intellectual
Capital (IC) in driving startup success, particularly within accelerator
programs in diverse geographical and socio-demographic contexts of emerging
markets. The findings could significantly impact founders, investors, and
policymakers, aiding in the design of programs that effectively support and
nurture emerging ventures.
By ensuring startups are equipped
with the necessary resources and enriched with appropriate skills and knowledge
from the outset, we can significantly enhance their chances of success and
longevity in the competitive business landscape.
Materials and methods
This study examines the impact of initial Intellectual
Capital (IC) intangibles on the survival of startups in emerging countries
during their pre-performance phase. For this purpose, the startups analyzed
were part of a subset that applied to accelerator programs and were sourced
from the Entrepreneurship Database Program (EDP) using survey data.
The Sample
EDP Characteristics
This study analyzes data
from a global cohort of entrepreneurs who applied to impact-focused
accelerators between 2013 and 2019. The data,
collected by Emory University’s Entrepreneurship Database Program (EDP),
includes application details and biannual follow-up survey results. After
removing duplicates and incomplete surveys, the EDP compiled a dataset of
14,457 new for-profit ventures. These ventures applied to approximately 370
programs run by over 130 organizations, with half being based in the United States,
Mexico, India, and Kenya. For this study, we focus on a subset of 4,106
ventures (28.4% of the total) that operate in countries classified as
upper-middle-income by the World Bank, which we define as emerging countries.
The EDP collected data at
the application stage and a year later from both successful and unsuccessful
applicants. The surveys, split into two sections, contain 91 questions. The
initial section includes contact information, entrepreneurship details, impact
metrics, operating model, financing, founding partners’ characteristics, and
understanding of new venture accelerators’ benefits. The follow-up section
gathers information about entrepreneurship goals, impacts, financial and
operational details, financing, and involvement in new venture accelerator
programs. The application data offers preliminary insights into the ventures,
founding teams, and pre-program performance.
Key issues for ventures in the initial EDP sample
Our analysis of the EDP
sample, which includes 14,457 for-profit ventures from 164 countries, reveals a strong social orientation and success
biases. Ventures that have been operational for at least three years
show a survival rate of 31% at the time of application, with over half
generating revenue and 78% expanding their workforce beyond the founding
members. Notably, 58% of these companies operate on proprietary technology.
About one-third of these
ventures have secured external equity investment, while a quarter have taken on
debt for startup expenses. Philanthropic contributions support a larger
portion. Ventures led by female founders are less likely to secure equity investments
but have a higher likelihood of having positive revenues in the preceding year.
Over 10% of the ventures in the sample are directed by women.
Ventures led by experienced
entrepreneurs or those with previous company founding experience tend to
attract more equity investments and report revenues and employees in the
preceding year. Similarly, ventures with founders who hold patents, copyrights,
or trademarks also show a higher tendency to attract equity investments and
report revenues and employees in the preceding year.
However, as expected, the
sample may exhibit a selection bias, as program selectors often favor ventures
with more established records (Hallen et al., 2020). Participants in these
programs are significantly more likely to report revenues in the preceding year
(GALI, 2020; GALI, 2021).
Procedures,
variables, and models
We performed an Exploratory
Factor Analysis (EFA) to examine the initial operational conditions of startups
in our dataset. This analysis accounted for both country
and startup conditions. We aimed to understand the interplay between specific
economic conditions, the venture’s operations, and the initial distribution of
founders’ intangible intellectual capital.
Following the work of Bontis (1998) we sought
to identify these factors in our dataset and evaluate their impact on the
startups’ survival probabilities.
We chose thirteen variables
from two primary sources: the World Bank Development Indicators (WDI) and the
Entrepreneurship Data Program at Emory University (EDP). The WDI supplied four
variables that mirror the economic conditions of each country, including
broadband subscriptions, control of corruption, rule of law, and internet usage
as a percentage of the population (World Bank, 2023). The EDP provided nine
variables associated with the initial allocation of founders’ intellectual
capital intangibles. These variables included factors like the founders’
previous for-profit experience, the venture’s social media presence, and
ownership of patents, inventions, copyrights, and trademarks. Table 1 presents
the descriptive statistics for these variables.
Table
1
Descriptive Statistics for Variables in the Principal Components
Calculations
|
Factorable Variables |
Type |
Mean |
Std. Deviation |
|
Hast Twitter Account
(Y/N) |
Binomial |
0.40 |
0.49 |
|
Has LinkedIn Page (Y/N) |
Binomial |
0.31 |
0.46 |
|
Invention Based Model
(Y/N) |
Binomial |
0.58 |
0.49 |
|
Has Patents (Y/N) |
Binomial |
0.14 |
0.35 |
|
Has Copyrights (Y/N) |
Binomial |
0.14 |
0.34 |
|
Has Trademarks (Y/N) |
Binomial |
0.34 |
0.47 |
|
Broadband subscriptions
(per 100 people) |
Numeric |
14.27 |
13.01 |
|
Rule Law |
Numeric |
0.21 |
0.94 |
|
Corruption |
Numeric |
0.03 |
0.97 |
|
Internet (%
population using the Internet) |
Numeric |
54.87 |
27.75 |
|
F1FPEXP (Has FP
Experience) |
Binomial |
0.69 |
0.46 |
|
F2FPEXP (Has FP
Experience) |
Binomial |
0.57 |
0.50 |
|
F3FPEXP (Has FP
Experience) |
Binomial |
0.55 |
0.50 |
Notes: N: 14,457. Source: Own Elaboration
The analysis identified five primary factors (Table 2): F1 Country
Context (economic, infrastructure, and legal conditions), F2 Specific Human
Capital (founders’ entrepreneurial experience), F3 Social Capital (social
networks), F4 Structural Capital (patents or invention-based models), and F5
Market Rights (trademarks or copyrights). While product innovation often
results in patents and copyrights, the analysis initially differentiated
between Structural Capital and Market Rights. However, these two components can
be associated with Organizational Capital, which includes copyrights, patents,
procedures, rules, and decision-making aids (Abdulaali,
2018).
The test results confirm the suitability of the factor analysis. The
Composite Reliability Indices exceed the recommended threshold of 0.7. The
Kaiser-Meyer-Olkin measure, which evaluates the sample’s adequacy, achieved a
value of 0.70, surpassing the suggested minimum of 0.6. This suggests that the
sample is appropriate for factor analysis. Bartlett’s test for sphericity was
statistically significant, with a p-value less than 0.001. The five components
extracted, detailed in Table 2, account for 70% of the total variance. Notably,
Factors 2 through 5 align with the IC classification criteria according to
existing literature.
Table 2
Exploratory factor analysis (N=14,457)
|
Source |
Label/explanation |
F1 Country Context |
F2 HCS |
F3 SC |
F4 STC |
F5 Market Rights |
|
WB |
Broadband subscriptions (per 100 people) |
0.95 |
|
|
|
|
|
WB |
Control of corruption (WB estimate) |
0.95 |
|
|
|
|
|
WB |
Rule of Law (WB estimate) |
0.94 |
|
|
|
|
|
WB |
Internet (% population using the Internet) |
0.89 |
|
|
|
|
|
Emory |
Founder 1 For Profit Experience (Y/N) |
|
0.80 |
|
|
|
|
Emory |
Founder 2 For Profit Experience (Y/N) |
|
0.73 |
|
|
|
|
Emory |
Founder 3 For Profit Experience (Y/N) |
|
0.72 |
|
|
|
|
Emory |
Venture has a LinkedIn Page (Y/N) |
|
|
0.84 |
|
|
|
Emory |
Venture has Twitter Acct (Y/N) |
|
|
0.83 |
|
|
|
Emory |
Venture has Patents (Y/N) |
|
|
|
0.80 |
|
|
Emory |
Venture has an Invention-Based Model (Y/N) |
|
|
|
0.77 |
|
|
Emory |
Venture has Copyrights (Y/N) |
|
|
|
|
0.81 |
|
Emory |
The venture has Trademarks (Y/N) |
|
|
|
|
0.73 |
|
|
Eigenvalues |
3.72 |
1.90 |
1.38 |
1.21 |
1.01 |
|
|
Variance |
28.49 |
14.62 |
10.60 |
9.31 |
7.70 |
|
|
Cumulative Variance |
28.49 |
42.87 |
53.47 |
62.78 |
70.48 |
|
|
Composite Reliability Index |
0.96 |
0.80 |
0.82 |
0.76 |
0.75 |
Notes:
For interpretative purposes, variables with factor loadings below 0.5 were not
included in the report. Extraction method: Principal component analysis.
Rotation Method: Varimax with Kaiser normalization. Source: WB = World Bank
Development Indicators; Emory = Emory Entrepreneurship Database Applications
Surveys. Source: Own Elaboration
Variables
in the Logistic Regression Model
The two most frequently used non-financial startup
performance measures are survival (Brüderl, 1998; Mas-Verdú
et al., 2015; Wamba et al., 2017; Adams et al., 2019), and growth (Haeussler et
al., 2019; Vanderstraeten et al., 2016). To provide
empirical validation for our research question and working hypotheses, we
selected Survival as our dependent variable (DV), a binomial variable that
takes the value 1 if the startup remains in operation three years after its
inception and zero otherwise (Hyytinen et al., 2015).
Regarding our predictive
variables, we focus on the influence of the founding teams’ IC intangibles on
the startups’ likelihood of survival, as categorized by three types: human
capital, organizational or structural capital, and social capital (Bontis, 1998; Wang & Chang, 2005; Yang & Lin,
2009).
Our Logistic Regression model integrates the initial
Intellectual Capital endowments, utilizing principal components extracted from
an Exploratory Factor Analysis (Aguilera et al., 2006). The resulting variables
are “F2 SHC Experience”, which reflects the founding team’s for-profit venture
experience; “F3 SC Media Presence” (Bandera & Thomas, 2018), indicating the
entrepreneur’s network via LinkedIn and Twitter (Song & Vinig,
2012), and two components, “F4 STC Innovation” and “F5 Market Rights” (Alvarez-Salazar & Seclen-Luna,
2023), related to the venture’s Structural or Organizational Capital, covering
aspects like intellectual assets, databases, organizational culture, structure,
patents, and trademarks (Abdulaali, 2018).
New business survival
depends on three key categories: personal, business-specific, and environmental
factors (Brüderl et al., 1992). To study the impact
of a venture’s initial Intellectual Capital (IC) endowment, we consider the
inclusion of various factors such as external economic conditions, team
dynamics, and venture specifics. Recognizing their influence on survival
probability, we include these factors as control variables in our analysis.
The “F1 Country Context” factor includes
country-specific variables (Economic Context) derived from World Bank
Indicators (WBI). These variables reflect the national economic environment
faced by startups.
To examine the impact of alleviating financial
constraints, which indirectly reflects the quality of the founder’s team on
survival probabilities (Fuertes-Callén et al., 2022;
Lee & Zhang, 2011; Wamba et al., 2017), we include the categorical variable
“Has Debt.” This variable is coded as follows: 0 indicates that the venture did
not receive any debt financing; 1 indicates that the venture obtained debt financing
either at its inception or in the year following the application; and 2
signifies that the venture secured financing on both occasions. The “Has Debt”
variable is crucial for understanding how financial constraints and access to
debt financing impact the survival probabilities of startups. The presence of
debt financing can indicate a startup’s financial health and its ability to
secure external funding, which positively influences survival probabilities.
Access to debt financing provides necessary resources for growth, operational
costs, and financial challenges, enhancing survival chances, especially in
early stages. It also indirectly reflects the quality and credibility of the
founder’s team, as lenders typically assess the team’s experience, skills, and
business plan before extending credit. Additionally, debt financing offers
operational flexibility, allowing startups to make strategic investments and
respond to market changes more effectively, which is crucial for long-term
survival and success.
Business accelerators significantly contribute to the
facilitation of new venture creation. Start-ups seek top-tier accelerators to
expedite their developmental journey (Salamzadeh
& Markovic, 2018). These accelerators provide early-stage funding
and essential mentorship, driven by their confidence in the startup’s
potential, personal interest in the concept, or admiration for the
entrepreneurial team (Radojevich-Kelley &
Hoffman, 2012). The characteristics of accelerator programs are known to
influence venture performance. Although we cannot explicitly measure specific
accelerator characteristics, we will account for program-specific unobserved
heterogeneity in our subsequent analyses, thereby indirectly accommodating
program differences.
The EDP examines two key variables related to
accelerator program characteristics: participation, which refers to ventures
that completed the program (GALI, 2021), and the program impact area,
indicating whether the program has a specific impact area (Lall, Chen, &
Roberts, 2020). Additionally, the analysis includes the use of an impact
measure, specifically the B Lab GIIRS (Global Impact Investing Rating System),
which assesses the social and environmental impact of companies and funds.
Impact measures, such as the B Lab GIIRS, are crucial for startup survival as
they assess the social and environmental impact of companies. These measures
help startups attract impact investors, enhance their credibility, and align
with sustainable business practices, leading to increased funding
opportunities, customer loyalty, and long-term success.
Table 3 presents the descriptive statistics
for the variables included in the logistic regression model.
Table 3
Descriptive Statistics for Variables in the LR Model
|
Source |
Variable/Label |
Type |
Mean |
Std. Dev. |
Min. |
Max |
|
EDP Coded |
DV Success
(Survival) |
Binomial |
0.3 |
0.46 |
0 |
1 |
|
EDP Factored |
F2 SHC (Experience) |
Numeric |
0.28 |
1.00 |
-3.09 |
1.81 |
|
EDP Factored |
F3 SC (media presence) |
Numeric |
-0.19 |
0.97 |
-3.61 |
2.68 |
|
EDP Factored |
F4 STC (Innovation) |
Numeric |
0.195 |
1.00 |
-2.57 |
4.50 |
|
EDP Factored |
F5 (Market
Rights) |
Numeric |
-0.24 |
0.96 |
-4.47 |
2.72 |
|
WBI Factored |
F1 Country Context |
Numeric |
-0.04 |
0.37 |
-3.48 |
1.20 |
|
EDP Coded |
Has Debt |
Categorical |
0.19 |
0.54 |
0 |
2 |
|
EDP Survey |
participated# program
impact area |
|||||
|
0 1 |
Binomial |
0.27 |
0.44 |
0 |
1 |
|
|
1 0 |
0.14 |
0.35 |
0 |
1 |
||
|
1 1 |
0.05 |
0.23 |
0 |
1 |
||
|
EDP Survey |
use impact measure |
Binomial |
0.06 |
0.25 |
0 |
1 |
Notes:
Binomial Variables are assigned a value of 1 when present and 0 when absent.
For Debt Presence, the categorical values are assigned as follows: a value of 1
is given if an investment is present either at inception or at the end of the
last calendar year, a value of 2 is given if an investment is present at both
inception and the end of the last calendar year, and a value of 0 is given in
all other cases. Source: Own Elaboration
Estimation
Techniques
For the validation
of our hypothesis, we use a Logarithmic Regression model (LR) which is
considered suitable when the response variable is dichotomous, and the effect
of predictors is linear. Our LR Model relies on the reduced form model: 
Where 
is the expected
value of 
given
(Aguilera et
al., 2006), In this case is the
probability of survival as a function of a set of available information about
the ventures. The analysis of the effects of the dimensions of IC over of
success as they´re operationalized requires a technique that adequately manages
the probabilities of attaining a successful performance. Logistic regression
(LR) is the appropriate technique when dealing with the relationship between a
dichotomous outcome and a set of explanatory variables. When a LR model estimates
a binary response outcome, we assume that its logit transformation has a linear
relationship with the predictor variables. Thereby the relationship between the
response variable and its covariates is interpreted through the odds ratio from
the parameters of the models. Measured in odds ratio (OR), if the parameter in
the regression is positive, the OR>1, and if its negative OR<1,
indicating the effect of the IV over the chances of survival. The logistic
regression model can be written as in equation 1:
= 
…
Equation 1: LR Model
The binary response variable 
being either 0
or 1, and 
.Then 
is interpreted
as 
for a given
combination of values of the predictor variables 
. We express the model as: 
, where 
, could only assume two values depending on whether is equal to
zero or one. The left-hand side of equation (1) is the log odds ratio, that is,
the logarithm of the odds that will equal 1,
for a given combination of the predictor variables. Our estimation uses the
Maximum Likelihood (ML) method.
The Proposed Model
To validate our hypothesis,
the following logistic regression model (LR) is proposed.
Equation 2: Proposed LR Model
Results from the LR model and discussion
Academic literature
identifies various factors influencing the survival of new companies, operating
in emerging markets, in their pre-performance phase (Strotmann, 2007; Santisteban &
Mauricio, 2017). Our research focused on understanding the impact of
recognized intangible Intellectual Capital (IC) dimensions on this survival. We
integrated control variables into our Logistic Regression (LR) model to
mitigate potential confounding effects. This method aimed to distinctly assess
the influence of IC dimensions on Survival Probabilities, thus facilitating the
effective validation of our working hypothesis.
Table 4 shows the results
of the Logistic Regression (LR) Model, which we used to test our hypothesis.
This hypothesis proposes a positive link between the initial Intellectual
Capital of the founding team and the performance of the startups operating in
emerging countries in our sample.
Table
4
LR Model
Results for Startups in Emerging Countries
|
(DV) Survival |
Odds |
Robust |
z |
P>z |
[95% Conf |
Interval] |
|
Ratio |
SE. |
|||||
|
IC Components |
||||||
|
F2 SHC (Experience) |
1.09 |
0.04 |
2.37 |
0.018** |
1.02 |
1.17 |
|
F3 SC (media presence) |
1.13 |
0.05 |
3.10 |
0.002** |
1.05 |
1.23 |
|
F4 STC (Innovation) |
1.31 |
0.06 |
6.32 |
0*** |
1.20 |
1.42 |
|
F5 (Market
Rights) |
1.11 |
0.04 |
2.73 |
0.006*** |
1.03 |
1.20 |
|
Control Variables |
||||||
|
The Business Environment |
||||||
|
F1
Country Context |
2.10 |
0.31 |
4.96 |
0*** |
1.57 |
2.81 |
|
Finances |
||||||
|
Has Debt |
2.01 |
0.13 |
11.01 |
0*** |
1.77 |
2.27 |
|
Program Characteristics |
|
|
|
|
|
|
|
Interaction Effects Participated
#Has program impact area |
||||||
|
0 1 |
1.26 |
0.11 |
2.62 |
0.009*** |
1.06 |
1.49 |
|
1 0 |
0.97 |
0.11 |
-0.24 |
0.81 |
0.78 |
1.21 |
|
1 1 |
2.19 |
0.33 |
5.17 |
0*** |
1.62 |
2.94 |
|
use impact
measures |
1.08 |
0.17 |
0.47 |
0.64 |
0.79 |
1.47 |
|
constant |
0.31 |
0.02 |
-21.36 |
*** |
0.28 |
0.35 |
Notes: ***p < .01, **p < .05, *p <
.10; Robust SE = Robust Standard Errors
Source: Own Elaboration
The resulting LR Model
The model’s results, expressed in terms of
odds ratios (expb), are as follows:
Equation 3: Estimated LR Model
The logistic regression model demonstrates a good fit
with the data, as indicated by the Wald chi2(10) value of 298.93 (p-value =
0.0000), suggesting overall statistical significance. The model explains
approximately 7.04% of the variance in the dependent variable (Success), as
reflected by the Pseudo R2 value. Key variables significantly impacting startup
survival include Has_Debt (Odds Ratio = 2.0074,
p-value = 0.000), (F1) Country Context
(Odds Ratio = 2.0986, p-value = 0.000), (F2) SHC (Odds Ratio = 1.0911, p-value = 0.018), F3 SC
(Odds Ratio = 1.1323, p-value = 0.002), F4 STC (Odds Ratio = 1.3101, p-value =
0.000), and Market Rights (F5), (Odds
Ratio = 1.1116, p-value = 0.006). Interaction terms show that participating in
a program with a specific impact area significantly increases the odds of
success (Odds Ratio = 2.1852, p-value = 0.000).
The analysis of odds ratios reveals several
significant variables impacting startup survival. At the 0.01 significance
level, Innovation (F4 STC), Market Rights (F5), (F1) Country Context, Has_Debt, and the interaction
effect of participating in a program with a specific impact area (1 1) are
significant. At the 0.05 significance level, Experience
(F2 SHC) and Media Presence (F3 SC) are significant. Additionally, the
interaction effect of participating in a program without a specific impact area
(0 1) is significant at the 0.01 level. These findings underscore the
importance of innovation, market rights, country context, financial health, and
targeted accelerator programs in enhancing startup survival.
By employing a Logistic Regression (LR) model, we were
able to measure the influence of various independent variables on the survival
probabilities of startups in the EDP sample. This also resulted in a predictive
model that can be used to determine the likelihood of success for a startup
during its pre-performance phase. The model correctly classified 72.4% of the
companies that survived. It had a sensitivity of 18.83%, indicating its ability
to correctly identify successful startups, and a specificity of 94.97%,
reflecting its ability to correctly identify unsuccessful startups. The
probability cutoff point was approximately 0.28. The area under the ROC curve
(0.6776) indicates a fair level of discrimination between successful and
unsuccessful startups
Discussion
Our analysis underscores the pivotal role of project
financing in shaping startups’ survival probabilities. Specifically, easing
financial constraints significantly increases a startup’s survival odds in the
pre-performance phase. Cooper et al. (1994) demonstrated that the total amount
of capital invested by the time of the first sale positively impacts the growth
and survival of new ventures. The literature widely acknowledges the
significance of startup capital for fledgling firms in their early stages (Cabral
& Mata, 2003; Figueroa-Armijos, 2019). Our empirical evidence suggests that
the most impactful control factor is the easing of these financial constraints.
During the pre-performance phase, ventures that have procured financing through
debt agreements (Has Debt) at inception, in the preceding year, or both,
demonstrate a 100% increase in survival likelihood (odds ratio 2.1) compared to
those constrained by external financing. These results align with other studies
that recognize the positive effect of debt financing on longer survival times
and higher revenues (Cole, 2018).
Our research highlights the significant role of
accelerator programs, particularly those with a well-structured curriculum, in
enhancing the survival probabilities of startups. We found a non-linear
interaction effect between variables indicating program completion and focus on
a specific impact area. Notably, when both indicators are present, the survival
probabilities of the venture increase by 119%. This is consistent with the
findings of Venâncio and Jorge (2022), who found that accelerated startups have
higher external equity ratios than non-accelerated startups, enhancing their
survival and growth probabilities. These findings also relate to the positive
effect of the entrepreneurial focus of EDP’s participating accelerator programs
on high-impact projects (Lall et al., 2020; GALI, 2021). The use of impact
measures increases the probabilities of survival by 8%, consistent with the
studies of Silva et al. (2022).
The study reveals that the wider economic environment,
as measured by WDI variables, increases survival probabilities by 110%. While
emerging countries are well-represented in the sample, the EDP primarily
reflects the entrepreneurial ecosystem in developed nations, particularly the
United States. This finding highlights the significant influence of the overall
economic context, suggesting that future research should explore the
operational dynamics of startups and accelerators across different country groups.
After evaluating the influence of both external and
internal variables from the EDP sample, we now turn our attention to the impact
of initial IC inventories on startup survival probabilities. These inventories
encompass three dimensions: Human Capital, Social Capital, and Structural or
Organizational Capital. Our primary focus is to validate, in our sample, the
hypothesis that the initial intangible IC assets of founding teams enhance
survival probabilities during the pre-performance phase.
According to our estimates, the SC assets identified
by the F3 SC (media presence) factor increase survival probabilities by 13%.
This result is generally consistent with the studies of Bandera and Thomas
(2018) and specifically aligns with the findings of Song and Vinig (2012), who associate positive performance in the
initial stages of ventures with expanded social media networks, particularly on
LinkedIn and Twitter.
The Structural or Organizational Capital (STC)
identified as F4 STC (Innovation) in the EDP sample, as indicated by variables
such as patent ownership and the adoption of invention-based models, increases
survival probabilities by 31%. This finding aligns with existing studies and
the fact that most ventures in the sample (58%) adopt an innovative-based
business model (GALI, 2020). It also corresponds with the natural selection
bias of acceleration programs in our sample (Radojevich-Kelley
& Hoffman, 2012), aligning with the idea that most accelerators want
concepts that have large upside potential that can be scaled to meet national
or global demand (Hallen et al., 2020).
In line with the F5 factor (Market Rights), owning
copyrights and trademarks enhances a venture’s survival chances of 11%. This
aligns with Abdulaali’s (2018) findings, which
highlight the positive impact of structural capital components on survival. It
also reflects the sample composition, where 33% of the ventures studied possess
trademarks and 13% have copyrights.
The founding team's experience in creating previous
for-profit ventures significantly influences survival probabilities, increasing
them by 9%. Prior studies have highlighted the capabilities that founders bring
to a venture due to their previous knowledge and experience, such as education
and industry experience (Bosma et al., 2004; Brüderl
et al., 1992; Geroski et al., 2010).
After incorporating control variables, empirical
evidence validated the general working hypothesis, demonstrating the positive
impact of the three dimensions of Intellectual Capital (IC) as recognized in
the existing literature and identified in the EDP sample. These dimensions,
represented by the intellectual capital assets of the founding teams,
significantly influence survival probabilities during the pre-performance
phase.
Our findings suggest several
implications for accelerator programs. First, accelerators could refine their
selection criteria to prioritize startups with strong intellectual capital
(IC), enhancing the likelihood of selecting ventures with higher survival
rates. Tailored support services, such as specialized training and mentorship,
could further develop the IC of participating in startups. Effective resource
allocation, focusing on educational workshops, can also be crucial.
Additionally, adopting new performance metrics to track the development of
founders’ skills and knowledge over time could better capture the impact of IC
on startup success. Accelerators might also advocate for policies supporting IC
development and consider expanding their programs to emerging markets, thereby
fostering a more inclusive global startup ecosystem.
Conclusions
This research builds on previous studies to examine
the influence of initial Intellectual Capital (IC) on the survival
probabilities of new ventures in accelerator programs operating in emerging
countries. It confirms the beneficial impact of IC assets on their survival, a
crucial success indicator for new ventures in the pre-performance phase, as
highlighted in broader contexts by Unger et al. (2011) and Sardo and Serrasqueiro (2019).
While most existing studies on startup survival have
been confined to advanced economies and have employed a single theoretical
approach (Azeem & Khanna, 2023), this research bridges the gap by
highlighting the significance of initial IC for startups in impact accelerator
programs in emerging countries. It shows that these IC accumulations,
encapsulating useful and applicable knowledge identified in surveys, enhance
the survival prospects of startups in the EDP-focused sub-sample. This
enhancement remains even after accounting for potential unobserved
heterogeneity and considering both external and internal influences on startup
operations. By examining the role of initial IC in a diverse range of
early-stage ventures in acceleration programs, the research expands our
understanding of startup survival determinants and broadens its scope to
encompass emerging economies and diverse accelerator programs.
Our findings are not just a result but a starting
point for further research. Future studies using the EDP information will
analyze the impact of knowledge intangibles on survival probability under
specific conditions, including socio-demographic coverage, heterogeneity of
acceleration programs, founding team composition, diversity, operational
sectors, size, funding source diversity, and differentiated effects. They will
also consider the contribution to the development of the countries where they operate,
and the innovation processes generated from their operation.
Additionally, there is a significant opportunity for
complementing this research. Due to the scarcity of studies analyzing the
mediating and moderating effects of IC components on performance, future
research will consider these complementary effects (Delmar & Shane, 2006),
particularly the relationships between human capital and financial constraints
as suggested by the research of authors such as Unger et al. (2011) and Salamzadeh et al. (2023). The lack of interaction studies
in this area highlights a gap in the literature, presenting a valuable avenue
for future exploration to better understand these dynamics.
Implications of the study
By implementing these practical strategies, startup
founders can better navigate the challenges of the initial stages and improve
their chances of long-term success. Our study underscores the importance of
early recognition and investment in key factors such as Intellectual Capital
(IC), financial planning, and participation in accelerator programs for startup
founders. By prioritizing the development of IC assets, including the knowledge
and skills of the founding team, startups can build a solid foundation for
navigating the early stages. Securing diverse funding sources and establishing
clear financial goals from the outset can further mitigate financial
constraints and improve survival probabilities.
Engaging in well-structured accelerator programs
offers valuable resources, mentorship, and networking opportunities, enhancing
the startup’s resilience and performance. Our research contributes to this
understanding by demonstrating that these elements significantly influence
startup survival, particularly in diverse and globally oriented contexts. By
recognizing and leveraging these critical factors early on, founders can better
position their ventures for long-term success and growth, adapting to challenges
and capitalizing on opportunities as they arise.
For policymakers and accelerator program developers,
our findings provide valuable insights for refining selection processes and
program development. Leveraging data from the EDP and the predictive strength
of our Logistic Regression (LR) model, we offer preliminary guidance for
creating more effective mentoring and support initiatives. The clear
distinction between the values of specificity and sensitivity in our LR model
indicates that survival and failure probabilities are independent entities,
challenging traditional assumptions and uncovering new research opportunities.
Further investigation into these factors can enhance the efficacy of support
initiatives, benefiting the startup ecosystem by fostering more resilient and
successful ventures.
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