Carlos Eduardo Canfield
Universidad Anahuac, Mexico
E-mail: carlos.canfield@gmail.com
Elvira Carlina Anzola
Universidad Anahuac, Mexico
E-mail: elvira.anzola@anahuac.mx
Submission: 20/03/2018
Revision: 02/04/2018
Accept: 06/04/2018
ABSTRACT
The mobilization of social resources for
addressing urgent societal needs under market assumptions is a major component
of the strategy for development. Social
enterprises as an alternative source of public goods and services attract the
attention of academics, practitioners and policy-makers to the efficient use of
entrepreneurial resources. Initially this study aims to provide a more
systematic understanding about the factors that affect the probabilities of
success of socially oriented undertakings and contributes to the literature by
answering the call for more empirical research about such effects over their
performance. Using a logistic regression model on data from a sample of
socially oriented ventures in 148 countries participating in the 2013-2016
Entrepreneurship Database Program at Emory University, the positive effects of
such factors were first validated. At a later stage, this quest attempted to
find differential behaviors of these effects by comparing operations in OECD
and developing countries. No conclusive evidence for dissimilarities between
groups was found. This result could be partially attributed to the
accelerator´s selection processes favoring companies with a proven record.
Important global policy implications are drawn in support of harmonized
social-entrepreneurship promotion programs and the adoption of standardized
impact measurement criteria. This argument raises ample academic and practical
possibilities for investigating the impact of socio-economic and cultural
influences on the efficacy of social enterprise´s interventions. After
controlling for the efficient use of entrepreneurial resources, teams made-up
of civil society organizations, businesses and government institutions can
allocate their attention to those country-specific situations affecting the
efficacy of development programs such as the problems to be solved, the
particularity of the eco-systems and the adequacy of the organizational arrays
adopted.
Keywords: Social Enterprises, Success
Factors, International Comparative Study, Global Accelerator Learning
Initiative, Logistic Regression
1. INTRODUCTION
The study of social
entrepreneurship (SE) as a mean to
address relevant societal problems in a market environment, has focused the
attention of practitioners, policy-makers and scholars in both developed and
developing countries (Brooks, 2009; Seelos; Mair, 2007
; Tracey; Jarvis, 2007; Chell et al., 2010;
Defourny; Nyssens,
2010; Wang, et al., 2015).
Despite the importance and growing popularity of this
topic, academics and practitioners have not reached a consensus on the meaning
of SE. Authors such as Choi and Majumdar (2014) argue that this conceptual
disagreement derives from the fact that social entrepreneurship is an
essentially contested concept, where many competing definitions exist and no
unifying conceptual framework of SE
has emerged. Many scholars believe that lacking a unified concept of
social entrepreneurship limits the theoretical advancement in the field (Mort et al., 2003;
Nicholls, 2010; Short et al., 2009).
Nicholls (2010)
considers that given the early stages of the research, the definition of social
enterprises and the SE domain have
not been established. Mair and Marti (2006)
make the case that the study of social entrepreneurship has been mainly
anecdotal and case driven, whereas Lepoutre et al.( 2013) argue that extant
quantitative research does not utilize a consistent definition or yield from
one large dataset that allows for a detailed empirical analysis of individual
drivers and antecedents of SE.
On the practitioners´ side, a wide array of SE promoting activities can be found.
Organizations such as Ashoka, the Skoll Foundation, and the Schwab Foundation
actively promote social entrepreneurship by highlighting the achievements of
individual social entrepreneurs (Dacin et
al., 2010).
Governments also support SE by establishing new organizational frameworks, ranging from
profit to non-profit, in order to encourage the formation of new SE initiatives and by providing in many
instances, funding to these projects. Universities have set up a great number
of social entrepreneurship centers and new scientific journals on social
entrepreneurship, social enterprise, and social innovation have been launched.
Also, the number of conferences and special issues in scientific journals
devoted to the topic has increased significantly (Choi; Majumdar, 2014).
On the subject of the specificity of social
enterprises, Defourny and Nyssens, (2010) deem that their cross-country and
regional singularities reside in the fact that their creation and their mode of
survival vary according to the socio-cultural tradition of each society. It has
been stablished in the literature that socioeconomic conditions shape the
development of social enterprises internationally, therefore they are created
to meet specific needs of that society by mobilizing diverse economic and social
resources and through interaction between different actors (Bacq; Janssen, 2011; Chell et al., 2010; Kerlin, 2010).
In this line of argument, with the aid of a logistic
regression model estimated over a rich data-set provided by the
Entrepreneurship Database Program at Emory University; supported by the Global
Accelerator Learning Initiative (GALI), initially the object of the present
study is to provide a more systematic understanding of the factors known to be
conducive to success in social enterprises across the world; and further, based
on additional empirical analysis, this search attempts to find differential
performance determinants originated by the specific socio-economic and
geographic divergences of the factors affecting the probability of success in a
sample of socially oriented ventures that graduated from accelerator programs,
in both OECD and developing
countries. Initially, the factors of success considered for the analysis derive
from the work of Sharir and Lerner (2006) with social ventures operating in
social settings in Israel and are further adapted to the specific conditions of
both the sample and the information collected in the Entrepreneurship Database
Program at Emory University in the 2013-2016 periods.
The two main questions posed in this research
are: What are the general factors
affecting the probability of success in socially oriented ventures that
participated in accelerator programs in our sample in 2013-2016? And, if a
differential success behavior regarding those factors exists in companies
operating in OECD or developing
countries?
The British Department of Trade and Industry (DTI),
defined social enterprise -a term that encompasses different types of arrays
and organizations- as a business with primarily social objectives whose
surpluses are principally reinvested for that purpose in the business or in the
community, rather than being driven by the need to maximize profit for
shareholders and owners (D.T.I., 2002).
Following Kerlin (2010), this investigation broadly considers
a socially oriented venture (SOV) as
an entity that uses nongovernmental market-based approaches to address social
issues, therefore providing a ‘‘business’’ source of revenue for many
types of socially oriented organizations and activities. In the sample under
study, SOV’s are market-oriented
businesses attempting to solve societal problems that i) have participated in
the 2013-2016 Emory University Database, ii) have expressed both a social
motive, and a social impact area for their creation by their founders and iii)
their ratio of philanthropic to total funding does not exceed 10%, thus relying
heavily on debt and equity backing.
2. LITERATURE REVIEW AND HYPOTHESES STATEMENT
As the subject of this
research, the study of SOV´s that
grow from accelerator programs around the world is framed under three settings:
The first one is a well-documented lack of a unified social venturing
framework, that fosters the use of more conventional entrepreneurship theory in
its understanding (Short et al. 2009; Zahra et al., 2009; Dacin et al., 2010).
The second is the evolution of social enterprises away
from institutional forms that focus on broad frame-breaking and innovation to a
narrower focus on market-based solutions and businesslike models, in alignment
with societal norms and expectations (Dart,
2004), situation that is favoring the generation of earned revenue from its
activities (Boschee;
McClurg, 2003; Alter, 2006; Lepoutre et al., 2013) and third, the arguments made around the
notion of social entrepreneurs as individuals in pursuit of opportunities with
emphasis in promoting social value and development (Chell, 2007; Mair; Marti, 2006); that
at the same time exhibit risk tolerance (Stevenson;
Jarillo, 1990; Lurtz; Kreutzer, 2017), decline to accept limitations,
use their resources efficiently to fulfill their activities (Peredo; McLean, 2006), and display a
heightened sense of accountability to the constituencies served and for the
outcomes created (Dees, 1998).
2.1.
Performance
measurement
The present research is quite aware of the ambiguities
and complexities of measuring SE
performance. The main goal of social enterprises is to create social value, yet
the challenge of measuring social change is great due to non-quantifiable,
multi-causal, temporal dimensions, and perceptive differences of the social
impact created (Austin; et al., 2006).
In the literature many approaches to measuring results
with respect to social, environmental, and economic impacts can be found (Arena et al., 2015). As a part of this vast approaches´ array, the
following two general categories can be identified: Based on sustainability, Social Return on Investment (SROI) is extensively applied in various
settings (Aeron-Thomas;
et al., 2004; Millar; Hall, 2013; Rotheroe; Richards,
2007; Ryan; Lyne, 2008).
Impact
Investment is a more recent approach to measure social
performance, and has been successfully used to increase funding. It can be
broadly considered as the mobilization of capital for investments intended to
create positive social impact beyond financial return (Jackson, 2013).
Built on the idea that impact measurement demonstrates
an investor’s true intent to have a positive social impact, this nascent
assessment industry has established different initiatives to develop a solid measurement
standard for the benefit of both investors and investees (GIIN, 2014).
Many success instances of the positive effect of the
use of Impact Investment can be found in the literature. Bugg-Levine et
al., (2012) pose as an example that loan
guarantees rather than direct loans help leverage private donations and reduce
the cost of debt as it was the case of a charter school in Houston that saved
10 million dollars in interests paid by having a loan guarantee by the Gates´
Foundation; or the social bonds launched in 2010 in the UK, that will only
repay interest if the social project succeeds.
Various impact measurement standards can be found
nowadays: As an example, the Impact Reporting and Investment Standards (IRIS)
project which provides a common set of definitions and terms for the field; The
Global Impact Investing Rating System (GIIRS), an analogue of the Standard and
Poor’s or Morningstar rating systems, that uses a common set of indicators to
measure the social performance of funds and companies that intend to create
impact (Jackson, 2013).
There are searchable online databases for the purpose
of sourcing investment products (ImpactBase, 2017) and renowned
universities such as Columbia University, have launched impact investing
initiatives (Höchstädter; Scheck, 2015).
2.2.
The
effects of socio-economic and geographical conditions over the factors
affecting the probability of success in social ventures:
Despite the above-mentioned lack of consensus around
the social entrepreneurship domain, authors such as Chell et al. (2010) pose that the central driver for
social entrepreneurship is the social problem being addressed in an innovative
and entrepreneurial way. Besides innovation, the emphasis now is in the particular
form of organization of the social venture. Austin et al. (2006) propose that
the entrepreneurial opportunity must effectively mobilize the resources needed
to solve societal problems therefore at times where philanthropic resources are
scarce and financial crises tend to translate government resources into
liquidity restoration programs, the focus is now on the financial
sustainability of the social enterprise (Aeron-Thomas
et al., 2004).
Entrepreneurship is a matter of recognizing and taking
advantages of opportunities. On one hand, as it’s the case of the so-called conventional-entrepreneurs, they find
and seize opportunities and transform them into economic value (Helfat;
Lieberman, 2002), on the other, social entrepreneurs find innovative solutions for social problems
and attempt to efficiently solve them in market conditions.
Zahra et al. (2009) propose
that globally, social founders take different approaches to recognizing an
entrepreneurial opportunity, therefore arrays deriving from these differences
might yield diverse results. Chell et
al. (2010) posed that the interaction of
the demand of public services by society, the supply of solutions to social
problems and their specific context and legal framework have an effect on the
development of social enterprises in different parts of the world.
Kerlin (2010)
analyzed regional differences of social enterprises, favoring the claim that
existing social structures and institutions shape and dictate the options
available for the development of social enterprise, leading to different
organizational models in different areas. Defourny and Borzaga (2001) studied
social enterprises in fifteen European countries finding variations attributed
to a number of systemic factors, among them: the level of development of the
economic and social structures; the characteristics of the welfare schemes and
of the traditional third sector; and the development of the countries´ legal
frameworks.
2.3.
Critical
success factors: looking for differential success behaviors in social ventures
Critical Success Factors (CSFs) have several potential
uses for any type of venture (Wronka,
2013). Based on the notion of the Pareto´s empirical principle (20/80 rule),
these CSF account for the majority of the determinants of a successful
enterprise. Rockart (1979, p. 85) defined CSFs as the limited number of areas
in which results, if satisfactory, will ensure successful competitive
performance for the organization.
On the same venue, other authors such as Lynch (2003)
describe them as the resources, skills and attributes of an enterprise that are
essential to deliver success; moreover, Bruno,
Leidecker and Harder (1987), considered them as the characteristics,
conditions and variables responsible for the organization´s success.
Various studies analyze the effect of the CSFs on
private enterprise performance (Gunasekaran et al., 2005; MOUZAS; ARAUJO, 2000; HO; LIN,
2004); and on Public-Private Partnerships
(LIU et al., 2014). The particular case of the effect of such
factors on social enterprises, were extensively examined by researchers Sharir
and Lerner (2006) on ventures operating in Israel. Their study showed eight
dimensions that contributed to the explanation of social entrepreneurial
success.
These dimensions
were: i) the entrepreneur’s social network; ii) total dedication to the
venture’s success; iii) the capital base at the establishment stage; iv) the
acceptance of the venture idea in the public discourse; v) the composition of
the venturing team, including the ratio of volunteers to salaried employees;
vi) forming co-operations in the public and nonprofit sectors in the long-term;
vii) the ability of the service to stand the market test; and viii) the
entrepreneurs’ previous managerial experience.
For the present investigation, these dimensions would
be adapted to both the nature of the sample and the specificity of the data
collected from the survey questions and used in the hypothesis validation
phase. At first, the proposed variables would be analyzed in the sample as a
whole in order to test their pertinence and then separately in groups formed by
OECD and developing countries SOV’s. This last stage would allow us to
gain additional insight about possible socio-economic and geographical
differential behaviors in both groups that could hinder the efficiency of social
enterprise´s interventions, particularly in developing countries.
2.4.
Hypotheses
statement
With respect to the first research question
established in this study, based on the literature, it is believed that the
factors considered to influence success in social enterprises have a positive
effect over the performance of socially oriented ventures graduating from
accelerator programs in the sample under analysis. For that matter, seven of
the eight success dimensions in the investigation of authors Sharir and Lerner
(2006) would be tested for their positive incidence over the probability of
success of the SOV’s in the whole
sample. The resulting null hypotheses are shown in Table 1
Table 1: Research hypotheses related to the effect of success
factors over the probability of venture´s success in the whole sample
Null
Hypotheses |
Factors |
Effect over the probability of success |
H1 |
The strength of the
entrepreneur’s social network |
Exists and increases the probability |
H2 |
The dedication
to the venture’s success by the founders |
Exists and increases the probability |
H3 |
the strength of the capital base at the
establishment stage |
Exists and increases the probability |
Table 1 Continued |
|
|
H4 |
the acceptance
of the venture idea in the public discourse |
Exists and increases the probability |
H5 |
the
composition of the venturing team |
Exists and increases the probability |
H6 |
the ability of
the service to stand the market test |
Exists and increases the probability |
H7 |
the
entrepreneurs’ previous managerial experience |
Exists and increases the probability |
Note: The alternative hypotheses Ha are defined as
not Ho
As per the second research question, the study wants
to validate the existence of a differential success behavior between SOV’s operating in OECD and developing
countries as it relates to factors having a positive effect on their success.
The resulting null hypotheses are exhibited in Table 2.
Table2: Research hypotheses related to the differential effect
of success factors over SOV´s operating in OECD and developing countries.
Null
Hypotheses |
Factors |
Effect over the probability of success |
H1A |
The strength of the
entrepreneur’s social network |
Have the same positive effect on both groups |
H2A |
The dedication
to the venture’s success by the founders |
Have the same positive effect on both groups |
H3A |
the strength of the capital base at the
establishment stage |
Have the same positive effect on both groups |
H4A |
the acceptance
of the venture idea in the public discourse |
Have the same positive effect on both groups |
H5A |
the composition
of the venturing team |
Have the same positive effect on both groups |
H6A |
the ability of
the service to stand the market test |
Have the same positive effect on both groups |
H7A |
the
entrepreneurs’ previous managerial experience |
Have the same positive effect on both groups |
Note: The alternative hypotheses Ha are defined as
not Ho
3. MATERIALS AND METHODS
As stated above, the objective of the present research
is to empirically investigate the effect of factors known in the literature
(SHARIR; LERNER, 2006) to be conducive to good venture performance in a sample
of SE´s that evolved from accelerator programs around the world. Specifically,
this analysis attempts to measure the magnitude and orientation of such
mentioned effects over the probabilities of success of SE´s under study.
For that matter, entrepreneurial data was gathered
through the Entrepreneurship Database Program at Emory University since 2013
and up to 2016 (GALI, 2017). This program
collected data from individual ventures during their application process at
contributing accelerators, and then entrepreneurs were resurveyed every six
months to gather follow-up data. The questions in the survey were structured
around four themes: i) Focus and goals; ii) structure and acceptance rates;
iii) funding sources and; iv) services and direct investment (GALI, 2017).
3.1.
The
sample
The 2013-2016 databases contain information from 8,666
early-stage ventures. Given the orientation of the accelerator partners,
roughly 80% are for-profit organizations. As it can be expected, the sample
exhibits a strong bias due to the venture selection process in accelerating
programs, that is, the sample reflects a strong orientation towards success in
its composition, because they encourage participation of enterprises with an established
track record, therefore applicants that end up participating in programs are
significantly more likely to report revenues in the prior year (GALI, 2017, p.
2).
Around 16% of the businesses report receiving prior
outside equity investment, and a little less report receiving debt and
philanthropic investments. Interestingly enough, less than half of the ventures
report positive revenues in the prior year, while almost two-thirds report
having at least one full-time or part-time employee at the end of that year
(GALI, 2017).
Based in the known features of the sample and using
the following broad definition of Socially
Oriented Ventures as market-oriented businesses attempting to solve
societal problems, a sub-sample is constructed using the following conditions:
i) For-profit enterprises that have participated in the 2013-2016 Emory
University Database, ii) have expressed both a social motive, and a social
impact area for their creation by their founders and iii) their ratio of
philanthropic to total funding does not exceed 10%, thus relying heavily on
debt and equity backing.
From the original 8,666 businesses, the analysis
collected information from 4,976 ventures on 148 nations, 44% of them operating
in OECD countries. As expected, the conformed sub-sample exhibits the same bias
as the original one, with respect to the effect of the proven track record as a
pre-requisite to participate in the acceleration programs. That is, 24% of
these ventures have been in operation for at least three years; 52% of them
reported having generated revenues from their operation since its inception and
60% having at least one employee beside the founders.
3.2.
The
operationalization of success factors
The present research is interested in validating
factors considered in the literature to have an influence over success in
social enterprises and at the same time, match the features of the ventures in
our sample with the information provided by the survey.
The choice of a suitable and practical definition of
success in the sample is a crucial task (MAIR; MARTI, 2006; SHARIR; LERNER,
2006). Its determination in our quest, bears in mind important sample´s
features, derived mainly by the bias in the accelerator program´s selection
processes, such as the profit-orientation of the companies, their proven track
record, their social motives and the expressed intention of founders to avoid
capital restrictions to fulfill a societal need. Given the generality of the
survey process, the exploratory nature of the study and the ample representation
of SOV´s in the sample, the dependent variable (DV) in this
investigation, Success was coded as 1, if the venture in the sample has both
generated revenue from operations and reported having full-time employees since
its creation, that is the case of roughly 41% of the business under
consideration, and 0 otherwise.
In a first impression, following Sharir and Lerner
(2006), seven of their main factors, contemplated in the literature to be
conducive to success, were matched against information around 23 selected
variables that were gathered in the Entrepreneurship Database Program at Emory
University for the periods 2013-2016. The initially selected variables, were
then factored with the aid of a factor analytical procedure using principal
components and an oblique rotation (oblimin), given the possibility that the
factors might be related. The initial tests favored the adequacy of the factor
analysis. The value of the Kaiser-Meyer-Olkin measure of sampling adequacy was
.68, above the commonly recommended value of .6, suggesting that the sample was
factorable; And Bartlett´s test for sphericity was highly significant at
p<.0001 level. Seven components were extracted and the corresponding factors
are exhibited in Table 3.
Table3: Summary of Exploratory Factor
Analysis Results for Social Enterprises´ Success Dimensions, using Principal
Components estimation (N = 4,979); obliquely rotated component loadings*
|
Factor Loadings |
|||||||
Item |
F1) Strength of social network |
F2) Ability to stand market test |
F3) Public acceptance of the venture’s idea |
F4) Dedication |
F5) capital base |
F6) Previous experience |
F7) Team Composition |
|
info_has_facebook |
.77 |
|||||||
info_has_linkedin |
.67 |
|||||||
info_has_website |
.59 |
|||||||
Table 3 continued |
|
|
|
|
|
|
|
|
model_procpack |
.77 |
|||||||
model_wholretail |
.75 |
|||||||
model_prodmanuf |
.69 |
|||||||
impact_use_iris |
-.77 |
|||||||
impact_use_blab_giirs |
-.72 |
|||||||
impact_use_othermeasure |
-.50 |
|||||||
report_any_prior_accelerator |
||||||||
selected |
.85 |
|||||||
finished |
.85 |
|||||||
time |
-.69 |
|||||||
inv_debtfrom_banks |
-.68 |
|||||||
inv_debtfrom_nonbankfin |
-.52 |
|||||||
Women_F1 |
-.57 |
|||||||
inv_equityfrom_angels |
.48 |
|||||||
model_has_copyrights |
.43 |
|||||||
model_has_trademarks |
.43 |
|||||||
att_demographic_group |
||||||||
Human Capital |
.74 |
|||||||
Women_F2 |
.71 |
|||||||
Eigenvalues |
2.53 |
1.99 |
1.53 |
1.40 |
1.25 |
1.20 |
1.13 |
|
% of variance |
11.01 |
8.65 |
6.67 |
6.09 |
5.45 |
5.20 |
4.91 |
Note:*Loadings =>.40
The independent variables thought to have an effect
over SOV’s success include those variables related to the Sharir and Lerner’s
factors in table 3 and additional classification variables, to conform the
Logistic Regression Model (LR) to be tested. The variable´s definitions are
presented in table 4.
Table 4: Operationalization of SOV’s
success factors
Variable |
Definition |
Origin |
Type |
Success Factor+ |
||
att_demographic_group |
Vulnerable demographic group impacted |
Coded |
Bernoulli |
Class |
||
Venture_Incomeclass |
Factor classifying countries
by income level. World Bank. |
Coded |
Categ.
|
Class |
||
Impact_area_education |
Declared impact area
education |
Surveyed |
Bernoulli |
Class |
||
Impact_area_health |
Declared impact area health
care |
Surveyed |
Bernoulli |
Class |
||
info_has_facebook |
-Has facebook page |
Surveyed |
Bernoulli |
F1 |
||
info_has_linkedin |
Has Linkedin page |
Surveyed |
Bernoulli |
F1 |
||
info_has_website |
Has website |
Surveyed |
Bernoulli |
F1 |
||
i.network value |
Sum of venture´s social
networks |
Coded |
1 to 4 |
F1 |
||
model_procpack |
Operational Model: Processing / Packaging |
Surveyed |
Bernoulli |
F2 |
||
model_wholretail |
Operational Model: Wholesale / Retail |
Surveyed |
Bernoulli |
F2 |
||
model_prodmanuf |
Operational Model: Production / Manufacturing |
Surveyed |
Bernoulli |
F2 |
||
impact_use_iris |
Venture uses IRIS measures |
Surveyed |
Bernoulli |
F3 |
||
impact_use_blab_giirs |
Venture uses GIIRS measures |
Surveyed |
Bernoulli |
F3 |
||
impact_use_othermeasure |
Venture uses another
measurement approach |
Surveyed |
Bernoulli |
F3 |
||
selected |
Indicate ventures that were
selected into programs |
Surveyed |
Bernoulli |
F4 |
||
finished |
Indicates the ventures that
finished programs |
Surveyed |
Bernoulli |
F4 |
||
time |
Ventures with 3 or more
years of creation |
coded |
Bernoulli |
F5 |
||
inv_debtfrom_banks |
Debt Source: From banks |
Surveyed |
Bernoulli |
F5 |
||
inv_debtfrom_nonbankfin |
Debt Source: From non-bank
financial institutions |
Surveyed |
Bernoulli |
F5 |
||
report_anyprior_accelerator |
founders participation in
any prior accelerator programs |
Surveyed |
Bernoulli |
F6 |
||
Women_F1 |
Woman as first founder |
Coded |
Bernoulli |
F6 |
||
inv_equityfrom_angels |
Equity Source: From angel
investors |
Surveyed |
Bernoulli |
F6 |
||
model_has_copyrights |
Have copyrights |
Coded |
Bernoulli |
F6 |
||
model_has_trademarks |
Have trademarks |
Coded |
Bernoulli |
F6 |
||
inv_equity_venturecap |
Equity Source: From venture
capitalists |
Surveyed |
Bernoulli |
F6 |
||
Human_Capital |
Calculated variable for
years of team´s education |
Calculated |
0 to 18 |
F7 |
||
Women_F2 |
Woman as second founder |
Coded |
Bernoulli |
F7 |
||
Note: Bernoulli variables coded as 1 if they are
present and 0 otherwise.+ Factors in Table 3
The classification factor includes categorical
variables: The attention to vulnerable groups considers children, women and the
elderly, the impact areas of education and health are reported variables in the
survey; The variable Venture_income_class categorizes countries according to
four World Bank´s classifications: Low income, Lower middle income, Upper
middle income and High Income. Factor 1, relates to the strength of the
venture´s social network and is operationalized by i.network value, coded as 0
to 4, summing up the number of social networks by the venture; Factor 2, the
ability to stand the market test is proxied by the proven operational model of
the venture, being packaging, whole sale or retail and manufacturing; Factor 3,
public acceptance of the venture´s idea is represented by the use of Impact
Investment measurement systems, being IRIS, GIIRS or other similar measure
reported; Factor 4, the total dedication to the venture´s operation, given the
features of the sample is characterized by the interaction between variables
that define those ventures that were selected into accelerator programs and
have successfully finished them (GALI, 2017);
Factor 5, the strength of the capital base, is expressed through a time
variable coded as 1 , if the venture has survived the first three years from
its creation and 0 otherwise, as well with variables expressing the existence
of bank or non-banking debt as an important source of funding; Factor 6
representing the prior entrepreneurial experience, is expressed through
founders’ participation_in_any_prior_accelerator_programs, Women_F1 (GALI, 2017) and property rights. The first
variable is easily understood, the second variable choice, that is, a woman
reported as the first founder in the venture is highly related to a sample
bias, related to the negative correlation between being a female and the
possibility of receiving outside equity funding (GALI,
2017), the third is the ownership of property rights (trademarks and
copyrights) as an indication of business maturity; Factor 7 refers to the
team´s composition. Human capital is a discrete variable representing the sum
of years of formal education in the team members (Unger et al., 2011)
and, the variable Woman_F2 represents the diversity in the team´s gender
composition (Carter
et al., 2003).
3.3.
Descriptive
statistics for variables in the model
From the teams in the sample, 41% of them showed a
good probability of achieving success whereas 24% have survived the threshold
of five years of existence since their inception. In Table 5, the descriptive
statistics for the variables in the model are shown.
Table 5: Descriptive statistics for
variables in the model
Variable |
Observations |
Mean |
Std. Dev. |
Min |
Max |
|
Success |
4979 |
0.41 |
0.49 |
0 |
1 |
|
att_demographic_group |
4979 |
0.63 |
0.88 |
0 |
3 |
|
time |
4979 |
0.24 |
0.43 |
0 |
1 |
|
report_any_prior_accelerator |
4979 |
0.27 |
0.44 |
0 |
1 |
|
selected# |
|
|||||
finished |
|
|||||
0 1 |
2205 |
0.00 |
0.06 |
0 |
1 |
|
1 0 |
2205 |
0.02 |
0.14 |
0 |
1 |
|
1 1 |
2205 |
0.13 |
0.33 |
0 |
1 |
|
Venture_incomeclass |
|
|||||
2 |
4979 |
0.32 |
0.47 |
0 |
1 |
|
Table 5 Continued |
|
|
|
|
|
|
3 |
4979 |
0.28 |
0.45 |
0 |
1 |
|
4 |
4979 |
0.28 |
0.45 |
0 |
1 |
|
i.network_value |
|
|||||
1 |
4979 |
0.31 |
0.46 |
0 |
1 |
|
2 |
4979 |
0.15 |
0.36 |
0 |
1 |
|
3 |
4979 |
0.19 |
0.40 |
0 |
1 |
|
4 |
4979 |
0.16 |
0.37 |
0 |
1 |
|
model_procpack# |
|
|||||
model_wholretail# |
|
|||||
model_prodmanuf |
|
|||||
0 0 1 |
4979 |
0.02 |
0.15 |
0 |
1 |
|
0 1 0 |
4979 |
0.08 |
0.27 |
0 |
1 |
|
0 1 1 |
4979 |
0.02 |
0.15 |
0 |
1 |
|
1 0 0 |
4979 |
0.15 |
0.35 |
0 |
1 |
|
1 0 1 |
4979 |
0.03 |
0.18 |
0 |
1 |
|
1 1 0 |
4979 |
0.05 |
0.23 |
0 |
1 |
|
1 1 1 |
4979 |
0.07 |
0.26 |
0 |
1 |
|
model_has_trademarks# |
|
|||||
model_has_copyrights |
|
|||||
0 1 |
4979 |
0.06 |
0.23 |
0 |
1 |
|
1 0 |
4979 |
0.23 |
0.42 |
0 |
1 |
|
1 1 |
4979 |
0.08 |
0.27 |
0 |
1 |
|
inv_equityfrom_angels |
4979 |
0.09 |
0.29 |
0 |
1 |
|
inv_equityfrom_venturecap |
4979 |
0.03 |
0.17 |
0 |
1 |
|
inv_debtfrom_banks |
4979 |
0.06 |
0.23 |
0 |
1 |
|
inv_debtfrom_non_banks |
4979 |
0.02 |
0.15 |
0 |
1 |
|
Women_F2
|
4979 |
0.23 |
0.42 |
0 |
1 |
|
Women_F1
|
4979 |
0.26 |
0.44 |
0 |
1 |
|
Human_Capital |
4979 |
7.35 |
4.88 |
0 |
18 |
|
impact_area_education |
4979 |
0.18 |
0.38 |
0 |
1 |
|
impact_area_health
|
4979 |
0.19 |
0.39 |
0 |
1 |
|
impact_use_iris |
4962 |
0.12 |
0.33 |
0 |
1 |
|
impact_use_blab_giirs |
4966 |
0.06 |
0.24 |
0 |
1 |
|
impact_use_othermeasure |
4968 |
0.20 |
0.40 |
0 |
1 |
|
3.4.
The
Logistic regression model
Our hypotheses testing rely on the reduced form model:
Where is the expected value of given. In our case is the probability of achieving success as a
function of a set of available information about the ventures surveyed.
Following Aguilera et al. (2006), the logistic regression model used for
testing the hypotheses is defined in the following way: Let be a set of continuous or categorical observed
variables and let us consider n observations of those variables represented in
the matrix = . Let Y = be a sample of a binary response variable , associated
with the observations in , where . The logistic regression
is defined by: (1) Where is the expected value of given and is modelled as: = , (1) where are the parameters defining the model and are the zero mean independent errors whose
variances are: , . We define the
logit transformation. Here ) stands for the
odds of response , for the
observed value of . The logistic regression model can be
estimated as a generalized linear model (GLM),
using the logit transformation as the link function. In matrix notation the logistic regression
model can be expressed as: , where ´ is the vector
of logit transformations as defined above, ( )´ is the vector
of parameters and X=, the design
matrix, with 1=(1,…,1)´ is a n-dimension vector of ones.
When a binary response outcome is modeled using
logistic regression, it is assumed that the logit transformation of the outcome
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. In equation (1), the exponential of the jth
parameter , is the odds ratio of success , when the jth
predictor variable is increased by one unit, maintaining the other predictors
constant. That is the exponential of the jth parameter of the
logistic regression model gives the multiplicative change in the odds of
success. The transformation from probability to odds is a monotonic
transformation, meaning the odds increase as the probability increases. The
logistic model will be estimated by the maximum the method and its goodness of
fit assessed through the Hosmer and Lemeshow test (Hosmer; Lemeshow, 1989).
As stated before, the dependent variable (DV)
in our regressions is Success, a coded binary response variable which is equal
to 1 when present and 0 otherwise. As it is the case, the hypotheses in this
research can be tested by the estimated values adopted by the vector of
parameters () in the model.
In this situation we want to test the model itself, by stating that the null
hypotheses propose that , or there is no
linear relationship in the population. Rejecting such a null hypothesis implies
that a linear relationship exists between X and the logit of Y, therefore
validating our research hypotheses. Moreover, in our case, if , the corresponding variable is considered to have an effect on the
probability of achieving success. The value of the coefficient determines the direction of the relationship
between X and the logit of Y. When larger (or smaller) X values are associated
with larger (or smaller) logits of Y. Conversely, if larger (or smaller) X values are associated
with smaller (or larger) logits of Y (Peng;
Lee; Ingersoll, 2002). For that matter if the parameter in the
regression is positive, the probability of success increases, and when it´s
negative, decreases (Hosmer; Lemeshow,
1989). In our case the (+/-) signs on the parameters would indicate that
the variables determines that the venture has better (worse) chances of being
successful.
4. ESTIMATION RESULTS
For the purpose of testing our hypotheses, in Table 6
we report the results from the LR
model, having Success as the DV. All the estimated coefficients
are significant at the 1% level, with the exception of the following variables:
report_any_prior_accelerator, the interaction of being selected but not
finishing the accelerator program, the models based on manufacturing and solely
on copyrights, the classification impact area factors and the interactions of
using only IRIS, IRIS and other measures and IRIS and GIIRS which are
significant at the 5% level.
Table 6: Summary of Logistic
Regression Analysis for Variables Predicting SOV´s Success
Success |
B |
Z |
P>(Z) |
Std. Error |
Odds ratio eB |
att_demographic_group |
.15** |
2.74 |
.01 |
.06 |
1.17 |
time |
1.50*** |
11.53 |
.00 |
.13 |
4.50 |
report_any_prior_accelerator |
.23* |
2.08 |
.04 |
.11 |
1.26 |
selected#finished |
|||||
0 1 |
.91 |
1.31 |
.19 |
.69 |
2.49 |
1 0 |
.66* |
1.98 |
.05 |
.33 |
1.93 |
1 1 |
.43 |
2.73 |
.01 |
.16 |
1.53 |
Table 6 Continued |
|
|
|
|
|
venture_incomeclass |
|||||
2 |
-.41** |
-2.7 |
.01 |
.15 |
0.66 |
3 |
-.92*** |
-5.52 |
.00 |
.17 |
0.40 |
4 |
-1.39** |
-7.26 |
.00 |
.19 |
0.25 |
i.network_value |
|||||
1 |
.58*** |
3.60 |
.00 |
.16 |
1.78 |
2 |
.80*** |
4.41 |
.00 |
.18 |
2.22 |
3 |
.66*** |
3.60 |
.00 |
.18 |
1.93 |
4 |
1.01*** |
5.22 |
.00 |
.19 |
2.75 |
model_prodmanuf#model_wholretail# model_procpack |
|||||
0 0 1 |
.53 |
1.56 |
.12 |
.34 |
1.71 |
0 1 0 |
.20 |
1.00 |
.32 |
.20 |
1.23 |
0 1 1 |
.25 |
.79 |
.43 |
.31 |
1.28 |
1 0 0 |
.35* |
2.29 |
.02 |
.15 |
1.42 |
1 0 1 |
.93*** |
3.45 |
.00 |
.27 |
2.54 |
1 1 0 |
.82*** |
3.14 |
.00 |
.26 |
2.27 |
1 1 1 |
.55*** |
2.87 |
.00 |
.19 |
1.74 |
model_has_trademarks#model_has_copyrights |
|||||
0 1 |
.46* |
2.10 |
.04 |
.22 |
1.58 |
1 0 |
.51*** |
4.12 |
.00 |
.12 |
1.67 |
1 1 |
.78*** |
3.84 |
.00 |
.20 |
2.18 |
inv_equityfrom_angels |
.54** |
2.74 |
.01 |
.20 |
1.72 |
inv_equityfrom_venturecap |
.48 |
1.54 |
.12 |
.31 |
1.62 |
inv_debtfrom_banks |
1.38*** |
4.48 |
.00 |
.31 |
3.98 |
inv_debtfrom_nonbankfin |
1.54*** |
3.19 |
.00 |
.48 |
4.68 |
Women_F2 |
.38** |
3.22 |
.00 |
.12 |
1.47 |
Women_F1 |
-.41*** |
-3.38 |
.00 |
.12 |
.66 |
Human_Capital |
.05** |
3.70 |
.00 |
.01 |
1.05 |
impact_area_educ |
.30* |
2.05 |
.04 |
.14 |
1.35 |
impact_area_health |
-.30* |
-2.02 |
.04 |
.15 |
0.74 |
impact_use_iris#impact_use_blab_giirs# impact_use_othermeasure |
|||||
0 0 1 |
.61*** |
4.11 |
.00 |
.15 |
1.83 |
0 1 0 |
-.37 |
-.94 |
.35 |
.40 |
.69 |
0 1 1 |
-.15 |
-.30 |
.76 |
.48 |
.86 |
1 0 0 |
.51* |
2.41 |
.02 |
.21 |
1.66 |
1 0 1 |
.61* |
1.99 |
.05 |
.31 |
1.84 |
1 1 0 |
.98* |
2.37 |
.02 |
.41 |
2.66 |
1 1 1 |
-.18 |
-.49 |
.62 |
.37 |
.83 |
_constant |
-1.94*** |
-9.12 |
0 |
.21 |
|
Notes: *p < .05. **p < .01. ***p < .001.
The Hosmer and Lemeshow test confirms that the model
is adequate in explaining success with a chi-square value of 12.83 (df=8), and
a significance of .12. Multi-collinearity is not significant since all SE´s of
coefficient estimates are smaller than 2. McFadden R2 for the binary
regression model is 21% and Nagelkerke´s R2 is 33%. The percentage
of successful ventures that are correctly classified is 79.08 and a test for
misspecification using STATA´s™ linktest was not significant at the 5% level.
Hence, the probability of achieving success for a SOV that originates from an accelerator program in the sample can
be obtained through equation 2:
(2)
The first set of hypotheses tested for the whole
sample: (H1 through H7) are those about the conduciveness to the success of the
seven Sharir and Lerner´s factors analyzed. In this case all Bi ´s are statistically
different from 0 at a significance level of 5%; hence the model´s null
hypotheses are rejected in favor of validating the existence of a positive
effect over the success of Factors 1 through 7. The reason for the negative
sign in the sixth factor around a female being the first founder, might reside
in the expressed sample bias, that refers that female founders around the world
have a lower probability of raising capital yet their ventures tend to generate
revenues from their operation (GALI, 2017).
Interestingly enough, going from a lower to a higher income country, as
manifested by the venture_incomeclass
categorical variable, reduces the probabilities of generating revenue
and hiring staff, expressing difficulties of such activities in social projects
in developed countries, while having a proven track record of performance
increases such probabilities, as reflected on the inv_equityfrom_angels
variable.
In table 7 we present the seventeen predictor
variables considered to be conducive to SOV´s success in our sample, as well as
their effect on the odds ratio. Variables are sorted by the magnitude of their
effect.
Table 7: Predictor variables´
coefficients and odd ratios, ordered by effect over the DV
Categorical Variables |
Predictor variables |
B |
|
Odds ratio eB |
Effect over
odds |
|
||
|
inv_debtfrom_nonbankfin |
1.54*** |
|
4.68 |
Increase |
|||
|
time |
1.50*** |
|
4.50 |
Increase |
|||
|
inv_debtfrom_banks |
1.38*** |
|
3.97 |
Increase |
|||
network_value |
4 |
1.01*** |
|
2.75 |
Increase |
|||
impact_use_iris#impact_use_blab_giirs#impact_use_othermeasure |
1 1 0 |
0.98* |
|
2.66 |
Increase |
|||
model_prodmanuf#model_wholretail#model_procpack |
1 0 1 |
0.93*** |
|
2.54 |
Increase |
|||
model_prodmanuf#model_wholretail#model_procpack |
1 1 0 |
0.82*** |
|
2.27 |
Increase |
|||
network_value |
2 |
0.80*** |
|
2.22 |
Increase |
|||
model_has_trademarks#model_has_copyrights |
1 1 |
0.78*** |
|
2.18 |
Increase |
|||
network_value |
3 |
0.66*** |
|
1.93 |
Increase |
|||
selected#finished |
1 0 |
0.66* |
|
1.93 |
Increase |
|||
impact_use_iris#impact_use_blab_giirs#impact_use_othermeasure |
1 0 1 |
0.61* |
|
1.84 |
Increase |
|||
impact_use_iris#impact_use_blab_giirs#impact_use_othermeasure |
0 0 1 |
0.61*** |
|
1.83 |
Increase |
|||
network_value |
1 |
0.58*** |
|
1.78 |
Increase |
|||
model_prodmanuf#model_wholretail#model_procpack |
1 1 1 |
0.55*** |
|
1.74 |
Increase |
|||
|
inv_equityfrom_angels |
0.54** |
|
1.72 |
Increase |
|||
model_has_trademarks#model_has_copyrights |
1 0 |
0.51*** |
|
1.67 |
Increase |
|||
impact_use_iris#impact_use_blab_giirs#impact_use_othermeasure |
1 0 0 |
0.51* |
|
1.66 |
Increase |
|||
model_has_trademarks#model_has_copyrights |
0 1 |
0.46* |
|
1.58 |
Increase |
|||
|
Women_F2 |
0.38** |
|
1.47 |
Increase |
|||
model_prodmanuf#model_wholretail#model_procpack |
1 0 0 |
0.35* |
|
1.42 |
Increase |
|||
|
impact_area_educ |
0.30* |
|
1.35 |
Increase |
|||
|
report_any_prior_accelerator |
0.23* |
|
1.26 |
Increase |
|||
|
att_demographic_group |
0.15** |
|
1.17 |
Increase |
|||
|
Human_Capital |
0.05** |
|
1.05 |
Increase |
|||
|
impact_area_health |
-0.30* |
|
0.74 |
Increase |
|||
venture_incomeclass |
2 |
-0.41** |
|
0.66 |
Decrease |
|||
|
Women_F1 |
-0.41*** |
|
0.66 |
Decrease |
|||
venture_incomeclass |
3 |
-0.92*** |
|
0.40 |
Decrease |
|||
venture_incomeclass |
4 |
-1.39** |
|
0.25 |
Decrease |
|||
The
second set of hypothesis tests for differential success behavior in OECD and developing countries in the
search for a dissimilar international impact of success factors derived from
specific socio-economic and cultural conditions. In Table 8 we present the 21
predictor variables considered to be conducive to success for our case, as well
as their effect on the odds ratio.
Table 8: Summary of Logistic
Regression Analysis for Variables Predicting SOV´s Success grouped by belonging
to an OECD country
Factor |
Predictor Variables |
Developing Countries |
|
|
OECD Countries |
|
|
|
B |
Std. Error. |
B |
Std. Error |
|||
C |
att_demographic_group |
.15* |
.07 |
.13 |
.10 |
||
F5 |
time |
1.45*** |
.16 |
1.59*** |
.23 |
||
F4 |
selected#finished |
||||||
|
0 1 |
.48 |
.82 |
-- |
|||
|
1 0 |
1.02* |
.46 |
.03 |
.59 |
||
|
1 1 |
.35 |
.20 |
.56* |
.25 |
||
C |
venture_incomeclass |
||||||
|
2 |
-.38** |
-- |
||||
|
3 |
-.49** |
.20 |
.27 |
.20 |
||
|
4 |
-- |
-- |
||||
F1 |
i.network_value |
||||||
|
1 |
.57** |
.18 |
.67 |
.43 |
||
|
2 |
.69** |
.21 |
1.15*** |
.43 |
||
|
3 |
.46* |
.22 |
1.10** |
.42 |
||
|
4 |
.99*** |
.24 |
1.26*** |
.43 |
||
|
model_prodmanuf#model_ wholretail#model_procpack |
||||||
F2 |
0 0 1 |
.33 |
.38 |
.97 |
1.18 |
||
|
0 1 0 |
.35 |
.26 |
-.04 |
.39 |
||
|
0 1 1 |
.24 |
.39 |
.33 |
.52 |
||
|
1 0 0 |
.24 |
.19 |
.55* |
.27 |
||
|
1 0 1 |
1.16*** |
.33 |
-.33 |
.94 |
||
|
1 1 0 |
.84*** |
.32 |
.86 |
.48 |
||
|
1 1 1 |
.42 |
.24 |
.83* |
.38 |
||
F6 |
model_has_trademarks# model_has_copyrights |
||||||
|
0 1 |
.38 |
.28 |
.56 |
.36 |
||
|
1 0 |
.64** |
.16 |
.34 |
.21 |
||
|
1 1 |
.73** |
.28 |
.85** |
.30 |
||
F6 |
inv_equityfrom_angels |
.24 |
.30 |
.75** |
.26 |
||
F5 |
inv_debtfrom_banks |
1.81*** |
.46 |
.77 |
.44 |
||
F5 |
inv_debtfrom_nonbankfin |
1.55* |
.63 |
2.02** |
.61 |
||
F7 |
Women_F2 |
.48*** |
.15 |
.24 |
.23 |
||
F6 |
Women_F1 |
-.41*** |
.15 |
-.46* |
.22 |
||
F7 |
Human_Capital |
.06*** |
.02 |
.05* |
.25 |
||
C |
impact_area_educ |
.20 |
.18 |
.50 |
.26 |
||
C |
impact_area_health |
-.29 |
.21 |
-.24 |
.23 |
||
F3 |
impact_use_iris#impact_use_giirs# _othermeasure |
||||||
|
0 0 1 |
0.76*** |
.18 |
.25 |
.28 |
||
|
0 1 0 |
-.87 |
.81 |
-.24 |
.37 |
||
|
0 1 1 |
.22 |
.96 |
-.39 |
.71 |
||
|
1 0 0 |
.55 |
.24 |
.36 |
.42 |
||
Table 8 Continued |
|
|
|
|
|
|
|
|
1 0 1 |
.73* |
.32 |
-.02 |
1.10 |
||
|
1 1 0 |
.86* |
.44 |
1.78 |
1.15 |
||
|
1 1 1 |
-.23* |
.46 |
.22 |
.62 |
||
|
_constant |
-1.99 |
.24 |
-3.58 |
.48 |
||
|
|
|
|
|
|
|
|
|
MacFadden’s R2 |
.19 |
|
|
.21 |
|
|
|
Nagelkerke´s R2 |
.31 |
|
|
.32 |
|
|
|
Linktest |
NS |
|
|
NS |
|
|
|
% Correctly classified (ROC) |
78 |
|
|
80 |
|
|
Notes: *p < .05. **p < .01. ***p < .001.;
C = Classification factor
Using the same LR
model as that one expressed in equation 2, in the groups formed by SOV´s with operations in Developing and OECD Countries, most of the variables representing Factors 1-7 were
significatively different from cero at the 5% level, with relatively minor
differences across groups that could be attributed to probable different
socio-economic and cultural conditions. These results did not conclusively
favor the rejection of the null hypotheses H1A through H7A in the study,
meaning that there are no significant differences of the positive effect of
Sharir and Lerner´s factors over success between SOV´s with operations in Developing
from those in OECD countries,
nevertheless some discrepancies were found.
In table 9 we present the predictor variables
considered to be conducive for SOV´s
success in our sample, as well as their effect over the odds ratio. Variables
are sorted by the magnitude of their effect over the developing countries
group.
Table 9: Predictor variables´
coefficients and odd ratios, ordered by effect over the DV in the Non-OECD
countries group
|
|
|
Non-OECD |
|
OECD |
|
|
|||
Factor |
Categorical Variable |
Predictor /values |
Odds Ratio |
Effect |
Odds Ratio |
Effect |
Diff. Behavior |
|||
F5 |
inv_debt_banks |
6.11 |
Increase |
2.16 |
Increase |
No |
||||
F5 |
inv_debt_nonbank |
4.71 |
Increase |
7.54 |
Increase |
No |
||||
F5 |
time |
4.26 |
Increase |
4.90 |
Increase |
No |
||||
|
model_prodmanuf# model_wholretail#pack |
1 0 1 |
3.19 |
Increase |
.72 |
Decrease |
Yes |
|||
F4 |
selected#finished |
1 0 |
2.77 |
Increase |
1.03 |
Increase |
No |
|||
F1 |
i.network_value |
4 |
2.69 |
Increase |
3.53 |
Increase |
No |
|||
F3 |
impact_use_iris#impact_use_gir #others |
1 1 0 |
2.36 |
Increase |
5.93 |
Increase |
No |
|||
F3 |
model_prodmanuf# model_wholretail#pack |
1 1 0 |
2.32 |
Increase |
2.36 |
Increase |
No |
|||
|
impact_use_iris#impact_use_gir#others |
0 0 1 |
2.14 |
Increase |
1.28 |
Increase |
No |
|||
|
Table 9 Continued |
|
|
|
|
|
|
|||
|
model_has_trademarks#model |
1 1 |
2.08 |
Increase |
2.34 |
Increase |
No |
|||
F3 |
impact_use_iris#impact_use_gir #others |
1 0 1 |
2.08 |
Increase |
.98 |
Decrease |
Yes |
|||
F1 |
i.network_value |
2 |
1.99 |
Increase |
3.16 |
Increase |
No |
|||
|
model_has_trademarks#model |
1 0 |
1.90 |
Increase |
1.40 |
Increase |
No |
|||
F1 |
i.network_value |
1 |
1.77 |
Increase |
1.95 |
Increase |
No |
|||
F3 |
impact_use_iris#impact_use_gir #others |
1 0 0 |
1.73 |
Increase |
1.43 |
Increase |
No |
|||
F2 |
Women_F2 |
1.62 |
Increase |
1.27 |
Increase |
No |
||||
F4 |
selected#finished |
0 1 |
1.62 |
Increase |
Yes |
|||||
F1 |
i.network_value |
3 |
1.58 |
Increase |
3.00 |
Increase |
No |
|||
|
model_prodmanuf# model_wholretail#pack |
1 1 1 |
1.52 |
Increase |
2.29 |
Increase |
No |
|||
|
model_has_trademarks#model |
0 1 |
1.46 |
Increase |
1.75 |
Increase |
No |
|||
F4 |
selected#finished |
1 1 |
1.42 |
Increase |
1.75 |
Increase |
No |
|||
|
model_prodmanuf# model_wholretail#pack pack# |
0 1 0 |
1.42 |
Increase |
.96 |
Decrease |
Yes |
|||
|
model_prodmanuf# model_wholretail#pack |
0 0 1 |
1.39 |
Increase |
2.64 |
Increase |
No |
|||
|
inv_equity _angels |
1.27 |
Increase |
2.12 |
Increase |
No |
||||
|
model_prodmanuf# model_wholretail# pack |
1 0 0 |
1.27 |
Increase |
1.73 |
Increase |
No |
|||
|
model_prodmanuf# model_wholretail#pack |
0 1 1 |
1.27 |
Increase |
1.39 |
Increase |
No |
|||
F3 |
impact_use_iris#impact_use_gir #others |
0 1 1 |
1.25 |
Increase |
.68 |
Decrease |
Yes |
|||
C |
impact_area_ educ |
1.22 |
Increase |
1.65 |
Increase |
No |
||||
C |
|
1.16 |
Increase |
1.14 |
Increase |
No |
||||
F7 |
Human_Capital |
1.06 |
Increase |
1.05 |
Increase |
No |
||||
F3 |
impact_use_iris#impact_use_gir #others |
1 1 1 |
.79 |
Decrease |
1.25 |
Increase |
Yes |
|||
|
impact_area_ health |
.75 |
Decrease |
.79 |
Decrease |
No |
||||
C |
venture_incomeclass |
2 |
.68 |
Decrease |
Yes |
|||||
|
Women_F1 |
.66 |
Decrease |
.63 |
Decrease |
No |
||||
C |
venture_incomeclass |
3 |
.61 |
Decrease |
1.31 |
Increase |
Yes |
|||
F3 |
impact_use_iris# impact_use_blab_giirs# other |
0 1 0 |
.42 |
Decrease |
.79 |
Decrease |
No |
|||
Note: # Interaction effect over variables; C=
Classification Factor
A venture based on a manufacturing and packaging based
models has 2.19 times more probability to generate revenue and hire employees
in Non-OECD countries, whereas the same type of ventures in developing
countries does not increase their success probabilities.
The same type of results could be found in those
developing countries´ ventures that declared the usage of two or more impact
investment measurement systems. The completion of accelerator programs seems to
be important in Non-OECD countries´ ventures. A proven retail strategy in
developing countries increases the probability of success, while the same
strategy is not as important in developed countries.
5. DISCUSSION AND FINAL REMARKS
Validation of hypotheses stating the positive effect
of clearly identified success factors found in the literature over SOV´s growing from accelerator programs
worldwide, and moreover the lack of conclusive evidence supporting the presence
of differential success behavior across country groups, classified by their economic
development level, provides valuable knowledge opportunities for practitioners
and policy makers.
Aside from cultural and socioeconomic differences,
that would certainly account for the specificity of the problems addressed by SOV´s and for disparities in the
dedication and the efficacy of individual entrepreneurial resources applied in
their solution, the assurance of globalized and homogeneous selection processes
as well as the use of sound standard performance measures, such as those
derived from impact investment methodologies, have a positive influence on
social venture´s success. This contention leverages plenty academic and
practical prospects for exploring the influence of socio-economic and cultural
influences over the efficacy of social enterprise´s interventions.
After controlling for efficiency in the disposition of
entrepreneurial resources, the organizations based on government, market and
civil society sectors can allocate their attention to those country specific
situations affecting the efficacy of development programs such as the problems
to be solved, the particular eco-systems and the suitability of the
organizational arrays adopted.
The present research contributed to bridge the gap
concerning empirical studies around success in social enterprises using rich
longitudinal datasets, based on multi-purpose surveyed data. Given the
expressed bias in the figures collected, generalization beyond the sample is
not simple. Nevertheless, this study leads the way for supplementary clarification
around the incidence of specific socio-economic and multicultural factors
affecting the effectiveness of international partnering efforts, based on
social enterprises, to provide social solutions to specific compelling problems
in all societies such as housing for the
urban poor, grassroots economic development, health care , education, income
growth among others, by reinforcing global efficiency standards and procedures
in developing programs around the world.
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