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Effect of Experience on Entrepreneurial Performance: Meta-Analytical Review

Authors:

Abstract

In our meta-analysis we investigated effect of experience on entrepreneurial performance. Performance were measured by three summarizing categories: size, profitability and growth. We report findings from 70 independent studies (N = 48425). We found small but significant relationship r = 0,065, CI [0,033; 0,097]. These findings are in line with other authors (Unger et. al., 2011) and expand our knowledge about human capital, which of experience are part. Experience have different effects on size of venture, profitability of venture and growth. Several moderators, such as type of the industry (technologically high vs low), age of the venture (young vs old), were tested, in attempt to explain high heterogeneity and variance, but their effects were non-significant and for some highly insignificant, for example for gender (men vs women vs mixed samples), or year of publication. Also industrial, managerial, entrepreneurial, startup and functional (marketing, finance etc.) experience generates different effects on performance. Our findings are contrary to many practises, routines, knowledge and advices used in business where experiences are valued very high. Results are relevant for several independent groups. Angel investors, venture capitalist and other groups which evaluate business proposals according to our findings overestimates total value of experiences and should correct weights they assign to this specific criteria.
Effect of Experience on Entrepreneurial Performance: Meta-Analytical
Review
Robert Hanák
Institute of Experimental Psychology, Centre of Social and Psychological Sciences, Slovak
Academy of Sciences
email: robohanak@gmail.com
Abstract
In our meta-analysis we investigated the effect of experience on entrepreneurial performance.
Performance was measured by three summarizing categories: size, profitability and growth.
We report findings from 70 independent studies (N = 48425). We found a small but significant
relationship r = 0,065, CI [0,033; 0,097]. These findings are in line with other authors (Unger
et. al., 2011) and expand our knowledge about human capital and experience are part of it.
Experiences have different effects on size of venture, profitability of venture and growth.
Several moderators, such as type of the industry (technologically high vs low), age of the
venture (young vs old) were tested, in attempt to explain high heterogeneity and variance, but
their effects were non-significant and for some highly insignificant, for example for gender
(men vs women vs mixed samples), or year of publication. Also industrial, managerial,
entrepreneurial, startup and functional (marketing, finance etc.) experience generates different
effects on performance. Our findings are contrary to many practises, routines, knowledges and
advices used in business where experiences are valued very high. The results are relevant for
several independent groups. Angel investors, venture capitalist and other groups, which
evaluate business proposals, overestimate according to our findings the total value of
experiences and should correct weights they assign to this specific criteria.
Introduction
Experiences are an integral part of the human capital (Becker, 1964) and in general
experiences are viewed as positive factor influencing individual performance. Entrepreneurial
performance measured by financial indicators (ROA, sales, assets, growth etc.) is also
expected to be positively affected by entrepreneur’s previous experience. These assumptions
are now integrated into our common business, entrepreneurial and investors’ practises. For
example angel investors and venture capitalists attached high importance to experiences of
entrepreneurs, when deciding about investment (MacMillan, Siegel, & Narasimha, 1985,
Babcock-Lumish, 2005). Maxwell, Jeffrey, & Lévesque (2011) found in their review study
that all authors, who investigated the investors decision criteria found that the experience of
entrepreneur was considered as important. Landström (1998) found that decision making
criteria used by investors attach highest value to those, which are related with human capital
and experience.
Despite all these positive views, which are manifested in decades lasting business practises,
evidence in the scientific studies are inconclusive and mixed. The relationship between
experiences and performance show high variation in magnitude and even in direction. While
some studies reported moderate positive relationship ( r = 0,24, Lerner, Almor, 2002; r =
0,24 Eisenhardt, Schoonhoven, 1990; r = 0,26, Spanjer, Witteloostuijn, 2017), other studies
reported much weaker positive relationship which in several cases are very close to zero (r =
0,02, Dencker, Gruber, 2015; r = 0,03, Dokko, Gaba, 2012) or even exactly r = 0 (Carpenter,
Pollock and Leary, 2003). And finally there are many studies which found negative
relationship (r = -0,23, Naldi, Davidsson, 2014, r = -0,14 Arthurs, et al. 2009, r =
-0,03, Yamakawa, Peng, Deeds, 2013) to name just a few from many.
There were several attempts to summarize our knowledge and to identify relationship between
experience and performance. Unger et al. (2011) with his co-authors published a meta-
analysis investigating relationship between human capital and success and found relatively
low relationship r = 0,098. Because experiences are only one part of human capital, the effect
for experience should be lower. Other authors, Peake and Marshall, (2009) performed
exploratory and ordered probit analyses to test the effect of experiences on the probability of
obtaining a positive estimate for the experience-performance relationship and also found
positive relationship. Therefore we think that experiences is positively related with
performance. We set our hypothesis:
Hypothesis 1:
The relationship between experience and performance is positive.
Different outcome demands different level of marksmanship. Growth of employees is most
easily reachable compared for example with sales. Hiring employees is based on internal
decision and process is not in fact limited by any very significant constraints or completion
from outside. Rising sales is more complicated because in this case entrepreneur has to
compete with others who also would like to sell at limited market and he must outperform
them. Profitability of assets (ROA), investments (ROI) or sales are even more complicated to
reach because entrepreneur must master not only markets but also internal company process
and flows. Therefore we think that profitably outcome is most difficult to reach and therefore
we set our second hypothesis this way:
Hypothesis 2:
The relationship between experiences and performance is lower for profitability performance
outcomes compared to size and growth performance outcomes.
In our research we would like also investigate several research questions:
To examine effect of different type of experience on performance.
To investigate different moderators such as country development, year of publication,
gender, industry level
Method
Selection criteria and collection of studies
We tried to identify all relevant studies reporting results about relationship between
performance and experiences. We applied following procedures. Firstly we used databases
(EBSCO, PsychINFO) search where we set the time interval 1985 -2017. We used all
variation of following keywords, for experience we used the keywords: experience,
managerial experience, startup experience, supervising experience etc. For performance we
used the keywords: growth, sales, employees, ROA, ROE, ROI, ROS, profit, income, success
and firm size. And for venture: startup, entrepreneur, business owner, small business and
small firm. We also used these databases tools to show similar studies when a relevant study
was found which helped us to find new studies previously not detected. The second search
process was manual search for databases of relevant journals in field. These journals were
manually searched: Journal of Business Venturing (1995 -2017), Entrepreneurship Theory and
Practise (1985 -2017), Journal of Small Business Management (1985 -2017), Academy of
Management Journal (1985 -2017) and Administrative Science Quarterly (1985 -2017). Third
search was Google scholar search engine where we found relevant papers and searched for
papers which cite the primary paper and were also relevant to the topic. Fourth search was
examining reference lists of studies to find new previously not identified studies. Fifth search
was performed with aim to avoid publication bias, therefore we searched in Google for not
published studies, theses, dissertations and reports.
All this searches resulted in 521 studies. After rejecting a) qualitative b) case studies, c)
excluding studies which did not provide indicators of experience and performance, d) studies
which didn´t report data needed to perform meta-analysis e) not receiving answers from
authors of studies, we have 70 independent samples.
Table 1 Samples included in meta-analysis
Author name,
year
Conceptualizatio
n of experience
Performance
indicator/s
Country of
origin
Industry Age in
years
Sampl
e size
Arthurs et. al.
(2009)
start-up size,
profitability
USA technology
based
2,02 313
Batjargal et. al.
(2013)
managerial size,
profitability
Internationa
l (China,
Russia,
France,
USA)
mixed 4,47 637
Beckman,
Burton, & O
´Reilly (2007)
managerial, start-
up
size USA mixed not
specified
161
Beckman &
Burton (2008)
functional size USA mixed (hi-tech) not
specified
158
Boeker (1997) industry size, growth USA semiconductor
s
not
specified
67
Boso, Story, &
Cadogan (2013)
entrepreneurial size, growth Ghana mixed not
specified
203
Camisón &
Villar-López
(2010)
functional profitability,
growth
Spain mixed not
specified
394
Capelleras et al.
(2010)
industry,
entrepreneurial
growth Internationa
l
(Argentina,
Brazil,
Chile, Peru)
not specified 7,21 647
Carpenter,
Sanders, &
Gregersen
(2001)
functional size,
profitability
Internationa
l
combined not
specified
245
Carpenter,
Pollock, &
Leary (2003)
functional size,
profitability
USA electrical &
electronic
6,29 97
Címerová
(2012)
industry size,
profitability
USA mixed not
specified
14483
Colombelli
(2015)
entrepreneurial size Internationa
l (Europe)
mixed 10 486
Dana,
Grandinetti, &
Mason (2016)
functional size Italy wine industry not
specified
100
Dalziel (2008) start-up profitability Canada not specified 21,58 52
Davidsson &
Honig (2003)
managerial, start-
up
size,
profitability
Sweden mixed (not hi-
tech)
not
specified
379
Debrulle &
Maes (2014)
industry profitability Belgium not specified not
specified
66
Dencker &
Gruber (2015)
industry,
entrepreneurial
size Germany mixed not
specified
451
DeVaughn &
Zheng (2016)
industry profitability USA bank industry not
specified
344
Dokko & Gaba
(2012)
functional size,
profitability
USA IT sector 3,05 375
Dyke, Fischer,
& Reuber
(1992)
(computer
services)
managerial,
entrepreneurial,
start-up
size,
profitability,
growth
USA computer
services
6,23 103
Dyke, Fischer,
& Reuber
(1992) (food
manufacturing)
managerial,
entrepreneurial,
start-up
size,
profitability,
growth
USA food
manufacturing
9,7 62
Dyke, Fischer,
& Reuber
(1992) (food
retail)
managerial,
entrepreneurial,
start-up
size,
profitability,
growth
USA food retail 12,37 73
Dyke, Fischer,
& Reuber
(1992) (food
wholesale)
managerial,
entrepreneurial,
start-up
size,
profitability,
growth
USA food wholesale 9,97 71
Dyke, Fischer,
& Reuber
(1992)
(furniture
manufacturing)
managerial,
entrepreneurial,
start-up
size,
profitability,
growth
USA furniture
manufacturing
8,78 77
Eggers & Song
(2014)
entrepreneurial size,
profitability,
growth
China mixed 12,1 219
Eisenhardt &
Schoonhoven
(1990)
industry size USA mixed (hi-tech) not
specified
66
Fernhaber & Li
(2010)
functional profitability,
growth
USA,
Canada
mixed 3,65 150
Gimeno et. al.
(1997)
managerial,
entrepreneurial,
functional
size USA mixed (not hi-
tech)
not
specified
1457
Gimmon &
Levie (2010)
industry,
managerial
size Israel mixed (hi-tech) 7,51 193
Gottschalk et.
al. (2014)
industry,
managerial
size Germany mixed not
specified
8400
Hayton (2005) industry size USA mixed (hi-tech) 3,41 237
Herrmann &
Datta (2006)
entrepreneurial,
functional
size,
profitability,
growth
USA manufacturing not
specified
380
Hmieleski &
Baron (2008)
entrepreneurial size,
profitability,
growth
Caucasus not specified 7,81 159
Hmieleski &
Baron (2009)
entrepreneurial size, growth USA mixed 5,74 201
Hmieleski &
Carr (2008)
entrepreneurial growth USA not specified not
specified
216
Chandler &
Hanks (1998)
entrepreneurial size, growth USA mixed (not hi-
tech)
3,52 102
Chandler &
Lyon (2009)
industry growth USA mixed not
specified
124
Jo & Lee
(1996)
industry,
managerial,
entrepreneurial,
start-up,
functional
profitability,
growth
South Korea mixed not
specified
48
Kallenberg &
Leicht (1991)
(men)
industry,
entrepreneurial
size USA health industry,
PC software,
eating &
drinking
13,19 878
Kallenberg &
Leicht (1991)
(women)
industry,
entrepreneurial
size USA health industry,
PC software,
eating &
drinking
10,55 261
Khayesi,
George, &
Antonakis
(2014)
functional size Uganda garment, info
and
communication
technology
5,41 242
Kor (2003) industry size, growth USA medical,
surgical, dental
10,69 340
Lee, Lee &
Pennings
(2001)
industry size Korea not specified 4,59 143
Lerner, Brush,
& Hisrich
(1997)
industry, start-up size,
profitability
Israel service, retail,
manufacturing
not
specified
218
Lerner & Haber
(2001)
industry,
entrepreneurial
profitability Israel tourism not
specified
53
Lerner & Almor
(2002)
industry size Israel not specified not
specified
220
Li & Zhang
(2007)
industry,
functional
size,
profitability
China mixed (hi-tech) 4,83 184
Matsuda &
Matsuo (2017)
industry,
managerial
profitability Japan mixed not
specified
1307
McGee,
Dowling, &
Megginson
(1996)
industry,
functional
size, growth USA mixed (hi-tech) not
specified
210
Miloud,
Aspelund, &
Cabrol (2012)
industry,
managerial, start-
up
size France mixed 15,46 102
Naldi &
Davidsson
(2014)
industry,
managerial
size,
profitability
Sweden not specified 35,76 138
Neville et al.
(2014)
entrepreneurial size, growth Canada not specified 3,3 2145
Oe &
Mitsuhashi
(2013)
industry, start-up size USA mixed not
specified
382
West & Noel
(2009)
start-up size,
profitability
USA technology
based
4,77 83
Pennings, Lee,
&
Witteloostuijn
(1998)
industry size Netherlands accounting 1,81 1851
Rauch &
Rijsdijk (2011)
industry,
managerial,
entrepreneurial
growth Germany trade, craft,
manufacturing
2,29 93
Reuber &
Fischer (1997)
functional size Canada software 11,16 49
Robb & Watson
(2012)
entrepreneurial size,
profitability
USA mixed not
specified
4016
Seghers,
Manigart, &
Vanacker
(2012)
industry size Belgium mixed not
specified
103
Shrader &
Siegel (2007)
industry, start-up,
functional
profitability,
growth
USA technology
based
not
specified
198
Spanjer &
Witteloostuijn
(2017)
industry,
entrepreneurial
size USA mixed not
specified
2120
Stone & Tudor
(2005)
managerial,
functional
profitability USA mixed not
specified
58
Uy, Foo, &
Song (2013)
start-up size Philippines mixed 3,86 156
Wasserman
(2003)
managerial size USA mixed not
specified
202
Weng & Lin
(2014)
industry size,
profitability
USA computer
industry
2,6 558
Yamakawa,
Peng, & Deeds
(2013)
industry size Japan not specified 6,47 203
Yang,
Zimmerman, &
Jiang (2011)
managerial size USA not specified 7,12 237
Zhao, Song, &
Storm (2013)
industry, start-up,
functional
profitability USA mixed not
specified
372
Zheng (2011) industry growth China mixed not
specified
98
Variable coding
In the following tables we are explaining coding of variables. Table 2 displays our operation
of experiences in studies included in meta-analysis and also its frequencies. In primary studies
experiences were described in various ways and after careful examination what was behind its
definition we summed and organized experiences into following categories. From data in this
table we can conclude that Industry experience is the most used type of experience (used 30
times), followed by Managerial experience (used 23 times) and others listed in Table 2.
Table 2. Coding and frequencies of human capital variables
Experience N
Industry experience years 26
Industry experience, yes/no 4
Managerial experience years 16
Managerial experience yes/no 7
Entrepreneurial experience years 13
Entrepreneurial experience yes/no 4
Startup experience years 13
Startup experience yes/no 5
Functional experience years 4
Functional experience yes/no 14
Measures of entrepreneurial performance are numerous and we use all those reported in
primary studies. According to Unger et al. (2011) we divided them into three groups: size,
growth, profitability. Specific categories are listed with their frequencies in the Table 3. The
most numerous measure in size category is Number of employees (used 57 times) followed by
Sales (used 45 times). For category Profitability most numerous were Profit and ROA equally
used 22 times. For category growth we found that authors in primary studies preferred
Composite growth as indicator to measure growth and its composition is different from study
to study. Comparing these three categories of performance we consider profitability as most
important for entrepreneurs, followed by growth and size.
Table 3. Coding and frequencies of performance variables
Size N Profitability N Growth N
Sales volume 4
5
Profit 22 Growth in sales 17
Number of employees 5
7
Income 4 Growth in employees 10
Assets 8 ROA 22 Growth in profit 1
Market valuation 5 ROS 7 Growth in assets 6
Earnings 4 Return on employees 6
Composite
profitability: ROA,
ROI, ROS average,
ROA + ROE+ profit
margin
13 Composite growth: sales
growth + profit growth,
employee + revenue
growth, growth of sales +
profit+ assets+
employees+ market
21
Stock market returns 1
Revenues 6
To measure publication bias we also controlled studies if they were published or not. But in
our sample of primary studies we identified only 3 studies which were not published
(Cimerova, 2012, Batjargal et. al., 2013 and Gottschalk et. al., 2014). This low number of
studies did not allow us to perform meta-analysis with relevant results.
We also coded studies according to level of development of countries interviewed
entrepreneurs came from. To identify studies from countries with low level of development
we were able to identify again only few studies: Boso, et al., 2013 Ghana, Khayesi et al.,
2014 – Uganda and Uy, et al., 2013 – Philipines, which we could classify as at lower level of
development. All other studies were from North America, Europe, South Korea, China and
Israel. Again we did not performed meta-analysis to avoid biased results.
To test the effect of company age, we divided studies into two groups: those reporting results
of companies younger than 8 years and those older than 8 years.
Also we tested industry effect. We divided studies into high industry and low industry. High
industry was electronics, computer industry, IT sector, software industry etc. All others such
as food manufacturing, textile and others plus combined studies were coded into low industry.
We also performed funnel plot to visually test distribution of effect of studies. We can see that
studies are evenly distributed. Classic fail – safe N test showed us 1448 studies.
Graph 1 Funnel plot
-2,0 -1,5 -1, 0 -0,5 0,0 0,5 1,0 1,5 2,0
0,00
0,05
0,10
0,15
0,20
Standard Error
Fisher's Z
Funnel Plot of Standard Error by Fisher's Z
Results
In Table 4 we report summary results. We expected that experience will have positive effect
on performance and our results support our first hypothesis (H1). As we can see effect size is
positive r = 0,065, CI [0,033; 0,097]. To make this effect insignificant according to Classic
fail – safe N test we have to find another 1448 studies.
Table 4 Results of meta-analysis on experience and performance
Variable K N Effec
t size
95%
CI
Lowe
r
95%
CI
Uppe
r
Tau
square
d
Standar
d Error
Z
valu
e
mod.
p
Fixed 7
0
4842
5
0,025 0,016 0,034 0,013 0,006
Random (H1) 7
0
4842
5
0,065 0,033 0,097
Industry
High industry 1
5
3728 0,045 -0,022 0,110 0,011 0,007
Low industry 5
2
4401
5
0,072 0,034 0,109 0,014 0,008 0,62 0,534
Age of business
Old 1
3
2769 0,018 -0,036 0,071 0,003 0,004
Young 2
4
9204 0,038 -0,005 0,082 0,007 0,004 -0,52 0,604
Performance measure
Profitability (H2) 3
3
2672
2
0,044 0,007 0,081 0,006 0,004
Size 5
6
4435
2
0,059 0,024 0,095 0,013 0,007
Growth 2
2
5717 0,019 -0,017 0,055 0,002 0,002
Type of experience
Industrial experience 3
0
3481
0
0,089 0,049 0,128 0,008 0,006
Entrepreneurial experience 1
7
5057 0,080 0,016 0,144 0,012 0,007
Startup experience 1
8
3443 0,122 0,031 0,211 0,030 0,015
Managerial experience 2
3
3055
5
0,053 -0,007 0,113 0,016 0,012
Functional experience 1
8
3377 0,076 -0,016 0,167 0,032 0,015 1,62 0,106
In literature experiences are operationalized in various ways such as industrial experience,
managerial, entrepreneurial, start-up or functional experience. To our knowledge, their effect
on performance had never been tested, therefore we selected specific studies with specific
type of experience and used them in meta-regression. As we can see in table 4, effect sizes are
different but also variance. Managerial experience seems to have the weakest effect, lower
than total effect. Taking into account variance, sample size and number of studies, industrial
experiences seems to have the strongest impact.
Effect of experience in high technology industry r = 0,045 was smaller compared to low
technology industry r = 0,072. This is with our expectation, because experience are in fact
gained in the past and high technology means de facto newness. But the moderator effect was
insignificant (z = 0, 62, p = 0.53).
Experience effect on performance evidently low in older r = 0,018 compared to younger
businesses r = 0,038. We would like to stress that both effects are very slow and in both cases
lower level of confidence interval is below zero. Moderator effect was also insignificant
(z = -0,52, p = 0.604).
Experience seems to have different effect on various indicators measuring performance.
Effect on growth seems to be the weakest r = 0,019, with lower level of confidence interval
below 0, which rises the question if it really exists. Effects on profitability are better r = 0,044
and with confidence interval not having 0. Strongest effect seems to be on size indicators, but
we have to be aware that there were more than twice number of studies (and 8 times more
cases) compared to growth, and despite that size model had highest variability in studies.
Therefore our hypothesis H2 is not supported.
We also tested the year of publication as a moderator which produced close to 0 intercept
which was highly insignificant. We also tested for gender differences and found it also
insignificant, which could be caused by low number of primary studies investigating only
women entrepreneurs.
Discussion
While the overall effect r = 0,065, CI [0,33; 0,097] is positive and in accordance with line of
findings from different authors (Unger, et al., 2011), it is low, compared with amount of
importance experience is receiving in business praxis and in theoretical discussions. We will
discuss several reasons for such low effect.
We consider first important reason for low effect objective characteristics of entrepreneurial
domain itself. In this domain entrepreneurs are performing their daily activities. When we
look at characteristics of this domain and environment, it is one of the worst possible of all
related to professional performance and expertise. Dynamic stimuli, work with people,
complex environment, many players, unique situations and tasks, rare events sometimes with
devastating effects, weak, delayed or missing feedback are real characteristics, in which
business is done every day. And all above listed factors are responsible for limited ability of
humans to reach level of expertise and produce good performance (Shanteau, 1992,
Kahneman, Klein, 2009). Absence of regular and straight feedback is especially problematic.
This makes learning very difficult and could lead to learning untrue, misleading and
erroneous knowledge. But supposing that experience naturally generates knowledge should be
also questioned as Unger et al. (2011, p. 353) wrote: “experience per se does not lead to
knowledge”.
Second reason for low effect and probably more significant, is useful and valid knowledge
transfer and application between different ventures. Every venture is different and effort to
use previous knowledge, without reflection and proper adjustment, in new venture could, and
probably will, lead to suboptimal results. This line of argumentation was described in field of
entrepreneurship by Reuber and Fisher (1994 and 1999). We think that knowledge transfer
between ventures, industries and time is limited. Despite experienced entrepreneurs possess
large amount of various types of knowledge and knowhow it is questionable how much are
these useful in new venture or even in new environment. If practical validity of this
knowledge/knowhow is low, and there are many reasons, arguments, evidence that it is low,
then applying this low validity knowledge brings detrimental effect. For example Henderson,
Miller, Hambrick, (2006) found that the longer time CEO spent in his tenure, the weaker
performance of organization he had. Reasons for that is probably losing touch with the
external environment.
Implications for future research
Variation of effect sizes emphasize moderator’s role which indicates that various types of
factors play a significant role. We could speculate that entrepreneur with his experiences is
much more influenced by his environment than we expected.
Large heterogeneity of results in primary studies leads us to think that in future research
scientists should concentrate at investigating carefully selected situations to identify under
which specific circumstances and conditions experiences generate positive effect and under
which they do not.
Experiences could be viewed as accumulated various and complex knowledge and therefore
we think, that our results are very closely related with entrepreneurial education. Throwing
some light at how specific type of experience influence specific outcomes could be the way
how systematically test those relationships. After detailed investigation we could say that
these activities create knowledge which improves performance.
From reading all those studies in our meta-analysis we think that there is little, or not enough,
consent about how various performance measures (profitability, size, growth) should be
related and what is the importance for entrepreneur. In which stage of development is specific
indicator of performance most important, how it should be related with others and are there
some/any indicators which should be not taken into account by entrepreneurs themselves?
Limitations
There are several limitations of this study. First and probably the most important limit is our
inability to identify all published studies. Experiences are often used as control variable.
Accidentally we found studies which were dealing with totally different topics (gender
differences, export or wine production and others) and they were reporting information about
experience, which we could use in this meta-analysis. We were not able to identify these
studies by any of our search steps (keywords in databases, title search, journal search). We
found these studies mostly by accident and by personal recommendation of researches
studying those unrelated topics. Therefore we performed Classic fail safe N test which
showed 1448 studies, which should exist to null our finding. Although we can be sure that
there are several studies which we did not identify, number 1448 gives us some guarantees
that the effect exists.
Conclusion
This meta-analysis extends our knowledge about how specific part of human capital -
experience, influence venture performance. Overall effect r = 0,065, as expected, is smaller
than human capital effect on success (r = 0,098; Unger et al., 2011). Taking into account the
confidence interval for overall effect CI [0,033; 0,097] and its lower estimate, which in fact is
not far from zero, our findings are contrary to many practises, routines, knowledges and
advices used in business where experiences are valued very high. Our findings could help
angel investors, venture capitalists, managers and business owners to make better and
evidence based decisions when dealing with experience.
Acknowledgements
This research was supported by a grant from Slovak Ministry of Education Science, Research
and Sport of the Slovak Republic VEGA 2/0118/17: Risk assessment in decision making of
individuals on the personal and company/business finances and business opportunities
awarded to Róbert Hanák
References1
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... Further, successful Niche marketing appears to require the use of specialization, relationship marketing, developing internal dynamic capabilities and building protective barriers. Companies are seriously striving to become more and more entrepreneurs [11]. Entrepreneurial orientation has been evaluated as an important antecedent in companies that positively impacts performance. ...
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