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International Journal on Advanced Science, Education, and Religion (IJoASER)
38
Assessing Startup Performance:
"Case Study at National Business Incubator"
Mayar Soeryo Prayogo
1*
,
Usep Suhud
2
, Agung Wahyu Handaru
1,2,3
Universitas Negeri Jakarta Jakarta, Indonesia
email:
*1
msprayogo@gmail.com;
2
usuhud@unj.ac.id;
3
ahandaru@unj.ac.id
Abstract— This study aims to examine the Startup Performance of the
national incubator which are includes Knowledge Management, Business
Incubator, and the effectiveness of innovation. Besides that this research is
expected to have tangible benefits toward the process of achieving Startup
goals that owned by business performers. This study will test five hypotheses
of direct influence (effect) and two hypotheses of mediating influence
(indirect effect). This study will use 339 respondents as sample with a Startup
analysis unit that operates and is affiliated with a local Startup development
business incubator located in Jabodetabek, West Java Province (excluding
Depok & Bekasi), Central Java and Bali. The intended startup on this
research is Person In Charge (PIC) / company founder / owner. The PIC /
founder / owner of the company here is not limited by age, gender and
position at the Startup. The company's PIC / founder / owner is only limited
towardStartup which is built by the business incubator. The research method
used is Confirmatory Factor Analysis (CFA) to test path analysis by
operating AMOS 22. The results of this study indicate that all proposed
hypotheses can be accepted statistically, both direct side and indirect effects
(mediation) side
Keywords
—
Startup Performance, Knowledge Management, Incubator
Business, Innovation Effectivity, confirmatory factor analysis (CFA),
AMOS22
I. INTRODUCTION
The growth and development of Startup is one of the focus of the Jokowi-
JK Government program through the growth of startup businesses (creative
people) along the value chain at the stages of creation, production,
distribution, consumption and conservation, where the creative economic
strength is more dependent on the excellence of human resources through
creative ideas of human thought as stated in Nawacita 2015-2019, where
1000 Technopreneur and Nexticorn (Next Indonesia Unicorn) became one of
the main programs.
CB Insight in its report in 2019 noted that there are currently more than
300 unicorns around the world, where there are 4 (four) Indonesian
companies that have become Global Unicorn Club, namely Traveloka (Travel
Technology), Bukalapak and Tokopedia (E-commerce) as well as Go-Jek
(On Demand). The term Startup level was originally coined by Lee (2013)
who used the term unicorn to refer to Startup worth 1 billion US dollars. The
IJoASER,Volume 2,Issue 3, November,2019
DOI: 10.33648/ijoaser.v2i2.40
Copyriht: STAI Al-Furqan Makassar,Indonesia
Content License: CC-BY-SA
39
Startup level phases which show 'class', are currently developing from
Cockroach (newly pioneered startup), Ponies (valuation of US $ 10 Million),
Centarurs (valuation of US $ 100 Million), Unicorn (valuation of US $ 1
billion), to Decacorn (valuation of US $ 10 Million), Centarurs(valuation of
US $ 100 Million), Unicorn (valuation of US $ 1 billion), to Decacorn
(valuation of US $ 10 Million) valuation of US $ 10 billion).
One of the challenges of Startups that is based on creative ideas is still
weak competitiveness, which refers to the results of the 2015 Martin
Prosperity Institute Global Creativity Index (GCI) survey, Indonesia is one of
the countries that is not yet creative. Based on the survey results, Indonesia
ranks 115 out of 139 countries. Indonesia's position is far below that of
neighboring countries such as Singapore (9), Malaysia (63), and Vietnam
(80). GCI is calculated using three factors, namely technology (technology),
creative people (talent), and tolerance (tolerance). Startup is a new company
that strives for existence. Startup entities are largely formed based on bright
ideas and grow to succeed through the stages of bootstarpping stage, seed
stage, and creation stage [1].
Startup Performance in the Technology-Based Starter Company (PPBT)
program conducted by the Ministry of Research, Technology and Higher
Education is measured by indicators of production performance, marketing
performance, finance performance, and performance personnel. Performance
problems are illustrated by not achieving the target indicators set at the
Startup under the guidance of the Semarang Telematics Creation and
Innovation Incubator (IKITAS). Performance problems also occur at Startups
located in Bandung Techno Park (BTP), namely Melisa.id with Point of Sales
/ POS Application products (POS applications integrated with chat systems),
Cross Variety Control that produces EGGQ products (egg quality detection
devices image processing-based domestic chickens), RasturaAyuGayo with
Atmatsya products (natural preservative liquid fish preservatives that are safe
for consumption), AgramaultInvestama with ProMix Agri-Biotech products
(Fertilizers that use six microbial protagonists that function as an accelerator
for faster composting time) that does not reach the target on sales (marketing
performance / sales turnover) and production targets (production
performance).
One of the phenomena of startup failure in Indonesia occurred in 2016,
where there were several startups that failed, including foodfanda,
YESBOSS, Diana, Shopious, Jade, Coral, ensogo, rakuten, and OpenRice
due to products and services that were not competitive. Startups that are not
able to produce products and services will be automatically excluded from
competition by competitors. Startup Performance is influenced by innovation,
where in the competitive era, the implementation of innovation plays an
important role in dealing with very rapid changes in the tastes and
preferences of customers from the market to create products and services in
accordance with market demands. Innovation is defined as the process of
creating new knowledge and ideas so as to produce different ways, processes,
International Journal on Advanced Science, Education, and Religion (IJoASER)
40
structures and technologies that are better for producing products and
services in accordance with customer needs and preferences [2].
Then the implementation of Knowledge Management at Startup has a
significant effect on improving performance through a positive contribution
to sustainable startup growth by increasing financial, environmental, human,
market, organizational, relational, technical and technological performance
[3]. Startup performance is influenced by the Startup ecosystem that has not
been fully developed, where the Startup ecosystem will form Knowledge
Management as an enabler in innovation by creating, storing, transferring,
and applying knowledge. Based on a survey conducted by CBI Insight on the
founder of a poor company, Startup Team is one of the 5 main causes of
startup failure. BTP as a business incubator identifies a bad team as a result
of Knowledge Management not running optimally because Startups only rely
on facilitation from business incubators that provide business coaching to
increase business capacity (scale up).
Business incubators are effective tools to accelerate the growth of startups
through clustering opportunities, business support services, networking
opportunities and space incubators [4]. Business incubators play an important
role in supporting startups who are most at risk of failure during the founding
phase. This was conveyed by PasarLaut.com (one of BTP's business
incubation startups), where in starting the initial step in creating an online sea
product trading portal, it received support from business incubators to sustain
in the form of supporting funding, physical infrastructure support, legal
support administration and networking support. However, business
incubators are considered to be still lacking in facilitating access to capital, in
the form of an effort to bring investors together to finance business (business
matching) and market access to help market products that have been
produced by startups. The business incubator acts as an innovation ecosystem
for startup growth and development. Weak business incubator seen in the
business incubation data at BTP shows the number of incubation tenants who
passed during the period of 2015 to 2017 is very minimal, in 2015 none of
the Startup tenants passed, whereas in 2016 only 2 (two) and 8 (eight) Startup
tenants in 2017 who passed from 21 (twenty one) who were scouted.
In the context of developing MSMEs conducted by Bank Indonesia, the
Business incubator study states that the Business incubator needs
infrastructure support, namely soft infrastructure and hard infrastructure.
Based on observations made at the Technical Implementation Unit (UPT) of
the Incubator Business Center (IBC) Semarang that foster 12 (twelve) tenants
/ startups, especially those located in Semarang, show that coworking space
and supporting facilities and infrastructure require improvements to support
the growth and development of startups. . In addition, facilitation of
assistance, guidance, training, facilitation of product development and access
to financial and marketing institutions provided to startups is still constrained
by the lack of local government budgets as responsible for the business
incubator. The above is marked by the phenomenon of the number of
incubation tenants who passed during the 2015 - 2017 period at BTP was
very minimal compared to the tenants who were fostered. Based on the
Technology Business Incubation Services Report that has been conducted at
IJoASER,Volume 2,Issue 3, November,2019
DOI: 10.33648/ijoaser.v2i2.40
Copyriht: STAI Al-Furqan Makassar,Indonesia
Content License: CC-BY-SA
41
KST, the number of tenants who passed to BTP during the period 2015 -
2017 only 10 tenants out of 21 tenants were fostered. This research will
conduct an in-depth study of the analysis of Startup Performance in the
national incubator.
II. M
ETHOD
The sample of this study is the population in the Jakarta area with an age
range of less than 18 years to more than 55 years that is easily found by
researchers and in accordance with the research criteria, application users
calling the application online. Measurement statements in the questionnaire
are measured using a Likert scale with a scale of 1-7. This study uses the
Structural Equation Modeling (SEM) analysis method, a multivariate
technique that combines aspects of multiple regression and factor analysis to
estimate correlations that are interdependent Meanwhile, conclude several
definitions that SEM has characteristics that are as analytical techniques that
function to be more asserting than explaining.SEM is a multivariate
technique that functions to combine aspects of multiple regression and factor
analysis to estimate the interdependence relationship between variables used
in the research model simultaneously [5].
SEM is a second generation multivariate analysis technique that combines
factor analysis with path analysis, thus enabling researchers to test and
estimate simultaneously (together) the relationship between multiple latent
independent variables and multiple latent dependent variables with many
indicators. Meanwhile, according to Santoso (2018), SEM is an increasingly
popular statistical analysis tool which is a combination of factor analysis and
regression analysis. SEM has the ability to measure latent variables that are
not directly measured but through the estimation of indicators or parameters.
Unit of analysis for this research is a Startup that operates and is affiliated
with a local Startup development business incubator located in Jabodetabek,
West Java Province (excluding Depok & Bekasi), Central Java and Bali. The
intended startup is Person In Charge (PIC) / company founder / owner. The
PIC / founder / owner of the company here is not limited by age, gender and
position at the Startup. The company's PIC / founder / owner is only limited
to Startup, which is built by the business incubator. This research will be
carried out in a local Startup development business incubator located in
Greater Jakarta, West Java Province (excluding Depok & Bekasi), Central
Java and Bali.
This study uses quantitative methods and the technique of this study is a
survey, using a questionnaire instrument. In this study using primary data.
The method used in collecting primary data is done through a questionnaire
International Journal on Advanced Science, Education, and Religion (IJoASER)
42
or questionnaire method to obtain data about the dimensions of the construct
being developed using a 1-5 item Likert scale. Dimension data of the
variables analyzed in this study will be given to respondents using a scale of
1-5 obtaining interval data. Comprehensive analysis of all variables in the
research at the multivariate analysis stage was carried out by modeling the
SEM structural equation. In general SEM analysis techniques according to
Ghozali (2014) can be divided into 2 main characteristics: 1) estimation of
multiple interdependence relationships of many variables; 2) the ability to
present unobserved concepts in these relationships by involving measures of
error in the estimation process then connecting between construct variables
through a system of simultaneous equations. Estimates of model parameters
use maximum likelihood estimates. SEM is a multivariate technique that
functions to combine aspects of multiple regression and factor analysis to
estimate the interdependence relationship between variables used in the
research model simultaneously [6].
This study uses the Structural Equation Modeling (SEM) analysis
method, which is a multivariate technique that combines aspects of multiple
regression and factor analysis to estimate correlations that are interdependent
simultaneously [7]. conclude several definitions that SEM has characteristics
that are as analytical techniques that function to be more asserting than
explaining [8]. SEM is a second generation multivariate analysis technique
that combines factor analysis with path analysis, allowing researchers to test
and estimate simultaneously (together) the relationship between multiple
latent independent variables and multiple latent dependent variables with
many indicators [9]. Meanwhile SEM is an increasingly popular statistical
analysis tool which is a combination of factor analysis and regression
analysis [10]. SEM has the ability to measure latent variables that are not
directly measured but through the estimation of indicators or parameters
[11].
III. R
ESULTS
A
ND
D
ISCUSSION
This research was conducted on technology-based startups engaged in
eight focus areas, namely: food, health and medicine, energy, transportation,
information and communication technology, defense and security, raw
materials, and advanced materials located in Jabodetabek, West Java
Province (not included Depok & Bekasi), Central Java and Bali. The amount
number of samples was taken was 302 respondents who were Person In
Charge (PIC) / founder / owner at startups affiliated with business incubators.
The profile of these respondents is scattered with various demographic
criteria which are described in table 2 below.
Table 2. Demographic Profile of Respondents
Criteria Group Frequency Persentage
position of respondent Founder 147 48,68%
IJoASER,Volume 2,Issue 3, November,2019
DOI: 10.33648/ijoaser.v2i2.40
Copyriht: STAI Al-Furqan Makassar,Indonesia
Content License: CC-BY-SA
43
Owner 140 46,36%
Manager 12 3,97%
Etc 3 0,99%
Startup Field
Food 9 2,98%
Healthy and Medicine 1 0,33%
Energy 0 0,00%
Transportation 2 0,66%
Teknologi Informasi dan Komunikasi (ICT) 270 89,40%
Security and defense 0 0,00%
Raw Material 1 0,33%
Advanced Material 2 0,66%
Etc (Tourism) 17 5,63%
Domicile
Jabodetabek 162 53,64%
Jawa Tengah 60 19,87%
Jawa Barat (Diluar Bogor, Depok, Dan
Bekasi) 42 13,91%
Bali Dan NTB 25 8,28%
Wilayah Lainnya 13 4,31%
Age
18-23 17 5,63%
24-29 160 52,98%
30-35 113 37,42%
36-41 9 2,98%
> 41 3 0,99%
Sex
Man 222 73,51%
Women 80 26,49%
Education
< SLTA 1 0,33%
SLTA 6 1,99%
Diploma 38 12,58%
S1 215 71,19%
S2 42 13,91%
S3 0 0,00%
Expected on role of
government
Market Access 77 25,50%
Infrastructure Facilitation 114 37,75%
International Journal on Advanced Science, Education, and Religion (IJoASER)
44
Capital Facilitation 20 6,62%
Regulations and Laws 4 1,33%
Human Resources Development 85 28,15%
Inexpextation 2 0,66%
Source: Processed by Researcher, (2019)
Based on the prerequisite tests conducted to test the indicators used in
the research model, the results obtained are all the indicators used to test the
model meet the required criteria. The research model has fulfilled the
normality requirements through testing using critical ratio skewness criteria.
The results of the normality test showed that the research data had normal
distribution because the value of cr skewness and univariate kurtosis of all
indicators were in the interval of -2.58 <z <2.58 as well as the multivariate cr
value of 2.311 indicating that the multivariate cr had been in the interval -
2.58 <z <2.58.
Reliability test using the AVE and CR values based on the test results
on the indicator instrument shows the AVE value is equal to 0.5 and the CR
value has exceeded 0.7 which means that overall it has fulfilled validity and
has been reliable. Thus, the analysis process can be continued at the next
stage, which is the prerequisite test phase of SEM analysis. Then the results
of testing the validity using the EFA showed no cross loading between
indicator items in the tested variable, besides that the EFA test results showed
that no pattern matrix was formed. This shows that the tested indicator items
can be tested at a later stage.
CFA model depicted in Figure 2 has been modified. Modifications are
made by eliminating the indicator items on the model. Modifications were
made so that all the criteria in goodness of fit were met as it was seen that the
CFA model could be further analyzed because it had a probability score (P)
greater than 0.05 which was 0.084 and the score in the goodness of fit criteria
had been met. The next process is to create a path analysis model. Then in the
next process in the results of Figure 3 there is a proposed hypothesis model
that meets the goodness of fit (GoF) criteria with a probability value of 0.19
and all GoF criteria have been met. This makes hypothesis testing can be
done with the model that has been made.
IJoASER,Volume 2,Issue 3, November,2019
DOI: 10.33648/ijoaser.v2i2.40
Copyriht: STAI Al-Furqan Makassar,Indonesia
Content License: CC-BY-SA
45
Figure 2.Confirmatory Factor Analysis Covarians
Hypothesis suitability test is done by looking at the t-value, which is the
value of the critical ratio (CR) in the regression weight with the provisions if
the value is ≥ 1.96, then the research hypothesis is accepted with a probability
value of 0.05 or with a sign that indicates the value of *** less than 0.01 or
close to zero. Meanwhile, for testing H6 and H7, the indirect effect will be
tested through mediating variables using AMOS output by looking at scores
on indirect effects.
After testing the model, the next step is to look at the regression weight
value to find out whether the tested hypothesis is accepted or not. In table 3
there are the results of calculations from the hypothesis test which states the
relationship between the variables tested. In the results of these calculations
stated that all hypotheses tested were accepted. Because it shows the value of
the critical ratio which is above 1.96 and the probability value (P) which is
below 0.05 as in table 3 below
After testing the model, the next step is to look at the regression weight
value to find out whether the tested hypothesis is accepted or not. In table 3
there are the results of calculations from the hypothesis test which states the
relationship between the variables tested. In the results of these calculations
stated that all hypotheses tested were accepted. Because it shows the value of
International Journal on Advanced Science, Education, and Religion (IJoASER)
46
the critical ratio which is above 1.96 and the probability value (P) which is
below 0.05 as in table 3 below.
Figure3.Research model in the path diagram
Tabel 3. Regression Weight Model
Estimate S.E. C.R. P Label
IN <--- KM 0.3
0.11
1
2.70
8
0.00
7 par_13
IN <--- IB 0.267
0.11
5
2.32
2
0.02
0 par_14
SP <--- KM 0.234
0.09
9
2.36
4
0.01
8 par_15
SP <--- IB 0.209
0.10
2
2.04
6
0.04
1 par_16
SP <--- IN 0.192
0.06
7
2.85
5
0.00
4 par_17
Source: SEM AMOS Data Processing Software (2019).
The p value of the influence of the variable Knowledge Management
(KM) on the effectiveness of innovation (KMIN) is significant (p value =
0.007) with cr having a positive sign of 2,708. Because the p value obtained
<0.05 and cr is positive> 1.96, Ho is rejected and it is concluded that
Knowledge Management (KM) has a positive and significant effect on the
effectiveness of innovation, this indicates that the higher Knowledge
Management (KM) then the higher the effectiveness of innovation, and on
IJoASER,Volume 2,Issue 3, November,2019
DOI: 10.33648/ijoaser.v2i2.40
Copyriht: STAI Al-Furqan Makassar,Indonesia
Content License: CC-BY-SA
47
contrary. The results of the hypothesis test are in accordance with research
conducted by Plessis [12] and Akram [2] which states that there is an
influence between Knowledge Management on the Effectiveness of
Innovation.
The p value of the influence of the Incubator Business (IB) variable on
the effectiveness of innovation (IBIN) is significant (p value = 0.020) with
a positive cr marking of 2,322. Because the p value obtained <0.05 and cr is
positive> 1.96, Ho is rejected and it is concluded that the Incubator Business
(IB) has a positive and significant effect on the effectiveness of innovation,
this indicates that the higher the Incubator Business (IB) then the higher the
effectiveness of innovation too, and on contrary. The results of the hypothesis
test are in accordance with research conducted by Allahar et.al (2016) which
in his research concluded that business incubation shows the potential for
innovation development in business. Then in Menon et.al (2007) states that
business incubation is an institutional institution that develops an atmosphere
for innovation and the creation of a startup through the establishment of a
network of active interactions between academia, industry, and government
in order to create a liquid situation for sharing ideas, knowledge, experience
and facilities and the development of new technologies.
The p value of the influence of the Knowledge Management (KM)
variable on Startup Performance (SP) (KM SP) is significant (p value =
0.018) with a positive cr marking of 2,364. Because the p value obtained
<0.05 and cr is positive> 1.96, Ho is rejected and it is concluded that
Knowledge Management (KM) has a positive and significant effect on
Startup Performance (SP), this indicates that the higher Knowledge
Management ( KM), will make higher the Startup Performance (SP) too, and
on contrary. The results of the hypothesis test are in accordance with research
conducted by Studies conducted by Imamoglu et.al [13] which in his
research explores the relationship between organizational learning
(organizational learning), TQM (Total Quality Management), and innovation
with Firm Performance, where research results shows that these variables
have a positive relationship with company performance. Then Rofiaty et.al
(2015) states that the effect of Knowledge Management directly on
organizational performance is less than the effect on organizational
performance mediated by strategy implementation.
The p value of the effect of the Incubator Business (IB) variable on
Startup Performance (SP) is very significant (p value = 0.041) with a positive
cr of 2.046. Because the p value obtained <0.05 and cr is positive> 1.96, Ho
is rejected and it is concluded that Incubator Business (IB) has a positive and
significant effect on Startup Performance (SP), this statement indicates that
the higher Incubator Business (IB), will make higher the Startup Performance
(SP) too, and on contrary so on. The results of the hypothesis test are in
accordance with research conducted by Studies conducted by Laskar et.al
(2016) showing that business incubators play a role in supporting
performance by avoiding and minimizing the risk of failure through
International Journal on Advanced Science, Education, and Religion (IJoASER)
48
clustering opportunities, business support services, networking opportunities
and incubator space. Then research conducted by Dutta [14] concluded that
business incubators can help in the growth and development of a business
startup.
The value of p value of the effect of the Innovation Effectiveness (IN)
variable on the company against Startup Performance (SP) (IN SP) is very
significant (p value = 0.004) with a positive cr marking of 2,855. Because the
p value obtained <0.05 and cr is positive> 1.96, Ho is rejected and it is
concluded that the Effectiveness of Innovation has a positive and significant
effect on Startup Performance (SP), this shows that the higher the
Effectiveness of Innovation (IN) will make the higher the Startup
Performance (SP) too, and on contrary so on. The results of the hypothesis
test are in accordance with research conducted by the Study conducted by
and who found a positive relationship between innovation and family
company performance. This is supported by research conducted by Price et.al
showing strong support for innovation in family and non-family companies.
In this study, the mediation test Innovation variable (IN) which acts as a
mediating variable / intervening on the indirect effect of Knowledge
Management (KM) and Incubator Business (IB) variables on Startup
Performance (SP) uses a large value of direct and indirect influence. For the
large value of direct and indirect effects can be seen in the following table:
Table 4. Standardized Direct Effects (Group number 1 - Default model)
IB KM IN
IN 0.307 0.356 0
SP 0.260 0.300 0.208
Standardized Indirect Effects (Group number 1 - Default model)
IB KM IN
IN 0.000 0.000 0.000
SP 0.064 0.074 0.000
Source: SEM AMOS Data Processing Software (2019).
Furthermore, the Knowledge Management (KM) variable, based on
the table above, can be seen that the direct influence of Knowledge
Management (KM) on Startup Performance (SP) is 0.300, while the indirect
effect of Knowledge Management (KM) on Startup Performance (SP)
mediated by the innovation variable is 0.074. This shows that the magnitude
of the indirect effect is lower than the magnitude of the direct effect, this
indicates the role of the innovation variable in mediating the indirect effect of
the Knowledge Management (KM) variable on Startup Performance (SP).
Based on the table above, it can be seen that the direct effect of the
Incubator Business (IB) variable on Startup Performance (SP) is 0.260, while
the indirect effect of the Incubator Business (IB) variable on the Startup
Performance (SP) mediated by the engagement variable is of 0.064. This
IJoASER,Volume 2,Issue 3, November,2019
DOI: 10.33648/ijoaser.v2i2.40
Copyriht: STAI Al-Furqan Makassar,Indonesia
Content License: CC-BY-SA
49
shows that the magnitude of the indirect effect is lower than the magnitude of
the direct effect, this indicates the role of the innovation variable in mediating
the indirect effect of the Incubator Business (IB) variable on Startup
Performance (SP).
V. CONCLUSION
Based on the results of data processing and analysis of the entire research
data, the conclusion that can be obtained is that all hypotheses tested can be
accepted. Knowledge Management Variable (KM) has a positive and
significant effect on the Effectiveness of Innovation (IN). Based on this it can
be concluded that, this shows that if Knowledge Management (KM) is
increasing, it will increase the Effectiveness of Innovation (IN), and vice
versa. The Incubator Business (IB) variable has a positive and significant
effect on the Effectiveness of Innovation (IN). Based on this it can be
concluded that, this shows that if the Incubator Business (IB) available
performs their functions properly. the better the business incubator, the better
the Effectiveness of Innovation (IN). Besides that, the Incubator Business
(IB) variable has a positive and significant effect on the Effectiveness of
Innovation (IN). Based on this it can be concluded that, this shows that if the
Incubator Business (IB) available performs their functions properly. the
better the business incubator, the more effective the business actors will be in
innovating and vice versa. Variable Effectiveness Innovation (IN) has a
positive and significant effect on Startup Performance (SP). Based on this it
can be concluded that, this shows that if the Effectiveness of Innovation (IN)
in the available startup companies perform their functions properly. the better
the business incubator, the more will increase Startup Performance (SP), and
vice versa. Based on the hypothesis test the company must be able to improve
aspects of effectiveness in innovating in order to improve the performance of
startup companies.
up.
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