MASTER THESIS WITHIN: Business Administration NUMBER OF CREDITS: 15 PROGRAMME OF STUDY: International Financial Analysis (1 year) AUTHOR: Alina R. M. Toganel, Mengyao Zhu JÖNKÖPING August 2017 Success factors of accelerator backed ventures Insights from the case of TechStars Accelerator Program
49
Embed
Success factors of accelerator backed ventures1134316/FULLTEXT01.pdfaffect the success of accelerator backed startups by using the TechStars Accelerator as an example. However, the
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
MASTER THESIS WITHIN: Business Administration
NUMBER OF CREDITS: 15
PROGRAMME OF STUDY: International Financial
Analysis (1 year)
AUTHOR: Alina R. M. Toganel, Mengyao Zhu
JÖNKÖPING August 2017
Success factors of accelerator backed ventures
Insights from the case of TechStars Accelerator Program
i
Acknowledgments
The authors of this paper truly appreciate the support and encouragement that have been
received when writing this thesis.
We especially want to thank our supervisors Michael Olsson and Pingjing Bo for the guidance
and inspiration we have received throughout the process of this thesis.
We also want to show our gratitude to our seminar colleagues for their support and continuous
feedback that has helped us improve this paper.
ii
Master Thesis in Business Administration
Title: Success factors of accelerator backed ventures: Insights from the case of TechStars Accelerator Program Authors: A.R.M. Toganel and M. Zhu Tutor: Michael Olsson and Pingjing Bo Date: 2017-08-07
Key terms: entrepreneurs, startups, accelerator, TechStars, Logit model
Abstract
Different types of business incubators have been established worldwide in the last decade. As the
latest generation of incubation models, the accelerator provides a mix of services including
mentorship, office space, access to the latest technology and a network of investors, with an aim
to help ventures survive in the market. Meanwhile, startups are important to the society because
they help balance the labor market and make contributions to the economic growth.
The aim of this paper is to find the factors which best predict the success of new ventures based
on characteristics of entrepreneurs and ventures. This research utilizes a case study of TechStars
Accelerator and includes 640 startups from all industries and geographical regions which
participated in the programs between 2007 and 2015. The analysis employs two statistical models,
namely the Logit Model and the Ordinary Least Squares (OLS) Model.
This study finds that technology intensive ventures founded by a team of entrepreneurs are more
likely to succeed. Also, other variables such as the amount of funding, previous industry
experience and location have a positive effect on the success of accelerator backed startups.
2.3 Comparison between different funding sources ........................................................... 9
2.4 Common characteristics of successful startup alumni ............................................... 10
2.4.1 Characteristics of the entrepreneur(s) .......................................................................... 10 2.4.1.1 Gender .............................................................................................................................................................. 11 2.4.1.2 Size of founding team .................................................................................................................................... 12 2.4.1.3 Level of education ........................................................................................................................................... 12 2.4.1.4 Previous industry experience ........................................................................................................................ 12 2.4.1.5 Entrepreneurial experience ............................................................................................................................ 13
2.4.2 Characteristics of the firm .............................................................................................. 14 2.4.2.1 Amount of funding throughout the program ............................................................................................ 14 2.4.2.2 Age of firm ....................................................................................................................................................... 14 2.4.2.3 Geographic location ....................................................................................................................................... 15 2.4.2.4 Industry ............................................................................................................................................................. 15 2.4.2.5 Technology intensive ...................................................................................................................................... 16
3. Data ............................................................................................... 18
3.1 Case study ......................................................................................................................... 18
3.2 Data collection ................................................................................................................. 18
First of all, we group the 640 observations into successful and unsuccessful based on
whether they have received further funding. Then we summarize the traits of the
entrepreneurs and startups and compare all these traits between each case. As the data
shows in Table 2, successful startups have a higher percentage in entrepreneurs’ higher
education background, related industry experience and prior entrepreneurial experience
than unsuccessful startups. Besides, the average funding amount that successful startups
received is 6,548,599.16 dollars, which is around 15 times more than the 429,197.53 dollars
that unsuccessful startups received.
Table 2: Descriptive statistics
Successful startups Unsuccessful startups
Entrepreneur traits: Gender: Male (%) 78 77 Female (%) 5 4 Mixed (%) 17 19 Number of founders (mean) 2.4958 2.2284 Education level: High school (%) 1 2 Bachelor degree (%) 43 49 Higher education (%) 56 48 Industry experience (%) 82 73 Entrepreneurial experience (%) 62 50 Startups’ traits: Amount of funding (mean) $6,548,599.16 $429,197.53 Startups’ age (mean) 1.0272 1.1975 Location (%) 88 86 Industry of operation: Retail (%) 11 10 Finance (%) 9 9 Media (%) 25 28 Education (%) 8 6 Information technology (%) 38 38 Health (%) 9 10 Technology intensive (%) 68 49
28
In addition, 68 percent of the startups in the successful group are technology intensive,
while the unsuccessful group only has 49 percent. Therefore, we should pay attention to
these different characteristics in the following regression analyses, since they are more likely
to be the success drivers.
4.2 Regression analysis
Before running the regressions, we first perform a correlation analysis to study the
relationship between the independent variables and assess the presence of multicollinearity.
The correlation matrix is presented in Appendix 1. According to the result, we can find
that the correlation between male and mixed variables is -0.8455, while the correlation
between bachelor degree and higher education variables is -0.9690. This means that the
mentioned variables are strongly correlated, the value of one variable can be predicted by
the value of the other one. Hence, we deleted the variables male and bachelor degree when
running the regressions. Apart from this, there is no issue of multicollinearity between the
remaining variables and we can proceed to run the Logit regression. However, the
indicators under the gender, education level and industry of operation factors are defined
as a dummy, and according to the rule of dummy variables, we should delete the variables
with the highest occurrence in each category (Princeton University Library, 2007).
4.2.1 Logistic regression
Table 3 shows the result of the Logistic regression. According to the data, the variable
amount of funding is significant at 5 percent level while three other variables are significant
at 10 percent level, namely number of founders, female and technology intensive. The
corresponding marginal effects imply that the four independent variables are positively
related to the dependent variable. If the number of founders increases with 1 people, the
probability of success will increase by 0.0388 percent, and if the entrepreneur is female, the
probability of success will increase with 0.1628 percent. When looking into the amount of
funding variable, it indicates that an increase with 1 dollar will increase the probability of
success with 2.65E-07 percent. Also, if the startup is technology intensive, its probability of
success will increase by 0.0919 percent.
29
Table 3: Logistic regression results
Variable Marginal effect Prob.
Number of founders 0.0388 0.0818** Female 0.1628 0.0808** Mixed -0.0373 0.5087 High school 0.0210 0.8864 Higher education 0.0475 0.2782 Industry experience 0.0653 0.2175 Entrepreneurial experience 0.0348 0.4090 Amount of funding 2.65E-07 0.0000*** Startups’ age -0.0028 0.8419 Location -0.0403 0.5046 Retail 0.0579 0.4507 Finance 0.0420 0.5948 Media 0.0188 0.7571 Education 0.1071 0.2129 Health 0.0286 0.7154 Technology intensive 0.0919 0.0547**
Note: The independent variables with a statistical significance of 5%, 10%, and 15% are marked with ***, ** and *, respectively.
Apart from this, from the original Eviews output presented in Appendix 2 we can see that
the value of McFadden R squared is 0.3115. The result shows that the goodness of fit of
our Logit model is relatively low. But unfortunately, we did not see any discussions of the
R squared in previous studies. Hence, we cannot evaluate how good or bad our model is
compared to other models. However, the goodness of fit in binary regression models is not
as important as it in other regression models. What really matters is the variables’
coefficients and statistical significance.
4.2.2 Ordinary Least Squares (OLS) regression
For the second regression, we take the amount of funding as the dependent variable. As it
can be seen from Table 4, there are two variables significant at 5 percent level, one
significant at 10 percent level and four significant at 15 percent level. Looking at the
variables that describe an entrepreneur’s characteristics, the coefficient indicates that an
increase in the number of founders will lead to 0.125 percent increase in the funding
amount. Meanwhile, if an entrepreneur has related industry experience, the amount of
funding will increase with 0.0658 percent. Second, among variables that describe a startup’s
characteristics, if the startup is located in the same country as the accelerator program, it
will increase the startups’ funding amount by 0.0889 percent. Also, if the startup is
technology intensive and if it operates in the health industry, the amount of funding will
increase by 0.0695 percent and 0.0765 percent, respectively. Thirdly, according to our study,
the higher education and startups’ age variables have a negative effect on the amount of
30
funding. If the entrepreneurs with a higher education background increase with 1 people,
this will lead to a decrease in the amount of funding by 0.064. Almost the same effect can
be seen for a one year increase in the age of startup, which leads to a decrease in the
amount of capital raised by 0.0612 percent.
Table 4: OLS regression results
Variable Coefficient Prob.
Number of founders 0.1250 0.0027*** Female -0.0417 0.3079 Mixed -0.0152 0.7090 High school -0.0211 0.59761 Higher education -0.0640 0.1170* Industry experience 0.0658 0.1137* Entrepreneurial experience -0.0220 0.5800 Startups’ age -0.0612 0.1271* Location 0.0889 0.0274*** Retail -0.0171 0.7005 Finance 0.0018 0.9657 Media -0.0322 0.5084 Education -0.0248 0.5610 Health 0.0765 0.0822** Technology intensive 0.0695 0.1143* Note: The independent variables with a statistical significance of 5%, 10%, and 15% are marked with ***, ** and *, respectively.
As it shows in Appendix 3, the adjusted R squared is 0.0279, which is very low. This means
that the residuals have a strong effect on the regression line, the estimated regression line
may be biased to the true regression line. Therefore, the model does not ideally fit the
observations and this can be considered as one of the limitations of our study.
New ventures are critical for the evolution of society and growth of economy. They can
create more job positions for people who face employment pressure, increase the diversity
of marketplace, and help eradicate poverty in least developed areas. Entrepreneurs want to
bring new ideas into the market to meet consumers’ expectations and they need help to
develop their ideas into reality.
There are different entities such as governments, universities and investors encouraging the
development of new ventures. However, they lack a complete support system to nurture
the entities in their earliest stages. This gap is filled by incubators and accelerators. In this
study, we chose to focus on accelerators because there is not enough quantitative research
regarding such programs.
This paper aimed to find the success factors of the accelerator backed ventures. In order to
fulfill this purpose, we conducted a case study focus on TechStars Accelerator Programs.
We analyzed 640 startups which participated in the programs between 2007 and 2015,
using the Logit Model and the OLS Model.
Our study shows that a venture is more likely to succeed if it is a technology intensive
venture founded by a team of entrepreneurs with previous industry experience and a
sufficient amount of capital. Also, the OLS regression finds location and startup’s age to be
significant determinants of a startup’s success. If the startup joins the accelerator program
soon after it was founded, and it is located in the same region as the program, it has a
higher chance to survive and grow in the long term. Despite the findings of previous
research, this study finds that female entrepreneurs are more likely to establish successful
ventures.
These findings give an insight to the success factors of accelerator backed ventures. The
accelerators can take these features into account when selecting the startups which apply
for their programs. Apart from this, other investors such as venture capitalists,
governments and universities can also focus on supporting enterprises which have
participated in such programs and have the characteristics found in this study. If our study
36
can help more startups to succeed, then these startups can provide more job positions,
create diverse products and promote economic development worldwide.
However, because this is a case study focusing only on TechStars startups, its findings
should not be extrapolated to the entire pool of accelerator backed ventures, and limit itself
to a hypothetical base that can be further investigated by the authors or others. For future
studies, we suggest researchers to increase the sample size, take more accelerators into
account, and get more convincing results. Besides, if the time allows, they can use face-to-
face interview or questionnaire to obtain more personal and accurate data. Researchers can
also include more variables such as government support, company’s size and
entrepreneur’s race, conducting a cross-section analysis to compare the operating
differences in each country.
37
7. References
Ahl, H. (2006). Why research on women entrepreneurs needs new directions. Entrepreneurship Theory and Practice, 595-621.
Allison, P. (2013, 2). What's the best R-squared for Logistic regression? Retrieved from Statistical Horizons: https://statisticalhorizons.com/r2logistic
Baluku, M., Kikooma, J., & Kibanja, G. (2016). Psychological capital and the startup capital - entrepreneurial success relationship. Journal of Small Business and Entrepreneurship, 27-54.
Bannock, G. (2005). The economics and management of small business. An international perspective. New York: Routledge.
Barkham, R. J. (1994). Entrepreneurial characteristics and the size of the new firm: a model and an econometric test. Small Business Economics, pp. 117-125.
Barkham, R. J. (1994). Entrepreneurial characteristics and the size of the new firm: A model and an econometric test. Small Business Economics, 117-125.
Barney, J. (1991). Firm resources and sustained competitive advantage . Journal of Management, 99-120.
Barringer, B. R., Jones, F. F., & Neubaum, D. O. (2005). A quantitative content analysis of the characteristics of. Journal of Business Venturing, pp. 663-687.
Barringer, B. R., Jones, F. F., & Neubaum, D. O. (2005). A quantitative content analysis of the characteristics of. Journal of Business Venturing, pp. 663-687.
Barringer, B., Jones, F., & Neubaum, D. (2005). A quantitative content analysis of the characteristics of rapid growth firms and their founders . Journal of Business Venturing , 663-687.
Bates, T. (2005). Analysis of young, small firms that have closed: delineating successful from unsuccessful closures. Journal of Business Venturing, 343-358.
Bates, T. (2005). Analysis of young, small firms that have closed: delineating successful from unsuccessful closures. Journal of Business Venturing , 343-358.
Battistella, C., De Toni, A., & Pessot, E. (2017). Open accelerators for start-ups success: a case study. European Journal of Information Management, 80-111.
Baum, J., Li, S., & Usher, J. M. (2000). Making the next move: How experiential and 129 vicarious learning shape the locations of chains’ acquisitions. Administrative Science Quarterly, 766-801.
Bingham, C., & Davis, J. (2012). Learning sequences: Their existence, effect and evolution. Academy of Management Journal , 611-641.
Black, B., & Gilson, R. (1998). Venture capital and the structure of capital markets: banks versus stock markets. Journal of Financial Economics, 243-277.
Broström, A., & Baltzopoulos, A. (2013, 6). Higher education experiences and new venture.
Bruderl, J., Preisendorfer, P., & Ziegler, R. (1992). Survival chances of newly founded business organizations. American Sociological Review, 227-242.
Bruneel, J., Ratinho, T., Clarysse, B., & Groen, A. (2012). The evolution of business incubators: Comparing demand and supply of business incubation services across different incubator generations. Technovation, 110-121.
Brunet, S., Grof, M., & Izquierdo, D. (2016). Global Accelerator Report. Retrieved from Gust: http://gust.com/global-accelerator-report-2015/
38
Carroll, G., Bigelow, L., Seidel, M.-D., & Tsai, L. (1996). The fates of de novo and de alio producers in the American Automobile Industry. Strategic Management Journal, 117-137.
Cohen, S., & Hochberg, Y. V. (2014). Accelerating startups: The seed accelerator phenomenon. Retrieved from http://ssrn.com/abstract=2418000
Collewaert, V. (2012). Angel investors’ and entrepreneurs' intentions to exit their ventures: A conflict perspective . Entrepreneurship Theory and Practice, 753-779.
Collins, J., & Porras, J. (1995). Build to last: successful habits of visionary companies. London: Century Business.
Cooper, A., & Woo, C. (1989). Entrepreneurship and the initial size of firms. Journal of Business Venturing , 317-332.
Cooper, A., Gimeno-Gascon, F., & Woo, C. (1994). Initial human and financial capital as predictors of new venture performance . Journal of Business Venturing , 371-395.
Crampton, N. (2016, December). Government funding and grants for small businesses. Retrieved from Entrepreneur Magazine: http://www.entrepreneurmag.co.za/advice/funding/government-funding-funding/government-funding-and-grants-for-small-businesses/
Darity Jr., W. A., & Mason, P. L. (1998). Evidence on Discrimination in Employment: Codes of Color, Codes of Gender. Journal of Economic Perspectiv, pp. 63-90.
Deeds, D., Decarolis, D., & Coombs, J. (2000). Dynamic capabilities and new product development in high technology ventures: An empirical analysis of new biotechnology firms. Journal of Business Venturing, 211-229.
Delmar, F., & Shane, S. (2006). Does experience matter: The effect of founding team experience on the survival and sales of newly founded ventures. Strategic Organization, 215-247.
Delmar, F., Davids, P., & Gartner, W. B. (2003). Arriving at the high-growth firm. Journal of Business Venturing, 189-216.
DiMaggio, P., & Powell, W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American Sociological Review, 147-160.
Dumitru, A. (2017, 2 24). How should tech entrepreneurs choose an incubator or accelerator? Retrieved from Nordic Business Report: https://www.nbforum.com/nbreport/tech-entrepreneurs-choose-incubator-accelerator/
Easton, G. (2010). Critical realism in case study research. Industrial Marketing Management, pp. 118-128.
Eisenhardt, K., & Schoonhoven, C. (1990). Organizational growth: Linking founding team, strategy, environment, and growth among U.S. semiconductor ventures, 1978-1988. Administrative Science Quarterly , 504-529.
Fortmann-Roe, S. (2012, 5). Accurately Measuring Model Prediction Error. Frost, J. (2013, 6 13). Multiple regression analysis: use adjusted R-squared and predicted R-squared to
include the correct number of variables. Retrieved from The Minitab Blog: http://blog.minitab.com/blog/adventures-in-statistics-2/multiple-regession-analysis-use-adjusted-r-squared-and-predicted-r-squared-to-include-the-correct-number-of-variables
Gaskill, L., Van Auken, H., & Manning, R. (1993). A factor analytic study of the perceived causes of small business failure. Journal of Small Business Management, 18.
Gazdik, M. (2014). The TechStars story: How an accelerator used the power of giving to conquer the world . Retrieved from Startup Grind: https://www.startupgrind.com/blog/the-techstars-story-how-an-accelerator-used-the-power-of-giving-and-conquered-the-world/
39
Giles, D. E. (2013, 8 10). Large and Small Regression Coefficients. Retrieved from Econometrics Beat: Dave Giles' Blog: http://davegiles.blogspot.se/2013/08/large-and-small-regression-coefficients.html
Gimeno, J., Folta, T., Cooper, A., & Woo, C. (1997). Survival of the fittest? Entrepreneurial human capital and the peristance of underperforming firms. Administrative Science Quarterly, 750-783.
Glowik, M., & Sadowski, F. (2014). Success factors of international new venture firms - empirical case study of German SME. Journal of Economics & Management, pp. 175-191.
Gujarati, D. N., & Porter, D. C. (2009). Basic Econometrics. Gust. (2015). Asia & Oceania accelerator report 2015. Retrieved from Gust:
http://gust.com/asian-oceanian-accelerator-report-2015/ Gust. (2015). Global Accelerator Report 2015 . Retrieved from Gust: http://gust.com/global-
accelerator-report-2015/ Gust. (2015). Middle east accelerator report 2015. Retrieved from Gust: http://gust.com/asian-
oceanian-accelerator-report-2015/ Hair, J. F., Anderson, R. E., Black , W. C., & Tatham, R. L. (1995). Multivariate Data
Analysis With Readings. Hamel, G. (1991). Competition for competence and inter partner learning within
international strategic alliances. Strategic Management Journal, 83-103. Harriford, D., & Thompson, B. (2010, 7 23). Feminist Activists Find Peace in Thailand.
Retrieved from Ms.Magazine: http://msmagazine.com/blog/2010/07/23/feminist-activists-find-peace-in-thailand/
Haunschild, P. R. (1993). Interorganizational imitation: The impact of interlocks on corporate acquisition activity. Administrative Science Quarterly , 564-592.
Haunschild, P. R., & Miner, A. S. (1997). Modes of interorganizational imitation: The effects of outcome salience and uncertainty . Administrative Science Quarterly , 472-500.
Hoffman, D. L., & Kelley, N. R. (2012). Analysis of Accelerator Companies:An Exploratory Case Study of Their Programs, Processes, and Early Results. Small Business Institute, pp. 54-70.
Hoffman, D., & Radojevich-Kelley, N. (2012). Analysis of accelerator companies: An exploratory case study of their programs, processes, and early results . Small Business Institute Journal, 54-70.
Hosmer, D. W., Hosmer, T., Le Cessie, S., & Lemeshow, S. (1997). A comparison of goodness-of-fit tests for the logistic regression model. Statistics in Medicine, pp. 965-980.
Ibrahim, D. M. (2012). The new exit in venture capital. Vanderbilt Law Review, 1-47. International Labour Organization. (2016). World employment social outlook. Jaffe, A., Trajtenberg, M., & Henderson, R. (1993). Geographic location of knowledge
spillovers as evidenced by patent citations. The Quarterly Journal of Economics, 577-598.
Kalleberg, A., & Leicht, K. (1991). Gender and organizational performance: Determinants of small business survival and success. The Academy of Management Journal, 136-161.
Kalnins, A., & Williams, M. (2014). When do female-owned businesses out-survive male-owned businesses? A disaggregated approach by industry and geography. Journal of Business Venturing, 822-835.
Kerr, W., Lerner, J., & Schoar, A. (2010). The consequences of entrepreneurial finance: A regression discontinuity analysis. Harvard Business School. Working Paper.
40
Klepper, S. (2002). The capabilities of new firms and the evolution of the US automobile industry. Industrial and Corporate Change, 645-666.
Klepper, S., & Simmons, K. (2000). Dominance by birthright: Entry of prior radio producers and competitive ramifications in the. Strategic Management, 997-1016.
Lemann, N. (2015, 10 12). The Network Man-Reid Hoffman's Big Dreams. Retrieved from The New Yorker: http://www.newyorker.com/magazine/2015/10/12/the-network-man
Li, Y. (2008). Duration analysis of venture capital staging: A real options perspective . Journal of Business Venturing , 497-512.
Lieberman, M. B. (1984). The learning curve and pricing in the chemical processing industry. Journal of Economics, 213-228.
Miller, D., & Shamsie, J. (2001). . Learning across the life cycle: Experimentation and performance among the Hollywood studio heads. Strategic Management Journal, 725-745.
Miner , A., & Haunschild, P. (1995). Population level learning. Research in Organizational Behaviour , 115.
Miner, A., Bassoff, P., & Moorman, C. (2001). Organizational improvisation and learning: A field study. Administrative Science Quarterly, 304-337.
Mitchell, W. (1989). Whether and when? Probability and timing of incumbents’ entry into emerging industrial subfields. Administrative Science Quarterly , 208-230.
Morrissette, S. (2007). A profile of angel investors. The Journal of Private Equity, 52-66. Morrissette, S. (2007). A profile of angel investors. The Journal of Private Equity, 52-66. Parker, S. (2005). The economics of entrepreneurship: What we know and what we don’t. Retrieved
from Max Planck Institute for Economics: http://www.econ.mpg.de/files/2005/egpsummerinst05/papers/sparker-what_we_know_and_what_we_dont.pdf
Pawels, C., Clarysse, B., Wright, M., & Van Hove, J. (2016). Understanding a new generation incubation model: The accelerator. Technovation, 13-24.
Phan, P., Siegel, D., & Wright, M. (2005). Science parks and incubators: observations, synthesis and future research . Journal of Business Venturing, 165-182.
Pisano, G. (1994). Knowledge, integration, and the locus of learning: An empirical analysis of process development. Strategic Management Journal, 85-100.
Porter, M. (1998). Clusters and the new economics of competition. Harvard Business Review, 77-90.
Pouder, R., & St. John, C. (1996). Hot spots and blind spots: Geographical clusters of firms and innovation. The Academy of Management Review, 1192-1225.
Princeton University Library. (2007). Working With Dummy Variables. Retrieved from Data and Statistical Services: http://dss.princeton.edu/online_help/analysis/dummy_variables.htm
Reynolds, P. (1987). New firms: societal contribution versus potential. Journal of Business Venturing , 231-246.
Robb, A., & Watson, J. (2012). Gender differences in firm performance: Evidence from new ventures in the United States. Journal of Business Venturing, 544-558.
Roure, J. B., & Keeley, R. H. (1990). Predictors of sucess in new technology based ventures. Journal of Business Venturing, 201-220.
Roure, J. B., & Keeley, R. H. (1990). Predictors of sucess in new technology based ventures. Journal of Business Venturing, 201-220.
Roure, J., & Maidique, M. (1986). Linking prefunding factors and high-technology venture success: An exploratory study. Journal of Business Venturing , 295-306.
41
Sapienza, H., & Grimm, C. (1997). Founder characteristics, start-up process and strategy/structure variables as predictors of shortline railroad performance. Entrepreneurship, theory and practice, 5-24.
Sappin, E. (2016, 10 20). 7 Ways entrepreneurs drive economic development. Retrieved from Entreperneur: https://www.entrepreneur.com/article/283616
Schoonhover, C., Eisenhardt, K., & Lyman, K. (1990). Speeding products to market: Waiting time to first product introduction in new firms. Administrative Science Quarterly, 177-207.
Singer, B. (1995). Contours of development. Journal of Business Venturing , 303-329. Smerdon, X. (2015). Young People Facing ‘Intense’ Employment Pressure. Soetanto, D., & Jack, S. (2013). Business incubators and the networks of technology-based
firms . The Journal of Technology Transfer, 432-453. Song, M., Podoynitsyna, K., van der Bij, H., & Halman, J. I. (2008). Success factors in new
ventures: a meta-analysis. The Journal of Product Innovation Management, pp. 7-27. Stake, R. E. (1995). The art of case study research. Startiene, G., Remeikiene, R., & Dumciuviene, D. (2010). Concept of self-employment.
Economics and Management, 262-274. Stiles, A., Halt, G., Donch , J., & Fesnak , R. (2016). Intellectual property and financing strategies
for technology startups. Springer. Taylor, Z. (2016, 12 7). What is the advantage of using the log likelihood function versus the likelihood
function for maximum likelihood estimation? Retrieved from Quora: https://www.quora.com/What-is-the-advantage-of-using-the-log-likelihood-function-versus-the-likelihood-function-for-maximum-likelihood-estimation
Teal, E., & Hofer, C. (2003). The determinants of new venture success. strategy, industry structure, and the founding entrepreneurial team. The Journal of Private Equity, 38-51.
Techstars. (2017). Techstars 2016 Impact Report. Thompson, P. (2005). Selection and firm survival: Evidence from the shipbuilding industry,
1825-1914. The Review of Economics and Statistics, 26-36. US Department of Labour. (2016, April 28). Business Employment Dynamics. Entrepreneurship
and the U.S. Economy. Retrieved from Bureau of Labour Statistics: https://www.bls.gov/bdm/entrepreneurship/bdm_chart5.htm
Watson, W., Stewart, W. H., & Barnir, A. (2003, 3). The effects of human capital, organizational demography, and interpersonal processes on venture partner perceptions of firm profit and growth. Journal of Business Venturing, pp. 145-164.
Wiltbank, R., & Boeker, W. (2007). Returns to angel investors in groups. Kansas City: Ewing Marion Kauffman Foundation.
Wiltbank, R., Read, S., Dew, N., & Sarasvathy, S. (2009). Prediction and control under uncertainty: Outcomes in angel investing. Journal of Business Venturing , 116-133.
Yin, R. K. (1981). The case study as a serious research strategy. Science Communication, pp. 97-114.
Yin, R. K. (1992). The case study method as a tool for doing evaluation. Current Sociology, pp. 121-137.
Zacharakis, A. L., Meyer, G. D., & De Castro, J. O. (1999). Differing perceptions of new venture failure: A matched exploratory study of venture capitalists and entrepreneurs. Journal of Small Business Management, 1-14.
Zahra, S. A., Matherne, B. P., & Carleton, J. M. (2003). Technological resource leveraging and the internationalisation of new ventures. Journal of International Entrepreneurship, pp. 163-186.
42
Appendix 1: Correlation matrix of independent variables
FOUNDERS MALE FEMALE MIXED HS BA HIGHER IND_EXP ENT_EXP FUNDING AGE LOCATION RETAIL FINANCE MEDIA EDU IT HEALTH TECH