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Software Startup
Ecosystems Evolution
The New York City Case Study
Daniel Cukier1, Fabio Kon1, and Thomas S. Lyons2
1 Univ...
Context
• Multiple case-study Tel-Aviv (2013/2014), 

São Paulo (2015) and New York (2015)
• Ecosystem conceptual framewor...
0
50
100
150
200
250
300
350
400
2005	
2006	
2007	
2008	
2009	
2010	
2011	
2012	
2013	
2014	
2015	
Literature
Source: Goog...
New York Case Study
• 25 semi-structured interviews with ecosystem agents
• initial contacts + Snowballing
• Entrepreneurs...
NYC Study Goals
• Map the NYC Ecosystem (minor)
• Validate and refine our 

Software Startup Ecosystem Maturity Model
• Fit...
Interview Protocol
• Mostly the same used in
• Tel-Aviv, Israel
• São Paulo
• A few new questions added to be able to answ...
Research Questions
• What are the minimum requirements for a startup
ecosystem to exist in its nascent stage?
• What are t...
Data sources
• Literature
• Crunchbase database
• 25 Interviews with experts
8
Findings about New York
• Evolved from M1 (late 1990s) to M4 in 2016
9
What are the stages that
ecosystems pass through?
M1
Nascent
M2
Evolving
M3
Mature
M4
Self-sustainable
10
Before the 2009 financial crisis
• NYC was mostly in the financial and services sectors.
• Engineers were comfortable with s...
Cultural shift in New York
City after the 2009 crisis
• Market crash made many tech talents loose their
jobs.
• They reali...
In addition to that
• Financial district office spaces were completely empty
and rental prices went down.
• To promote the ...
0	
100	
200	
300	
400	
500	
600	
700	
800	1996	1998	
2000	2002	2004	2006	2008	
2010	2012	2014	
Founded	 First	Investment	 ...
NYC startups
acquisitions and IPOs
Source: Our graph from raw Crunchbase data.
0	
20	
40	
60	
80	
100	
120	
140	
1996	
199...
• Bottom-up / entrepreneur-led
• Inclusive: foreign founders, 2x more
women than SV, from elderly to
children…
• Events: N...
What are the minimum requirements for a
startup ecosystem to exist in its nascent
stage?
• Talent and engaged entrepreneur...
What are the requirements for a startup
ecosystem to exist as a mature self-
sustainable ecosystem?
• At least three gener...
What are the stages that
ecosystems pass through?
M1
Nascent
M2
Evolving
M3
Mature
M4
Self-sustainable
19
Maturity Metric M1 M2 M3 M4
Exit Strategies none a few
several
M&A and
few IPO
several
M&A and
several IPO
Entrepreneurshi...
Metrics importance
Maturity Metric M1 M2 M3 M4
Exit Strategies
Entrepreneurship in universities
Angel Funding
Culture valu...
Can they regress or die?
• Yes, it is possible, but rare (natural disasters, wars,
persistent economic crisis)
• Transitio...
Can people proactively interfere in the evolution of
ecosystems? Is it possible to exist other M4
ecosystems?
• “what has ...
Conclusions
• New York, from 1990s to 2016 evolved from M1 to M4
• It is now a big global center for Fintech and Media
sta...
Limitations and Future Work
• Interviewees are biased by their knowledge of what
today is considered a successful path for...
We want your collaboration!
• Get in touch with us to
• provide your feedback on the maturity model
• include your local e...
from this slide on, slides are
optional and can be used for
question and answer session
27
Generalized Map of a Software Startup Ecosystem
28
Simplified Generalized Map
29
4 Maturity Levels
M1
Nascent
M2
Evolving
M3
Mature
M4
Self-sustainable
30
4 levels - Nascent (M1)
When the ecosystem is already recognized as a
startup hub, with already some existing startups, a
...
4 levels - Evolving (M2)
Ecosystems with a few successful
companies, some regional impact,
job generation and small local
...
4 levels - Mature (M3)
Ecosystems with hundreds of startups,
where there is a considerable amount
of investment deals, exi...
4 levels - Self-sustainable (M4)
Ecosystems with a high startups and
investment deals density, at least a 2nd
generation o...
• Bottom-up / entrepreneur-led
• Inclusive
• Rallying points (events)
• Long-term perspective
M4 aligned with Brad Feld’s
...
Objectives
• Propose a methodology to measure ecosystem maturity
based on multiple factors
• Base the maturity model on th...
Methodology
• Elements of the conceptual model become factors
• For each factor, we defined 3 levels
• started with our ini...
Differences in Version 2
• New Angel Funding essencial factor
• Access to funding changed to summing factor
• Factors measu...
Maturity Model - Long
version
• 22 factors - 10 essential, 12 summing
• Maturity Level is not a binary measurement,
classi...
FACTORS L1 L2 L3
Exit strategies 0 1 >=2
Global market <10% 10-40% > 40%
Entrepreneursip in universities < 2% 2 - 10% > 10...
FACTORS L1 L2 L3
Human capital quality > 20th 15 - 20th < 15th
Culture values for entrepreneurship < 0.5 0.5 - 0.75 > 0.75...
Absolute measured factors
FACTORS L1 L2 L3
Number of startups < 200 200 - 1k > 1k
Access to funding # of deals / year < 50...
Exit strategies Accelerators quality
Global market High-tech companies presence
Entrepreneursip in universities Establishe...
TEL AVIV SÃO PAULO NEW YORK
Essential
Factors
L3 (9) L2 (9) L3 (10)
Summing
Factors
L2 (7), L3 (6) L1 (8), L2 (5) L2 (4), ...
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Software Startup Ecosystems Evolution - The New York City Case Study

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Startup ecosystems started to emerge in different parts of the world since the 2000s. They normally focus on specific areas and present different characteristics and dynamics. Also, each ecosystem has its own path of evolution over time. In this paper, we analyze the dynamics of these ecosystems by taking the New York case study as an example. We show what stages ecosystems pass through, the requirements for each of these phases and why it is possible for many ecosystems to evolve until reaching a self-sustainable level of maturity. This research can bring a better understanding of the evolution of software startup ecosystems and what role their actors play in this development. The results can be used as a basis for ecosystem stakeholders to reflect and act concretely on improving the maturity of their local environments.

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Software Startup Ecosystems Evolution - The New York City Case Study

  1. 1. Software Startup Ecosystems Evolution The New York City Case Study Daniel Cukier1, Fabio Kon1, and Thomas S. Lyons2 1 University of São Paulo - Dep. of Computer Science, Brazil 2 City University of New York, New York, NY, USA 1
  2. 2. Context • Multiple case-study Tel-Aviv (2013/2014), 
 São Paulo (2015) and New York (2015) • Ecosystem conceptual framework and its core elements • Each ecosystem has its own characteristics and must find ways to evolve • Ecosystem characterization is a dynamic process and it must be analyzed over time 2
  3. 3. 0 50 100 150 200 250 300 350 400 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Literature Source: Google Scholar Papers containing the term "startup ecosystem" 3
  4. 4. New York Case Study • 25 semi-structured interviews with ecosystem agents • initial contacts + Snowballing • Entrepreneurs (14), investors (4), scholars (4), and other players (3) • 17 males and 8 females • Average age: 42 (std 11) • CEO, COO, CTO, lawyer, professor, manager, founding partner, and writer • 100% undergraduate degree, 38% had a master’s or MBA and 13% were PhDs 4
  5. 5. NYC Study Goals • Map the NYC Ecosystem (minor) • Validate and refine our 
 Software Startup Ecosystem Maturity Model • Fit NYC into our maturity model • Investigate the evolution of an ecosystem over time 5
  6. 6. Interview Protocol • Mostly the same used in • Tel-Aviv, Israel • São Paulo • A few new questions added to be able to answer the following research questions. • Available at 
 ccsl.ime.usp.br/startups/publications 6
  7. 7. Research Questions • What are the minimum requirements for a startup ecosystem to exist in its nascent stage? • What are the requirements for a startup ecosystem to exist as a mature self-sustainable ecosystem? • What are the stages that ecosystems pass through? Can they regress or die? • Can people proactively interfere in the evolution of ecosystems? Is it possible to create ecosystems as evolved as, e.g., the Silicon Valley? 7
  8. 8. Data sources • Literature • Crunchbase database • 25 Interviews with experts 8
  9. 9. Findings about New York • Evolved from M1 (late 1990s) to M4 in 2016 9
  10. 10. What are the stages that ecosystems pass through? M1 Nascent M2 Evolving M3 Mature M4 Self-sustainable 10
  11. 11. Before the 2009 financial crisis • NYC was mostly in the financial and services sectors. • Engineers were comfortable with salaries paid in the financial market. • But, in 2009 the financial market crashed... 11
  12. 12. Cultural shift in New York City after the 2009 crisis • Market crash made many tech talents loose their jobs. • They realized they were not as safe as they believed. • The opportunity cost of starting a new company seemed smaller and taking the risk was no longer such an issue. • Investors started to look for new investment opportunities (outside of financial markets). 12
  13. 13. In addition to that • Financial district office spaces were completely empty and rental prices went down. • To promote the recovery of real state, financial district owners offered free co-working space for new startups, with the hope that their growth in the future could bring more real state business to the district. • Many tech people decided to invest in their education (Masters, PhDs) => more basis for tech companies 13
  14. 14. 0 100 200 300 400 500 600 700 800 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 Founded First Investment acquisitions Companies founded in NYC and first investment deals Source: Our graph from raw Crunchbase data. 14
  15. 15. NYC startups acquisitions and IPOs Source: Our graph from raw Crunchbase data. 0 20 40 60 80 100 120 140 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Acquisitions IPOs 15
  16. 16. • Bottom-up / entrepreneur-led • Inclusive: foreign founders, 2x more women than SV, from elderly to children… • Events: NY Tech Meetup, Digital.NYC, entrepreneurship programs • Long-term perspective: Cornell Tech, New York Angels, 3 generations M4 - Boulder thesis alignment [Brad Feld 2012] 16
  17. 17. What are the minimum requirements for a startup ecosystem to exist in its nascent stage? • Talent and engaged entrepreneurs from the beginning • High-quality research Universities • Presence of big tech companies 17
  18. 18. What are the requirements for a startup ecosystem to exist as a mature self- sustainable ecosystem? • At least three generations reinvesting their wealth • Angel investors and mentorship • Exit opportunities: M&A and IPOs • Media keeps momentum and entrepreneurship awareness 18
  19. 19. What are the stages that ecosystems pass through? M1 Nascent M2 Evolving M3 Mature M4 Self-sustainable 19
  20. 20. Maturity Metric M1 M2 M3 M4 Exit Strategies none a few several M&A and few IPO several M&A and several IPO Entrepreneurship in universities < 2% 2-10% ~ 10% >= 10% Angel Funding irrelevant irrelevant some many Culture values for entrepreneurship < 0.5 0.5 - 0.6 0.6 - 0.7 > 0.7 Specialized Media no a few several plenty Ecosystem data and research no no partial full Ecosystem generations 0 0 1-2 >=3 Events monthly weekly daily > daily Maturity Model - Short version 20
  21. 21. Metrics importance Maturity Metric M1 M2 M3 M4 Exit Strategies Entrepreneurship in universities Angel Funding Culture values for entrepreneurship Specialized Media Ecosystem data and research Ecosystem generations Events Legend very important important not important 21
  22. 22. Can they regress or die? • Yes, it is possible, but rare (natural disasters, wars, persistent economic crisis) • Transition between stages is smooth and takes years • “the ecosystem evolution is a one-way street, because created conditions are self-reinforced” • “These are very rare situations [to regress or die], thus the natural path is evolution” 22
  23. 23. Can people proactively interfere in the evolution of ecosystems? Is it possible to exist other M4 ecosystems? • “what has happened in NYC can happen anywhere that has the entrepreneurial spirit and the freedom to innovate” • “You can create a vibrant long-term startup community anywhere in the world” • Culture is key: it changes but takes time 23
  24. 24. Conclusions • New York, from 1990s to 2016 evolved from M1 to M4 • It is now a big global center for Fintech and Media startups • Financial crisis of 2009 played important role • It was a combination of the presence of human talent with proper support from institutions 24
  25. 25. Limitations and Future Work • Interviewees are biased by their knowledge of what today is considered a successful path for technology startups. • Medium and small local (non-tech) companies were not included in the study and need to be further investigated • Is culture a limiting factor? • How to measure ecosystem connectivity and density? 25
  26. 26. We want your collaboration! • Get in touch with us to • provide your feedback on the maturity model • include your local ecosystem in the classification • Prof. Fabio Kon <fabio.kon@ime.usp.br> • Daniel Cukier <danicuki@ime.usp.br> 26
  27. 27. from this slide on, slides are optional and can be used for question and answer session 27
  28. 28. Generalized Map of a Software Startup Ecosystem 28
  29. 29. Simplified Generalized Map 29
  30. 30. 4 Maturity Levels M1 Nascent M2 Evolving M3 Mature M4 Self-sustainable 30
  31. 31. 4 levels - Nascent (M1) When the ecosystem is already recognized as a startup hub, with already some existing startups, a few investment deals and maybe government initiatives to stimulate or accelerate the ecosystem development, but no great output in terms of job generation or worldwide penetration. 31
  32. 32. 4 levels - Evolving (M2) Ecosystems with a few successful companies, some regional impact, job generation and small local economic impact. To be in this level, the ecosystem must have all essential factors classified at least at L2, and 30% of summing factors also on L2 32
  33. 33. 4 levels - Mature (M3) Ecosystems with hundreds of startups, where there is a considerable amount of investment deals, existing successful startups with worldwide impact, a first generation of successful entrepreneurs who started to help the ecosystem grow and be self- sustainable. To be in this level, the ecosystem must have all essential factors classified at least at L2, 50% of summing factors also on L2, and at least 30% of all factors on L3 33
  34. 34. 4 levels - Self-sustainable (M4) Ecosystems with a high startups and investment deals density, at least a 2nd generation of entrepreneur mentors, specially angel investors, a strong network of successful entrepreneurs compromised with the long term maintenance of the ecosystem, an inclusive environment with many startups events and presence of high quality technical talent. To be in M4, the ecosystem must have all essential factors classified as L3, and 80% of summing factors also in L3. 34
  35. 35. • Bottom-up / entrepreneur-led • Inclusive • Rallying points (events) • Long-term perspective M4 aligned with Brad Feld’s model 35
  36. 36. Objectives • Propose a methodology to measure ecosystem maturity based on multiple factors • Base the maturity model on the ecosystem core elements (taken from the conceptual framework) • Help ecosystem agents to identify what are the next steps required for evolution • Propose a theory about Startup Ecosystem evolution and dynamics • Secondary: compare ecosystems 36
  37. 37. Methodology • Elements of the conceptual model become factors • For each factor, we defined 3 levels • started with our initial guess • refined in 2 steps with a dozen experts from at least 3 ecosystems • Version 1 published and workshopped • Version 2 refined from • Workshop feedback • New York ecosystem observations and experts feedback 37
  38. 38. Differences in Version 2 • New Angel Funding essencial factor • Access to funding changed to summing factor • Factors measured by absolute values changed to relative • Short version • Metrics importance table optional slide 38
  39. 39. Maturity Model - Long version • 22 factors - 10 essential, 12 summing • Maturity Level is not a binary measurement, classification is fuzzy • Some factors measurements are relative to size and there is no linearity when going to higher levels 39
  40. 40. FACTORS L1 L2 L3 Exit strategies 0 1 >=2 Global market <10% 10-40% > 40% Entrepreneursip in universities < 2% 2 - 10% > 10% Mentoring quality < 10% 10-50% > 50% Bureaucracy > 40% 10 - 40% < 10% Tax Burden > 50% 30 - 50% < 30% Accelerators quality (% success) < 10% 10 - 50% > 50% Access to funding US$ / year <200M 200M-1B > 1B Maturity Model - Long version 40
  41. 41. FACTORS L1 L2 L3 Human capital quality > 20th 15 - 20th < 15th Culture values for entrepreneurship < 0.5 0.5 - 0.75 > 0.75 Technology transfer processes < 4.0 4.0 - 5.0 > 5.0 Methodologies knowledge < 20% 20 - 60% > 60% Specialized media players < 3 3-5 > 5 Startup Events monthly weekly daily Ecosystem data and research not available partially fully Ecosystem generations 0 1 2 Maturity Model - Long version 41
  42. 42. Absolute measured factors FACTORS L1 L2 L3 Number of startups < 200 200 - 1k > 1k Access to funding # of deals / year < 50 50 - 300 > 300 Angel Funding # of deals / year < 5 5 - 50 > 50 Incubators / tech parks 1 2 - 5 > 5 High-tech companies presence < 2 2 - 10 > 10 Established companies influence < 2 2 - 10 > 10 per 1 million inhabitants 42
  43. 43. Exit strategies Accelerators quality Global market High-tech companies presence Entrepreneursip in universities Established companies influence Number of startups Human capital quality Access to funding US$ / year Culture values for entrepreneurship Angel Funding Technology transfer processes Access to funding # of deals / year Methodologies knowledge Mentoring quality Specialized media players Bureaucracy Ecosystem data and research Tax Burden Ecosystem generations Incubators / tech parks Startup Events Essential / Summing factors 43
  44. 44. TEL AVIV SÃO PAULO NEW YORK Essential Factors L3 (9) L2 (9) L3 (10) Summing Factors L2 (7), L3 (6) L1 (8), L2 (5) L2 (4), L3 (8) Maturity Level Mature (M3) Evolving (M2) Self-sustainable (M4) Ecosystems Comparison 44

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