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The new enterprise model and the startup ecosystem during the crisis in Greece

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The new enterprise model and the startup ecosystem during the crisis in Greece

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The seminar will present the data sets, methodology and key findings of two AUEB funded research projects on the new entrepreneurial paradigm and the startup ecosystem that emerged during the recent economic crisis in Greece.

The seminar will present the data sets, methodology and key findings of two AUEB funded research projects on the new entrepreneurial paradigm and the startup ecosystem that emerged during the recent economic crisis in Greece.

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The new enterprise model and the startup ecosystem during the crisis in Greece

  1. 1. The new enterprise model and the startup ecosystem during the crisis in Greece Ioanna Sapfo Pepelasisa , Aimilia Protogeroub Ioannis Besisc, Spiros Paraskevasd a Professor Emerita, Department of Economics, AUEB, ipepelasis@aueb.gr b Researcher, Laboratory of Industrial and Energy Economics, NTUA, protoger@gmail.com c Business Analyst, Athens Stock Exchange, ioannisbesis1@gmail.com d Data Scientist, SPhears AI, spirosparaskevas@yahoo.gr 10 March 2021
  2. 2. Part 1 Ioanna Sapfo Pepelasis and Aimilia Protogerou (2018) , “A break with the past? The Shift from inward looking to internationally competitive and born global firms.” , Managerial and Decision Economics. Part 2 Part 2.1. Ioannis Besis and Ioanna Sapfo Pepelasis (2020), “Incubated early stage startuppers and their initiatives in Athens during the Crisis (2010‐2016)” Working Paper Series 01-2020, Department of Economics, AUEB. Part 2.2 Ioannis Besis, Ioanna Sapfo Pepelasis & Spiros Paraskevas (forthcoming 2021) In Progress Part 3 Next Steps General Introduction - Project Updates
  3. 3. Basic Research Questions 1. International Literature • Alexander (1964) • Cassis & Pepelasis Minoglou (2004) • Sifneos (2011) • Bitros & Pepelasis Minoglou (2007) • Hassid & Karayanis (1999) • Makridakis (1997) • Spanos (2001) • Protogerou, Caloghirou & Lioukas (2015) • Protogerou (2015) • Notta & Vlachvei (2014) • Doukidis (2015) • Malerba & MacKelvey (2016) Pepelasis, Protogerou (2018) - Introduction Part 1 Part 2.1. Part 2.2. Part 3
  4. 4. Figure 1: GDP growth in Greece (1960-2014) in comparison to the European South and the EU average (Source: World Bank) Figures 0 50 100 150 200 250 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Mining Construction Manufacturing Figure 2: Index of Greek industrial production: mining, construction and manufacturing (Source: ICAP HELLAS) 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 20000 Industry Services Figure 3: Total number of industrial and service firms in Greece (2004- 2014) (Source: ICAP HELLAS) Part 1 Part 2.1. Part 2.2. Part 3
  5. 5. The continuities as understood from our personal research and that of others in the existing literature are a mixture of the prevalence of: • micro-business (MB) • family capitalism (FC) • introversion (I) • unproductive entrepreneurship (UE) • low-level entrepreneurship (LLE) • shallow entrepreneurship (SE) • rent-seeking entrepreneurship (RSE). Having presented above the features of the dominant entrepreneurship paradigm, we should acknowledge that there have been two positive exceptions. The Primacy of Inward-looking orientation and Rent-seeking Entrepreneurship The traditional model of Greek entrepreneurship has displayed certain constant features, regardless of time period, some of which have been seen as an indication of Greece’s differentness from Western Europe. The one is intercontinental shipping, in which Greek-owned ships have been the number one global fleet for many decades now. The other is the few industrial pioneers of the past who, on the basis of innovation and extrovert entrepreneurial strategies, created Greek-based multinationals. Three important cases are those of: • Fillipou Brothers • Aristovoulos Petzetakis • Karelia Fine Tobaccos Part 1 Part 2.1. Part 2.2. Part 3
  6. 6. Our sample consists of 21 “successful firms,” in both manufacturing and services that had rising sales during the crisis and are export oriented. Exports varied from around 50% to 100% of sales and were usually nearer the latter figure. In fact, some of these firms were actually born global i.e. they operate internationally right from the start. We take into account 5 features and search for which one(s) accounted for success? Sample and Methodology Part 1 Part 2.1. Part 2.2. Part 3
  7. 7. Introversion Extroversion New Entrepreneurial Ecosystem Today.. A break with the Past? High (technical) level of education Born Global Use of innovations- technologies in both low and high-tech industries B2B Draw elements from the Greek tradition Self-financing Object: A study of 21 success stories (manufacturing and creative industries) Part 1 Part 2.1. Part 2.2. Part 3
  8. 8. • Our sample of firms contains a body of over 40 entrepreneurs. • Communalities. The protagonist entrepreneurs • First, almost all are Greek in origin, and the few foreigners are present basically in the team‐ based firms. • Moreover, they are mostly male who have founded their firms in their 30s and 40s or even younger. • Females appear sporadically. Second, entrepreneurs are well educated (some have studied and/ or worked abroad), internationalized in outlook, and surround themselves with highly qualified managerial personnel. Most importantly, we find entrepreneurs who have degrees in engineering or have had other forms of technical education, even in low‐technology sectors such as fashion and food. Third, the vast body of entrepreneurs has an important background in terms of financial and/or human capital. • The firms in our sample are the creations of amazing personalities: Greeks who dared to risk and who were flexible, perhaps unwittingly resembling somewhat the example of Greek diaspora merchants two centuries ago. • The new innovative and extrovert paradigm, embodying characteristics of high‐level and Baumolian productive entrepreneurship, does not share the characteristics of the mainstream traditional way of enterprising described in Section 4, except one: The presence of family in business although in a different context. In a nutshell, over a wide spectrum of activities (ranging from medicine to gaming) the protagonist entrepreneurs have created global cutting‐edge products by combining in a masterful way diverse elements such as high technological expertise and managerial skills, in many cases coupled with knowledge and practices embodied in Greek cultural heritage. Part 1 Part 2.1. Part 2.2. Part 3
  9. 9. THE NEW ENTREPRENEURIAL PARADIGM: WHY NOW? It is not easy to answer “Why now and not before?” • Changing environmental conditions due to the crisis—and also to the maturation of other long‐term trends unrelated (at least directly) to the crisis. • Post‐2008, the following three “crowding out” conditions specifically no longer hold true: The New Innovative and Extrovert Paradigm Part 1 Part 2.1. Part 2.2. Part 3
  10. 10. • This paper illustrates the emerging extrovert ecosystem of re-industrialization during the crisis years in Greece in an environment of slower growth in the global economy. It is important to underline the existence within the new ecosystem of companies founded pre- and post-2008, both in high- and low-tech sectors. • The new ecosystem constitutes a break with the past, but we cannot fail to note that there are certain persisting continuities, these being the positive side of family involvement in business and traditional handicrafts intermingling with modern technology (innovation), especially in the organization of production, marketing and branding. Inspiration is drawn from the specificities of Greek agricultural and pastoral traditions and Greek civilization and culture. There is a vision of “Greekness”, a Greek way of life and philosophy to be sold to the world, drawing upon the beauty of the country and the fact that Western civilization, democracy and philosophy were born in Greece. Suddenly “made in Greece” has become a brand name, a value added General Conclusions Part 1 Part 2.1. Part 2.2. Part 3
  11. 11. Part 2
  12. 12. Startup Ecosystem during the Crisis Part 2.1. Part 2.2. Ioannis Besis, Ioanna Sapfo Pepelasis & Spiros Paraskevas (forthcoming 2021) In the above dataset (300 startups), a combination of a feature selection algorithm and three different modeling families was tested out in order to predict the characteristics of startups that survived in 2020. Ioannis Besis and Ioanna Sapfo Pepelasis (2020) This research was constructed using a unique dataset of sixteen key socio- economic indicators for initially 255 (now 300) incubated early stage initiatives/startups and their founders in Athens throughout the crucial years of 2010-2016. The project maps this ecosystem, examines its drivers and makes preliminary predictions using descriptive statistics methodology. Part 1 Part 2.1. Part 2.2. Part 3
  13. 13. Besis, Pepelasis (2020) – International Literature • Albort-Morant, G. & Ribeiro-Soriano, D. (2015) • Antoniades, V., Giakoumelos, M., Petkakis, T. & Zacharia, Z. (2018) • Apergis, N., & Fafaliou, I. (2014) • Åstebro, T., & Bernhardt, I. (2003) • Bakouros, Y. L., Mardas, D. C., & Varsakelis, N. C. (2002) • Bosma, N. & Kelley, D. (2019) • Burgel, O., & Murray, G. C. (2000) • Cassis, Y. and Pepelasis Minoglou, I. (2006) • Cavusgil, S. T., & Knight, G. (2015) • Cefis, E., Marsili, O., (2003) • Coeurderoy, R., Cowling, M., Licht, G., Murray G., (2012) • Cook, R., Campbell, D., Kelly, C., (2012) • Enterprise Greece (2019) • Found.ation, EIT Digital & Velocity Partners. (2019) • Tsakanikas, A., Giotopoulos, I., Valavanioti, E. & Stavraki, S. (2019) • Kanellos S. N. (2013) • Lambropoulos, S., (2015) • Max Kuhn; Kjell Johnson (2013) • Mian, S. A. (1996) • Nicolò D., Nania I., (2017) • Pepelasis, I.S., Besis, I., Bournakis, I. and Papanastassiou, M. (2019) • Pepelasis, I. S., & Protogerou, A. (2018) • Protogerou, A., Caloghirou, Y., & Markou, F. (2015) • Ratinho, T. and Mitsopoulos, M, (2017) • Sofouli, E., & Vonortas, N. S. (2007) • Tortella, G. & Quiroga, G. (eds) (2013) • Vlachopoulou, M., Ziakis, C. & Petridis, K. (2017) • Vliamos, S. J., & Tzeremes, N. G. (2012) Part 1 Part 2.1. Part 2.2. Part 3
  14. 14. This paper is based on a unique database for Greece and it is the first to chart the features of start-up(per)s that have been incubated and it consists of information on: The Startup Ecosystem in the Crisis 443 Individual Entrepreneurs 255 Incubated Young Enterprises These were crucial years as the economic crisis was deepening. Our purpose is to provide insights into the socio- economic features of start-uppers and explain/discover what factor(s) determine(s) survival among new ventures / startups in Greece. We focused on the initial stage - i.e., nascent ventures and we measure success by checking longevity and survival. Part 1 Part 2.1. Part 2.2. Part 3
  15. 15. This paper is based on a unique database for Greece and it is the first to chart in detail the features of start-up(per)s and their ‘enterprises’ that have been incubated We have drawn our material from the following seven incubators, the largest in Athens (Greece) at the time : Data & Methods Egg ACEin MIT EF Greece Orange Grove Iqbility Ekinisi Lab NBG Seeds For our analysis we have relied on written material and videos from the following supplementary sources : 1. The web sites of incubators 2. The web sites of start-ups 3. Social media sites (LinkedIn, Facebook) and 4. Articles/interviews in the press on start-uppers and their enterprises. Also, we have relied on answers of start-upper ‘incubees’ to our questionnaire (the total number of questionnaires answered is twenty two); . The data set covers 443 individual entrepreneurs/founders and 255 incubated young enterprises for the years 2010-2016. Part 1 Part 2.1. Part 2.2. Part 3
  16. 16. Socio-Economic Indicators The first group consists of seven startupper/founder specific indicators: 1. Age 2. Gender 3. Level of Education 4. Field(s) of education 5. Variety in skills 6. Whether the company sector related to founder’s education 7. Whether the founder has experience abroad The second group consists of nine startup/firm specific indicators: 1. Geographical Location 2. Business Sector 3. High Tech vs Low Tech in terms of Sector 4. High Tech vs Low Tech in terms of process of production 5. Whether the good offered is a Physical Product or Service 6. Whether the good offered is b2c or b2b 7. Whether the startup has customers abroad 8. Number of founders per startup 9. Whether among the founders there exist relatives Part 1 Part 2.1. Part 2.2. Part 3
  17. 17. 0,26% 2,90% 36,94% 41,16% 3,96% 7,65% 3,17% 3,96% 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Distribution of the level of education and educational / professional status Entrepreneurs Characteristics (I) – Descriptive Statistics Part 1 Part 2.1. Part 2.2. Part 3
  18. 18. Entrepreneurs Characteristics (II) – Descriptive Statistics Part 1 Part 2.1. Part 2.2. Part 3 26,02% 73,98% Variety in skills (digression between undergraduate-graduate studies) Yes No 46,95% 53,05% Experience Abroad Yes No 50,40% 49,60% Company Sector related to founder's education Yes No
  19. 19. Startup Characteristics (I) – Descriptive Statistics Part 1 Part 2.1. Part 2.2. Part 3
  20. 20. Startup Characteristics (II) – Descriptive Statistics Part 1 Part 2.1. Part 2.2. Part 3
  21. 21. Startup Characteristics (III) – Descriptive Statistics Part 1 Part 2.1. Part 2.2. Part 3 18,91% 81,09% High Tech vs Low Tech High Low 23,83% 76,17% High Tech vs Low Tech (in Terms of Process) High Low
  22. 22. Conclusions – The profile of the incubee Startupper The startupper was predominantly under thirty, male; and with studies (in order of importance) in the following fields: engineering; business and economics; and computer science. Startup The initiative/ startup was predominantly established in Athens, but it was also the case that a little over one in ten was based abroad. The top sectors of startups in order of importance were: hardware- software; the creative industries; agriculture and leisure. Part 1 Part 2.1. Part 2.2. Part 3
  23. 23. Subgroups – Survival and Internationalization Part 1 Part 2.1. Part 2.2. Part 3
  24. 24. Univariate Analysis Survival Pearson's Chi-squared test (X-squared = 15.898, df = 8, p-value = 0.04386) Internationalization Pearson's Chi-squared test with Yates' continuity correction (X-squared = 17.41, df = 1, p-value = 3.013e-05) Part 1 Part 2.1. Part 2.2. Part 3
  25. 25. Model Mean Accuracy (10 fold Cross Validation) Lift from Naive Model # of Independent Variables Naive Model* 50% 0 0 GLM + MRMR 64.41% 28.82% 9 RF + MRMR 70.09% 40.18% 27 CART + MRMR 63.35% 26.7% 8 • Founding members average educational level • Founding members Educational Level Variance • Founding members mixed gender • Having achieved customers abroad • Lack of information on headquarters existence • Postgraduate studies on economics • Founding members experience abroad • Business sector in Health/Medicine, Tourism, Creative Industries, Agriculture • High tech processes internally • Studies in Engineering & Computer Science Startup Survival (log odds) = 0.097 +0.53*High_Low_Tech_Process(=Yes) - 0.69 * Education_Level_Variance (among founding members) + 0.63 * Education_Digression (=Yes) + 1.10 * Customer_Type_B2B_B2C + 0.91 * Customers_Abroad (=Yes) • Having achieved customers abroad • Applying high tech processes internally • Founding Members digressing from original studies • Founding members average educational level • Educational level variance among founding members Startups Survival Modeling Results In Progress Logistic Regression + MRMR Random Forest + MRMR Decision Trees + MRMR Part 1 Part 2.1. Part 2.2. Part 3
  26. 26. Surviving Startups Profiles 1. Achieved customers abroad & whose founding members attained highest education level. 4% of total population with 82% survival probability. 2. Achieved customers abroad & whose founding members have similar education levels. 12% of total population with 76% survival probability 3. Achieved customers abroad, whose founding members lower education level is compensated by applying high tech processes internally. 11% of the total population with 65% survival probability. Non Surviving Startups Profiles 1. No customers abroad, whose founding members educational level varied and did not digress from original studies. 38% of total population with 24% survival probability. 2. No customers abroad and low tech processes applied internally. 15% of total population with 42% survival probability. 3. No customers abroad, lower than average educational level and low tech internal processes. 9% of the total population with 38% survival probability. Startups Survival Modeling Insights Part 1 Part 2.1. Part 2.2. Part 3
  27. 27. In Progress Logistic Regression + MRMR Random Forest + MRMR Decision Trees + MRMR Startups Extroversion Modeling Results Model Mean Accuracy (10 fold Cross Validation) Lift from Naive Model # of Independent Variables Naive Model 50% 0 0 GLM + MRMR 66.71% 33.42% 11 RF + MRMR 72.73% 45.46% 29 CART + MRMR 60.67% 21.34% 10 Startup Customers Abroad (log odds) = - 0.47 + 0.89 * High_Tech (=Yes) - 1.11 * Engineering_Studies (=Yes) + 1.13 * Experience_Abroad • Founding members average educational level • Founding members Educational Level Variance • Founding members mixed gender • Having achieved customers abroad • Lack of information on headquarters existence • Postgraduate studies on economics • Founding members experience abroad • Business sector in Health/Medicine, Tourism, Creative Industries, Agriculture • High-tech processes internally • Studies in Engineering & Computer Science • Having members with experience abroad, • Studies in non engineering domain, • Having digressed from original studies & • Engaged in High-Tech business. • Founding members with Engineering studies. Understood as a revealing variable of business domain Part 1 Part 2.1. Part 2.2. Part 3
  28. 28. Startups Extroversion Modeling Insights Extrovert Startups Profiles 1. Founding members experience abroad, non engineering studies (in the sense of non applicable business type). 8% of the total population with 78% probability. 2. Founding members experience abroad, non engineering studies (in the sense of non applicable business type) & digression from original studies. 8% of the total population with 68% probability of extroversion. Decrease in extroversion probability from previous profile comes from the uncertainty wrt to the type of offering services regarding the customer. 3. Founding members with experience abroad, non engineering studies (in the sense of non applicable business type) have digressed from original studies and doing business engaged in High Tech domains. 14% of total population ~ 60% extroversion probability. Introvert Startups Profiles: 1. Founding members with no experience abroad and decreased educational level. 59% of total population with 25% extroversion probability. 2. An interesting profile: founding members with experience abroad, engaged in Low Tech business domains, & despite displaying higher education levels have not digressed from original studies. 6% of overall population, with 15% extroversion probability. Part 1 Part 2.1. Part 2.2. Part 3
  29. 29. Part 3
  30. 30. Final Conclusions and Next Steps Still trying to integrate the descriptive statistics with the Machine Learning Methods Few differences in the results of 2 databases (255, 300) There is no a great differences in the results between the 2 survival dates (2018, 2020) 2021: A new Era Part 1 Part 2.1. Part 2.2. Part 3
  31. 31. Thank you! Ευχαριστούμε!

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