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Technology Startups in Tunisia: Specificities and finding challenges
- 1. 1 Technology Startups in Tunisia Specificitiesand funding challenges Elaborated by: Kaouther ADHOUM Tutored by: Professor Chokri Mamoghli
- 2. 2 AGENDA Introduction Review articles Sampleand variables Methodology and Empirical Results Conclusion
- 3. 3 AGENDA Introduction Review articles Sampleand variables Methodology and Empirical Results Conclusion
- 4. 4 Theinnovation’smarket isavery competitivemarket, and theideas which areconsidered astheorigin of thesecompaniesarerarely converted to aformal study and aconcreteconceptualization to becomeafter thisphasethestarting point of thecompany. In general, only 20% of theproposed ideasturn into concreteBusiness opportunities(Adamaand al, 1994). Introduction Review Articles Sampleand variables Methodology and results Conclusion 4
- 5. 5 Startupsareintroducing anew BusinessModel and they are changing theway theeconomy isevolving. Technology entrepreneursarefacing many challengesto enter marketsmainly in funding: Lack of funding at early stages Inadequacy of classical funding sourceswith their needs 5 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 6. 6 6 New funds New Markets Innovation Entrepreneurship Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 7. 7 Objective: Identify theparticularitiesof Tech startupsand thegap factors between existing funding sourcesand thespecific needsof Inovativeenterprises. 7 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 8. 8 Problem: How Tech startupsaregetting funded in Tunisia, what arethe challengesthey arefacing and how thegapsbetween their needsand theexisting funding areaffecting them ? 8 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 9. 9 Research Questions: What aretheparticularitiesof Tech funding and their impact on theBusinessmodel and financial structureof Technology Startups? Arethefinancial challengesthat aTechnological entrepreneur facestoday atruerestraint to thedevelopment of Tech entrpreneurship? What arethecriteriataken in consideration for thechoiceof a funding sourceover another? What arethealternativefunding sourcesthat could beadequate to Tech startups? 9 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 10. 10 AGENDA Introduction Review articles Sampleand variables Methodology and Empirical Results Conclusion
- 11. 11 Startupsemerged to bethenew faceof entrepreneurship, a moreinnovativeopportunistic and dynamic way to create valueand set up abusiness. 11 Entrepreneurship as an economic Growth Vector Audretsch(2006) p58, globalization combined to technological change, in particular breakthrough information technology and communication emerged entrepreneurship asa significant organizational form that generatesinnovation and economic growth. Marchesnay (1995) « Theconcept of entrepreneurship isoneof themost controversial and meaningful concepts of strategic analysis». In fact, dueto its economic contribution and the interactionswith different market actors, thereisno onesingleunanimous definition of entrepreneurship. Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 12. 12 12 Tech Entrepreneurship Specificities Albert and Mougenot (1988) Theinnovativefirm isacompany facing intentionally theeconomic uncertainty. Thisuncertainty hastwo aspects. It could be“technical/ technological” or it could berelated to competitors. RedisJ. (2007) “A startup isayoung company, with acoreactivity that hasan intensivetechnological content and had received aVC funding.” Park (2005) High Tech enterprisesarecompaniesthat useor invest in an emerging technology or arapidly-growing technology asakey development factor of their products, their production cycleor their marketing strategy. Albert, 2000; Benavent and Verstraete, 2000; Bernasconi, 2000; Samuelson, 2001 • A high demand for resources • It’sahigh intellectual intensity field that requiresadependenceto acertain expertiseand aknow-how that iscommunicated and cumulated in thehuman resources • A high dependenceto new technologies, adifficulty encountered in developing, launching and marketing of thenew product/service • Theimportanceof partnership relationshipswith other social actors. Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 13. 13 13 Venture Capital Angel Investors Seed Funding Investment Funds Private/Public Entrepreneur Personal Savings Bootrstrapping RegularDebt Private/Public Credit institutions Funding Sources Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 14. 14 14 Funding sources and alternatives forTech startups financingFunding sources and alternatives forTech startups financing [McMillan, Zemann and Subbanarasimba (1987); Pratt (1995); Amit, Branderand Zott (1998); Sapienza and De Clercq (2000); Davila, Fosterand Gupta (2003); Chang (2004)]VentureCapital funding emblematic funding sourceadapted to theinvestment donein technological field, being a major successfactor for the technological startup performance Manigart and Struyf (1997), Venture capitalistsand Business Angels are not competing funding sources, business angelsaremore present in thepre- startup phase(pre- creation) whilethe VentureCapital institutionsprefer to invest on more advanced phaseswith moreimportant amounts. Bronwyn H. Hall (2002), thedifference between external and internal funding: • information asymmetry between theinventor and theinvestor • themoral hazard related to theinventor or the innovativeentrepreneur • Agency costsand fiscal regulation that differs between external funding sourcesand thenon distributed earnings. Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 15. 15 15 Funding Technology obeys to new selection criteria Information Assymetry Entrepreneur Behaviour Shumpeter(1942), Nelson (1959) and Arrow (1962). Innovation wastoo risky of an investment mainly dueto thefact that it's considered asarisky activity and theROI isgenerally uncertain and not steady in time. Modigliani and Miller(1958); Brigham and Gapenski (1997) Classic Models: theVentureCapital funding isconsidered by the entrepreneur asan expensivefunding source. Thereisaclear preference for themultipledebt financing techniquesbut technological startups, by their nature, areconsidered astoo risky for adebt funding becauseof lack of assessment toolsavailableto financial institutionsfor such projects. Manigart and Struyf (1997), thechoiceof afunding source includesalso thecomponent of company capital dilution and thelevel of involvement of theentrepreneur along with hisstrategy for the company's’ longevity, aswell astherisk that could beinduced by seeking for each funding sources. Risk Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 16. 16 Authorswho tried investigating thevariablesin funding decision: Minola and Girogino (2008), their objectivewasto identify themost determinant variablesin theselection of afunding sourcefor an innovativestartup in Italy, determine thenatureof correlation between thosevariablesand thepossiblefinancing alternativesand how they can influencethefundscollection process. Koch, Kuhn, Gruenhagen, and Hisrich (2010), they tried to model thestructureof new technological startup financing, allowing them to explain therelation between the averagecost of capital and debt ratio for astartup and then show thelimitsof MM theorem in theexplanation of financing choicesin thepreciseframework of high technology sector. Berglof (1994) orRepullo and Suarez (2000). Cassamatta (2003) and Inderst and Mueller(2003) studied theoptimal contracting between an entrepreneur and an investor in thecasewherethemoral-hazard problem isdouble-sided. Ueda (2002) offered amodel of entrepreneurial financewhereventure-capitalistsand banksaretwo modesof financing characterized by different contracting inefficiencies: venture-capitalistsarewell informed about theentrepreneur’sproject but can possibly appropriatetheentrepreneur’sidea. By contrast, bankscannot, but they contract under asymmetric information. 16 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 17. 17 Tech Startups in Tunisia Thenumber of companiesoperating in thetechnology R&D field according to theAPI and FIPA numbersare9 companiesin 2013 and thefirmsoperating in theservicesfield is nearly 2717, they createbetween 3000 and 4000 jobsevery year. Thereare7 cyberparcs with startup incubatorsthat host nearly 50 projectsevery year. Technology firmsin Tunisiabenefit from multipleincentives: Tax reduction and incentives, Investment bonus, Dedicated funds(RIICTC), Public (BFPME/BTS) and rivatefunding (VentureCapital, Seed funding, BusinessAngelsand PrivateBanks). 17 Number of startupsbased in Technpolisin Tunisia Source: Tunisian Ministry of ICT Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 18. 18 AGENDA Introduction Review articles Sampleand variables Methodology and Empirical Results Conclusion
- 19. 19 19 Sample Population consisting of 79 startupsbased in thedifferent technopolises (El Ghazala, Rades, Sfax, Monsatir). Final sample: 34 firms Selection criteria: astartup isayoung company (lessthan 10 years) working in the technological field, bringing innovation by thetypeof serviceand/or product offeringsshe has. Theconcept of Innovation taken in consideration hereisabreakthrough technology innovation such asan innovation or adiscovery and/or an added-valueor alteration to an existing product or service, achangein theway of production, theprocessof delivery or even thebrand imagecould beconsidered asan innovation. Period: 08/01/2013- 31/03/2013 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 20. 20 20 Data Creation date Equity Funding sourceschosen Company Size(number of employees) Financial structure Financial Ratio Debt Ratio Geographical implantation/Location Funding source Theentrepreneur'sgender Thetax regimeadopted Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 21. 21 21 Variables: CapSoc: for Social capital of thestartup, acontinuousvariable, weselected 6 intervalsof study Size: measured by thenumber of employees, discretevariable Age: discretevariables EF: Entrepreneur funding ratio (contribution in thetotal startup capital), ratio variable BA: BusinessAngelsfunding ratio, ratio variable SeedFunds: Seed fundsfunding ratio, ratio variable VC: Venturecapital funding ratio, ratio variable MotherC: Mother Company funding ratio, ratio variable FOPRODIS: FOPRODISfunding ratio Other: Other funding sourcesratio Location: Location, nominal variable FiscalRegiman: Totally exporting or partially exporting startup, nominal variable EntGender: Entrepreneur gender, nominal variable Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 22. 22 AGENDA Introduction Review articles Sampleand variables Methodology and Empirical Results Conclusion
- 23. 23 Methodology: To describetherelationship between theidentified variablesand thefunding source choicewechosetheMultiplecorrespondenceanalysis(MCA) which isaderivation or for othersthecounterpart of theprincipal component analysis(PCA). The particularity of thisdataanalysistechniqueisthat it hasan explanatory effect on categorical qualitativedata, it aimsmainly to reducethenumber of dimensionsset in thedataand present in abi-dimensional format. For our analysisin SPSS, Thevariableschosen aredescriptivecategory variables p=13 and thenumber of objectswehaveisn=34 23 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 24. 24 Model Theobjectiveof theanalysisisto find amatrix X and aset of matrix (for j= 1,.....,p) for thefollowing theobjectivefunction to beminimal Under thenormalization constraint : wheretheobject-matrix is: And isthe sx sidentity matrix. 24 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 25. 25 1. MCA Analysis: TheCronbach’sAlphaAnalysis: - Dimension 1: 87,3% - Dimension 2: 83,1% Eigen Value: High descriptivepower of thedimension and ability to transcript clearly the behavior of thevariablesin thedimensionspresented. Inertia: Those2dimensionsexplain or synthesize72,6% of theinitial raw information extracted from thevariables. 25 Thereliability index israted assatisfactory for both dimensionsasit'sincluded between 0,7 and 0,9. Models summary Represented Variance Dimension Cronbach's Alpha Total (eigen value) Inertia 1 ,873 5,148 ,396 2 ,831 4,295 ,330 Total 9,443 ,726 Average ,854a 4,721 ,363 a. The average Cronbach's Alpha is based on the proper average value. Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 26. 26 2. Correlation Analysis a. Correlation Matrix b. Correlation Matrix Axis Analysis Axis1 iscorrelated with thenumber of employees, theVC funding ratio and theFOPRODIS funding ratio. Wecan assimilatethisaxisasreferenceof thestartup maturity. It refersto the criteriathat could impact afunding choicefrom savvy external sourcesthat usually opt for cyclical funding and prefer developed startupsover beginning ones. Axis2 iscorrelated with theFOPRODISfunding ratio, other sourcesfunding ratio and startup location. Thisaxisrefer to thestartup attractivenessand readinessfor external funding sources. 26 Business Angels Funding Ratio Entrepreneur Funding Ratio VC Funding Ratio Startup Capital Company Size Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 27. 27 3. Homogeneity Analysis Most of theobservationsareconcentrated towardsthecenter which showsahigh homogeneity and showsthat most of thestartupsarefunded by oneor two external institutional investors. Only two extremevaluesweredetected (funded by morethan 4 sources) which leadsto say that thecapital structureisavaluablefactor in theconstruction of theaxeswith ahigh descriptivevalue. 4. Discrimination Analysis • dimension 1 islinked to thestartup age, entrepreneur funding ratio, BusinessAngelsfunding ratio. • For dimension 2 startup location and other internal sourcesarethe closest value, it reflectstheintrinsic valueof thestartup. 27 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 28. 28 5. Main Coordinates Analysis 28 Axis1 Indicator for readinessof thestartup and itsopennessto external investors Axis2 Intrinsic valueof thestartup Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 29. 29 Results: • Euclidean distance between the variables allowsusto assessthemodalities’ scattering. 1. BusinessAngelsFunding ratio and Seed fund ratio arereally closewhich could be explained by the fact that thosetwo opportunistic funding sourcesvaluethe technology proposed by thestartup and itspositioning on themarket asafirst choicecriteriathat could bereflected in thelow distancebetween thestudies variables. 2. thecapital structuredependson thedifferent interactionsbetween thefunding sourcesand they often aremotivated or repulsed by thesamestartup specificities and related opportunities. • Correlation detectssimilarity in thebehaviour between funding sourcesthat have identical operation modeand selection process. Thebehavior of thevariablesis mainly explained by thetwo dimensions(thestartup maturity and thestartup intrinsic value). Although thosevariablesareevenly weighted but variableslike funding sourcesratiosand thestartup equity sutructurehaveamoreimportant explanatory weight in thefunding decision. 29 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 30. 30 AGENDA Introduction Review articles Sampleand variables Methodology and Empirical Results Conclusion
- 31. 31 Thereisno school of thought that advocatesaclear funding sourcethat could competewith debt financing and venturecapital funding, and at thesametimebeing adapted to thenatureof thetechnological startup activity. Still therearemany researchesthat were done to explorenew funding sources. Funding decision relieson aset of criteriaand environmental characteristicsthat affect thedecision of choosing afunding sourceover another and thosecriteriaare mainly induced by the reality of thefinancial market and theentry gatesof certain financing sources. Funding decisionsarenot only influenced by thescarcity of resourcesor thefunding cycletimelines, it’salso highly linked to theentrepreneur’snature, theimportanceof theinvestment and thespecific innovation brought to themarket by thestartup. Therearenew funding sourcesthat areviableand can support theriskinessof the startup Businessmodel: Crowdfunding, Bootstrapping. 31 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 32. 32 Contribution Describethedifferent relationshipsthat might exist between thefunding decision and other internal and external factorsto thestartup. Definetheparticularitiesof theTunisian Startupsbehaviour in their funding search. 32 Introduction Review Articles Sampleand variables Methodology and results Conclusion
- 33. 33 Future Research Implications Litteratureislimited in termsof analysisof theTechnological entrepreneur behaviour and itsimpact on thefinancial structureof astartup Theexisting studiesdid not present aclear valueof thealternativenew funding sourcesand their impact on thefinancial structureof astartup, on profitability and what aretheclear cutting advantagescompared to moretraditional ones. 33 Introduction Review Articles Sampleand variables Methodology and results Conclusion
Editor's Notes
- Enrepreneurship drives new sources of funding and create Business opportunities, they often either enter established markets with an innovation and create a new market/A new need by their innovation
- “technical/ technological” uncertainty: or This is the case of when there is no guarantee when starting the business and that the startup will be able to find or develop the needed technical and human requirements for the new activity (Allegret and al, 1998, p.10). Uncertainity related to competitors When the company is not able to predict the offer and demand evolution in the new markets and it can not confirm when or whether or not the innovative service or product will be accepted by the targeted market.
- Bhide (1992), referring to bootstrapping funding, made the following statement “Without question, some start-ups powered by other people’s money have rocketed to success. . . Agency costs: the separation between the prerogatives of the shareholder and those of the manager
- New technological startups are often killed at birth because of the lack or absence of funding sources at the early phases of creation (Bygrave et Timmons 1992)
- Minola and Giorgino (2008): The model is based on the study of different variables: the competencies and the profiles of management team, entrepreneurship orientation and growth evolution, the project profitability, the usage of external funding according to the timing and evolution phase of the project, the technology, the size, the assets of the company and the market on which it operates. The contribution of this research has a double interest: first of all, the model is based on reliable evaluation criteria for complex and ambiguous dimensions like technological risk and the attention to the market; second of all, they developed a holistic approach of the firm understanding from the entrepreneur point of view and the investor point of view Koch, Kuhn, Gruenhagen, and Hisrich (2010), the authors developed a model of capital costs dedicated to startups and innovative SMEs starting at the assumptions issued by Modigliani and Miller regarding the existence of an optimal capital structure, markets efficiency and constant interest rates. This analysis allowed to explain the relation between the average cost of capital and debt ratio for a startup and then show the limits of MM theorem in the explanation of financing choices in the precise framework of high technology sector. Ueda (2002) Depending on the nature of the project and the strength of property rights, one or the other mode will be optimal. If property rights are perfectly enforced, venture-capital is always the optimal form of financing. The proposed model endogenizes the staging of investment as the solution to an optimal contracting problem.
- Technoplois: dispose of startup incubators, they support entrepreneurship initiatives and new businesses
- Multiple Correspondence Analysis (MCA) also known as homogeneity analysis is a factorial method to study the link that may exist between a number of qualitative variables ( more than 2). It's a descriptive exploratory technique for analyzing multidimensional data to describe the relationship between two or more categorical variables ( ordinal or nominal ) providing a graphical representation of their categories in the form of a cloud of points ( points bands) projected into a sub- space of small dimensions . MCA may also be applied as a principal component analysis of categorical data . When there is no linear relationship between variables or when the variables are nominal , the homogeneity analysis is preferred over a normed principal components analysis. The main advantage of MCA is that it could process almost any type of data (organized or not, discrete, continuous. We rather talk about categories than variables, it's either the values, the modalities or the classes.
- The introduction of the filter allows us to control the fact that none of the active observed values is out of the interval [1, ]. The object matrix defines for each object i the set of active observations of the analysis (= 1) and the supplementary observations (=0) The factorial coordinates of each object are centered which can be written in this format: U’ O X=O where u is the n x 1- constant vector of scalar component equal to 1. n: number of observations or objects p: number of variables (or criteria) s: number of dimensions (or factors) for each criteria j, j = 1,....,m vector nx1 of categorical observations number of categories or modalities of the criteria j Matrix n x of modalities indicator for the criteria j Matrix element of = {1 if the observation i belongs to the category k of the criteria j , 0 otherwise} Filter matrix n x n of the observations indicators for the criteria j Matrix element of = {1 if the observation i belongs to the interval [1, ], 0 otherwise} Diagonal matrix of weights containing the marginal effectives of criteria modalities D Diagonal matrix of the marginal of modalities effectives The matrix of the factorial coordinates are: X matrix n x s of factorial coordinates of observations (objects) according to s dimensions matrix x s of factorial coordinates for the modalities of the criteria j according to s dimensions. Y Concatenated matrix x p of the factorial coordinates for the whole set of modalities
- The maximum number of dimensions doesn't exceed generally: Number of modalities - number of variables not having any missing value OR Number of observations - 1 In our case we kept it for a number of 2 dimensions, it's always more simple to interpret the results of a low number of dimensions especially when the added associations don't add much value to the factors analysis.
- The startup capital-Entrepreneur funding ratio Correlation: entrepreneur's own equity is correlated to the startup capital. This could be explained by the fact that the entrepreneur is the first and in most of the cases the main investor in the startup and the capital evolution is induced by the entrepreneur shares evolution. The entrepreneur funding ratio - size of the company Correlation: Researches showed that the first funding source for technological startups is the entrepreneur's own resources. This makes the size of the business highly conditioned by the ability of the entrepreneur of funding his firm and how big he can leverage those funds to extend his business. Size of the company - Venture capital funding ratio Correlation as Venture capital generally appear at more advanced stages in the startup lifecycle. Being at a mature size and already showing signs of viability, it's the perfect time for a startup to attract VC funding. This also impacts the relationship between VC funding ratio and startup age, more advanced startups are more attractive and showed to be good candidates for VC funding. Business Angels funding ratio - Venture Capital funding ratio Correlation. As seen through the literature review, Business Angels and Venture Capitalists are not competing funding sources, they are more complementary. They usually approach the startup at different evolution stages but they are motivated by the same business reasons: capital yield, interest in the technology and market expertise that allow them to be on the high-potential projects.
- Other variables like startup capita, entrepreneur age and fiscal regimen are very close to the origin, it's understandable as those variables are common to both dimensions and the startup could be similar in the amount of capitals invested, in the fiscal regimen and in the entrepreneur gender. The discrimination measurement is the variance of the variable thought a given dimension. The discrimination diagram indicate the discriminatory variables for that dimension.
- we excluded the entrepreneur's gender and the tax regimen, they are considered as additional variables with low impact on the dimension structure and minor correlation with the rest of the variables. Axis 1 high concentration of Entrepreneur funding ratio, Business Angels funding, Venture capital funding, startup capital and startup age variables => this axis should be the maturity indicator for the startup. It indicates the readiness of the startup and its openness to external investors. Axis 2 we can see that the condensation: age of the startup, Mother company funding ratio and startup capital. This axis describes more the intrinsic value of the startup and it's own survival assets given its maturity.
- In a study done on biotechnology startups in California during the early 90s, Willoughby & Blakely demonstrated that only 6% of the studied population and venture capital was the first funding source of 12% of those startups. It means that 4 out 5 of the studied startups started their businesses with funds collected from informal and non institutional sources.