Technology Startups in Tunisia: Specificities and finding challenges

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Technology Startups in Tunisia

Specificities and funding challenges

Elaborated by: Kaouther ADHOUMTutored by: Professor Chokri Mamoghli

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AGENDA Introduction Review articles Sample and variables Methodology and Empirical Results Conclusion

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The innovation’s market is a very competitive market, and the ideas which are considered as the origin of these companies are rarely converted to a formal study and a concrete conceptualization to become

after this phase the starting point of the company. In general, only 20% of the proposed ideas turn into concrete Business opportunities (Adama and al,

1994).

Introduction Review Articles

Sample and variables

Methodology and results

Conclusion

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Startups are introducing a new Business Model and they arechanging the way the economy is evolving.

Technology entrepreneurs are facing many challenges to entermarkets mainly in funding: Lack of funding at early stages Inadequacy of classical funding sources with their

needs

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Conclusion

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New funds

New Markets

InnovationEntrepreneurship




Conclusion

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Objective:Identify the particularities of Tech startups and the

gap factorsbetween existing funding sources and the specific

needs ofInovative enterprises.

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Conclusion

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Problem:How Tech startups are getting funded in Tunisia, what are thechallenges they are facing and how the gaps between theirneeds and the existing funding are affecting them ?

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Conclusion

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Research Questions: What are the particularities of Tech funding and

their impact on the Business model and financial structure of Technology Startups?

Are the financial challenges that a Technological entrepreneur faces today a true restraint to the development of Tech entrpreneurship?

What are the criteria taken in consideration for the choice of a funding source over another?

What are the alternative funding sources that could be adequate to Tech startups? 9




Conclusion

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Startups emerged to be the new face of entrepreneurship, amore innovative opportunistic and dynamic way to create

value and set up a business.

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Entrepreneurship as an economic Growth Vector

Audretsch (2006) p58, globalization combined to technological change, in particular breakthrough information technology and communication emerged entrepreneurship as a significant organizational form that generates innovation and economic growth.

Marchesnay (1995) « The concept of entrepreneurship is one of the most controversial and meaningful concepts of strategic analysis». In fact, due to its economic contribution and the interactions with different market actors, there is no one single unanimous definition of entrepreneurship.




Conclusion

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Tech Entrepreneurship SpecificitiesAlbert and Mougenot (1988)

The innovative firm is a company facing intentionally the economic uncertainty. This uncertainty has two aspects. It could be “technical/ technological” or it could be related to competitors.

Redis J. (2007) “A startup is a young company, with a core activity that has an intensive technological content and had received a VC funding.”

Park (2005) High Tech enterprises are companies that use or invest in an emerging technology or a rapidly-growing technology as a key development factor of their products, their production cycle or their marketing strategy.

Albert, 2000; Benavent and Verstraete, 2000; Bernasconi, 2000; Samuelson, 2001

• A high demand for resources• It’s a high intellectual intensity field that requires a dependence to a certain expertise and a know-how that is communicated and cumulated in the human resources• A high dependence to new technologies, a difficulty encountered in developing, launching and marketing of the new product/service• The importance of partnership relationships with other social actors.




Conclusion

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Venture Capital

Angel Investors

Seed Funding

Investment FundsPrivate/ Public

EntrepreneurPersonal SavingsBootrstrapping

Regular Debt Private/ Public

Credit institutions

FundingSources




Conclusion

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Funding sources and alternatives for Tech startups financing

[McMillan, Zemann and Subbanarasimba (1987); Pratt (1995); Amit, Brander and Zott (1998); Sapienza and De Clercq (2000); Davila, Foster and Gupta (2003); Chang (2004)]Venture Capital funding emblematic funding source adapted to the investment done in technological field, being a major success factor for the technological startup performance

Manigart and Struyf (1997), Venture capitalists and Business Angels are not competing funding sources, business angels are more present in the pre-startup phase (pre-creation) while the Venture Capital institutions prefer to invest on more advanced phases with more important amounts.

Bronwyn H. Hall (2002), the difference between external and internal funding: • information asymmetry between the inventor and the investor • the moral hazard related to the inventor or the innovative entrepreneur• Agency costs and fiscal regulation that differs between external funding sources and the non distributed earnings.




Conclusion

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Funding Technology obeys to new selection criteria

InformationAssymetry

Entrepreneur

Behaviour

Shumpeter (1942), Nelson (1959) and Arrow (1962).Innovation was too risky of an investment mainly due to the fact that it's considered as a risky activity and the ROI is generally uncertain and not steady in time.Modigliani and Miller (1958); Brigham and Gapenski (1997) Classic Models: the Venture Capital funding is considered by the entrepreneur as an expensive funding source. There is a clear preference for the multiple debt financing techniques but technological startups, by their nature, are considered as too risky for a debt funding because of lack of assessment tools available to financial institutions for such projects.Manigart and Struyf (1997), the choice of a funding source includes also the component of company capital dilution and the level of involvement of the entrepreneur along with his strategy for the company's’ longevity, as well as the risk that could be induced by seeking for each funding sources.

Risk




Conclusion

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Authors who tried investigating the variables in funding decision:

Minola and Girogino (2008), their objective was to identify the most determinant variables in the selection of a funding source for an innovative startup in Italy, determine the nature of correlation between those variables and the possible financing alternatives and how they can influence the funds collection process.

Koch, Kuhn, Gruenhagen, and Hisrich (2010), they tried to model the structure of new technological startup financing, allowing them 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.

Berglof (1994) or Repullo and Suarez (2000). Cassamatta (2003) and Inderst and Mueller (2003) studied the optimal contracting between an entrepreneur and an investor in the case where the moral-hazard problem is double-sided.

Ueda (2002) offered a model of entrepreneurial finance where venture-capitalists and banks are two modes of financing characterized by different contracting inefficiencies: venture-capitalists are well informed about the entrepreneur’s project but can possibly appropriate the entrepreneur’s idea. By contrast, banks cannot, but they contract under asymmetric information.

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Conclusion

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Tech Startups in Tunisia

The number of companies operating in the technology R&D field according to the APIand FIPA numbers are 9 companies in 2013 and the firms operating in the services

field isnearly 2717, they create between 3000 and 4000 jobs every year. There are 7

cyberparcswith startup incubators that host nearly 50 projects every year.

Technology firms in Tunisia benefit from multiple incentives : Tax reduction andincentives, Investment bonus, Dedicated funds (RIICTC), Public (BFPME/BTS) andrivate funding (Venture Capital, Seed funding, Business Angels and Private Banks).

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Number of startups based in Technpolis in Tunisia

Source: Tunisian Ministry of ICT




Conclusion

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Sample

Population consisting of 79 startups based in the different technopolises (El Ghazala, Rades, Sfax, Monsatir).

Final sample: 34 firms

Selection criteria: a startup is a young company (less than 10 years) working in the technological field, bringing innovation by the type of service and/or product offerings she has. The concept of Innovation taken in consideration here is a breakthrough technology innovation such as an innovation or a discovery and/or an added-value or alteration to an existing product or service, a change in the way of production, the process of delivery or even the brand image could be considered as an innovation.

Period: 08/01/2013- 31/03/2013




Conclusion

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Data

Creation date Equity Funding sources chosen Company Size (number of employees) Financial structure Financial Ratio Debt Ratio Geographical implantation/Location Funding source The entrepreneur's gender The tax regime adopted




Conclusion

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Variables:

CapSoc: for Social capital of the startup, a continuous variable, we selected 6 intervals of study

Size: measured by the number of employees, discrete variable Age: discrete variables EF: Entrepreneur funding ratio (contribution in the total startup capital),

ratio variable BA: Business Angels funding ratio, ratio variable SeedFunds: Seed funds funding ratio, ratio variable VC: Venture capital funding ratio, ratio variable MotherC: Mother Company funding ratio, ratio variable FOPRODIS: FOPRODIS funding ratio Other: Other funding sources ratio Location: Location, nominal variable FiscalRegiman: Totally exporting or partially exporting startup, nominal

variable EntGender: Entrepreneur gender, nominal variable




Conclusion

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Methodology: To describe the relationship between the identified variables and the

funding sourcechoice we chose the Multiple correspondence analysis (MCA) which is a

derivationor for others the counterpart of the principal component analysis (PCA). Theparticularity of this data analysis technique is that it has an explanatory

effect oncategorical qualitative data, it aims mainly to reduce the number of

dimensions set inthe data and present in a bi-dimensional format.

For our analysis in SPSS, The variables chosen are descriptive category variables

p=13 and the number of objects we have is n=34 23




Conclusion

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Model

The objective of the analysis is to find a matrix X and a set of matrix (for j= 1,.....,p)

for the following the objective function to be minimal

Under the normalization constraint : where the object-matrix is: And is the s x s identity matrix.

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Conclusion

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1. MCA Analysis:

The Cronbach’s Alpha Analysis:- Dimension 1: 87,3%- Dimension 2: 83,1%

Eigen Value: High descriptive power of the dimension and ability to transcript clearly the

behavior of the variables in the dimensions presented.Inertia: Those 2dimensions explain or synthesize 72,6% of the initial raw informationextracted from the variables.

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The reliability index is rated as satisfactory for both dimensions as it's included 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.




Conclusion

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2. Correlation Analysisa. Correlation Matrix

b. Correlation Matrix Axis Analysis Axis 1 is correlated with the number of employees, the VC funding ratio and the

FOPRODIS funding ratio. We can assimilate this axis as reference of the startup maturity. It refers to the criteria that could impact a funding choice from savvy external sources that usually opt for cyclical funding and prefer developed startups over beginning ones.

Axis 2 is correlated with the FOPRODIS funding ratio, other sources funding ratio and startup location. This axis refer to the startup attractiveness and readiness for external funding sources.

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Business Angels

Funding Ratio

Entrepreneur

FundingRatio

VC Funding

Ratio

Startup Capital

CompanySize




Conclusion

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3. Homogeneity Analysis Most of the observations are concentrated towards the center which

shows a high homogeneity and shows that most of the startups are funded by one or two external institutional investors.

Only two extreme values were detected (funded by more than 4 sources)

which leads to say that the capital structure is a valuable factor in the construction of

the axes with a high descriptive value.

4. Discrimination Analysis • dimension 1 is linked to the startup age, entrepreneur

funding ratio, Business Angels funding ratio. • For dimension 2 startup location and other internal

sources are the closest value, it reflects the intrinsic value of the startup. 27




Conclusion

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5. Main Coordinates Analysis

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Axis 1 Indicator for readiness of the startup and its openness to external investors

Axis 2 Intrinsic value of the startup




Conclusion

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Results:• Euclidean distance between the variables allows us to assess the

modalities’ scattering.

1. Business Angels Funding ratio and Seed fund ratio are really close which could be explained by the fact that those two opportunistic funding sources value the technology proposed by the startup and its positioning on the market as a first choice criteria that could be reflected in the low distance between the studies variables.

2. the capital structure depends on the different interactions between the funding sources and they often are motivated or repulsed by the same startup specificities and related opportunities.

• Correlation detects similarity in the behaviour between funding sources that have identical operation mode and selection process. The behavior of the variables is mainly explained by the two dimensions (the startup maturity and the startup intrinsic value). Although those variables are evenly weighted but variables like funding sources ratios and the startup equity sutructure have a more important explanatory weight in the funding decision.

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Conclusion

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There is no school of thought that advocates a clear funding source that could compete with debt financing and venture capital funding, and at the same time being adapted to the nature of the technological startup activity. Still there are many researches that were done to explore new funding sources.

Funding decision relies on a set of criteria and environmental characteristics that affect the decision of choosing a funding source over another and those criteria are mainly induced by the reality of the financial market and the entry gates of certain financing sources.

Funding decisions are not only influenced by the scarcity of resources or the funding cycle timelines, it’s also highly linked to the entrepreneur’s nature, the importance of the investment and the specific innovation brought to the market by the startup.

There are new funding sources that are viable and can support the riskiness of the startup Business model: Crowdfunding, Bootstrapping.

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Conclusion

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Contribution Describe the different relationships that might exist between the

funding decision and other internal and external factors to the startup.

Define the particularities of the Tunisian Startups behaviour in their funding search.

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Conclusion

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Future Research Implications

Litterature is limited in terms of analysis of the Technological entrepreneur behaviour and its impact on the financial structure of a startup

The existing studies did not present a clear value of the alternative new funding sources and their impact on the financial structure of a startup, on profitability and what are the clear cutting advantages compared to more traditional ones.

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Conclusion

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Thanks for your attention

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Technology Startups in Tunisia: Specificities and finding challenges