Assessing Commercial Viability of Technology Start-up Businesses in a Government Venture Capital under Intuitionistic Fuzzy Environment

Abstract

Governments around the world are increasingly showing keen interests in venture capital investments in technology start-up businesses. However, determining the commercial potential of a new Technology start-up business is generally seen as a complex exercise especially in a government-controlled setting where selection of candidates can be clouded by several peripheral considerations. To generate more interests in decision-making models aimed at assessing the commercial viability of candidate start-up businesses in a government-run venture capital, this study (1) provides a modified form of the Strategic Technology Evaluation Program (STEP) called G-STEP as a new selection criteria for a government-controlled venture capital scheme (2) adopts a comprehensive intuitionistic fuzzy TOPSIS framework with a sensitivity analysis component for the assessment of early stage but high potential tech start-up firms and (3) demonstrates its applicability with a numerical example assessing the commercial potential of start-up businesses in a Government technology venture capital program. The proposed decision-making framework could be useful in the assessment and selection problems in other government priority areas.

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References

  1. 1.

    Afful-Dadzie, E., Afful-Dadzie, A., Oplatková, Z.K.: Measuring progress of the millennium development goals: a fuzzy comprehensive evaluation approach. Appl. Artif. Intell. 28(1), 1–15 (2014)

    Article  Google Scholar 

  2. 2.

    Aloini, D., Dulmin, R., Mininno, V.: A peer IF-TOPSIS based decision support system for packaging machine selection. Expert Syst. Appl. 41(5), 2157–2165 (2014)

    Article  Google Scholar 

  3. 3.

    Alperovych, Y., Hübner, G., Lobet, F.: How does governmental versus private venture capital backing affect a firm’s efficiency? Evidence from Belgium. J. Bus. Ventur. (2014). doi:10.1016/j.jbusvent.2014.11.001

    Google Scholar 

  4. 4.

    Altuntas, S., Dereli, T.: An evaluation index system for prediction of technology commercialization of investment projects. J. Intell. Fuzzy Syst. 23(6), 327–343 (2012)

    Google Scholar 

  5. 5.

    Atanassov, K.T.: Intuitionistic fuzzy sets. Fuzzy Sets Syst. 20(1), 87–96 (1986)

    MathSciNet  Article  MATH  Google Scholar 

  6. 6.

    Audretsch, D.B.: Standing on the shoulders of midgets: the US Small Business Innovation Research program (SBIR). Small Bus. Econ. 20(2), 129–135 (2003)

    Article  Google Scholar 

  7. 7.

    Bandarian, R.: Measuring commercial potential of a new technology at the early stage of development with fuzzy logic. Int. J. Technol. Manag. Innov. 2(4), 73–85 (2008)

    Google Scholar 

  8. 8.

    Baum, J.A., Silverman, B.S.: Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups. J. Bus. Ventur. 19(3), 411–436 (2004)

    Article  Google Scholar 

  9. 9.

    Bertoni, F., Tykvová, T.: Which form of venture capital is most supportive of innovation?. ZEW-Centre for European Economic Research Discussion, Paper, 12-018 (2012)

  10. 10.

    Bertoni, F., Colombo, M.G., Grilli, L.: Venture capital financing and the growth of high-tech start-ups: disentangling treatment from selection effects. Res. Policy 40(7), 1028–1043 (2011)

    Article  Google Scholar 

  11. 11.

    Bertoni, F., Tykvová, T.: Does governmental venture capital spur invention and innovation? Evidence from young European biotech companies. Res. Policy 44(4), 925–935 (2015)

    Article  Google Scholar 

  12. 12.

    Boran, F.E., Boran, K., Menlik, T.: The evaluation of renewable energy technologies for electricity generation in Turkey using intuitionistic fuzzy TOPSIS. Energy Sources Part B 7(1), 81–90 (2012)

    Article  Google Scholar 

  13. 13.

    Boran, F.E., Genç, S., Kurt, M., Akay, D.: A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method. Expert Syst. Appl. 36(8), 11363–11368 (2009)

    Article  Google Scholar 

  14. 14.

    Brander, J. A., Egan, E., & Hellmann, T. F.: Government sponsored versus private venture capital: Canadian evidence. National Bureau of Economic Research, No. w14029 (2008)

  15. 15.

    Büyüközkan, G., Feyzıo\({\tilde{g}}\)lu, O.: A fuzzy-logic-based decision-making approach for new product development. Int. J. Prod. Econ. 90(1), 27–45 (2004)

  16. 16.

    Cao, Q., Wu, J., Liang, C.: An intuitionisitic fuzzy judgement matrix and TOPSIS integrated multi-criteria decision making method for green supplier selection. J. Intell. Fuzzy Syst. 28(1), 117–126 (2015)

    Google Scholar 

  17. 17.

    Christofidis, C., Debande, O.: Financing innovative firms through venture capital. EIB Sector Papers (2001). Available at : http://37.157.154.50/attachments/pj/vencap.pdf

  18. 18.

    Chen, T. Y.: IVIF-PROMETHEE outranking methods for multiple criteria decision analysis based on interval-valued intuitionistic fuzzy sets. Fuzzy Optim. Decis. Making, pp. 1–26 (2014)

  19. 19.

    Cho, J., Lee, J.: Development of a new technology product evaluation model for assessing commercialization opportunities using Delphi method and fuzzy AHP approach. Expert Syst. Appl. 40(13), 5314–5330 (2013)

    Article  Google Scholar 

  20. 20.

    Chorev, S., Anderson, A.R.: Success in Israeli high-tech start-ups. Critical factors and process. Technovation 26(2), 162–174 (2006)

    Article  Google Scholar 

  21. 21.

    Chu, Y., Ma, J.: Software piracy and spending across different economic groups. Int. J. Bus. Syst. Res. 3(1), 78–92 (2009)

    Article  Google Scholar 

  22. 22.

    Cumming, D., Dai, N.: Local bias in venture capital investments. J. Empir. Finance 17(3), 362–380 (2010)

    Article  Google Scholar 

  23. 23.

    Daneshvar Rouyendegh, B.: The DEA and intuitionistic fuzzy TOPSIS approach to departments’ performances: a pilot study. J. Appl. Math. (2011)

  24. 24.

    Deng, H., Yeh, C.H., Willis, R.J.: Inter-company comparison using modified TOPSIS with objective weights. Comput. Oper. Res. 27(10), 963–973 (2000)

    Article  MATH  Google Scholar 

  25. 25.

    Dereli, T., Altun, K.: A novel approach for assessment of candidate technologies with respect to their innovation potentials: quick innovation intelligence process. Expert Syst. Appl. 40(3), 881–891 (2013)

    Article  Google Scholar 

  26. 26.

    Dimov, D., Shepherd, D.A., Sutcliffe, K.M.: Requisite expertise, firm reputation, and status in venture capital investment allocation decisions. J. Bus. Ventur. 22(4), 481–502 (2007)

    Article  Google Scholar 

  27. 27.

    Ernst & Young.: Adapting and evolving Global venture capital insights and trends (2014). Available at: http://emergingmarkets.ey.com/adapting-evolving-global-venture-capital-insights-trends-2014/

  28. 28.

    Ernst & Young.: The power of three: together, governments, entrepreneurs and corporations can spur growth across the G20. The EY G20 Entrepreneurship Barometer 2013 (2013). Available at: http://www.ey.com/Publication/vwLUAssetsPI/The_EY_G20_Entrepreneurship_Barometer_2013/$FILE/EY-G20-main-report.pdf

  29. 29.

    Feeney, L., Haines Jr, G.H., Riding, A.L.: Private investors’ investment criteria: insights from qualitative data. Ventur. Cap. 1(2), 121–145 (1999)

    Article  Google Scholar 

  30. 30.

    Franke, N., Gruber, M., Harhoff, D., Henkel, J.: Venture capitalists’ evaluations of start-up teams: trade-offs, knock-cut criteria, and the impact of VC experience. Entrep. Theory Pract. 32, 459–483 (2008)

    Article  Google Scholar 

  31. 31.

    Fried, V. H., Hisrich, R. D.: Toward a model of venture capital investment decision making. Financ. Manag. 28–37 (1994)

  32. 32.

    Gage, D.: The venture capital secret: 3 out of 4 start-ups fail. Wall Street J. (WSJ) (2012). Available at: http://www.wsj.com/news/articles/SB10000872396390443720204578004980476429190

  33. 33.

    Galbraith, C.S., DeNoble, A.F., Ehrlich, S.B.: Predicting the commercialization progress of early-stage technologies: an ex-ante analysis. Eng. Manag. IEEE Trans. 59(2), 213–225 (2012)

    Article  Google Scholar 

  34. 34.

    Giardino, C., Wang, X., Abrahamsson, P.: Why early-stage software startups fail: A behavioral framework. Software Business. Towards Continuous Value Delivery, pp. 27–41. Springer International Publishing, Heidelberg (2014)

    Google Scholar 

  35. 35.

    Gompers, P.A., Lerner, J.: The Venture Capital Cycle. MIT Press, Cambridge (2004)

    Google Scholar 

  36. 36.

    Government of South Africa: The Venture Capital Trust Fund (VCTF) (2015). Available at: http://www.venturecapitalghana.com.gh/?launch=aboutus

  37. 37.

    Grilli, L., Murtinu, S.: Government, venture capital and the growth of European high-tech entrepreneurial firms. Res. Policy 43(9), 1523–1543 (2014)

    Article  Google Scholar 

  38. 38.

    Haines Jr, G.H., Madill, J.J., Riding, A.L.: Informal investment in Canada: financing small business growth. J. Small Bus. Entrep. 16(3–4), 13–40 (2003)

    Article  Google Scholar 

  39. 39.

    Hall, J., Hofer, C.W.: Venture capitalists’ decision criteria in new venture evaluation. J. Bus. Ventur. 8(1), 25–42 (1993)

    Article  Google Scholar 

  40. 40.

    Hsu, D.H.: Venture capitalists and cooperative start-up commercialization strategy. Manag. Sci. 52(2), 204–219 (2006)

    Article  Google Scholar 

  41. 41.

    Hu, J.W.S., Hu, Y.C., Bein, H.C.: Constructing a corporate social responsibility fund using fuzzy multiple criteria decision making. Int. J. Fuzzy Syst. 13(3), 195–205 (2011)

    Google Scholar 

  42. 42.

    Hwang, C., Yoon, K.: Multiple attribute decision making methods and application. Springer, New York (1981)

    Book  MATH  Google Scholar 

  43. 43.

    Iheduru, O.C.: Black economic power and nation-building in post-apartheid South Africa. J. Mod. Afr. Stud. 42(1), 1–30 (2004)

    Article  Google Scholar 

  44. 44.

    Jain, R.K., Martyniuk, A.O., Harris, M.M., Niemann, R.E., Woldmann, K.: Evaluating the commercial potential of emerging technologies. Int. J. Technol. Transf. Commerc. 2(1), 32–50 (2003)

    Google Scholar 

  45. 45.

    Kahraman, C., Onar, S.C., Oztaysi, B.: Fuzzy multicriteria decision-making: a literature review. Int. J. Comput. Intell. Syst. 8(4), 637–666 (2015)

    Article  Google Scholar 

  46. 46.

    Kakati, M.: Success criteria in high-tech new ventures. Technovation 23(5), 447–457 (2003)

    Article  Google Scholar 

  47. 47.

    Kaplan, D.S., Piedra, E., Seira, E.: Entry regulation and business start-ups: evidence from Mexico. J. Pub. Econ. 95(11–12), 1501–1515 (2011)

    Article  Google Scholar 

  48. 48.

    Khatibi, V., Montazer, G.A.: Intuitionistic fuzzy set vs. fuzzy set application in medical pattern recognition. Artif. Intell. Med. 47(1), 43–52 (2009)

    Article  Google Scholar 

  49. 49.

    Kirsch, D., Goldfarb, B., Gera, A.: Form or substance: the role of business plans in venture capital decision making. Strateg. Manag. J. 30(5), 487–515 (2009)

    Article  Google Scholar 

  50. 50.

    Klir, G., Yuan, B.: Fuzzy sets and fuzzy logic, p. 4. Prentice Hall, New Jersey (1995)

    MATH  Google Scholar 

  51. 51.

    Knoesen, S. G.: The Politics of Distribution in South Africa. In WGPE 2007, 15–16 (2009). Available at: http://www.sscnet.ucla.edu/polisci/wgape/papers/16_Knoesen.pdf

  52. 52.

    Kucukvar, M., Gumus, S., Egilmez, G., Tatari, O.: Ranking the sustainability performance of pavements: an intuitionistic fuzzy decision making method. Automation in Construction 40, 33–43 (2014)

    Article  Google Scholar 

  53. 53.

    Kung, C.Y., Wen, K.L.: Applying grey relational analysis and grey decision-making to evaluate the relationship between company attributes and its financial performance—a case study of venture capital enterprises in Taiwan. Decis. Support Syst. 43(3), 842–852 (2007)

    Article  Google Scholar 

  54. 54.

    Landström, H.: Informal investors as entrepreneurs. Technovation 18(5), 321–333 (1998)

    Article  Google Scholar 

  55. 55.

    Leleux, B., Surlemont, B.: Public versus private venture capital: seeding or crowding out? A pan-European analysis. J. Bus. Ventur. 18(1), 81–104 (2003)

    Article  Google Scholar 

  56. 56.

    Lerner, J.: The government as venture capitalist: the long-run effects of the SBIR program (No. w5753). National Bureau of Economic Research (1996)

  57. 57.

    Lerner, J.: When bureaucrats meet entrepreneurs: the design of effective 'public venture capital’ programmes. Econ. J. 112(477), F73–F84 (2002)

    Google Scholar 

  58. 58.

    Li, D.F.: Decision and Game Theory in Management with Intuitionistic Fuzzy Sets, vol. 308, pp. 1–441. Springer, Heidelberg (2014)

    Google Scholar 

  59. 59.

    Luukkonen, T., Deschryvere, M., Bertoni, F.: The value added by government venture capital funds compared with independent venture capital funds. Technovation 33(4), 154–162 (2013)

    Article  Google Scholar 

  60. 60.

    MacMillan, I.C., Siegel, R., Narasimha, P.S.: Criteria used by venture capitalists to evaluate new venture proposals. J. Bus. Ventur. 1(1), 119–128 (1986)

    Article  Google Scholar 

  61. 61.

    Mason, C., & Stark, M.: What do investors look for in a business plan? A comparison of the investment criteria of bankers, venture capitalists and business angels. Int. Small Bus. J. 22(3), 227–248 (2014)

    Article  Google Scholar 

  62. 62.

    Meyer, T., Mathonet, P.Y.: Beyond the J curve: Managing a portfolio of venture capital and private equity funds, vol. 566. John Wiley & Sons, New York (2011)

    Google Scholar 

  63. 63.

    Munari, F., Toschi, L.: Assessing the impact of public venture capital programmes in the United Kingdom: do regional characteristics matter? J. Bus. Ventur. 30(2), 205–226 (2015)

    Article  Google Scholar 

  64. 64.

    Muzyka, D., Birley, S., Leleux, B.: Trade-offs in the investment decisions of European venture capitalists. J. Bus. Ventur. 11(4), 273–287 (1996)

    Article  Google Scholar 

  65. 65.

    Nattrass, N., Seekings, J.: Democracy and distribution in highly unequal economies: the case of South Africa. J. Mod. Afr. Stud. 39(3), 471–498 (2001)

    Article  Google Scholar 

  66. 66.

    Nkusu, M.: Nonperforming loans and macrofinancial vulnerabilities in advanced economies. IMF Working Papers, 1–27 (2011)

  67. 67.

    OECD: Entrepreneurship at a Glance 2013. OECD Publishing, Paris (2013). doi:10.1787/entrepreneur_aag-2013-en

    Google Scholar 

  68. 68.

    Petty, J.S., Gruber, M.: In pursuit of the real deal: a longitudinal study of VC decision making. J. Bus. Ventur. 26(2), 172–188 (2011)

    Article  Google Scholar 

  69. 69.

    Pina-Stranger, A., Lazega, E.: Bringing personalized ties back in: their added value for biotech entrepreneurs and venture capitalists interorganizational networks. Sociol. Quart. 52(2), 268–292 (2011)

    Article  Google Scholar 

  70. 70.

    Riquelme, H., Rickards, T.: Hybrid conjoint analysis: an estimation probe in new venture decisions. J. Bus. Ventur. 7(6), 505–518 (1992)

    Article  Google Scholar 

  71. 71.

    Rogerson, C.M.: The impact of the South African government’s SMME programmes: a ten-year review (1994–2003). Dev. South. Afr. 21(5), 765–784 (2004)

    MathSciNet  Article  Google Scholar 

  72. 72.

    Silva, J.: Venture capitalists’ decision-making in small equity markets: a case study using participant observation. Ventur. Capital 6(2–3), 125–145 (2004)

    Article  Google Scholar 

  73. 73.

    Sorenson, O., Rogan, M.: (When) Do organizations have social capital? Annu. Rev. Sociol. 40, 261–280 (2014)

    Article  Google Scholar 

  74. 74.

    Swamidass, P.M.: University startups as a commercialization alternative: lessons from three contrasting case studies. J. Technol. Transf. 38(6), 788–808 (2013)

    Article  Google Scholar 

  75. 75.

    Szmidt, E.: Distances and Similarities in Intuitionistic Fuzzy Sets. Studies in Fuzziness and Soft Computing. Springer International Publishing, Heidelberg (2014)

    Book  MATH  Google Scholar 

  76. 76.

    Tao, Z., Chen, H., Zhou, L., Liu, J.: A generalized multiple attributes group decision making approach based on intuitionistic fuzzy sets. Int. J. Fuzzy Syst. 16(2), 184 (2014)

    MathSciNet  Google Scholar 

  77. 77.

    Tyebjee, T.T., Bruno, A.V.: A model of venture capitalist investment activity. Manage. Sci. 30(9), 1051–1066 (1984)

    Article  Google Scholar 

  78. 78.

    Van Osnabrugge, M., & Robinson, R. J.: Angel Investing: Matching Start-up Funds with Start-up Companies: A Guide for Entrepreneurs and Individual Investors. Jossey-Bass (2000)

  79. 79.

    Varol, B.P., & Aygün, H.: Fuzzy soft topology. Hacet. J. Math. Stat. 41(3), (2012)

  80. 80.

    Wang, L.X.: A Course in Fuzzy Systems. Prentice-Hall Press, New Jersey (1999)

    Google Scholar 

  81. 81.

    Woike, J. K., Hoffrage, U., Petty, J. S.: Picking profitable investments: the success of equal weighting in simulated venture capitalist decision making. Journal of Business Research (2015)

  82. 82.

    Xu, Z.: Intuitionistic fuzzy aggregation operators. Fuzzy Syst. IEEE Trans. 15(6), 1179–1187 (2007)

    Article  Google Scholar 

  83. 83.

    Ye, F.: An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection. Expert Syst. Appl. 37(10), 7050–7055 (2010)

    Article  Google Scholar 

  84. 84.

    Yeh, C.H., Deng, H., Wibowo, S., Xu, Y.: Fuzzy multicriteria decision support for information systems project selection. Int. J. Fuzzy Syst. 12(2), 170–174 (2010)

    Google Scholar 

  85. 85.

    Zacharakis, A.L., Meyer, G.D.: The potential of actuarial decision models: can they improve the venture capital investment decision? J. Bus. Ventur. 15(4), 323–346 (2000)

    Article  Google Scholar 

  86. 86.

    Zadeh, L.A.: Fuzzy sets. Inf. Control 8(3), 338–353 (1965)

    Article  MATH  Google Scholar 

  87. 87.

    Zhang, X.: Venture capital investment selection decision-making base on fuzzy theory. Phys. Proc. 25, 1369–1375 (2012)

    Article  Google Scholar 

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Acknowledgments

This work was supported by Grant Agency of the Czech Republic–GACR P103/15/06700S, further by financial support of research project NPU I no. MSMT-7778/2014 by the Ministry of Education of the Czech Republic and also by the European Regional Development Fund under the Project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089. Further, this work was supported by Internal Grant Agency of Tomas Bata University under the project nos. IGA/FAI/2015/054 and IGA/FaME/2014/007, IGA/FaME/2015/023.

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Afful-Dadzie, E., Afful-Dadzie, A. & Oplatková, Z.K. Assessing Commercial Viability of Technology Start-up Businesses in a Government Venture Capital under Intuitionistic Fuzzy Environment. Int. J. Fuzzy Syst. 19, 400–413 (2017). https://doi.org/10.1007/s40815-016-0141-9

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Keywords

  • Government venture capital (GVC)
  • Commercialization
  • Technology start-up businesses
  • Intuitionistic fuzzy TOPSIS (IFS)
  • Multi-criteria decision making