Identifying Factors Associated with the Survival and Success of Grassroots Educational Innovations

  • Ivan SmirnovEmail author
Part of the Science, Technology and Innovation Studies book series (STAIS)


There is a general consensus that education needs for innovation to stay relevant in the modern world, and yet surprisingly little is known about innovation in education. One particularly underexplored area is grassroots innovation, and the reasons behind its success or failure. We present the results from an empirical study that identifies factors associated with success of grassroots educational innovations in a Russian context. We use data about 240 applications to an innovation competition to build a predictive model of projects success. The generalizability of the model was tested on data about another 250 projects (AUC = 0.83). We show that characteristics of a project team play more important role than characteristics of innovation itself. We also discovered that expert evaluation has low predictive power and is inferior to statistical approach. Our study demonstrates the potential power of data-driven approaches to decision making with respect to innovations in education and vulnerability of traditional approaches based on experts’ evaluation.


  1. Adams, R., Bessant, J., & Phelps, R. (2006). Innovation management measurement: A review. International Journal of Management Reviews, 8(1), 21–47.CrossRefGoogle Scholar
  2. Blank, S. (2013). The four steps to the epiphany. Pescadero, CA: K&S Ranch.Google Scholar
  3. Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32.CrossRefGoogle Scholar
  4. Caliendo, M., & Kritikos, A. S. (2008). Is entrepreneurial success predictable? An ex-ante analysis of the character-based approach. Kyklos, 61(2), 189–214.CrossRefGoogle Scholar
  5. Carter, N. M., Gartner, W. B., & Reynolds, P. D. (1996). Exploring start-up event sequences. Journal of Business Venturing, 11(3), 151–166.CrossRefGoogle Scholar
  6. Chen, C. C., Greene, P. G., & Crick, A. (1998). Does entrepreneurial self-efficacy distinguish entrepreneurs from managers? Journal of Business Venturing, 13(4), 295–316.CrossRefGoogle Scholar
  7. Christensen, C. M., Baumann, H., Ruggles, R., & Sadtler, T. M. (2006). Disruptive innovation for social change. Harvard Business Review, 84(12), 94.Google Scholar
  8. Cooper, A. C., Gimeno-Gascon, F. J., & Woo, C. Y. (1994). Initial human and financial capital as predictors of new venture performance. Journal of Business Venturing, 9(5), 371–395.CrossRefGoogle Scholar
  9. Davidsson, P., & Honig, B. (2003). The role of social and human capital among nascent entrepreneurs. Journal of Business Venturing, 18(3), 301–331.CrossRefGoogle Scholar
  10. Dawes, R. M., Faust, D., & Meehl, P. E. (1989). Clinical versus actuarial judgment. Science, 243, 1668–1674.CrossRefGoogle Scholar
  11. Dees, J. G. (2007). Taking social entrepreneurship seriously. Society, 44(3), 24–31.CrossRefGoogle Scholar
  12. Edelman, L. F., Manolova, T. S., & Brush, C. G. (2008). Entrepreneurship education: Correspondence between practices of nascent entrepreneurs and textbook prescriptions for success. Academy of Management Learning & Education, 7(1), 56–70.CrossRefGoogle Scholar
  13. Edwards, A. W. (1963). The measure of association in a 2× 2 table. Journal of the Royal Statistical Society Series A (General), 125(1), 109–114.CrossRefGoogle Scholar
  14. Fisher, R. A. (1922). On the interpretation of χ2 from contingency tables, and the calculation of P. Journal of the Royal Statistical Society, 85(1), 87–94.CrossRefGoogle Scholar
  15. Google. (2016). The change in the usage of the words ‘innovation’ and ‘novelty’ with time according to Google Ngram Viewer.
  16. Grove, W. M., & Meehl, P. E. (1996). Comparative efficiency of informal (subjective, impressionistic) and formal (mechanical, algorithmic) prediction procedures: The clinical–statistical controversy. Psychology, Public Policy, and Law, 2(2), 293.CrossRefGoogle Scholar
  17. Huang, J., & Ling, C. X. (2005). Using AUC and accuracy in evaluating learning algorithms. IEEE Transactions on Knowledge and Data Engineering, 17(3), 299–310.CrossRefGoogle Scholar
  18. Khan, S. (2012). The one world schoolhouse: Education reimagined. New York, NY: Twelve.Google Scholar
  19. Koroleva, D., & Khavenson, T. (2015). The portrait of a twenty-first century innovator in education. Russian Education and Society, 57, 338–357.CrossRefGoogle Scholar
  20. Ling, C. X., Huang, J., Zhang, H. (2003). AUC: A better measure than accuracy in comparing learning algorithms. Proceedings of 16th Canadian Conference on Artificial Intelligence, pp. 329–341.Google Scholar
  21. Marvel, M. R., & Lumpkin, G. T. (2007). Technology entrepreneurs’ human capital and its effects on innovation radicalness. Entrepreneurship Theory and Practice, 31(6), 807–828.CrossRefGoogle Scholar
  22. Mosteller, F. (1968). Association and estimation in contingency tables. Journal of the American Statistical Association, 63(321), 1–28.Google Scholar
  23. OECD. (2005). Oslo manual (3rd ed.). Paris: OECD.Google Scholar
  24. OECD. (2014). Measuring innovation in education. Paris: OECD.CrossRefGoogle Scholar
  25. Perren, L., & Sapsed, J. (2013). Innovation as politics: The rise and reshaping of innovation in UK parliamentary discourse 1960–2005. Research Policy, 42, 1815–1828.CrossRefGoogle Scholar
  26. Pevsner, N. (Ed.). (1999). A dictionary of architecture and landscape architecture. London: Penguin Books.Google Scholar
  27. Reimers, F. (2010). Pathways for change educational innovation in Latin America. Americas Quarterly, Fall.Google Scholar
  28. Robinson, K. (2010). Changing education paradigms.
  29. Rogers, E. M. (1983). Diffusion of innovations (3rd ed.). New York: Free Press of Glencoe.Google Scholar
  30. Schumpeter, J. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle. Cambridge, MA: Harvard University Press.Google Scholar
  31. Schumpeter, J. (1942). Capitalism, socialism and democracy. New York, NY: Harper & Brothers.Google Scholar
  32. Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities. Organization Science, 11, 448–469.CrossRefGoogle Scholar
  33. Shea, P., Pickett, A., & Li, C. S. (2005). Increasing access to higher education: A study of the diffusion of online teaching among 913 college faculty. The International Review of Research in Open and Distributed Learning, 6(2).Google Scholar
  34. Šidák, Z. (1967). Rectangular confidence regions for the means of multivariate normal distributions. Journal of the American Statistical Association, 62(318), 626–633.Google Scholar
  35. Simonton, D. (Ed.). (2006). The Routledge history of women in Europe since 1700. Abingdon: Routledge.Google Scholar
  36. Soffer, T., Nachmias, R., & Ram, J. (2010). Diffusion of web supported instruction in higher education-the case of Tel-Aviv university. Educational Technology & Society, 13(3), 212–223.Google Scholar
  37. Spiering, K., & Erickson, S. (2006). Study abroad as innovation: Applying the diffusion model to international education. International Education Journal, 7(3), 314–322.Google Scholar
  38. Taddei, F. (2009). Training creative and collaborative knowledge-builder: A major challenge for 21st century education. Paris: OECD.Google Scholar
  39. Ucbasaran, D., Westhead, P., & Wright, M. (2008). Opportunity identification and pursuit: Does an entrepreneur’s human capital matter? Small Business Economics, 30(2), 153–173.CrossRefGoogle Scholar
  40. UNESCO. (1999). Jan Amos Comenius. Paris: UNESCO.Google Scholar
  41. UNICEF. (2015). The state of the world’s children 2015. New York: UNICEF.CrossRefGoogle Scholar
  42. Warford, M. K. (2005). Testing a diffusion of innovations in education model (DIEM). The Innovation Journal, 10(3).Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of EducationNational Research University Higher School of EconomicsMoscowRussia

Personalised recommendations