Analysing the Diffusion of the Ideas and Knowledge on Economic Open Problems on Female Entrepreneur in US Over Time: The Case of Wikipedia (Year 2015–2017)

  • Paola PaoloniEmail author
  • Carlo Drago
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


An important problem on the entrepreneurship field is the precise comprehension of the diffusion dynamics of the ideas and knowledge. In fact ideas can have an important impact on the business and on the managerial decisions. So in this sense the analysis of the evolution of the ideas need to be carefully considered and evaluated. In this work we will propose a time-series cluster analysis of pageviews data of selected topics on Gender in Wikipedia. Results give relevant insights on the evolution of relevant topics as the gender pay and role at work over time. These points can provide useful relevant informations in real business contexts.


Entrepreneurship Gender Culture Clustering Time series clustering Exploratory data analysis 


  1. Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering—A decade review. Information Systems, 53, 16–38.CrossRefGoogle Scholar
  2. Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). ClValid: An R package for cluster validation. Journal of Statistical Software, 25(4), 1–22.
  3. Charrad, M., Ghazzali, N., Boiteau, V., & Niknafs, A. (2014). NbClust: An R package for determining the relevant number of clusters in a data set. Journal of Statistical Software, 61, 1–36.
  4. Choi, H., & Varian, H. (2012). Predicting the present with Google trends. Economic Record, 88(s1), 2–9.CrossRefGoogle Scholar
  5. Drago, C. (2017a). Forecasting the measured perceived touristic interest using autoregressive neural networks and big data: The case of Florence. In Conference: Convegno Nazionale AIQUAV 2017 Qualità della vita e sostenibilità, At Florence, November 2017.Google Scholar
  6. Drago, C. (2017b). Measuring the interest and the attraction for the heritage over time using social big data: The case of Florence (December 28, 2017). Available at SSRN:
  7. Drago, C., & Paoloni, P. (2018). Measuring and evaluating the interest on management and gender topics in United States 1990–2017: A time series clustering approach. In P. Paoloni & R. Lombardi (Eds.), Gender issues in business and economics, selections from the 2017 Ipazia Workshop on Gender. Springer International Publishing.Google Scholar
  8. Kassambara, A., & Mundt, F. (2017). factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.5.
  9. Paoloni, P., & Demartini, P. (2016, December). Women in work and management research: A literature review (2005–2015). Palgrave Communication, 2.Google Scholar
  10. Paoloni, P., & Lombardi, R. (2017, December). Exploring the connection between relational capital and female entrepreneurs. African Journal of Business Management, 11(24), 40–750.Google Scholar
  11. Theodoridis, S., & Koutroumbas, K. (2008). Pattern recognition (2nd ed.). New York: Academic Press.Google Scholar
  12. Tukey, J. W. (1977). Exploratory data analysis. Pearson. ISBN 978-0201076165.Google Scholar
  13. V.A. (2017). Wikipedia introduction. In Wikipedia. Retrieved September 20, 2017, from

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of Rome “N. Cusano”RomeItaly
  2. 2.CED—Center for Economic Development & Social ChangeNaplesItaly

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