Abstract
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.
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References
Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering—A decade review. Information Systems, 53, 16–38.
Brock, G., Pihur, V., Datta, S., & Datta, S. (2008). ClValid: An R package for cluster validation. Journal of Statistical Software, 25(4), 1–22. https://www.jstatsoft.org/v025/i04.
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. http://www.jstatsoft.org/v61/i06/paper.
Choi, H., & Varian, H. (2012). Predicting the present with Google trends. Economic Record, 88(s1), 2–9.
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.
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: https://ssrn.com/abstract=3093991.
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.
Kassambara, A., & Mundt, F. (2017). factoextra: Extract and visualize the results of multivariate data analyses. R package version 1.0.5. https://CRAN.R-project.org/package=factoextra.
Paoloni, P., & Demartini, P. (2016, December). Women in work and management research: A literature review (2005–2015). Palgrave Communication, 2.
Paoloni, P., & Lombardi, R. (2017, December). Exploring the connection between relational capital and female entrepreneurs. African Journal of Business Management, 11(24), 40–750.
Theodoridis, S., & Koutroumbas, K. (2008). Pattern recognition (2nd ed.). New York: Academic Press.
Tukey, J. W. (1977). Exploratory data analysis. Pearson. ISBN 978-0201076165.
V.A. (2017). Wikipedia introduction. In Wikipedia. Retrieved September 20, 2017, from https://en.wikipedia.org/wiki/Wikipedia:Introduction.
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Paoloni, P., Drago, C. (2019). 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). In: Paoloni, P., Lombardi, R. (eds) Advances in Gender and Cultural Research in Business and Economics. IPAZIA 2018. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-030-00335-7_13
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DOI: https://doi.org/10.1007/978-3-030-00335-7_13
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