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Measuring and Evaluating the Interest on Management and Gender Topics in the United States in 1990–2017: A Time Series Clustering Approach

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

Abstract

In the last years, there has been an increased interest in the “gender studies” on which the topics related to women, managers and entrepreneurs are analysed from an approach combining different disciplines. These different approaches reflect also the different topics and the different problems considered on these fields. In this work, by using selected Google queries, we have studied the evolution of the different queries related to “gender studies” over the time. In particular we have used a time series clustering approach in order to detect a common dynamics for the considered Google queries. We found that the queries between the glass ceiling and the pay gap between men and women show a relevant increase over the time.

Keywords

Gender studies Glass ceiling Gender pay gap Time series clustering Google queries 

References

  1. Adler, N. J., & Harzing, A.-W. (2009). When knowledge wins: Transcending the sense and nonsense of academic rankings. The Academy of Management Learning and Education, 8(1), 72–95.CrossRefGoogle Scholar
  2. Aghabozorgi, S., Shirkhorshidi, A. S., & Wah, T. Y. (2015). Time-series clustering–a decade review. Information Systems, 53, 16–38.CrossRefGoogle Scholar
  3. Allan, S., Branston, G., & Carter, C. (Eds.). (2002). News, gender and power. Abingdon: Routledge.Google Scholar
  4. Broadbridge, A., & Hearn, J. (2008). Gender and management: New directions in research and continuing patterns in practice. British Journal of Management, 19(s1), S38–S49.CrossRefGoogle Scholar
  5. Cesaroni, F. M., & Sentuti, A. (2012). Women and Family Businesses. What role for women when the leader is a man?. Paper presented at 2e Journée d'études internationale “Travail en Famille, Travail non Remunere en Europe (XVe-XXIe siècle)”, Université De Rouen (France) 5 Octobre 2012.Google Scholar
  6. Choi, H., & Varian, H. (2012). Predicting the present with Google trends. The Economic Record, 88(s1), 2–9.CrossRefGoogle Scholar
  7. De Carlo J.F., & Lyons, P.R. (1979). A comparison of selected personal characteristics of minority and non-minority female entrepreneurs. Journal of business Management, 17(4), 369–373.Google Scholar
  8. Drago, C., Scepi, G., & Lauro, C. (2012). Multiple beanplots GCCA in a temporal framework. In A. Javier, M. Carlos, B. Paula, & N. F. Monique (Eds.), 3rd workshop in symbolic data analysis: Book of abstracts, 7–9 Noviembre 2012, Madrid.Google Scholar
  9. Drago, C., Lauro, C., & Scepi, G. (2013). Visualization and analysis of large datasets by beanplot PCA. SIS conference proceedings conference advances in latent variables: Methods, models and applications. In E. Brentari & M. Carpita (Eds.), Advances in latent variables. Milan: Vita e Pensiero.Google Scholar
  10. Dunn, J. C. (1974). Well separated clusters and fuzzy partitions. Journal on Cybernetics, 4, 95–104.CrossRefGoogle Scholar
  11. ECU. (2011). Equality in higher education: Statistical report 2011. Part 1: Staff. London: Equality Challenge Unit.Google Scholar
  12. Essers, C., Benschop, Y., & Dooreward, H. (2010). Female ethnicity: Understanding Muslim immigrant businesswomen in The Netherlands. Gender, Work and Organization, 17(3), 320–339.CrossRefGoogle Scholar
  13. Google Trends. (2017). Google; www.google.com/trends. Accessed 22 January 2017.
  14. Guillaume, C., & Pochic, S. (2009). What would you sacrifice? Access to top management and the work–life balance. Gender, Work & Organization, 16(1), 14–36.Google Scholar
  15. Guthrie, J., Ricceri, F., & Dumay, J. (2012). Reflections and projections: A decade of intellectual capital accounting research. The British Accounting Review, 44, 68–82.CrossRefGoogle Scholar
  16. Halkidi, M., Batistakis, Y., & Vazirgiannis, M. (2001). On clustering validation techniques. Journal of Intelligent Information Systems, 17(2), 107–145.CrossRefGoogle Scholar
  17. HEFCE. (2010). Staff employed at HEFCE-funded HEIs. Trends and profiles 1995–96 to 2008–09. Issues paper 2010/06. Bristol: Higher Education Funding Council for England.Google Scholar
  18. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM computing surveys (CSUR), 31(3), 264–323.CrossRefGoogle Scholar
  19. Liao, T. W. (2005). Clustering of time series data—A survey. Pattern Recognition, 38(11), 1857–1874.CrossRefGoogle Scholar
  20. Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al. (2011). Big data: The next frontier for innovation, competition, and productivity. McKinsey Global Institute: New YorkGoogle Scholar
  21. Massicotte, P., & Eddelbuettel, D. (2016). gtrendsR: Perform and display Google trends queries. R Package Version, 1(3), 5. https://CRAN.R-project.org/package=gtrendsR.Google Scholar
  22. McAfee, A., & Brynjolfsson, E. (2012). Big data. The management revolution. Harvard Business Review, 90(10), 61–67.Google Scholar
  23. Montero, P., & Vilar, J. A. (2014). TSclust: An R package for time series clustering. Journal of Statistical Software, 62(1), 1–43. https://www.jstatsoft.org/article/view/v062i01.CrossRefGoogle Scholar
  24. Paoloni, P. (2011). la dimensione relazionale delle imprese femminili. Milano: Franco Angeli.Google Scholar
  25. Paoloni, P., & Demartini, P. (2012). The relational capital in female Smes. Journal of Academy of Business and Economics, 12(1), 23–32.Google Scholar
  26. Paoloni, P., & Demartini, P. (2016). Women in management: Perspectives on a decade of research (2005–2015). Palgrave Communications. doi: 10.1057/palcomms.2016.94.
  27. Paoloni, P., & Dumay, J. (2015). The relational capital of micro-enterprises run by women: The start-up phase. Vine, 45(2), 172–197.CrossRefGoogle Scholar
  28. Phipps, J. B. (1971). Dendrogram topology. Systematic Biology, 20(3), 306–308.Google Scholar
  29. Probert, B. (2005). ‘I just couldn’t fit it in’: Gender and unequal outcomes in academic careers. Gender, Work & Organization, 12(1), 50–72.Google Scholar
  30. R Development Core Team. (2008). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna. isbn: 3-900051-07-0, URL http://www.R-project.org.
  31. Schrier J.W. (1975). The female entrepreneur. A pilot study, The centre for venturing management, Milwaukee.Google Scholar
  32. Schwartz E.B. (1976). Entrepreneurship: A new female frontier. Journal of Contemporary business, 5(1), 47–76.Google Scholar
  33. Shamir, J., & Shamir, M. (2000). The anatomy of public opinion. Ann Arbor, MI: University of Michigan Press.CrossRefGoogle Scholar
  34. Smithson, J., & Stokoe, E. H. (2005). Discourses of work–life balance: negotiating ‘genderblind’ terms in organizations. Gender, Work & Organization, 12(2), 147–168.Google Scholar
  35. Tlaiss, H., & Kauser, S. (2011). The importance of wasta in the career success of Middle Eastern managers. Journal of European Industrial Training, 35(5), 467–486.Google Scholar
  36. Tienari, J., Holgersson, C., et al. (2009). Gender, management and market discourse: The case of gender quotas in the Swedish and Finnish media. Gender, Work and Organization, 16(4), 501–521.CrossRefGoogle Scholar
  37. Tukey, J. W. (1977). Exploratory data analysis. London: Pearson.Google Scholar
  38. Vosen, S., & Schmidt, T. (2011). Forecasting private consumption: Survey-based indicators vs. Google trends. Journal of Forecasting, 30(6), 565–578.CrossRefGoogle Scholar
  39. Waterman, M. S., & Smith, T. F. (1978). On the similarity of dendrograms. Journal of Theoretical Biology, 73(4), 789–800.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Department of UNISU, Faculty of EconomicsNiccolò Cusano UniversityRomeItaly
  2. 2.University of Rome “Niccolò’ Cusano”, Via Don Carlo GnocchiRomeItaly

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