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Effective Personnel Selection and Team Building Using Intelligent Data Analytics

  • Ayeshaa Parveen Abdul WaheedEmail author
  • Mojgan Moshirpour
  • Mohammad Moshirpour
  • Jon Rokne
  • Reda Alhajj
Chapter
  • 1.3k Downloads
Part of the Studies in Big Data book series (SBD, volume 27)

Abstract

Building a successful team is essential for any organization as it enhances creativity, innovation, and productivity. Mismatched staffing is very costly for employers as it can lead to loss of time and resources spent on training and recruitment, loss of productivity, and project failure. Existing personnel selection approaches are focused on determining if the candidates’ skills and personalities fit the job in question. However studies suggest that compatibility of personality traits of team members with respect to the overall team performance must also be considered. This should be done without creating a level of homogeneity and agreeableness which would adversely affect productivity. Factors such as cohesion and consistency between the team members’ personalities are analyzed using the Big Five model to organize and recognize compatible personality traits that would result in more effective teamwork. This research proposed a solution which utilizes intelligent data analytics to provide effective and efficient decision support for personnel selection in order to increase team performance. The result of the proposed research could save significant amount of time and resources by contributing to the increase of employee satisfaction, reducing turnover, and increasing team performance and project success rates.

Keywords

Personnel Selection Increase Team Performance Efficient Decision Support Project Success Rate Potential Team Members 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Agrawal R, Srikant R, et al. Fast algorithms for mining association rules. In: Proceedings of 20th international conference on very large data bases, VLDB, vol. 1215; 1994, p. 487–99.Google Scholar
  2. 2.
    Azar A, Sebt MV, Ahmadi P, Rajaeian A. A model for personnel selection with a data mining approach: A case study in a commercial bank. SA J Hum Resour Manag. 2013; 11(1):10.CrossRefGoogle Scholar
  3. 3.
    Barry B, Stewart GL. Composition, process, and performance in self-managed groups: the role of personality. J Appl Psychol. 1997; 82(1):62.CrossRefGoogle Scholar
  4. 4.
    Big five personality test dataset. http://personality-testing.info/_rawdata/BIG5.zip.
  5. 5.
    Brown S, Garino G, Martin C. Firm performance and labour turnover: Evidence from the 2004 workplace employee relations survey. Econ Modell. 2009;26(3):689–95.CrossRefGoogle Scholar
  6. 6.
    Buchanan LB. The impact of big five personality characteristics on group cohesion and creative task performance. Doctoral dissertation, Virginia Polytechnic Institute and State University; 1998.Google Scholar
  7. 7.
    Chien C-F, Chen L-F. Data mining to improve personnel selection and enhance human capital: A case study in high-technology industry. Expert Syst Appl. 2008; 34(1):280–90.CrossRefGoogle Scholar
  8. 8.
    Egolf D, Chester S. Forming storming norming performing: Successful communication in groups and teams. Bloomington: IUniverse; 2013.Google Scholar
  9. 9.
    French KA, Kottke JL. Teamwork satisfaction: exploring the multilevel interaction of teamwork interest and group extraversion. Act Learn High Educ. 2013;14(3):189–200.CrossRefGoogle Scholar
  10. 10.
    Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn. 1997;29(2–3):131–63.CrossRefGoogle Scholar
  11. 11.
    Gosling SD, Rentfrow PJ, Swann WB. A very brief measure of the big-five personality domains. J Res Pers. 2003;37(6):504–28.CrossRefGoogle Scholar
  12. 12.
    Han J, Pei J, Kamber M. Data mining: concepts and techniques. Burlington: Elsevier; 2011.Google Scholar
  13. 13.
    Hooper RS, Galvin TP, Kilmer RA, Liebowitz J. Use of an expert system in a personnel selection process. Expert Syst Appl. 1998;14(4):425–32.CrossRefGoogle Scholar
  14. 14.
    Leavitt HJ. Some effects of certain communication patterns on group performance. J Abnorm Soc Psychol. 1951;46(1):38.CrossRefGoogle Scholar
  15. 15.
    Ledesma RD, Sánchez R, Díaz-Lázaro CM. Adjective checklist to assess the big five personality factors in the argentine population. J Pers Assess. 2011; 93(1):46–55.CrossRefGoogle Scholar
  16. 16.
    Liang PJ, Rajan MV, Ray K. Optimal team size and monitoring in organizations. Account Rev. 2008;83(3):789–22.CrossRefGoogle Scholar
  17. 17.
    Linoff GS, Berry MJA. Data mining techniques: for marketing, sales, and customer relationship management. New York: Wiley; 2011.Google Scholar
  18. 18.
  19. 19.
    Neuman GA, Wagner SH, Christiansen ND. The relationship between work-team personality composition and the job performance of teams. Group Org Manag. 1999;24(1):28–45.CrossRefGoogle Scholar
  20. 20.
    Nussbaum M, Singer M, Rosas R, Castillo M, Flies E, Lara R, Sommers R. Decision support system for conflict diagnosis in personnel selection. Inf Manage. 1999;36(1):55–62.CrossRefGoogle Scholar
  21. 21.
    Salas E, Sims DE, Shawn Burke C. Is there a “big five” in teamwork? Small Group Res. 2005;36(5):555–99.CrossRefGoogle Scholar
  22. 22.
    Schmitt DP, Allik J, McCrae RR, Benet-Martínez V. The geographic distribution of big five personality traits patterns and profiles of human self-description across 56 nations. J Cross-Cult Psychol. 2007;38(2):173–212.CrossRefGoogle Scholar
  23. 23.
    Survey confirms high cost of turnover. http://seattle.bizjournals.com/seattle/stories/1998/08/17/focus6.html; 1998.
  24. 24.
    Tai W-S, Hsu C-C. A realistic personnel selection tool based on fuzzy data mining method. In: 9th Joint international conference on information sciences (JCIS-06). Amsterdam: Atlantis Press; 2006.CrossRefGoogle Scholar
  25. 25.
    Tan P-N, Steinbach M, Kumar V. Association analysis: basic concepts and algorithms. Introduction to data mining. Boston: Pearson Addison Wesley; 2005.Google Scholar
  26. 26.
    Tett RP, Burnett DD. A personality trait-based interactionist model of job performance. J Appl Psychol. 2003; 88(3):500.CrossRefGoogle Scholar
  27. 27.
    Woolley AW, Gerbasi ME, Chabris CF, Kosslyn SM, Hackman JR. Bringing in the experts how team composition and collaborative planning jointly shape analytic effectiveness. Small Group Res. 2008;39(3):352–71.CrossRefGoogle Scholar
  28. 28.
    Zhai Q, Willis M, O’Shea B, Zhai Y, Yang Y. Big five personality traits, job satisfaction and subjective wellbeing in China. Int J Psychol. 2013;48(6):1099–108.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Ayeshaa Parveen Abdul Waheed
    • 1
    • 2
    Email author
  • Mojgan Moshirpour
    • 1
    • 2
  • Mohammad Moshirpour
    • 3
  • Jon Rokne
    • 1
    • 2
  • Reda Alhajj
    • 3
  1. 1.Department of Computer ScienceUniversity of CalgaryCalgaryCanada
  2. 2.Department of Electrical and Computer EngineeringUniversity of CalgaryCalgaryCanada
  3. 3.Department of Electrical & Computer EngineeringUniversity of CalgaryCalgaryCanada

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