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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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.
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.
Barry B, Stewart GL. Composition, process, and performance in self-managed groups: the role of personality. J Appl Psychol. 1997; 82(1):62.
Big five personality test dataset. http://personality-testing.info/_rawdata/BIG5.zip.
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.
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.
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.
Egolf D, Chester S. Forming storming norming performing: Successful communication in groups and teams. Bloomington: IUniverse; 2013.
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.
Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers. Mach Learn. 1997;29(2–3):131–63.
Gosling SD, Rentfrow PJ, Swann WB. A very brief measure of the big-five personality domains. J Res Pers. 2003;37(6):504–28.
Han J, Pei J, Kamber M. Data mining: concepts and techniques. Burlington: Elsevier; 2011.
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.
Leavitt HJ. Some effects of certain communication patterns on group performance. J Abnorm Soc Psychol. 1951;46(1):38.
Ledesma RD, Sánchez R, DĂaz-Lázaro CM. Adjective checklist to assess the big five personality factors in the argentine population. J Pers Assess. 2011; 93(1):46–55.
Liang PJ, Rajan MV, Ray K. Optimal team size and monitoring in organizations. Account Rev. 2008;83(3):789–22.
Linoff GS, Berry MJA. Data mining techniques: for marketing, sales, and customer relationship management. New York: Wiley; 2011.
Naive bayes classifier. http://en.wikipedia.org/wiki/Naive_Bayes_classifier.
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.
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.
Salas E, Sims DE, Shawn Burke C. Is there a “big five” in teamwork? Small Group Res. 2005;36(5):555–99.
Schmitt DP, Allik J, McCrae RR, Benet-MartĂnez 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.
Survey confirms high cost of turnover. http://seattle.bizjournals.com/seattle/stories/1998/08/17/focus6.html; 1998.
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.
Tan P-N, Steinbach M, Kumar V. Association analysis: basic concepts and algorithms. Introduction to data mining. Boston: Pearson Addison Wesley; 2005.
Tett RP, Burnett DD. A personality trait-based interactionist model of job performance. J Appl Psychol. 2003; 88(3):500.
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.
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this chapter
Cite this chapter
Waheed, A.P.A., Moshirpour, M., Moshirpour, M., Rokne, J., Alhajj, R. (2018). Effective Personnel Selection and Team Building Using Intelligent Data Analytics. In: Moshirpour, M., Far, B., Alhajj, R. (eds) Highlighting the Importance of Big Data Management and Analysis for Various Applications. Studies in Big Data, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-60255-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-319-60255-4_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-60254-7
Online ISBN: 978-3-319-60255-4
eBook Packages: EngineeringEngineering (R0)