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Here to stay or go? Connecting turnover research to applied attrition modeling

Published online by Cambridge University Press:  01 July 2019

Andrew B. Speer ,
Subhadra Dutta ,
Menghan Chen  and
Glenn Trussell
Andrew B. Speer*
Affiliation:
Wayne State University, Detroit, Michigan, USA
Subhadra Dutta
Affiliation:
Stitch Fix, San Francisco, California, USA
Menghan Chen
Affiliation:
Twitter, San Francisco, California, USA
Glenn Trussell
Affiliation:
American Family Insurance, Madison, Wisconsin, USA
*
*Corresponding author. E-mail: [email protected]
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Abstract

Attrition modeling is a direct application of extant turnover research that can favorably impact workforce planning and action planning. However, while academic research enables practitioners insights into understanding turnover phenomena, there is no single document that comprehensively translates this work to give guidance as to the many practical decisions that must be made when modeling turnover, as well as how to apply psychological research to messier operational data. This focal article introduces and provides guidance on attrition modeling by outlining early considerations when planning a study, describing how to mesh theory with operational considerations when identifying turnover predictors within organizational settings, highlighting analytical strategies to model turnover, and considering how to appropriately share results. Collectively, this article serves as a guide to conducting attrition modeling within organizations and offers suggestions for future research to inform best practices.

Keywords

turnoverattrition modelingpredictionretentionbig data
Type
Focal Article
Information
Industrial and Organizational Psychology , Volume 12 , Issue 3 , September 2019 , pp. 277 - 301
Copyright
© Society for Industrial and Organizational Psychology 2019 

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Footnotes

We would like to thank the two reviewers of this manuscript for their insightful suggestions and improvements to the article.

References

Adams, G. A., & Beehr, T. A. (1998). Turnover and retirement: A comparison of their similarities and differences. Personnel Psychology, 51, 643–665.CrossRefGoogle Scholar
Allen, D. G., Bryant, P. C., & Vardaman, J. M. (2010). Retaining talent: Misconceptions with evidence-based strategies. Academy of Management Perspectives, 24, 48–64.Google Scholar
Allen, D. G., Hancock, J. I., Vardaman, J. M., & McKee, D. N. (2014). Analytical mindsets in turnover research. Journal of Organizational Behavior, 35, 61–86.CrossRefGoogle Scholar
Aquino, K., Griffeth, R. W., Allen, D. G., & Hom, P. W. (1997). Integrating justice constructs into the turnover process: A test of a referent cognitions model. Academy of Management Journal, 40, 1208–1227.Google Scholar
Barrick, M. R., Mount, M. K., & Strauss, J. P. (1994). Antecedents of involuntary turnover due to a reduction in force. Personnel Psychology, 47, 515–535.CrossRefGoogle Scholar
Becker, W.J., & Cropanzano, R. (2011). Dynamic aspects of voluntary turnover: An integrated approach to curvilinearity in the performance-turnover relationship. Journal of Applied Psychology, 96, 233–246.CrossRefGoogle ScholarPubMed
Becker, G. S. (1962). Investment in human capital: A theoretical analysis. Journal of Political Economy, 70, 9–49.CrossRefGoogle Scholar
Berk, R. A. (2006). An introduction to ensemble methods for data analysis. Sociological Methods and Research, 34, 263–295.CrossRefGoogle Scholar
Bersin, J. (2015, October 19). People analytics takes off: Ten things we’ve learned. [LinkedIn post]. Retrieved from https://www.linkedin.com/pulse/people-analytics-takes-off-ten-things-weve-learned-josh-bersin.Google Scholar
Bersin, J., Collins, L., Mallon, D., Moir, J., & Straub, R. (2016, February 29). People analytics: Gaining speed. Deloitte University Press. Retrieved from https://dupress.deloitte.com/dup-us-en/focus/human-capital-trends/2016/people-analytics-in-hr-analytics-teams.html.Google Scholar
Breaugh, J. A. (2014). Predicting voluntary turnover from job applicant biodata and other applicant information. International Journal of Selection and Assessment, 22, 321–332.CrossRefGoogle Scholar
Breiman, L. (1996). Bagging predictors. Machine Learning, 26, 123–140.CrossRefGoogle Scholar
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32.CrossRefGoogle Scholar
Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and regression trees. New York, NY: Chapman & Hall.Google Scholar
Bycio, P., Hackett, R. D., & Alvares, K. M. (1990). Job performance and turnover: A review and meta-analysis. Applied Psychology, 39, 47–76.CrossRefGoogle Scholar
Chambers, R. (2016). So you want to predict risk of loss for employees. Paper presented at the 31st Annual Conference of the Society for Industrial & Organizational Psychology, Anaheim, CA.Google Scholar
Chamorro-Premuzic, T., Winsborough, D., Sherman, R., & Hogan, R. (2016). New talent signals: Shiny new objects or a brave new world? Industrial and Organizational Psychology, 3, 621–640.CrossRefGoogle Scholar
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Erlbaum.Google Scholar
Cohen, J., Cohen, P., West, S. G., & Aiken, L. S. (2003). Applied multiple regression/correlation analysis for the behavioral sciences (3rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
Conway, J., & Frick, S. (2017). A predictive turnover model for global private banking relationship managers. Paper presented at the 32nd Annual Conference of the Society for Industrial & Organizational Psychology, Orlando, FL.Google Scholar
Davenport, T. H., Harris, J., & Shapiro, J. (2010). Competing on talent analytics. Harvard Business Review, 88, 52–58.Google ScholarPubMed
Deloitte. (2015). Global human capital trends 2015: Leading in the new world of work. New York, NY: Deloitte University Press.Google Scholar
Deloitte. (2016). Global human capital trends 2016: The new organization. New York, NY: Deloitte University Press.Google Scholar
Deloitte. (2017). Global human capital trends 2017: Rewriting the rules for the digital age. New York, NY: Deloitte University Press.Google Scholar
Es-Sabahi, N., & Deluca, D. (2017). Utilizing machine learning to predict turnover. Paper presented at the 32nd Annual Conference of the Society for Industrial & Organizational Psychology, Orlando, FL.Google Scholar
Electronic Privacy Information Center (2017, October 11). hiQ Labs, Inc. v. LinkedIn Corp. Retrieved from https://epic.org/amicus/cfaa/linkedin/.Google Scholar
Feldman, D. C. (1994). The decision to retire early. Academic Management Review, 19, 285–311.CrossRefGoogle Scholar
Felps, W., Mitchell, T. R., Hekman, D. R., Lee, T. W., Holtom, B. C., & Harman, W. S. (2009). Turnover contagion: How coworkers’ job embeddedness and job search behaviors influence quitting. Academy of Management Journal, 52, 545–561.CrossRefGoogle Scholar
Friedman, J. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29, 1189–1232.CrossRefGoogle Scholar
George, J. M., & Bettenhausen, K. (1990). Understanding prosocial behavior, sales performance, and turnover: A group-level analysis in a service context. Journal of Applied Psychology, 75, 698–709.CrossRefGoogle Scholar
Graen, G. B., Liden, R., & Hoel, W. (1982). Role of leadership in the employee withdrawal process. Journal of Applied Psychology, 67, 868–872.CrossRefGoogle Scholar
Gray, C. (2017). Digging into the data: Using analytics to mine for top talent. Paper presented at the Human Capital Institute 2017 Workforce Planning & People Analytics Conference, Miami, FL.Google Scholar
Griffeth, R. W., Hom, P. W., & Gaertner, S. (2000). A meta-analysis of antecedents and correlates of employee turnover: Update, moderator tests, and research implications for the next millennium. Journal of Management, 26, 463–488.CrossRefGoogle Scholar
Guzzo, R. A., Fink, A. A., King, E., Tonidandel, S., & Landis, R. S. (2015). Big data recommendations for industrial–organizational psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice, 8, 491–508.CrossRefGoogle Scholar
Hackman, J. R., & Oldham, G. R. (1980). Work redesign. Reading, MA: Addison-Wesley.Google Scholar
Harrison, D. A., Newman, D. A., & Roth, P. L. (2006). How important are job attitudes? Meta-analytic comparisons of integrative behavioral outcomes and time sequences. Academy of Management Journal, 49, 305–325.CrossRefGoogle Scholar
Harrison, D. A., Virick, M., & William, S. (1996). Working without a net: Time, performance, and turnover under maximally contingent rewards. Journal of Applied Psychology, 81, 331–345.CrossRefGoogle Scholar
Heavey, A. L., Holwerda, J. A., & Hausknecht, J. P. (2013). Causes and consequences of collective turnover: A meta-analytic review. Journal of Applied Psychology, 98, 412–453.CrossRefGoogle ScholarPubMed
Hom, P. W., Lee, T. W., Shaw, J. D., & Hausknecht, J. P. (2017). One hundred years of employee turnover theory and research. Journal of Applied Psychology, 102, 530–545.CrossRefGoogle ScholarPubMed
Holtom, B. C., Mitchell, T. R., Lee, T. W., & Eberly, M. B. (2008). Turnover and retention research: A glance at the past, a closer review of the present, and a venture into the future. The Academy of Management Annals, 2, 231–274.CrossRefGoogle Scholar
Hom, P. W., Mitchell, T. R., Lee, T. W., & Griffeth, R. W. (2012). Reviewing employee turnover: Focusing on proximal withdrawal states and an expanded criterion. Psychological Bulletin, 138, 831–858.CrossRefGoogle ScholarPubMed
Hosmer, D. W., & Lemeshow, S. (2000). Applied logistic regression (2nd ed.). New York, NY: Wiley.CrossRefGoogle Scholar
Hulin, C. L. 1991. Adaptation, persistence, and commitment in organizations. In Dunnette, M. D. & Hough, L. M. (Eds.), Handbook of industrial and organizational psychology, vol. 2 (2nd ed., pp. 445–505). Palo Alto, CA: Consulting Psychologists Press.Google Scholar
Huselid, M. A., & Day, N. E. (1991). Organizational commitment, job involvement, and turnover: A substantive and methodological analysis. Journal of Applied Psychology, 76, 380–391.CrossRefGoogle Scholar
Jackofsky, E. F. (1984). Turnover and job performance: An integrated process model. Academy of Management Review, 9, 74–83.CrossRefGoogle Scholar
Jermier, J. M., Slocum, J. W., Louis, W., & Fry, J. G. (1991). Organizational subcultures in a soft bureaucracy: Resistance behind the myth and façade of an official culture. Organization Science, 2, 170–194.CrossRefGoogle Scholar
Kahabka, J., Peterson, C., & Padalia, C. (2017). Aurora’s journey to create a sustainable workforce strategy. Paper presented at the Human Capital Institute 2017 Workforce Planning & People Analytics Conference, Miami, FL.Google Scholar
Kemery, E. R., Dunlap, W. P., & Griffeth, R. W. (1988). Correction for variance restriction in point-biserial correlations. Journal of Applied Psychology, 73, 688–691.CrossRefGoogle Scholar
Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. PNAS Proceedings of the National Academy of Sciences of the United States of America, 110, 5802–5805.CrossRefGoogle Scholar
Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. New York, NY: Springer.CrossRefGoogle Scholar
Kuhn, K. M. (2016). The rise of the “gig economy” and implications for understanding work and workers. Industrial and Organizational Psychology: Perspectives on Science and Practice, 9, 157–162.CrossRefGoogle Scholar
Kulik, C. T., Treuren, G., & Bordia, P. (2012). Shocks and final straws: Using exit-interview data to examine the unfolding model’s decision paths. Human Resource Management, 51, 25–46.CrossRefGoogle Scholar
Lee, T. W., Mitchell, T. R., Holtom, B. C., McDaniel, L. S., & Hill, J. W. (1999). The unfolding model of voluntary turnover: A replication and extension. Academy of Management Journal, 42, 450–462.Google Scholar
Little, R. J., & Rubin, D. B. (1989). The analysis of social science data with missing values. Sociological Methods & Research, 18, 292–326.CrossRefGoogle Scholar
Litano, M. L., Collmus, A. B., & Zhang, D. C. (2018). Lost in translation: Visually communicating validity evidence. The Industrial-Organizational Psychologist, 554.Google Scholar
Lüdtke, O., Robitzsch, A., & Grund, S. (2017). Multiple imputation of missing data in multilevel designs: A comparison of different strategies. Psychological Methods, 22, 141–165.CrossRefGoogle ScholarPubMed
Mael, F. A. (1991). A conceptual rationale for the domain and attributes of biodata items. Personnel Psychology, 44, 763–792.CrossRefGoogle Scholar
Maltarich, M. A., Nyberg, A. J., & Reilly, G. (2010). A conceptual and empirical analysis of the cognitive ability–voluntary turnover relationship. Journal of Applied Psychology, 95, 1058–1070.CrossRefGoogle ScholarPubMed
March, J. G., & Simon, H. A. (1958). Organizations. New York, NY: John Wiley.Google Scholar
McAbee, S. T., Landis, R. S., & Burke, M. I. (2017). Inductive reasoning: The promise of big data. Human Resource Management Review, 27, 277–290.CrossRefGoogle Scholar
McCloy, R. A., Smith, E. A., & Anderson, M. G. (2016). Predicting voluntary turnover from engagement data. Paper presented at the 31st Annual Conference of the Society for Industrial & Organizational Psychology, Anaheim, CA.Google Scholar
McKnight, P. E., McKnight, K. M., Sidani, S., & Figueredo, A. J. (2007). Missing data: A gentle introduction. New York, NY: Guilford Press.Google Scholar
Mitchell, D., Blair, M., & Speer, A.B. (2015). Big data at Sprint: Front-line employee insights. Paper presented at the 30th Annual Conference of the Society for Industrial & Organizational Psychology, Philadelphia, PA.Google Scholar
Mitchell, T. R., Holtom, B. C., Lee, T. W., Sablynski, C. J., & Erez, M. (2001). Why people stay: Using job embeddedness to predict voluntary turnover. Academy of Management Journal, 44, 1102–1121.Google Scholar
Morgeson, F. P., & Humphrey, S. E. (2006). The work design questionnaire (WDQ): Developing and validating a comprehensive measure for assessing job design and the nature of work. Journal of Applied Psychology, 91, 1321–1339.CrossRefGoogle Scholar
Morita, J. G., Lee, T. W., & Mowday, R. T. (1989). Introducing survival analysis to organizational researchers: A selected application to turnover research. Journal of Applied Psychology, 74, 280–292.CrossRefGoogle Scholar
Morita, J. G., Lee, T. W., & Mowday, R. T. (1993). The regression-analog to survival analysis: A selected application to turnover research. Academy of Management Journal, 36, 1430–1464.Google Scholar
Nishii, L. H., & Mayer, D. M. (2009). Do inclusive leaders help to reduce turnover in diverse groups? The moderating role of leader–member exchange in the diversity to turnover relationship. Journal of Applied Psychology, 94, 1412–1426.CrossRefGoogle ScholarPubMed
Nunnally, J. C. (1978). Psychometric theory. New York, NY: McGraw-Hill Book.Google Scholar
Ones, D. S., Kaiser, R. B., Chamorro-Premuzic, T., & Svensson, C. (2017). Has industrial-organizational psychology lost its way? TIP. Retrieved from http://www.siop.org/tip/april17/lostio.aspx.Google Scholar
O’Reilly, C. A., Caldwell, D. F., & Barnett, W. P. (1989). Work group demography, social integration, and turnover. Administrative Science Quarterly, 34, 21–37.Google Scholar
Pelled, L. H., & Xin, K. R. (1999). Down and out: An investigation of the relationship between mood and employee withdrawal behavior. Journal of Management, 25, 875–895.CrossRefGoogle Scholar
Pieper, J. R. (2015). Uncovering the nuances of referral hiring: How referrer characteristics affect referral hires’ performance and likelihood of voluntary turnover. Personnel Psychology, 68, 811–858.CrossRefGoogle Scholar
Porter, C. M., Woo, S. E., & Campion, M. A. (2016). Internal and external networking differentially predict turnover through job embeddedness and job offers. Personnel Psychology, 69, 635–672.CrossRefGoogle Scholar
Putka, D. J., Beatty, A. S., & Reeder, M. C. (2018). Modern prediction methods: New perspectives on a common problem. Organizational Research Methods, 21, 689–732.CrossRefGoogle Scholar
Rahman, M. S., Ambler, G., Choodari-Oskooei, B., & Omar, R. Z. (2017). Review and evaluation of performance measures for survival prediction models in external validation settings. BMC Medical Research Methodology, 17, 1–15.CrossRefGoogle ScholarPubMed
Reeder, M. C., Purl, J., Hughes, M., Wolters, H. M., & Kirkendall, C. D. (2016). Cognitive and non-cognitive antecedents of turnover: A multidimensional, longitudinal approach. Paper presented at the 31st Annual Conference of the Society for Industrial & Organizational Psychology, Anaheim, CA.Google Scholar
Rice, M. E., & Harris, G. T. (2005). Comparing effect sizes in follow-up studies: ROC area, Cohen’s d, and r. Law and Human Behavior, 29, 615–620.CrossRefGoogle ScholarPubMed
Rosett, C. R., & Leinweber, K. (2017). Predicting frontline turnover: A practical approach yielding early results. Paper presented at the 32nd Annual Conference of the Society for Industrial & Organizational Psychology, Orlando, FL.Google Scholar
Rubenstein, A. L., Eberly, M. B., Lee, T. W., & Mitchell, T. R. (2018). Surveying the forest: A meta-analysis, moderator investigation, and future-oriented discussion of the antecedents of voluntary employee turnover. Personnel Psychology, 71, 23–65.CrossRefGoogle Scholar
Schervish, P. G. (1983). The structural determinants of unemployment, vulnerability and power in market relations. New York, NY: Academic Press.Google Scholar
Shagam, D. (2017). Do you have the answer? Breaking people analytics myths. Paper presented at the Human Capital Institute 2017 Workforce Planning & People Analytics Conference, Miami, FL.Google Scholar
Shaw, J. D., Gupta, N., & Delery, J. E. (2005). Alternative conceptualizations of the relationship between voluntary turnover and organizational performance. Academy of Management Journal, 48, 50–68.CrossRefGoogle Scholar
Sinharay, S., Stern, H. S., & Russell, D. (2001). The use of multiple imputation for the analysis of missing data. Psychological Methods, 6, 317–329.CrossRefGoogle ScholarPubMed
Starbuck, C. (2017, January 17). Building and scaling a people analytics practice with limited resources: 10 guidelines for success. [LinkedIn post]. Retrieved from https://www.linkedin.com/pulse/building-scaling-people-analytics-practice-limited-10-craig?trk=hp-feed-article-title-like.Google Scholar
Streiner, D. L. (2003). Diagnosing tests: Using and misusing diagnostic and screening tests. Journal of Personality Assessment, 81, 209–219.CrossRefGoogle ScholarPubMed
Stumpf, S. A., & Dawley, P. K. (1981). Predicting voluntary and involuntary turnover using absenteeism and performance indices. Academy of Management Journal, 24, 148–163.Google Scholar
Sturman, M. C., & Trevor, C. O. (2001). The implications of linking the dynamic performance and turnover literatures. Journal of Applied Psychology, 86, 684–696.CrossRefGoogle ScholarPubMed
Tabachnick, B. G., & Fidell, L. S. (2001). Using multivariate statistics (4th ed.). Boston, MA: Allyn and Bacon.Google Scholar
Terborg, J. R., & Lee, T. W. (1984). A predictive study of organizational tenure rates. Academy of Management Journal, 27, 793–810.Google Scholar
Wells, D. L., & Muchinsky, P. M. (1985). Performance antecedents of voluntary and involuntary managerial turnover. Journal of Applied Psychology, 70, 329–336.CrossRefGoogle Scholar
Yu, H. (2017). Validating machine-generated sentiment and emotion scores for qualitative data. Paper presented at the 32nd Annual Conference of the Society for Industrial & Organizational Psychology, Orlando, FL.Google Scholar
Zimmerman, R. D. (2008). Understanding the impact of personality traits on individuals’ turnover decisions: A meta-analytic path model. Personnel Psychology, 61, 309–348.CrossRefGoogle Scholar
Zimmerman, R. D., Swider, B. W., Woo, S. E., & Allen, D. G. (2016). Who withdraws? Psychological individual differences and employee withdrawal behaviors. Journal of Applied Psychology, 101, 498–519.CrossRefGoogle ScholarPubMed