Hostname: page-component-cd9895bd7-gxg78 Total loading time: 0 Render date: 2024-12-28T02:51:56.893Z Has data issue: false hasContentIssue false
  • English
  • Français

Big Data Techniques and Talent Management: Recommendations for Organizations and a Research Agenda for I-O Psychologists

Published online by Cambridge University Press:  19 June 2018

Michael C. Campion ,
Michael A. Campion  and
Emily D. Campion
Michael C. Campion*
Affiliation:
Campion Services and University of Texas Rio Grande Valley
Michael A. Campion
Affiliation:
Purdue University
Emily D. Campion
Affiliation:
Old Dominion University
*
Correspondence concerning this article should be addressed to Michael C. Campion, Campion Services, 3336 Dubois St., West Lafayette, IN 47906. E-mail: michael@campion-services.com
Rights & Permissions [Opens in a new window]

Extract

Big data and its applicability to talent management (TM) as defined by Rotolo et al. (2018) has already been recognized by many outside the field of I-O psychology. The market is beginning to include offerings from vendors for products that use some combination of big data techniques to process vast amounts of data or previously unanalyzable data, which they claim will improve components of TM for organizations. Unfortunately, as noted in the focal article, this “frontier” issue makes it difficult for organizations to separate the wheat from the chaff. Further, with few exceptions, I-O psychology is just beginning to inform organizations about whether and how big data can be used for the purposes of TM.

Type
Commentaries
Information
Industrial and Organizational Psychology , Volume 11 , Issue 2 , June 2018 , pp. 250 - 257
Copyright
Copyright © Society for Industrial and Organizational Psychology 2018 

Big data and its applicability to talent management (TM) as defined by Rotolo et al. (Reference Rotolo, Church, Adler, Smither, Colquitt, Shull and Foster2018) has already been recognized by many outside the field of I-O psychology. The market is beginning to include offerings from vendors for products that use some combination of big data techniques to process vast amounts of data or previously unanalyzable data, which they claim will improve components of TM for organizations. Unfortunately, as noted in the focal article, this “frontier” issue makes it difficult for organizations to separate the wheat from the chaff. Further, with few exceptions, I-O psychology is just beginning to inform organizations about whether and how big data can be used for the purposes of TM.

For the purposes of this article, we define big data techniques as those that use advanced computer programs that apply a wide range of statistical and other analytic frameworks and procedures, including text mining, to analyze large datasets to discover relationships, create models, and predict outcomes to help make decisions in TM. Currently, most examples exist in selection contexts, but the potential applicability of these techniques could range far broader to many other human resource practices such as job analysis, performance and succession management, turnover prediction, engagement surveys, and so on.

In addition to our own experience researching the use of big data techniques in staffing contexts (e.g., recruitment, scoring of essays, interviewing; e.g., Campion & Campion, Reference Campion and Campion2018; Campion, Campion, Campion, & Reider, Reference Campion, Campion, Campion and Reider2016) and ongoing consulting projects where we develop and administer such systems, we are often asked to advise organizations who are considering big data products from vendors. In that role, we serve as outside evaluators to help determine whether the companies should adopt the products and how to improve the quality (and prove the value) of those products.

Based on this, we respond to the recommendation of the focal article to end bad talent management by helping to preemptively prevent the adoption of big data products in the selection domain that are not reasonably grounded in science. First, we present a list of recommendations for organizations to consider when faced with an opportunity to adopt new technology from outside vendors that claim to enable an organization to leverage big data for the purposes of TM. Second, we provide a research agenda for I-O psychologists that attempts to ensure that future research aligns with important questions organizations have or will have moving forward regarding whether to, or how best to, leverage big data for TM purposes. This agenda is not intended to be exhaustive but rather to focus on topics of more immediate importance in terms of their potential to inform organizations. These recommendations and agenda are provided in Tables 1 and 2, respectively.

Table 1. Recommendations to Improve the Application of Big Data to Talent Management

Table 2. Future Research Directions

References

Campion, M. C., & Campion, E. D. (2018, April). Using text mining to identify and quantify strategically aligned applicant brands. Paper presented at the 33rd Annual Conference of the Society for Industrial and Organizational Psychology, Chicago, IL.Google Scholar
Campion, M. C., Campion, M. A., Campion, E. D., & Reider, M. H. (2016). Initial investigation into computer scoring of candidate essays for personnel selection. Journal of Applied Psychology, 101, 958–975.Google Scholar
Rotolo, C. T., Church, A. H., Adler, S., Smither, J. W., Colquitt, A. L., Shull, A. C., . . . Foster, G. (2018). Putting an end to bad talent management: A call to action for the field of I-O psychology. Industrial and Organizational Psychology: Perspectives on Science and Practice, 11 (2), 176–219.Google Scholar
Song, Q., Wee, S., & Newman, D. A. (2017). Diversity shrinkage: Cross-validating pareto-optimal weights to enhance diversity via hiring practices. Journal of Applied Psychology, 102, 1636–1657.Google Scholar
Uniform guidelines on employee selection procedures. (1978). Federal Register, 43, 38290–38315.Google Scholar
Figure 0

Table 1. Recommendations to Improve the Application of Big Data to Talent Management

Figure 1

Table 2. Future Research Directions