Using Big Data to Enhance Staffing

Vast Untapped Resources or Tempting Honeypot? 1

Authored by: Richard N. Landers , Alexis A. Fink , Andrew B. Collmus

Handbook of Employee Selection

Print publication date:  March  2017
Online publication date:  March  2017

Print ISBN: 9781138915190
eBook ISBN: 9781315690193
Adobe ISBN:


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The overall purpose of organizational staffing is to deliver fresh hires into organizations. Efforts to improve staffing have historically involved pursuing two primary goals: improving job applicant quality and improving the process used to quantify and make decisions about those applicants. Industrial/-organizational (I-O) psychologists, based upon decades of research, have many specific processes they commonly employ to meet these goals. Despite this, a family of technologies commonly referred to as big data has begun to appear in staffing processes without much, if any, validation from I-O psychologists. Data scientists have claimed that such technologies have the potential to “disrupt” the bedrock staffing procedures on which much of modern I-O psychology has been built. The truth of this claim is difficult to determine for many reasons, but most glaringly because data scientists and I-O psychologists come from such different theoretical perspectives that it is often difficult to find common ground even in casual conversation.

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