The Unrealized Potential of Technology in Selection Assessment

Abstract

The Unrealized Potential of Technology in Selection Assessment Over the past two decades, assessment for employee selection has undergone radical changes due to technological advances (see Tippins, 2015, for a review). Examples include changes in assessment delivery (computerized, online, mobile, use of adaptive testing), changes in assessment content (greater use of video and audio, graphics, gamification), changes in interactivity (recording of applicants, video interviewing), changes in scoring and reporting (quicker, internet delivered), and changes in many other aspects (e.g., use of algorithms and data mining). The purpose of this paper is not to provide another review of these advances as quality treatments exist (Scott, Bartram, & Reynolds, 2017; Tippins, 2015) but to discuss 3 ways incorporation and use of technological advances in assessment practice and research could be strengthened. First, we discuss the need to think more creatively regarding what is measured. That is, despite the promise of technology as a means of measuring new KSAOs (i.e., knowledge, skills, abilities, and other constructs like motivation) and richer content, many of our assessments are just "transported" traditional tools. As an example, gamified assessments (Bhatia & Ryan, 2018) most often are measuring traditional constructs in environments with game elements layered on.

Personnel selection in the digital age: a review of validity and applicant reactions, and future research challenges

“…In our review, we focus on the digital presentation of tests through the internet (online testing), and more recently through mobile devices. This differs to simple computerized tests of course in that applicants typically log-on remotely via an internet connection and may complete non-proctored version of tests (Ryan & Derous, 2019;Scott, Bartram & Reynolds, 2018).…”

Intelligent video interview agent used to predict communication skill and perceived personality traits

“…Many related works have developed facial expression detection tools to assess job applicants' characteristics automatically, but those studies focused on "how" (i.e., methods to assess) and not "what" (constructs to be assessed), which is important for the purposes of validation, explainability, and acceptability in personnel selection and assessment [45]. By contrast, this study not only developed a detection tool but also assessed job applicants' communication skills and personality traits, which have been identified as important criteria in employment selection.…”

“…Commonly used selection tools include structured interviews, including face-to-face interviews, phone interviews, and conference or synchrony video interviews, which require considerable amounts of human effort and time and indirect financial cost. With the emergence of AI, people may imaging that AI agents can automatically perform work similar to a group of experienced interviewers to make the hiring process more efficient (for both employers and candidates) [17,45]. In addition to the cost-efficient benefits of automation, AI decision agents can be adopted to decrease human biases (implicit or explicit) that may impact how cues from the interviewee are interpreted because the AI agent would evaluate all interviewees with the same criteria, which could make the judgment of communication skills and personality traits more consistent and fair.…”

“…This study applied a novel DL model with high concurrent validity and accuracy to automatically predict an interviewee's communication skills and personality traits, which may provide a key way to bridge the gap between human imagination and computer reality and realize the potential of data-driven AI in selection assessment and human-centric computing [45].…”

New Insights on Technology and Assessment: Introduction to JWOP Special Issue

“…In an extraordinary effort, the authors develop a four-year longitudinal study in which they relate the cognitive and emotional components of SWB with job performance. As Ryan and Derous (2019) indicate in their article in this Special Issue, there is an important need to explore new constructs that help improve the prediction of professional performance. The recall is also valid, as authors do, of a construct with a long psychological tradition that is especially relevant today.…”