1 CALL for PAPERS Personnel Psychology Special Issue Part 2

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CALL FOR PAPERS Personnel Psychology Special Issue Part 2: Applying Machine Learning and Artificial Intelligence to Personnel Selection

Guest Editors: Michael A. Campion and Emily D. Campion

Machine learning, artificial intelligence, and related advanced analytics are several of the top trends in recent surveys of Industrial and Organizational Psychology (I-O) (e.g., “SIOP Top 10 Workplace Trends”). Practitioners of Personnel Selection/Staffing, being one of the largest areas of practice in I-O, have begun to adopt these techniques to improve assessments and other hiring procedures. Academics have similarly explored the use of advanced analytics to enable a deeper theoretical understanding of Personnel Selection/Staffing and related Human Resource functions. Examples include scoring candidate essays (Campion, Campion, Campion, & Reider, 2016), deriving selection content from work history (Sajjadiani, Sojourner, Kammeyer-Mueller, & Mykerezi, 2019), gamifying situational judgment tests (Landers, Auer, & Abraham, 2020), assessing the sentiment of narrative performance evaluations (Speer, 2018), and exploring organizational recruitment signals (Banks et al., 2019). Yet, there is much more to be done in this domain to serve our dual aim of informing theory and practice. Therefore, the purpose of the second part of this special issue is to promote and call for research that uses machine learning, artificial intelligence, and related advanced analytics in Personnel Selection/Staffing.

Examples of specific topics of interest include: 1. New selection procedures based on machine learning and artificial intelligence. 2. Use of new types of data for selection procedures based on machine learning and artificial intelligence (e.g., text analysis, video, etc.). 3. New analytic and scoring approaches to existing selection procedures. 4. The interpretability and construct validity of machine learning and artificial intelligence techniques to address the “black box” problem. 5. Advanced ways to assess the impact of recruitment methods and their effectiveness of attracting highly qualified candidates. 6. Novel mechanisms or procedures designed to combat spurious relationships, capitalization on chance, and other “dustbowl empiricism” problems with “data mining” (e.g., machine learning). 7. Explorations of the likelihood, detection, and remediation of adverse impact associated with procedures developed using machine learning and artificial intelligence. 8. How to make machine learning and artificial intelligence techniques accessible to I/O Psychologists and those in related disciplines (e.g., Organizational Behavior) who have not been trained in these techniques. 9. How machine learning and artificial intelligence can enhance understanding of basic theory and fundamental knowledge in Personnel Selection/Staffing.

Articles will likely present original empirical research, but may also include critical reviews of the literature and theoretical pieces. Moreover, articles might include a master tutorial component to educate and train readers on these techniques. Interdisciplinary research may be especially relevant because other disciplines are more advanced in machine learning and artificial intelligence (e.g., Data Science, Information Technology, etc.). We especially encourage research from those in applied settings because there is likely to be much more 2 cutting-edge work in organizations than what is currently documented in academic scientific journals.

A special issue editorial board of 13 scientists who have extensive experience applying machine learning and artificial intelligence to staffing will assist with the review process to evaluate the technical aspects and the practical importance of submissions. Biographies of the guest editorial board are at the end of this call.

Submission Process and Timeline

To be considered for the Special Issue, manuscripts must be submitted between August 1 and August 31, 2021 by 8:00pm U.S. Eastern Standard Time. During this period, papers will be reviewed on an ongoing, rolling basis. Papers for this Special Issue cannot be submitted prior to August 1, 2021. Submitted papers will undergo a double-blind review process and will be evaluated by at least two reviewers and a special issue editor. Final acceptance is contingent on the review team’s judgments of the paper’s contributions on the following four dimensions:

1. Theoretical contribution: Does the article offer new and innovative ideas and insights or meaningfully extend existing theory? Are the articles embedded in the relevant literature? 2. Empirical contribution: Does the article offer new and unique findings, and are the study design, data analysis, and results rigorous and appropriate in testing the hypotheses or examining the research questions? 3. Practical contribution: Does the article contribute to the improved management of people in organizations? 4. Contribution to the special issue topic. Does the article contribute to the literature on employee selection/staffing through the use of artificial intelligence and machine learning?

Authors should prepare their manuscripts for blind review according to the directions provided in the Publication Manual of the American Psychological Association (7th ed.). Formatting guidelines are also provided on Personnel Psychology’s website, under “author guidelines”: http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1744-6570. Be sure to remove any information that may potentially reveal the identity of the authors to the review team. Manuscripts should be submitted electronically at: http://mc.manuscriptcentral.com/ppsych

Questions? Please direct all of your questions about the Special Issue Part 2 to Michael A. Campion ([email protected]).

References

Banks, G. C., Woznyj, H. M., Wesslen, R. S., Frear, K. A., Berka, G., Heggestad, E. D., & Gordon, H. L. (2019). Strategic recruitment across borders: An investigation of multinational enterprises. Journal of Management, 45(2), 476-509.

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(7), 958 - 975.

Landers, R. N., Auer, E. M., & Abraham, J. D. (2020). Gamifying a situational judgment test with immersion and control game elements. Journal of Managerial Psychology.

Sajjadiani, S., Sojourner, A. J., Kammeyer-Mueller, J. D., & Mykerezi, E. (2019). Using machine learning to translate applicant work history into predictors of performance and turnover. Journal of Applied Psychology.

SIOP Top 10 Workplace Trends. (nd). Retrieved from https://www.siop.org/Business- Resources/Top-10-Workplace-Trends

Speer, A. B. (2018). Quantifying with words: An investigation of the validity of narrative‐ derived performance scores. Personnel Psychology, 71(3), 299-333.

Biographies of Guest Editorial Board

• Emily D. Campion is an Assistant Professor of Management at Old Dominion University. She co-authored the first application of machine learning to personnel selection in a top I/O journal (Campion et al., 2016), authored several articles under review, and is an active researcher developing new procedures in the area of text mining currently. • Michael A. Campion is the Herman C. Krannert Distinguished Professor of Management at Purdue University. He co-authored the first application of machine learning to personnel selection in a top I/O journal (Campion et al., 2016), authored several articles under review, is researching and developing new procedures currently, and is a consultant to several major government and private sector organizations on this topic. • Alexis Fink is Vice President, People Analytics and Workforce Strategy at Facebook. She has spent more than two decades leading Talent Analytics, Workforce Strategy, Talent Management and large-scale organizational change teams at global organizations, including Facebook, Microsoft and Intel. She has done extensive work applying advanced analytical methods to human capital problems. • Robert E. Gibby is IBM’s Chief Talent Scientist responsible for data, technology, assessment, and learning solutions that help people choose the best candidates for opportunities across the employee lifecycle. He was one of three winners of IBM’s 2016 Cognitive Build from over 8,000 team submissions for ways to leverage IBM’s Watson AI capability. • Genetha Grey manages the Data Science & Research group of the Employee Success Strategy & Analytics (ESSA) team at Salesforce. ESSA focuses on optimizing the employee experience through the use of data. Previously, she was an analytics research scientist at Intel Corporation where she worked on both engineering and people analytics applications. • Nick Koenig is Principal Data Scientist at Modern Hire where he has built, validated, and productionized machine learning models designed to replicate expert human raters in the assessment and interview space. He is the primary member of the winning team from the 4

inaugural SIOP machine learning competition. He has also co-hosted the SIOP ML competition for the past two years. • Richard N. Landers holds the John P. Campbell Distinguished Professorship of Industrial- Organizational Psychology as an Associate Professor at the University of Minnesota and is a Fellow of the Society for Industrial and Organizational Psychology. He researches innovative technologies in psychometric assessment, employee selection, adult learning, and research methods. • Keith McNulty is Global Head of People Analytics for McKinsey & Company. He leads a large team focused on applying advanced analytics, machine learning, natural language processing and other artificial intelligence methods in the areas of hiring, performance, development, learning and engagement. He has built and implemented numerous AI processes in these fields including classification and reinforcement learning algorithms. • Nathan Mondragon is Chief I/O Psychologist at HireVue and has managed the creation and research of the first machine learning based interview assessment system used for selection, authored several articles under review, created additional novel approaches, and managed consultants to organizations on this topic. • Dan J. Putka is a Principal Scientist at the Human Resources Research Organization in Alexandria, Virginia. He focuses on advancing psychometric and analytic methods that are sensitive to the demands of applied research and practice, most recently in the area of machine learning. He has delivered numerous presentations on big data and data analytics at national conferences, and his work on machine learning prediction methods won the 2018 Organizational Research Methods (ORM) paper of the year award. • Daniel Schmerling is a Senior Machine Learning Engineer at Wonderlic. He has over a decade of experience in researching, developing, and deploying talent assessment systems. Among his many applications of machine learning, he created the Automated Job Analysis using machine learning. He is also a past first place winner of the annual SIOP Machine Learning Competition and has presented on machine learning and artificial intelligence at many I/O Psychology and Artificial Intelligence conferences. • Andrew Speer is an Assistant Professor of Industrial and Organizational Psychology at Wayne State University. He has published articles on natural language processing (PPsych, Speer, 2018; JBP, Speer et al., 2018), turnover modeling (IOP, Speer et al., 2019), and has several machine learning articles currently under review that deal with diverse methods (random forests, elastic net, neural nets) and consequences of using these methods in organizational settings. • Scott Tonidandel, is a Professor of Management at University of North Carolina at Charlotte). He co-edited the SIOP Frontiers series volume titled Big Data at Work: The Data Science Revolution and Organizational Psychology, has several articles on big data, was a member of the SIOP Big Data Task Force, and recently completed work on an NSF funded project that uses sensors to understand team interactions and the impact of diversity.