Improving Hiring Decisions: Algorithmic and Expert Judgment
Making accurate hiring decisions is key for organizational performance. When making hiring decisions, managers typically incorporate various pieces of information (e.g., impressions from CVs, personal statements, interviews, and assessment centers). In this workshop, you will learn how such information can best be combined to improve your hiring decisions. How can simple algorithms and decision rules help improve decision accuracy? How do algorithms have to be used such that they are accepted by stakeholders, and what are advantages and disadvantages of algorithmic procedures? How accurate is a manager’s expert judgment? How can we best make use of a manager’s knowledge and experience?
In this workshop, dr. Marvin Neumann presents the latest scientific insights on improving decision making in selection. Participants take part in an experiment that sheds light on their decision strategies, and it points out strengths and weaknesses. We will reflect on the results of this experiment and derive practical, hands-on implications that managers can use immediately to improve their decision accuracy when selecting future talent, making promotion decisions, or assigning people to challenging on-the-job trainings.
“Marvin Neumann conducted two workshops on “working with decision rules” at LTP Business Psychologists. In these inspiring meetings, Marvin got our consultants thinking about this important topic. We, as scientist practitioners, are definitely going to benefit from the valuable new insights. Marvin is a scientist who knows how to build bridges to practice and we really appreciate that.”
Aims:
1. Understand how you make hiring decisions.
2. Learn strategies that help you improve hiring decisions within your organization.
3. Understand the role of algorithms and expert judgment in hiring decisions.
Find out more:
Meijer, R. R., Neumann, M., Hemker, B. T., & Niessen, A. S. M. (2020). A tutorial on mechanical decision-making for personnel and educational selection. Frontiers in Psychology, 10, 3002. Link
Neumann, M., Niessen, A. S. M., Linde, M., Tendeiro, J. N., & Meijer, R. R. (2023). “Adding an egg” in algorithmic decision making: Improving stakeholder and user perceptions, and predictive validity by enhancing autonomy. European Journal of Work and Organizational Psychology, 0(0), 1–18. Link
Neumann, M., Niessen, A. S. M., & Meijer, R. R. (2021). Implementing evidence-based assessment and selection in organizations: A review and an agenda for future research. Organizational Psychology Review, 11(3), 205–239. Link