TY - JOUR
T1 - “Adding an egg” in algorithmic decision making: improving stakeholder and user perceptions, and predictive validity by enhancing autonomy
AU - Neumann, Marvin
AU - Niessen, A. Susan M.
AU - Linde, Maximilian
AU - Tendeiro, Jorge N.
AU - Meijer, Rob R.
PY - 2023
Y1 - 2023
N2 - Decision makers often combine multiple pieces of information to make performance predictions and hiring decisions. More valid predictions are made when information is combined algorithmically (mechanical prediction) rather than in the decision-maker’s mind (holistic prediction). Yet, decision makers rarely use algorithms in practice. One reason is that decision makers are worried about negative evaluations from other stakeholders such as colleagues when using algorithms. We hypothesized that such stakeholders evaluate decision makers more positively when they use autonomy-enhancing algorithmic procedures (AEAPs, holistically adjust predictions from a prescribed algorithm or self-design an algorithm), than when they use a prescribed algorithm. Relatedly, we hypothesized that decision makers who use AEAPs are less worried about negative stakeholder evaluations, and more likely to use algorithms in performance predictions. In Study 1 (N = 582), stakeholders evaluated decision makers more positively when they used AEAPs rather than a prescribed algorithm. In Study 2 (N = 269), decision makers were less worried about negative stakeholder evaluations and more likely to use AEAPs compared to a prescribed algorithm. Importantly, using AEAPs also resulted in substantially higher predictive validity than holistic prediction. We recommend the use of self-designed algorithms to improve perceptions and validity.
AB - Decision makers often combine multiple pieces of information to make performance predictions and hiring decisions. More valid predictions are made when information is combined algorithmically (mechanical prediction) rather than in the decision-maker’s mind (holistic prediction). Yet, decision makers rarely use algorithms in practice. One reason is that decision makers are worried about negative evaluations from other stakeholders such as colleagues when using algorithms. We hypothesized that such stakeholders evaluate decision makers more positively when they use autonomy-enhancing algorithmic procedures (AEAPs, holistically adjust predictions from a prescribed algorithm or self-design an algorithm), than when they use a prescribed algorithm. Relatedly, we hypothesized that decision makers who use AEAPs are less worried about negative stakeholder evaluations, and more likely to use algorithms in performance predictions. In Study 1 (N = 582), stakeholders evaluated decision makers more positively when they used AEAPs rather than a prescribed algorithm. In Study 2 (N = 269), decision makers were less worried about negative stakeholder evaluations and more likely to use AEAPs compared to a prescribed algorithm. Importantly, using AEAPs also resulted in substantially higher predictive validity than holistic prediction. We recommend the use of self-designed algorithms to improve perceptions and validity.
U2 - 10.1080/1359432X.2023.2260540
DO - 10.1080/1359432X.2023.2260540
M3 - Article
SN - 1359-432X
VL - 33
SP - 245
EP - 262
JO - European Journal of Work and Organizational Psychology
JF - European Journal of Work and Organizational Psychology
IS - 3
ER -