Abstract
Purpose
The purpose of this paper is to investigate National Hockey League (NHL) expansion draft decisions to measure divestment aversion and endowment effects, and analyze bias and its affect on presumed rational analytic decision making.
Design/methodology/approach
A natural experiment with three variables (age, minutes played and presence of a prior relationship with a team’s management), filtered athletes that were exposed or protected to selection. A machine learning algorithm trained on a group of 17 teams was applied to the remaining 13 teams.
Findings
Athletes with pre-existing management relationships were 1.7 times more likely to be protected. Athletes playing fewer relative position minutes were less likely to be protected, as were older athletes. Athlete selection was predominantly determined by time on ice.
Research limitations/implications
This represents a single set of independent decisions using publicly available data absent of context. The results may not be generalizable beyond the NHL or sport.
Practical implications
The research confirms the affect of prior relationships on decision making and provides further evidence of measurable sub-optimal decision making.
Social implications
Decision making has implications throughout human resources and impacts competitiveness and productivity. This adds to the need for managers to recognize and implement de-biasing in areas such as hiring, performance appraisal and downsizing.
Originality/value
This natural experiment involving high-stakes decision makers confirms bias in a setting that has been dominated by students, low stakes or artificial settings.
Subject
Marketing,Strategy and Management,Tourism, Leisure and Hospitality Management,Business and International Management
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献