The authors gratefully acknowledge the valuable comments provided by the current and previous Editors, and the reviewers.
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ABSTRACT EXPERIMENTAL EVIDENCE FOR AGENCY MODELS OF SALESFORCE
COMPENSATIONAcademic work on sales compensation plans feature agency models prominently, and these models have also been used to build decision aids for managers. However, empirical support remains sketchy. We conducted three experiments to investigate three unresolved predictions involving the incentive-insurance trade-off posited in the model. First, compensation should be less incentive-loaded with greater effort-output uncertainty so as to provide additional insurance to a risk-averse agent. Second, flat wages should be used for verifiable effort so as to avoid unnecessary incentives. Third, less incentive-loaded plans should be used with more riskaverse agents so as to provide additional insurance.Our design implemented explicit solutions from a specific agency model, which offers greater internal validity compared to extant laboratory designs that either did not implement explicit solutions or excluded certain parameters. In Experiment I, data from working manager subjects supported the first prediction, but only when risk-averse agents undertook non-verifiable effort. We interpret this as disclosing the model's "core" circumstance wherein it orders the data when the incentive-insurance trade-off is relevant. Thus, when verifiable effort made incentives moot, as is the case for the second prediction, the model failed to order the data.Building on these results, we reasoned that the third prediction should find support among risk-averse agents, but not among risk−neutral agents, since insurance is a moot point with the latter agents. To this end, we added risk-neutral utility functions for agents in Experiment II. Data from MBA student subjects supported the predictions, but only when risk-averse agents