The Impact of Perceived Environmental Uncertainty and Perceived Agent Effectiveness on the Composition of Compensation Contracts
通过实验室实验检验感知环境不确定性和感知代理人有效性对薪酬合同总价值及薪酬-激励组合的影响，发现代理人有效性提高会增加总薪酬和绩效薪酬比例，但环境不确定性的假设被拒绝。
Recently, a stream of theoretical development concerning the design of optimal compensation plans has emerged in the marketing literature. Little empirical work has been done so far to validate the theoretical predictions. The findings from the few field studies are equivocal and conflicting which we ascribe to failure to account for the major assumptions underlying the theoretical predictions. We argue that, in order to enhance the rigor of ‘theory testing’, the field studies must be complemented by laboratory experiments where the conditions stipulated by the assumptions of the theory can be systematically imposed and/or relaxed. This should help in the reconciliation of the differences found in the field studies. The experiment reported in this paper is a first step towards filling this void in the empirical research in this area. Here, we assess the impact of perceived environmental uncertainty and perceived agent effectiveness on the magnitude (total pay) and composition (salary-incentive mix) of employment compensation. The hypotheses stem from propositions analytically derived by Basu, Lal, Srinivasan, and Staelin (1985) and Lal and Srinivasan (1988). The insurance industry middle management compensation problem served as the experimental context. The results of the experiment ratified the hypothesis that an increase in perceived agent effectiveness leads to award of compensation contracts of larger total expected value and a larger proportion of performance-contingent pay. However, the hypothesis that an increase in perceived environmental uncertainty prompts the principal to award compensation contracts of smaller total expected value and a smaller proportion of performance-contingent pay was rejected. We discuss possible alternative explanations for the conflicting results associated with the uncertainty variable and identify future research opportunities in this area.