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Salesforce Compensation and Two‐Sided Ambiguity: Robust Moral Hazard with Moment Information

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  • Zhaolin Li
  • Samuel N. Kirshner

Abstract

We analyze a salesforce principal‐agent model where both the firm and sales agent have limited information on the effort‐dependent demand distribution, creating two‐sided ambiguity. Under the max–min decision criteria, the firm offers a contract to the agent who exerts unobservable effort to influence the demand distribution. We formulate the problem as a semi‐infinite program and use the agent's shadow prices to construct the least expensive contract. Next, we use the least expensive contract to create a non‐linear optimization model, which provides the firm's optimal robust contract. Due to the problem's complexity, we focus our attention on the class of distribution‐free contracts. We show that using a distribution‐free contract is a necessary condition for achieving the first‐best outcome. Our analysis reveals that the index of dispersion determines whether the optimal distribution‐free contract is linear or quadratic. Finally, we extend our model to incorporate quota‐bonus contracts and inventory considerations. Overall, our results demonstrate that variance information plays a critical role in designing contracts under distributional ambiguity and provides justification for the application of quadratic contracts in practice.

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  • Zhaolin Li & Samuel N. Kirshner, 2021. "Salesforce Compensation and Two‐Sided Ambiguity: Robust Moral Hazard with Moment Information," Production and Operations Management, Production and Operations Management Society, vol. 30(9), pages 2944-2961, September.
    • Handle: RePEc:bla:popmgt:v:30:y:2021:i:9:p:2944-2961
    DOI: 10.1111/poms.13412
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    Cited by:

    1. Tian Bai & Samuel N. Kirshner & Meng Wu, 2021. "Managing Overconfident Newsvendors: A Target‐Setting Approach," Production and Operations Management, Production and Operations Management Society, vol. 30(11), pages 3967-3986, November.
    2. Li, Zhaolin, 2021. "Robust Moral Hazard with Distributional Ambiguity," Working Papers BAWP-2021-01, University of Sydney Business School, Discipline of Business Analytics.

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