The Comprehensive Effects of Sales Force Management: A Dynamic Structural Analysis of Selection, Compensation, and Training

Doug J. Chung, Byungyeon Kim, Byoung G. Park

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


This study provides a comprehensive model of an agent’s behavior in response to multiple sales management instruments, including compensation, recruiting/ termination, and training. The model takes into account many of the key elements that constitute a realistic sales force setting: allocation of effort, forward-looking behavior, present bias, training effectiveness, and employee selection and attrition. By understanding how these elements jointly affect agents’ behavior, the study provides guidance on the optimal design of sales management policies. A field validation, by comparing counterfactual and actual outcomes under a new policy, attests to the accuracy of the model. The results demonstrate a tradeoff between adjusting fixed and variable pay; how sales training serves as an alternative to compensation; a potential drawback of hiring high-performing, experienced salespeople; and how utilizing a leave package leads to sales force restructuring. In addition, the study offers a key methodological contribution by providing formal identification conditions for hyperbolic time preference. The key to identification is that under a multiperiod nonlinear incentive system, an agent’s proximity to a goal affects only future payoffs in nonpecuniary benefit periods, providing exclusion restrictions on the current payoff.

Original languageEnglish (US)
Pages (from-to)7046-7074
Number of pages29
JournalManagement Science
Issue number11
StatePublished - Nov 2021
Externally publishedYes

Bibliographical note

Funding Information:
The authors thank the department editor, associate editor, and three anonymous reviewers for their constructive feedback during the review process. The authors also thank Dongkyu Chang, Murat Gumrukcu, Sunil Gupta, Vineet Kumar, Rajiv Lal, Kash Rangan, Hakan Sahin, Alvin Silk, Karin Soydan, K. Sudhir, Hasan Ulusoy, and Yi Xin; seminar participants at Harvard University, Johns Hopkins University, McKinsey & Company, New York University, the University of Texas at Austin, and the University of Toronto; and conference participants at the 2018 INFORMS Marketing Science Conference, 2018 China India Insights Conference, 2018 Frontiers in Empirical Marketing Conference, 2019 Marketing Dynamics Conference, and 2020 Marketing Strategy Consortium for their comments and suggestions. The authors also thank the anonymous firm for cooperating in this study.

Publisher Copyright:
Copyright: © 2021 INFORMS


  • Dynamic structural models
  • Exclusion restriction
  • Hyperbolic discounting
  • Identification
  • Present bias
  • Recruiting
  • Sales force compensation
  • Selection
  • Termination
  • Training


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