Project Contracting Strategies for Managing Team Dynamics

George Georgiadis*, Christopher S. Tang

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

2 Scopus citations

Abstract

In this chapter we study a team dynamic problem in which a group of agents collaborate over time to complete a project. The project progresses at a rate that depends on the agents’ efforts, and it generates a payoff upon completion. First, we show that agents work harder the closer the project is to completion, and members of a larger team work harder than members of a smaller team—both individually and on aggregate—if and only if the project is sufficiently far from completion. Second, we analyze the problem faced by a manager who is the residual claimant of the project and she chooses the size of the team and the agents’ incentive contracts to maximize her discounted payoff. We show that the optimal symmetric contract compensates the agents only upon completion of the project. Finally, we endogenize the size of the project, where a bigger project is one that requires greater cumulative effort and generates a larger upon completion. We show that if the manager can commit to her optimal project size at the outset of the game, then she will choose a smaller project relative to the case without commitment. An implication of this result is that without commitment, the manager is better off delegating the decision rights over the project size to the agents.

Original languageEnglish (US)
Title of host publicationSpringer Series in Supply Chain Management
PublisherSpringer Nature
Pages89-105
Number of pages17
DOIs
StatePublished - 2017

Publication series

NameSpringer Series in Supply Chain Management
Volume5
ISSN (Print)2365-6395
ISSN (Electronic)2365-6409

Keywords

  • Decision Rights
  • Dynamic Team Problem
  • Incentive Contracts
  • Project Size
  • Residual Claimant

ASJC Scopus subject areas

  • Management of Technology and Innovation
  • Management Science and Operations Research
  • Control and Optimization

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