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Monitoring mechanisms in new product development with risk-averse project manager

Author

Listed:
  • Kai Yang

    (Tianjin University)

  • Yanfei Lan

    (Tianjin University)

  • Ruiqing Zhao

    (Tianjin University)

Abstract

It is necessary for one senior executive (she) to monitor her project manager (he) who conducts early research stage followed by a later development stage in new product development. In this paper, we analyze two monitoring mechanisms: (1) the idea information-based monitoring (IM) mechanism wherein the senior executive engages one supervisor to monitor the project manager’s idea information; (2) the effort-based monitoring (EM) mechanism wherein the senior executive engages another supervisor to monitor the project manager’s effort. Within the framework of uncertainty theory, we first present two classes of bilevel uncertain principal-agent monitoring models, and then derive their respective optimal incentive contracts. We find that the senior executive should set the incentive term as high as possible to motivate each supervisor to monitor the project manager’s idea information and effort no matter how much the design idea value is. We also find that EM mechanism can always dominate IM mechanism when the monitoring costs are equal. Moreover, comparing with a no monitoring scenario, we identify two values of monitoring: the value of monitoring idea information and the value of monitoring effort. Our results show that adopting IM and EM mechanisms can improve the senior executive’s profits obtained in the no monitoring scenario when the revenue uncertainty is sufficiently low. The results also indicate that the value of monitoring idea information decreases as the risk aversion level of the project manager improves, while the value of monitoring effort shows the opposite feature.

Suggested Citation

  • Kai Yang & Yanfei Lan & Ruiqing Zhao, 2017. "Monitoring mechanisms in new product development with risk-averse project manager," Journal of Intelligent Manufacturing, Springer, vol. 28(3), pages 667-681, March.
  • Handle: RePEc:spr:joinma:v:28:y:2017:i:3:d:10.1007_s10845-014-0993-5
    DOI: 10.1007/s10845-014-0993-5
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    References listed on IDEAS

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    2. Yuhui Liu & Hecheng Li & Hong Li, 2020. "Evolutionary algorithm using surrogate models for solving bilevel multiobjective programming problems," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-31, December.

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