Kieron Meagher, Suraj Prasad

Optimal Incentives in Problem Solving Teams

Introduction

Optimal incentives in problem solving teams. Discover how implicit incentives in problem-solving teams reduce free riding, making group incentive pay highly profitable, even surpassing costless monitoring.

Abstract

Workers in problem solving teams- these are short term teams that are set up to generate ideas for improving a production process or a product- are often rewarded through group incentive pay. This is even though group incentives give workers an incentive to free ride. In our paper, we show how problem solving creates implicit incentives to reduce free riding, which in turn lowers the cost of using group incentive pay. In fact, when an employer has initial bargaining power and implicit incentives are strong, group incentive pay yields higher profits than monitoring workers, even when monitoring is costless.

Review

The paper, "Optimal Incentives in Problem Solving Teams," tackles a highly relevant issue concerning the design of compensation schemes for short-term teams, particularly those focused on idea generation for process or product improvement. It addresses the well-known challenge of free-riding that typically accompanies group incentive pay, a common practice despite this inherent drawback. The central contribution lies in demonstrating how the nature of problem-solving tasks itself can generate significant implicit incentives, which effectively mitigate the free-riding problem and thereby reduce the overall cost of utilizing group-based rewards. This leads to the striking conclusion that, under conditions of employer bargaining power and strong implicit incentives, group incentive pay can actually yield superior profits compared to even costless individual monitoring. The strength of this work lies in its novel theoretical contribution to the literature on team incentives and organizational design. By identifying and modeling the endogenous generation of "implicit incentives" within the problem-solving context, the authors provide a compelling justification for the widespread, yet seemingly suboptimal, use of group incentives in such teams. This insight moves beyond simplistic models of free-riding by recognizing the inherent motivational elements embedded within collaborative problem-solving, which is a common and critical activity in many organizations. The paper's finding that group incentives can outperform even a hypothetically perfect and free monitoring system is particularly impactful, suggesting a re-evaluation of conventional wisdom regarding optimal team compensation strategies. While the abstract presents a compelling argument, a full appreciation of the paper's claims would require a deeper dive into the specific mechanisms through which "implicit incentives" are generated and modeled within the problem-solving framework. Clarification on the nature of the employer's "initial bargaining power" and its role in reinforcing the profitability of group incentives would also be crucial. Furthermore, understanding the specific assumptions underpinning the comparison with "costless monitoring" will be key to evaluating the generalizability of the results. This paper likely offers a robust theoretical model, and future extensions could explore empirical validation of these implicit incentives in real-world problem-solving teams or consider how team composition and task complexity might moderate their strength and effectiveness.

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