An Experimental Investigation of Managing Quality Through Monetary and Relational Incentives
An Experimental Investigation of Managing Quality Through Monetary and Relational Incentives
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DOI：
10.1287/mnsc.2016.2716
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发表时间：
2018-05-01
影响因子：
5.4
通讯作者：
Hyndman, Kyle
中科院分区：
文献类型：
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作者：
Davis, Andrew M.;Hyndman, Kyle
我们研究了货币和关系激励对于管理两级供应链中产品质量的有效性。在我们的环境中，零售商为产品提供供应商合同条款，其中产品的质量可以是低质量或高质量。供应商可以选择付出较高的努力，虽然成本较高，但可以保证高质量，也可以选择付出较低的努力，但不能保证高质量。我们比较了货币激励（例如零售商收到高质量产品时向供应商支付的奖金）和关系激励（例如存在惩罚威胁的长期关系的双方）如何影响整体质量和供应链效率。我们的两个主要结果表明：（1）无论是否存在货币激励，关系激励都能提高质量和供应链效率；（2）当存在关系激励时，增加货币激励的影响是非单调的：效率较低的货币激励似乎会挤出关系激励的好处，甚至导致供应链效率下降，而更有效的货币激励实际上是对关系激励的补充。 激励措施并导致质量和供应链效率的显着提高。然后，我们继续证明公平的行为模型可以很好地组织数据。
We investigate the efficacy of monetary and relational incentives for managing the quality of a product in a two-tier supply chain. In our setting, a retailer offers a supplier contract terms for a product, where the product can be low or high quality. The supplier can choose to exert high effort, which is costly but guarantees high quality, or low effort, which does not assure high quality with certainty. We compare how monetary incentives, such as a bonus that is paid to the supplier when high quality is received by the retailer, and relational incentives, such as the two parties engaging in a long-term relationship where there is the threat of punishment, affect overall quality and supply chain efficiency. Two of our primary results suggest that (1) relational incentives improve both quality and supply chain efficiency, regardless of whether a monetary incentive is present, and (2) when relational incentives are present, the impact of adding monetary incentives is nonmonotonic: less efficient monetary incentives appear to crowd out the benefits of relational incentives leading even to a reduction in supply chain efficiency, whereas more efficient monetary incentives actually complement relational incentives and lead to significant increases in both quality and supply chain efficiency. We then proceed by demonstrating that a behavioral model of fairness can organize the data quite well.