后悔
收入
波动性(金融)
营销
计算机科学
产品(数学)
背景(考古学)
经济
算法
业务
计量经济学
机器学习
财务
数学
生物
古生物学
几何学
出处
期刊:Management Science
[Institute for Operations Research and the Management Sciences]
日期:2021-03-01
卷期号:67 (3): 1670-1695
被引量:59
标识
DOI:10.1287/mnsc.2020.3599
摘要
This paper develops a new algorithm for increasing the revenue in a dynamic product assortment problem. Then, it identifies the challenges faced by managers in practice and discusses the conditions under which workers follow the algorithm. To do so, I conducted a field experiment with a beverage vending machine business. The experiment shows that, on average, workers are reluctant to follow the algorithmic advice; however, the workers are more willing to conform once their forecasts are integrated into the algorithm. Analyses using nonexperimental variations highlight the importance of taking worker and context heterogeneity into account to maximize the benefit from adopting a new algorithm. Higher worker’s regret, sales volatility, and fewer delegations increase the conformity, while they mitigate the effects of integration. Workers avoid high-traffic vending machines and focus on machines with high sales volatility when adopting the algorithm. The effects on the sales are largely similar to the effects on product assortments. The results emphasize the gap between nominal and actual performance of an algorithm and several practical issues to be resolved. This paper was accepted by Matthew Shum, marketing.
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