This paper develops a new algorithm for increasing the revenue in a dynamic product assortment problem. Then, it identies the challenges faced by managers in practice and discusses the conditions under which workers follow the algorithm. To do so, I conducted a eld experiment with a beverage vending machine business. The exper- iment 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 non-experimental variations highlight the impor- tance of taking worker and context heterogeneity into account to maximize the benet from adopting a new algorithm. Higher worker's regret, sales volatility, and fewer dele- gations increase the conformity, while they mitigate the eects of integration. Workers avoid high-trac vending machines and focus on machines with high sales volatility when adopting the algorithm. The eects on the sales are largely similar to the eects on product assortments. The results emphasize the gap between nominal and actual performance of an algorithm and several practical issues to be resolved.
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