Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion

Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented work performance. To reconcile these perspectives, we theorize that domain experience affects algorithm-augmented performance via two distinct countervailing forces—ability and aversion. On one hand, workers’ domain experience can complement algorithms, due to an increased ability to judge the accuracy of an algorithm’s advice. On the other hand, workers with more domain experience tend to exhibit more aversion to accepting helpful algorithmic advice. Each force varies in its influence on workers with different levels of domain experience. Ability developed through learning-by-doing increases at a decreasing rate over the range of experience, while algorithmic aversion is more intense for experts. Therefore, more domain experience will increase algorithm-augmented performance for workers with low levels of domain experience, but will decrease the algorithm-augmented performance of workers with high levels of domain experience. We test this by exploiting a within-subjects experiment in which corporate Information Technology support workers were assigned to resolve problems both manually and using an algorithmic tool. We confirm that the difference between performance with the algorithmic tool vs. without the tool was characterized by an inverted U-shape over the range of domain experience. Only workers with moderate domain experience did significantly better using the algorithm than resolving tickets manually.