Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented worker performance. Reconciling these perspectives, we theorize that intermediate levels of domain experience are optimal for algorithm-augmented performance, due to the interplay between two countervailing forces—ability and aversion. Although domain experience can increase performance via increased ability to complement algorithmic advice (e.g., identifying inaccurate predictions), it can also decrease performance via increased aversion to accurate algorithmic advice. Because ability developed through learning by doing increases at a decreasing rate, and algorithmic aversion is more prevalent among experts, we theorize that algorithm-augmented performance will first rise with increasing domain experience, then fall. 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 versus 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. These findings highlight that, even if greater domain experience increases workers’ ability to complement algorithms, domain experience can also trigger other mechanisms that overcome the positive ability effect and inhibit performance. Additional analyses and participant interviews suggest that, even though the highest experience workers had the greatest ability to complement the algorithmic tool, they rejected its advice because they felt greater accountability for possible unintended consequences of accepting algorithmic advice.

Abstract Research Summary Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post hoc analysis of regression results to detect patterns that may have gone unnoticed. However, ML models should not be treated as the result of a deductive causal test. To demonstrate the application of ML for pattern discovery, we implement ML algorithms to study employee turnover at a large technology company. We interpret the relationships between variables using partial dependence plots, which uncover surprising nonlinear and interdependent patterns between variables that may have gone unnoticed using traditional methods. To guide readers evaluating ML for pattern discovery, we provide guidance for evaluating model performance, highlight human decisions in the process, and warn of common misinterpretation pitfalls. The Supporting Information section provides code and data to implement the algorithms demonstrated in this article. Managerial Summary Supervised machine learning (ML) methods are a powerful toolkit that might help managers and researchers discover interesting patterns in large and complex data. We demonstrate this by using several ML algorithms to investigate the drivers of employee turnover at a large technology company. We evaluate the performance of the models, and use visual tools to interpret the patterns revealed. These patterns can be useful in understanding turnover, but we caution not to confuse correlation with causation. These methods should be viewed as “exploratory” and not conclusive proof of relationships in the data. Our guidance can be helpful for managers evaluating analysis conducted by data scientists in their organizations.

Abstract More and more companies are turning to emerging markets as sources of global innovation to help transform business and society. However, building innovation capabilities in emerging markets is still elusive for most companies. To understand how some companies are successfully building these capabilities, we examined workers within R&D units in China across six foreign multinational corporations. In contrast with prior literature that emphasizes a structural view of who the workers interacted with to innovate, our inductive analysis highlights a behavioral view of how R&D unit personnel interact during the problem and solution search process. We identified two key behaviors associated with the problem and solution search: (1) observing customers in their everyday context, and (2) uncovering general knowledge principles from internal experts. Respectively, these behaviors helped R&D workers to question assumptions about existing products as they relate to customers and to apply useful principles from expert knowledge rather than copying solution templates. Our findings offer an alternative path to building global innovation capabilities in markets where structural constraints exist for the company.