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...

The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic behavior by agents. We theorize...

We describe how to employ machine learning (ML) methods in theory development. Compared to traditional causal inference methods, ML methods make far fewer a priori assumptions about the functional form of the underlying model that best represents the data. Given this, researchers could use such...

Can generative artificial intelligence (AI) transform the role of the CEO by effectively automating CEO communication? This study investigates whether AI can mimic a human CEO and whether employees’ perception of the communication’s source matter. In a field experiment with a firm, we extend...