Profit Expectations Informed by Data: A Noise Intolerance Theory on SMEs’ Use of Data

Abstract

Data are hailed as solutions to the ambiguity and uncertainty of looking into the future. This paper investigates a cognitive constraint to entrepreneurs using their venture’s data to inform their profit expectation. Specifically, we study how an entrepreneur’s ambiguity intolerance and uncertainty intolerance affect how much their profit expectation differs from a prediction based on their venture’s profit data. We develop theory on data informing expectations and the role of ambiguity and uncertainty intolerance. We test our hypotheses in a unique sample of 233 firms participating in a large-scale consulting project. We find that ambiguity intolerance and uncertainty intolerance each decrease the difference between expectation and prediction if that prediction’s uncertainty is low. Uncertainty intolerance increases that difference if prediction uncertainty is high.