Building Models in Two Worlds: From Latent Constructs to Behavioral Signals

Academic modeling often seeks to explain human behavior by building abstract concepts, like "privacy concern," from multiple survey questions using methods like structural equation modeling. In contrast, industry modeling focuses on predicting actions by using concrete, observable signals such as user clicks and purchase history from large databases. This distinction shifts the core challenge from validating theoretical constructs with scarce data to managing messy, large-scale data for predictive accuracy and business value. Interestingly, the statistical property of correlated inputs is desirable for building reliable constructs in academia but becomes a problematic issue to be managed in many industry models.