Two response variables that look identical and aren’t. My PhD models tried to explain why people engage, while industry models predict who will. The statistics barely changed, but the underlying data did.

In academia I modeled senior citizens’ *intention to engage* with a brand’s social media using constructs like perceived usefulness, ease of use, privacy concern, and brand trust. These predictors are latent; they must be built from survey items, which introduces measurement error. Structural equation modeling (SEM) fits a measurement model (=~) and a structural model (~) simultaneously, allowing the error to be modeled rather than ignored.

After refining the constructs, I found that privacy concern and brand trust did not predict intention, while usefulness, ease of use, brand awareness, and brand interaction did. In industry, the target is engagement itself—clicks or purchases—stored directly in databases. Here the variables already exist, so I use supervised learning (e.g., XGBoost) on behavioral features like recency, frequency, and monetary value.

The difference is that in academia I must validate the proxy, whereas in industry I assume the proxy is valid until something breaks. This contrast highlights how modeling decisions shift when moving from latent constructs to observable behavioral signals.