Questionnaire Item Budgets and Incremental Predictive Value for Self-Reported AI-Enabled Consumer Service Use: Temporal Evidence from a Korean Panel

We examined whether additional questionnaire items improve prediction of self-reported artificial intelligence (AI)-enabled consumer service use. Models were developed with 1565 Korean panel respondents who hesitated to shop online because their activity might be recorded. They were evaluated on 1520 such respondents one wave later. Item sets were selected under limits of 0–30 additional items, with or without prior use. All four original models using every candidate item had higher log loss (prediction error; lower is better) than their benchmarks: for ridge regression with prior use, 0.69961 versus 0.66062 for a benchmark based on prior use alone. Three models remained worse after adjustment for multiple comparisons, and no original budget policy established improvement. A single expected-benefit item gave favorable estimates, but its added value remained uncertain. Collapsing regional categories and retuning made demographic estimates slightly favorable; stronger regularization was associated with smaller excess losses, and random forests had favorable estimates. The full panel also included respondents outside the hesitation-defined group. Gains in that broader panel remained uncertain among people absent from model development. These findings concern particular fitted specifications. How information is encoded and regularized may matter as much as whether it is collected.