Changing Attitudes and Demand for Bioengineered Soybean Oil

Abstract

Consumer acceptance of bioengineered foods has stalled in repeated international surveys, and the modal explanation, health concerns and environmental scepticism, does not adequately explain the dispersion.

The food-policy and consumer-behaviour literatures have begun to explore non-cognitive consumer dispositions (most prominently **food-technology neophobia**, the dispositional preference for familiar food technologies over novel ones) as the missing explanatory channel. The behavioural-finance methodological literature has begun to use machine-learning methods, particularly Random Forest classifiers combined with SHAP attribution, to recover non-linear interactions between cognitive and dispositional drivers that the linear logit benchmark suppresses. This paper combines those two literatures.

Using a stratified random consumer survey (n = 1,000) with validated TPB, Risk-Perception, and TAM scales, we apply a two-stage Random Forest framework. Stage 1 classifies binary purchase versus non-purchase; Stage 2 classifies product-variant choice among purchasers. SMOTE oversampling addresses class imbalance, 10-fold cross-validation with grid search optimises hyperparameters, MICE multiple imputation handles missing values, and SHAP attribution interprets feature importance.

Stage-2 multiclass results are currently pending a diagnostic review of the labelling structure before publication. The paper is positioned as a documentation-stage exemplar of methods-applied behavioural-finance work and serves as a methodological reference for the wider research programme.

Data & Methods