Setting an appropriate reference point is crucial in reference-dependent choice modeling, as it directly influences the reliability of utility estimates and the interpretation of consumer decision-making. However, many prior studies have relied on generalized or fixed reference points—such as status quo or past experiences—without accounting for individual-level heterogeneity. To address this limitation, this study proposes a reference-dependent artificial neural network (RD-ANN) that integrates the structure of reference-dependent choice models into a neural network framework. RD-ANN is designed to learn individual- and alternative-specific reference points based on consumer and alternative attributes, thereby providing a flexible and data-driven approach to reference point estimation. Empirical validation using smartphone and automobile choice data shows that RD-ANN outperforms benchmark models in various predictive performance metrics including accuracy, recall, precision, and F1 score. The model also captures behavioral patterns such as brand loyalty and status quo bias more effectively. In the empirical contexts considered, RD-ANN was found to better reflect consumer heterogeneity and may help provide more accurate estimates of price sensitivity compared to models using a fixed status quo reference point. These findings suggest that the proposed approach offers a promising direction for integrating behavioral theory and machine learning in discrete choice modeling.
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