Optimizing Innovation Decisions with Deep Learning: An Attention–Utility Enhanced IPA–Kano Framework for Customer-Centric Product Development

Abstract

This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit attention–implicit utility discrepancy, we extend the traditional IPA–Kano model by incorporating an attention dimension, thereby constructing a three-dimensional optimization framework with eight decision spaces. This enhanced framework enables the following: (1) fine-grained classification of customer requirements by distinguishing between an attribute’s perceived salience and its actual impact on satisfaction; (2) strategic resource allocation, differentiating between quality enhancement priorities and cognitive expectation management to maximize innovation impact under resource constraints. To validate the model, we conducted a case study on wearable watches for the elderly, analyzing 12,527 online reviews to extract 41 functional attributes. Among these, 14 were identified as improvement priorities, 9 as maintenance attributes, and 7 as low-priority features. Additionally, six cognitive management strategies were formulated to address attention–utility mismatches. Comparative validation involving domain experts and consumer interviews confirmed that the proposed IPAA–Kano model, leveraging deep learning, outperforms the traditional IPA–Kano model in classification accuracy and decision relevance. By integrating deep learning with optimization-based decision models, this research offers a practical and systematic methodology for translating customer attention and satisfaction data into actionable innovation strategies, thus providing a robust, data-driven approach to resource-efficient product development and technological innovation.