EGinterview: A Tool for Enhancing the Evaluation Grid Method with Big Data and Generative AI

Abstract

The Evaluation Grid Method (EGM) is a well-established research approach in Kansei Engineering. EGM is a semi-structured interview and analysis technique that captures hierarchical evaluation structures by identifying the factors underlying participant evaluations. Although EGM is a powerful tool for constructing qualitative relationships, its application has traditionally been limited to small sample sizes due to the labor-intensive nature of data collection (interviews) and analysis (manual construction of evaluation structures). While tools such as E-Grid have been developed, they remain constrained in scalability and cross-participant comparison. To address these limitations, the authors introduce EGinterview (EGi), a novel system designed for large-scale EGM-based Kansei analysis. EGi enables efficient visualization and comparison of evaluation structures across multiple participants while maintaining compatibility with EGM's theoretical foundations. Its architecture supports flexible data management, advanced visual analytics, and future integration of natural language processing technologies, including large language models. This study outlines EGi's design rationale and key features and illustrates its practical utility through two case studies grounded in prior research: emotional responses to various tourist sites and value perceptions of sustainability and premium. These cases demonstrate EGi's scalability, adaptability, and potential to advance research in the context of complex and large-scale emotional data.