Kansei Engineering in the Evolving Service Sector: A Decade of Insights

Kansei Engineering (KE) has increasingly been applied beyond product design into service contexts, responding to the growing importance of emotional satisfaction, experiential quality, and human-centered service design. Despite its expanding use, a comprehensive understanding of how KE has evolved methodologically, theoretically, and contextually within service research remains limited. This study aims to critically review KE applications in services over the last decade to identify key trends, contributions, and research gaps.

A semi-systematic literature review was conducted using a two-phase Define-Refine protocol. A structured search was performed using the Scopus database covering publications from 2010 to 2023. The review followed PRISMA-guided screening and refinement procedures, resulting in the selection of 28 peer-reviewed journal articles. The selected studies were analysed through iterative thematic synthesis, methodological comparison, and cross-industry analysis.

The findings reveal four major thematic clusters of KE applications in services: (1) KE for service quality enhancement, (2) data-driven and analytics-based KE, (3) KE in digital and smart service systems, and (4) behavioral and psychophysiological KE. The review demonstrates a significant methodological shift from traditional attribute-response models toward more data-driven and computational approaches, including text mining, machine learning, sentiment analysis, and advanced statistical modelling. In addition, KE increasingly serves as an integrative framework, combining service quality models, decision-support systems, and intelligent service technologies. A cross-sector comparison further reveals varying levels of methodological maturity across logistics, hospitality, transportation, healthcare, and digital services.

This study provides both theoretical and practical insights into the evolving role of KE in service research and development. By mapping the methodological evolution and thematic diversification of KE applications, the review highlights the growing importance of emotionally informed, data-driven, and human-centered approaches in contemporary service design. The findings also identify emerging opportunities for integrating KE with artificial intelligence, adaptive systems, and culturally sensitive service innovation. The study is limited by its reliance on a single database and the interpretive nature of the semi-systematic review approach.