User Demand Prioritization for Multifunctional Spaces in Small Apartments: A KANO-AHP Model Approach

This study proposes an integrated KANO-Analytic Hierarchy Process (AHP) framework to optimize multifunctional spaces in small apartments within high-density urban environments.Existing research primarily relies on qualitative assessments or single evaluation methods, lacking systematic classification and quantitative evaluation of user demands.This limitation hampers the precision of space optimization strategies.To address this gap, this study employs the KANO model to classify user demands and AHP to quantify their relative importance, providing a structured decision-making framework.Findings reveal that Must-be (M) demands are fundamental for space efficiency and residential comfort, comprising 63.3% of total demand weight, with material quality (21.25%), lighting and color (14.61%), and storage design (9.91%) as the most critical factors.One-dimensional (O) demands account for 26.1%, emphasizing user habit compatibility (9.83%) and spatial adaptability (6.92%).Attractive (A) demands represent 10.6%, with modular design (6.92%) and smart home systems (3.68%) receiving the highest attention.AHP results confirm that basic infrastructure should precede purely innovative features in optimizing small apartment spaces.Despite offering a quantitative optimization framework, this study has limitations.The sample specificity may limit generalizability, and AHP relies on expert evaluations, introducing potential subjectivity.Future research should explore cross-cultural validation, refine decision-making methodologies through advanced statistical modeling, and assess economic factors and cost-benefit trade-offs to develop scalable, economically viable solutions.This study bridges a gap in the quantitative analysis of multifunctional space demands and introduces a practical optimization framework for architects, urban planners, and smart home developers.It also lays a foundation for future interdisciplinary research, fostering data-driven approaches to small apartment design in compact urban environments.