Dynamic perceived quality analysis using social media data at macro- and micro-levels

Purpose This paper aims to propose a method for dynamic product perceived quality analysis using social media data and to achieve a macro–micro combination analysis. The method enables the prioritization of perceived quality attributes and provides perception causes. Design/methodology/approach To rationalize the macro–micro combination, ANOVA and multiple linear regression were used to identify the main factors affecting perceived quality which served as the combination basis; by using the combination basis for consumer segmentation, macro-knowledge (i.e. attribute importance and quality category of the attribute) is achieved by term frequency-inverse document frequency (TF-IDF)-based attribute importance calculation and KANO-based attribute classification, which is combined with micro-quality diagnostic information (i.e. perceived quality, perception causes and quality parameters). Further, dynamic perception Importance-Performance Analysis (IPA) is built to present the attribute priority and perception causes. Findings The framework was validated by the new energy vehicle (NEV) data of Autohome. The results show that price and purchase purpose are the most influential factors of perceived quality and that dynamic perception IPA can effectively prioritize attributes and mine perception causes. Originality/value This is one of the first studies to analyze dynamic perceived quality using social media data, which contributes to the research on perceived quality. The paper also contributes by achieving a combined macro–micro analysis of perceived quality. The method rationalizes the macro–micro combination by identifying the factors influencing perceived quality, which provides ideas for other studies using social media data.