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

Abstract

PurposeThis 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/approachTo 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.FindingsThe 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/valueThis 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.

Bridging Gaps towards the 2030 Agenda: A Data-Driven Comparative Analysis of Government and Public Engagement in China towards Achieving Sustainable Development Goals

“…After data collection and preprocessing from muti-source data based on the SDG keywords list, we analyzed the relative frequency of references to the 17 SDGs and popular SDG topics made by the public and the government, discerning consistencies or disparities in the attention directed towards these goals. Then, following previous studies [31,32], we took the relative frequency of references to the 17 SDGs as the perceived importance and the SDG realization status in the annual report on the SDGs published by the United Nations as the performance score. On this basis, in this study, we used the IPA analysis to understand the attention towards and prioritization of the SDGs comprehensively.…”

“…In this study, we employed the SDGs as a coding scheme to gauge the relative frequency of each SDG from the total comments, aiming to explore the attention devoted to these goals by both the public and government [31,32]. We assessed the frequency of SDG comments to identify any consistencies or differences in attention towards these SDGs.…”

Knowing how satisfied/dissatisfied is far from enough: a comprehensive customer satisfaction analysis framework based on hybrid text mining techniques

“…Despite its simplicity, such methods suffer from consumer memory bias, inflated ratings and delayed response (Chen et al, 2022;Kwon, 2022). With the emergence of e-commerce and social media, consumers are generating a large number of online reviews that are objective, authentic and insightful (Yang et al, 2023a(Yang et al, , 2023b. Research on such big data can yield new insights into service attributes that have been extensively studied in the hospitality field (Zarezadeh et al, 2022).…”