Modelling Consumer Satisfaction Based on Online Reviews Using the Improved Kano Model From the Perspective of Risk Attitude and Aspiration

Abstract

With the development of e-commerce, an increasing number of online reviews can serve as a promising data source for enterprises to improve online products. This paper proposes a method for modelling consumer satisfaction based on online reviews using the improved Kano model from the perspective of risk attitude and aspiration. Firstly, the attributes concerned by consumers are extracted from online reviews, and sentiment analysis of the extracted attributes is carried out using Standford CoreNLP. Secondly, to identify the types of product attributes, an improved Kano model is proposed based on the effects of product attributes on consumer total utility. On this basis, different attribute types are illustrated from the perspective of risk attitude. Then, the consumer aspirations are mined based on the risk attitudes of different attributes and the attribute impact on consumer satisfaction. According to the risk attitudes and aspirations of different attributes, the quantified satisfaction functions are constructed to provide more objective and accurate improvement suggestions. Finally, the proposed method is applied to the hotel service improvement to illustrate the effectiveness.

Exploring Bidirectional Performance of Hotel Attributes through Online Reviews Based on Sentiment Analysis and Kano-IPA Model

“…Existing studies suggested that online textual reviews indicate details on customers' demands and perceptions of hotel attributes [6,9]. Although numerical textual reviews have been studied widely to extract critical hotel attributes and their sentiment [14,15,46], few studies have distinguished between positive reviews and negative reviews to identify wellperformed attributes contributing to satisfied customers and poorly performed attributes resulting in dissatisfied customers. Therefore, this study attempts to explore the difference between the good performance and poor performance of the same attribute with respect to positive reviews and negative reviews concerning different star hotels through sentiment analysis.…”

“…Based on the TF-IDF algorithm, we obtained the weight of each sub-attribute with respect to the six attributes concerning four hotel star ratings. Then the importance of the six attributes concerning four hotel star ratings was calculated respectively by Equation (15), as shown in Table 11. On the whole, the importance of value, service and sleep quality varies across the hotel star ratings, while other attributes' importance fluctuates slightly and are considered as very important for all hotels.…”

“…Recently, with the development of data mining techniques, online reviews serve as the promising data source for customer satisfaction analysis. Several scholars have analyzed the attribute performance through online reviews using sentiment analysis methods, and hence found the determinants of customer satisfaction in the hotel industry [14,15]. However, these studies processed hotel reviews as a whole dataset, neglecting discriminating positive and negative reviews.…”

Research on Personalized Product Integration Improvement Based on Consumer Maturity

“…Cohen et al [14] measured consumer satisfaction along with several consumer behavior factors (e.g., emotion, loyalty, and trust). However, existing research mainly evaluates the relationship between product (or service) attributes [35]- [38] and consumer satisfaction through subjective methods [39], such as questionnaires, interviews, etc., most of which focus on customer perceptions and attitudes instead of more concrete objective criteria [20]. Besides, according to the definition of user satisfaction in these studies [24], [34], consumers' evaluation behavior should be a considered process, and the satisfaction score is a well-thought-out result generated in evaluation process.…”

“…In conclusion, satisfaction decision-making is the result of consumers' well-thought-out evaluation process. Although user satisfaction has been extensively studied by scholars both in the field of data mining [35]- [38] and traditional empirical research [39]. However, most of these studies only focus on product attributes, few studies have fully explored the effect of consumer heterogeneity (personality characteristics) on satisfaction decision making.…”