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Online shopping is becoming more prevalent, with consumers turning to e-commerce platforms to search for information about the goods and services they need. Users will usually check other consumer reviews on the platform as a reference while shopping. Online retailers can collect and analyze these online reviews to monitor consumer opinions about product quality, logistics services, packaging and other attributes to provide an accurate basis for product improvement and service optimization. This paper applies the Latent Dirichlet Allocation (LDA) algorithm to extract the critical factors that affect consumer satisfaction. More than 30,000 reviews of seven kinds of 3C (computer, communication, and consumer electronic) product categories obtained by crawler technology are analyzed. Then, the DEMATEL-ANP (DANP) method is applied to the extracted framework to build a cause-and-effect diagram of 3C product satisfaction model. The innovative LDA-DANP hybrid model clarifies the causal influence of the evaluation dimensions for 3C products sold online. The results show that brand value is the most important dimension affecting consumer online product satisfaction. Appearance design, logistics awareness service and product performance also have a positive influence on perceived service and brand value. Finally, some management implications and practical suggestions are proposed.

With the development of internet platforms, consumers increasingly rely on online product reviews during the shopping process. Customer online reviews can guide product design and provide valuable insights for businesses. Therefore, analyzing customer online reviews has become a research hotspot. However, existing studies suffer from some limitations, including the lack of fine-grained sentiment analysis and word clustering analysis. To address these issues, this study proposes a method based on Long Short-Term Memory (LSTM) for identifying and predicting customer preferences, aiming to provide more accurate information support for product design improvement personnel. This research first conducts data collection, preprocessing, and word vector computation. Subsequently, dictionary and sentiment labels are extracted. Based on a topic clustering system, sentiment labels are quantified for polarity classification, and sentiment scores and importance values are calculated as evaluation metrics for customer preference data. The preference-identified data are then applied to an LSTM prediction model, and the dataset is partitioned accordingly. Finally, the trained model is used for LSTM-based prediction of importance values and sentiment scores. The research results demonstrate that this method can accurately predict product preferences based on dynamic online reviews.

Studies have shown that online product reviews significantly affect consumer purchase decisions. However, it is difficult for the consumer to read online product reviews one by one because the number of online reviews is very large. Thus, to facilitate consumer purchase decisions, how to rank products through online reviews is a valuable research topic. This paper proposes a method for ranking products through online reviews based on sentiment classification and the interval-valued intuitionistic fuzzy Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). The method consists of two parts: (1) identifying sentiment orientations of the online reviews based on sentiment classification and (2) ranking alternative products based on interval-valued intuitionistic fuzzy TOPSIS. In the first part, the online reviews of the alternative products concerning multiple attributes are preprocessed, and an algorithm based on support vector machine and one-versus-one strategy is developed for classifying the sentiment orientations of online reviews into three categories: positive, neutral, and negative. In the second part, based on the percentages of the online reviews with different sentiment orientations and the numbers of online reviews of different products crawled from the website, an interval-valued intuitionistic fuzzy number is constructed to represent the performance of an alternative product with respect to the product attribute. Additionally, the interval-valued intuitionistic fuzzy TOPSIS method is employed to determine a ranking of the alternative products. Finally, a case analysis is provided to illustrate the application of the proposed method.

From the theoretical perspective of the elaboration likelihood model and service quality, this study attempts to provide an empirical study of patients’ medical choice behaviour and the moderating effect of disease risk under different paths with a real data set from an online healthcare community. The results demonstrate that factors from the central and peripheral routes affect patients’ behavioural intention regarding online medical choices. Disease risk plays an important moderating role. Low-risk patients are mainly influenced by factors from the peripheral route including doctors’ service reputation and disclosure of service qualifications, while high-risk patients are mainly influenced by factors from the central route including service quality of treatment and reviews quality.

This paper investigates the impact of aesthetics in early game development based on a quantitative analysis of 367 early access games. We identified the relationship between aesthetic perception in early video games reflected in the user reviews, comments, and subsequent positive and negative video game recommendations over time. We find that customer co-creation in product innovation is increasingly negative feedback over time when the game’s aesthetic early impression is perceived as negative. The implications for innovation management are that aesthetics design impacts the response to customer-ready prototypes. Managers should take the aesthetic design and user perception in early development into account and not delay the attention to aesthetics to a later product release stage.

Product snippets on e-commerce platforms are expected to attract consumers with appealing featured information about products. Related researches focus mainly on search traffic optimization, which, however, is usually less of attraction, i.e., showing weakness on catching consumers’ eyebrows. Meanwhile, online reviews contain versatile features that consumers highlight, which are found potentially useful to enhance the attraction of snippets. This paper proposes an iterative multi-criteria optimization method, by taking both snippet keywords and review features weighted by search intensity and review intensity, respectively, into consideration. The method can output enhanced snippets with optimized potential attraction and search traffic.