Customer-centric service benchmarking using online reviews: a case study of Bangkok hotels

Online hotel reviews is an important source for customers to find out the products and service quality. Online hotel reviews consist of reviews and ratings. The best online reviews website in TripAdvisor. In this era, online reviews are really important and could affect customer preferences in choosing a hotel. The aim of this research is to analyze in what extent do online reviews and ratings affect customer preferences. This study use survey by distributing a total of 100 questionnaires in order to gain the data. Then, the data will be proceed through SPSS to test the validity, reliability, classical assumption, and multiple regression analysis. The results indicatedthat both of the variables (online reviews and ratings) positively impact customer preferences in choosing a hotel. However, online reviews has a bigger impact towards customer preferences rather than online ratings. It is recommended for TripAdvisor to weed out the fake reviews and guarantee the trustworthy reviews. For the hotel managers, it is recommended to response both, positive and negative reviews that made by customers in TripAdvisor, because it could change someone’s perceptions into the good feeling of the hotel.

The exponentially growing online reviews have become a great wealth of information into which many researchers have started tapping. Using online reviews as a source of customer feedback, product designers are able to better understand customers’ preferences and improve product design accordingly. However, while predicting future product demand as a function of product attributes and customer heterogeneity has proved to be effective, not many literatures have studied the impact of non-product-related features, such as number of reviews and average ratings, on product demand using a large-scale dataset. As such, this paper proposes a data-driven methodology to investigate the influence of online ratings and reviews in purchase behavior by using discrete choice analysis. In the absence of information about the true customer choice set, we generate an estimated customer choice set based on a probability sampling using customer clustering and product clustering. In order to examine the effect of number of reviews and average rating, we have computed, for all the laptops in the choice set of each customer, the number of reviews and thus average rating at the date of this particular customer’s review. Using laptops for our case study, our experiment has shown that the number of reviews and average ratings are statistically significant, and the inclusion of these features will greatly improve the predictive ability of the model.

Due to the development of the e-commerce platform and the internet technology, the inclination of consumers for online shopping is shooting up. To lure consumers and gratify consumers, it’s necessary for enterprise to explore and excavate the purchase intention evolution mechanism so that enterprises can customize the marketing strategies and get consumers to purchase products. Previous studies have shown that consumers’ purchase intention is influenced significantly by online reviews. However, the mechanism by which consumers’ real purchase intentions change when they refer to online reviews is unclear. In fact, the process that consumers browse online reviews is truly an opinion interaction process between recipients (consumers who buy goods) and reviewers (consumers who post online reviews). Interaction between opinions may lead to changes in consumers’ purchase intentions. Therefore, an opinion dynamics model, the Deffuant–Weisbuch (D-W) model, is introduced and improved to explore the dynamic evolution of consumers’ purchase intention. Firstly, online reviews are executed. Then, fuzzy quantification of sentimental opinion values is performed through trapezoidal fuzzy numbers. Secondly, the improved D-W model is constructed considering the influence of the personality of recipients and the professionalism of reviewers on opinion interaction and the “negative bias” mechanism. Finally, a case study is constructed with online reviews of a cell phone by using the above method. In addition, sensitivity analyses are conducted for the personality coefficient of recipients, professionalism of reviewers, and size of heterogeneous consumers, respectively, through which, the validity of the proposed method is expounded. This study not only contributes to an in-depth discussion about the influencing factors of purchase intention, but also provides references for enterprises to better utilize online reviews to promote products and attract consumers.

As an essential information resource, online reviews play an important role in consumers’ decision-making processes. To solve the product ranking problem through online reviews, two important issues are involved: sentiment analysis (SA) for online reviews and product ranking based on multi-criteria decision-making (MCDM) methods. However, merely a few studies have considered the impact of SA accuracy, which can significantly affect the final decision-making process. This paper proposes a novel data-driven method for ranking products through online reviews based on interval type-2 fuzzy sets (IT2FSs) and SA. In this method, after acquiring online reviews, the explicit and implicit attributes are extracted from the website itself and the latent Dirichlet allocation (LDA) model, respectively. Thereafter, a deep learning model is adopted to identify the five sentiment intensities of online reviews, based on which the SA results are represented as IT2FSs by considering the classification effect. After type-reduction for IT2FSs, the ranking order is obtained based on the exponential TODIM (ExpTODIM) method. Furthermore, a case study on ranking travel products from Trip.com Group through online reviews is provided to illustrate the effectiveness and applicability of the proposed method.

An integrated method for product ranking through online reviews based on evidential reasoning theory and stochastic dominance

The credibility of online reviews sites is questioned due to the prevalence of review manipulation, leading to decreased consumers’’ trust and satisfaction. This study aimed to examine the relationship between online reviews, consumer trust, and their influence on purchasing decisions among senior high school students. Using a descriptive-correlational design, data were collected from 300 senior high school students through modified and adapted survey questionnaires. The mean and Pearson’s correlation were used to analyze the data. The results revealed a very high level of reliance on online reviews among students, as well as a very high level of consumer trust in these reviews. Moreover, the analysis showed a strong, significant relationship (r = 0.724, p < 0.05) between online reviews and consumer trust, indicating that the consumer trust of senior high school students significantly influences how much they trust in making choices when shopping online. However, the study also highlights the need for the critical evaluation of online information to mitigate the risks of misinformation and biased reviews. These findings suggest that while students are highly engaged with online reviews and trust them significantly, they may not always be equipped to discern credible information from misleading content. Consequently, the study recommends that educational institutions incorporate media literacy and critical thinking modules into senior high school curricula, particularly in subjects like Media and Information Literacy and Consumer Education. These modules should focus on teaching students how to evaluate online reviews critically, identify potential biases, verify sources, and differentiate between genuine and misleading information. Furthermore, schools should conduct interactive workshops, discussions, and case studies on real-life instances of deceptive online reviews to enhance students’ analytical skills. By integrating these strategies, students will be better equipped to make informed purchasing decisions and navigate the digital marketplace responsibly.

Kano analysis and importance-performance analysis (IPA) are widely used for needs analysis, product positioning, and strategic planning in product design. Previous research uses customer surveys and online reviews as the main data sources. However, these data carry inevitable subjective bias. In contrast, product maintenance records provide objective information on product quality issues and failure patterns, which can be cross-validated with customers’ personal experience from online reviews. In this paper, we propose a systematic approach for conducting Kano-IPA analysis from online reviews and product maintenance records synthetically. An attribute-keyword dictionary is first established using keyword extraction and clustering methods from online reviews and maintenance records. After that, semantic groups including product attributes and associated descriptions are extracted by dependency parsing analysis. The sentiment scores of identified attributes are calculated by a self-supervised representation learning approach (Sentiment Knowledge Enhanced Pre-training, SKEP) from the built semantic groups. Sentiment scores and occurrence frequencies of attributes in online reviews are utilized for Kano analysis. The importance of product attributes in IPA is estimated from the impact of sentiments of each product attribute on product ratings, while the performance is estimated from the sentiment scores of online reviews or the quality statistics from maintenance records. A case study of passenger vehicles shows that integrated data can provide more comprehensive results and richer insights. The proposed approach enables automatic data processing and can support companies to make efficient design decisions with broader perspectives from multi-source data.

Hotel managers can learn about hotel customer satisfaction by analyzing online reviews. There are differences in the experience of reviewers, the release time and the degree of recognition for different online reviews, which leads to different reliability in reflecting customer satisfaction. The issue of how to determine hotel customer satisfaction more rationally with this reliability in mind is still a problem. To address this problem, this paper proposes a method for measuring hotel customer satisfaction based on the Dempster-Shafer (D-S) evidence theory, which considers the reliability of online reviews and information from multiple online travel review websites. This method is composed of three stages. First, considering the difference in online reviews’ reliability, the basic probability assignment is generated based on online reviews. Next, the aggregation of the evaluation information is conducted based on the entropy weight method and Dempster’s rule of combination. Finally, the utility function is used to calculate the expected utility, and customer satisfaction is analyzed based on the calculated expected utility. Therefore, improvement strategies of customer satisfaction can be developed according to customer satisfaction ranking. To verify the feasibility and effectiveness of this method, a case study for four hotels is presented. The proposed method is expected to help hotel managers understand the hotel’s customer satisfaction, and develop corresponding improvement strategies to enhance its competitiveness.

Online reviews published on the e-commerce platform provide a new source of information for designers to develop new products. Past research on new product development (NPD) using user-generated textual data commonly focused solely on extracting and identifying product features to be improved. However, the competitive analysis of product features and more specific improvement strategies have not been explored deeply. This study {fully uses} the rich semantic attributes of online review texts and proposes a novel online review--driven modeling framework. This new approach can extract fine-grained product features; calculate their importance, performance, and competitiveness; and build a competitiveness network for each feature. As a result, decision-making is assisted, and specific product improvement strategies are developed for NPD beyond existing modeling approaches in this domain. Specifically, online reviews are first classified into redesign- and innovation-related themes using a multiple embedding model, and the redesign and innovation product features can be extracted accordingly using a mutual information multilevel feature extraction method. Moreover, the importance and performance of features are calculated, and the competitiveness and competitiveness network of features are obtained through a personalized unidirectional bipartite graph algorithm. Finally, the importance—performance—competitiveness analysis plot is constructed, and the product improvement strategy is developed via a multistage combined search algorithm. Case studies and comparative experiments show the effectiveness of the proposed method and provide novel business insights for stakeholders, such as product providers, managers, and designers.

With the growth of internet technology, customers are sharing up their experiences. Hence, these types of customer experiences are spreading rapidly as a source of online reviews. For this reason, online reviews have become a critical source of information that influences customers’ purchase intentions and behavior. Thus, businesses should monitor online reviews to understand the customer experience and increase customer satisfaction and loyalty. This study attempts to identify essential characteristics for positive online reviews of wine bars and examine the structural relationships of these attributes. To accomplish this purpose, a total of 1,337 online reviews were collected from Google Travel and analyzed. The frequency analysis was performed using text mining to determine the most frequently referred to attributes, and the semantic network analysis, factor analysis, and regression analysis were conducted to understand customer experience and satisfaction of wine bars located in Busan, South Korea. The results show that the top 50 keywords identified from the online reviews were categorized as four groups—‘Atmosphere’, ‘Service’, ‘Date and Location’, and ‘Menu’. The results of the factor analysis reduced the original dimension of 48 keywords to 16 keywords and classified them into six factors, namely, ‘Service’, ‘Staff’, ‘Menu’, ‘Environment’, ‘Recommendation’ and ‘Atmosphere’. Based on these results, implications for sustainable wine bar marketing strategies were suggested.

Mining customer requirements from online reviews: A product improvement perspective

Mining online reviews for ranking products: A novel method based on multiple classifiers and interval-valued intuitionistic fuzzy TOPSIS

A novel Multi-Criteria Decision Making framework based on Evidential Reasoning dealing with missing information from online reviews

Wisdom of crowds: Conducting importance-performance analysis (IPA) through online reviews

Comparison of free-comment online product reviews and central location product testing for sensory product characterisation: A case study with coffee consumers