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This study aims to elicit the preferences of potential travelers for different property listings' attributes (online review number, positive valence rate of reviews and discount strategy) when selecting hotels and peer-to-peer (P2P) accommodation sharing on online booking platforms.

A discrete choice experiment (DCE) was conducted with 291 respondents with accommodation needs. They were asked to choose between pairs of listings.

The authors found that when booking accommodation online, complex discount strategies were not determinant both in selecting hotels and P2P accommodations. Positive valence rate of reviews has a higher impact on the selection of traditional hotels than P2P accommodations, while the number of online reviews has a higher impact on the selection of P2P accommodations than traditional hotels. The authors further discuss the effect of each attribute on online accommodation selection in terms of price ranges of the property listings.

The findings provide suggestions for platform operators and product/service providers to improve their marketing strategies and optimize their management efforts.

To the best of the authors’ knowledge, this is one of the first studies that investigate the role of property listings' attributes on the selections between hotels and P2P accommodations. The findings from this research study could be generalized to other online platforms and electronic commerce-related transactions.

To better understand the online reviews and help potential consumers, businessmen and product manufacturers effectively obtain users’ evaluation on product aspects, this paper aims to explore the distribution regularities of users’ attention and sentiment on product aspects from the temporal perspective of online reviews.

Temporal characteristics of online reviews (purchase time, review time and time intervals between purchase time and review time), similar attributes clustering and attribute-level sentiment computing technologies are used based on more than 340k smartphone reviews of three products from JD.COM (a famous online shopping platform in China) to explore the distribution regularities of users’ attention and sentiment on product aspects in this paper.

The empirical results show that a power-law distribution can fit users’ attention on product aspects, and the reviews posted in short time intervals contain more product aspects. Besides, the results show that the values of users’ sentiment on product aspects are significantly higher/lower in short time intervals which contribute to judging the advantages and weaknesses of a product.

This paper cannot acquire online reviews for more products with temporal characteristics to verify the findings because of the restriction on reviews crawling by the shopping platforms.

This work reveals the distribution regularities of users’ attention and sentiment on product aspects, which is of great significance in assisting decision-making, optimizing review presentation and improving the shopping experience.

The purpose of this paper is to detect predefined service attributes and their sentiments from online restaurant reviews, and then to measure the effects of customer sentiments toward service attributes on customer satisfaction (CS) and revisit intention (RVI) simultaneously.

This study proposed a supervised framework to model CS and RVI simultaneously from restaurant reviews. Specifically, the authors detected the predefined service dimensions from online reviews based on random forest. Then, the sentiment polarities of the reviews toward each predefined dimension were identified using light-gradient boosting machine (LightGBM). Finally, the effects of attribute-specific sentiments on CS and RVI were evaluated by a bagged neural network-based model. The proposed framework was evaluated by 305,000 restaurant comments collected from DianPing.com, a Yelp-like website in China.

The authors obtained a hierarchal importance order of the investigated service themes (i.e. location, service, environment, price and food). The authors found that food played the most important role in affecting both CS and RVI. The most salient attribute with respect to each service theme was also identified.

Unlike prior work relying on the data collected from surveys, this study is among the first to model the relationship among service attributes, CS and RVI simultaneously from real-world data. The authors established a hierarchal structure of eighteen attributes within five service themes and estimated their effects on both CS and RVI, which will broaden our understanding of customer perception and behavioral intention during service consumption.

The purpose of this paper is to propose a model to test whether the combined effects of valence and objectivity/subjectivity of online review have an effect on consumer judgment and whether e-WOM platforms have a moderating effect.

In total, 480 respondents participated in online experiments with a four (positive+objective, positive+subjective, negative+objective, and negative+subjective online review) by two (marketer-generated vs consumer-generated brand community web sites) between subject design.

The experiment showed that: an objective negative online review was rated higher in terms of message usefulness compared to the other types of online reviews; positive reviews, whether they are objective or subjective, were rated higher in terms of attitudes toward and intention to purchase the reviewed product, and the effects of online reviews moderated by e-WOM platforms on consumer judgment were supported.

The present study, based on an established theoretical foundation, will help the research community to gain a deeper understanding of the combined effects of online review valence and attributes on consumer judgment and whether user-generated web community is better for consumers to consult product experience.

The findings of this study can provide interested firms with useful strategies and tactics to enhance users’ acceptance of online reviews in terms of who operates the web sites.

With increasing use of consumers’ online reviews, the present study proposed and tested a comprehensive research model integrating both the valence and objectivity/subjectivity of online review, which has rarely been addressed in previous research.

The helpfulness vote is a type of aggregate user representation that, by measuring the quality of an online review based on certain criteria, can allow readers to find helpful reviews more quickly. Although widely applied in practice, the effectiveness of the voting mechanism is unsatisfactory. This paper uses the heuristic–systematic model and the theory of dynamics of reviews to shed light on the effect of various information cues (product ratings, word count and product attributes in the textual content of reviews) on online reviews’ aggregative voting process. It proposes a conceptual model of seven empirically tested hypotheses.

A dataset of user-generated online hotel reviews (n = 6,099) was automatically extracted from Ctrip.com. In order to measure the variable of product attributes as a systematic cue, the paper uses Chinese word segmentation, a part-of-speech tag and word frequency statistics to analyze online textual content. To verify the seven hypotheses, SPSS 17.0 was used to perform multiple linear regression.

The results show that the aggregative process of helpfulness voting can be divided into two stages, initial and cumulative voting, depending on whether voting is affected by the previous votes. Heuristic (product ratings, word count) and systematic cues (product attributes in the textual content) respectively exert a greater impact on the two stages. Furthermore, the interaction of heuristic and systematic cues plays an important role in both stages, with a stronger impact on the cumulative voting stage and a weaker one on the initial stage.

This paper’s findings can be used to explore improvements to helpfulness voting by aligning it with an individual’s information process strategy, such as by providing more explicating heuristic cues, developing different methods of presenting relevant cues to promote the voting decision at different stages, and specifying the cognitive mechanisms when designing the functions and features of helpfulness voting.

This study explores the aggregative process of helpfulness votes, drawing on the study of the dynamics of online reviews for the first time. It also contributes to the understanding of the influence of various information cues on the process from an information process perspective.

This study aims to investigate the online customer review behavior and determinants of overall satisfaction with hotels of travelers in various travel group compositions.

The author collected data from online reviews of travelers in various travel group compositions from 600 hotels in 100 of the largest cities in the USA from Booking.com and used latent semantic analysis (LSA) to identify the positive and negative factors from online reviews of travelers in various travel group compositions. Then, text regression was used to determine the influential factors of overall satisfaction of travelers in various travel group compositions.

It was found in this study that not all the positive and negative textual factors mined from travelers’ online reviews significantly influenced their overall satisfaction. In addition, the determinants of traveler satisfaction were different when travelers were in different travel group compositions.

The author found similar online review behavior, but different basic, excitement and performance factors of travelers in different travel group compositions.

This study helps hoteliers understand customers’ perception of the specific attributes of their products and services, which provides a guideline for businesses to design the priority rule to improve these corresponding attributes and use market segmentation strategy when dealing with customers in different travel group compositions.

The author examined and compared the online review behavior and determinants of satisfaction using the factors mined from online reviews between travelers in various travel group compositions. This study combined customer ratings with textual reviews and predicted customer ratings from the factors extracted from textual reviews using LSA and text regression.

This paper aims to propose a bed and breakfast (B&B) recommendation method that takes into account review timeliness and user preferences to help consumers choose the most satisfactory B&B.

This paper proposes a B&B ranking method based on improved intuitionistic fuzzy sets. First, text mining and cluster analysis are combined to identify the concerns of consumers and construct an attribute set. Second, an attribute-level-based text sentiment analysis is established. The authors propose an improved intuitionistic fuzzy set, which is more in line with the actual situation of sentiment analysis of online reviews. Next, subjective-objective combinatorial assignments are applied, considering the consumers’ preferences. Finally, the vlsekriterijumska optimizacija i kompromisno resenje (VIKOR) algorithm, based on the improved score function, is advised to evaluate B&Bs.

A case study is presented to illustrate the use of the proposed method. Comparative analysis with other multi-attribute decision-making (MADM) methods proves the effectiveness and superiority of the VIKOR algorithm based on the improved intuitionistic fuzzy sets proposed in this paper.

Proposing a B&B recommendation method that takes into account review timeliness and user customization is the innovation of this paper. In this approach, the authors propose improved intuitionistic fuzzy sets. Compared with the traditional intuitionistic fuzzy set, the improved intuitionistic fuzzy set increases the abstention membership, which is more in line with the actual situation of attribute-level sentiment analysis of online reviews.

The authors investigate the varying impact of three categories of conflicting consumer reviews (i.e. conflicting opinions on attributes of a product item, conflicting ratings of an item and the intensity of conflicting reviews of an item) on the potential customers' perceived informativeness, which is expected to affect the perceived correct purchase.

To test their proposed hypotheses, the authors conducted an experiment using a 2 × 2 × 2 factorial design for each conflict type comprising two levels (low vs high).

The results of this study found that conflicting opinions on product attributes can enhance potential customers' perceptions of informativeness and subsequent correct purchase decisions while conflicting ratings and the intensity of conflicting reviews can diminish potential customers' perceptions of informativeness. In addition, conflicting ratings negatively moderate the effect of conflicting attributes on perceived informativeness such that the positive effect of conflicting attributes on perceived informativeness will be less prominent when conflicting ratings are present (vs absent).

While potential customers are browsing product descriptions, reviews and comments from other purchasers are also playing a role in influencing a potential customer's purchase decision. However, given the different experiences and temperaments of individuals, the subjective remarks and ratings of individuals are sometimes inconsistent or even conflicting, which can lead to confusion among potential customers. The authors categorize the positive or negative effects of the three conflicting reviews based on the two dimensions of ease of capture and product diagnosticity. The findings can help platforms optimize the display of product reviews to help potential customers make more accurate purchase decisions.

This study explores the direct relationship between social media user-generated content (UGC), online search traffic and offline light vehicle sales of different models.

The long-run equilibrium relationship and short-run dynamic effects between the valence and volume of UGC, online search traffic and offline car sales are analyzed by applying the autoregressive distribution lag (ARDL) model.

The study found the following. (1) In the long-run relationship, the valence of online reviews on social media platforms is significantly negatively correlated with the sales of all models. However, in the short-run, the valence of online reviews has a significant positive correlation with all models in different lag periods. (2) The volume of online reviews is significantly positively correlated with the sales of all models in the long run. However, in the short run, the relationship between the volume of online reviews and the sales of lower-sales-volume cars is uncertain. There is a significant positive correlation between the volume of reviews and the sales of higher-sales-volume cars. (3) Online search traffic has a significantly negative correlation with the sales of all models in the long run. However, in the short run, there is no consistent conclusion on the relationship between online search traffic and car sales.

This study provides a reference for managers to use in their efforts to improve offline high-involvement product sales using online information.

This study aims to investigate the impacts of online review and source features upon travelers’ online hotel booking intentions.

This study developed a research model and empirically examined the model by collecting data from business travelers in the Mainland China. Factor analysis was adopted to identify features of online reviews content and source attribute. Regression analysis was used to examine impacts of these attributes upon travelers’ online booking intention.

Six features of online reviews content and one source attribute were identified, namely, usefulness, reviewer expertise, timeliness, volume, valence (negative and positive) and comprehensiveness. Regression analysis results testified positive causal relationships between usefulness, reviewer expertise, timeliness, volume and comprehensiveness and respondents’ online booking intentions. A significantly negative relation between negative online reviews and online booking intentions was identified, whereas impacts from positive online reviews upon booking intentions were not statistically significant.

The major limitation of this study is that interrelationships among features of online reviews, which were discussed in other similar studies, were not considered. Still, this study benefited researchers from scrutinizing features of online reviews, rather than several of them. As such, it offered more comprehensive suggestions for practitioners in how to better utilize online reviews as a marketing tool.

Hospitality practitioners could enhance consumer review management by applying the six underlying factors of online review in the present study to find out the ways of increasing consumers’ booking intentions in the specific hotel contexts.

A major theoretical contribution of this paper is its comprehensiveness in examining features of review content as well as its source simultaneously. This study also offered areas worthy of more research efforts from perspectives of practitioners and researchers.