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The current study investigates the impact on perceived review helpfulness of the simultaneous processing of information from multiple cues with various central and peripheral cue combinations based on the elaboration likelihood model (ELM). Thus, the current study develops and tests hypotheses by analyzing real-world review data with a text mining approach in e-commerce to investigate how information consistency (rating inconsistency, review consistency and text similarity) influences perceived helpfulness. Moreover, the role of product type is examined in online consumer reviews of perceived helpfulness.

The current study collected 61,900 online reviews, including 600 products in six categories, from Amazon.com. Additionally, 51,927 reviews were filtered that received helpfulness votes, and then text mining and negative binomial regression were applied.

The current study found that rating inconsistency and text similarity negatively affect perceived helpfulness and that review consistency positively affects perceived helpfulness. Moreover, peripheral cues (rating inconsistency) positively affect perceived helpfulness in reviews of experience goods rather than search goods. However, there is a lack of evidence to demonstrate the hypothesis that product types moderate the effectiveness of central cues (review consistency and text similarity) on perceived helpfulness.

Previous studies have mainly focused on numerical and textual factors to investigate the effect on perceived helpfulness. Additionally, previous studies have independently confirmed the factors that affect perceived helpfulness. The current study investigated how information consistency affects perceived helpfulness and found that various combinations of cues significantly affect perceived helpfulness. This result contributes to the review helpfulness and ELM literature by identifying the impact on perceived helpfulness from a comprehensive perspective of consumer review and information consistency.

This study aims to adopt a cognitive heuristic approach to investigate the interaction effect of a message source characteristic (reviewer expertise [RE]) and two message structure characteristics (review rating consistency [RC] and review valence [RV]) on the perceived credibility of hotel online reviews.

Data were collected from 242 university students and were analyzed by three-way analysis of variance through a 2 × 2 factorial experiments using a simulated hotel review page on TripAdvisor.

Results show a three-way interaction effect of RE, RC and RV on the perceived credibility of hotel online reviews. The main effects of the three factors are also determined. Higher perceived credibility scores are found for negative reviews, reviews written by experts and reviews with a consistent rating.

This study adopts an experimental approach and is the first to investigate the three-way interactions of message source and message structure characteristics of online hotel reviews. Data were collected from students in a university in Hong Kong. Results may not be generalizable to other markets.

Results suggest that reviews written by experts have higher perceived credibility. Hotels should pay attention to the content of online reviews and the expertise level of reviewers. Efforts should be exerted to create positive experiences for hotel guests that motivate expert reviewers to write positive reviews. Note that negative reviews have higher perceived credibility than positive ones. Hotels should promptly address negative reviews and provide professional responses to reviewers. Platform operators of user-generated content (UGC) should create well-defined reviewer profiles that can serve as cues that communicate the different expertise of reviewers.

This study is the first to test the three-way interaction effect of RE, RC and RV on the perceived credibility of hotel online reviews. Results provide recommendations to hotels and UGC operators and enable them to benefit from emerging UGC usage.

The existing collaborative filtering algorithm may select an insufficiently representative customer as the neighbor of a target customer, which means that the performance in providing recommendations is not sufficiently accurate. This study aims to investigate the impact on recommendation performance of selecting influential and representative customers.

Some studies have shown that review helpfulness and consistency significantly affect purchase decision-making. Thus, this study focuses on customers who have written helpful and consistent reviews to select influential and representative neighbors. To achieve the purpose of this study, the authors apply a text-mining approach to analyze review helpfulness and consistency. In addition, they evaluate the performance of the proposed methodology using several real-world Amazon review data sets for experimental utility and reliability.

This study is the first to propose a methodology to investigate the effect of review consistency and helpfulness on recommendation performance. The experimental results confirmed that the recommendation performance was excellent when a neighbor was selected who wrote consistent or helpful reviews more than when neighbors were selected for all customers.

This study investigates the effect of review consistency and helpfulness on recommendation performance. Online review can enhance recommendation performance because it reflects the purchasing behavior of customers who consider reviews when purchasing items. The experimental results indicate that review helpfulness and consistency can enhance the performance of personalized recommendation services, increase customer satisfaction and increase confidence in a company.

This paper aims to offer a framework explaining how product experience (i.e. think vs feel) and product involvement (high vs low) influence the helpfulness of online reviews. It also reexamined how online consumer review dimensions help to build online review helpfulness under different contexts.

Data were collected using content analysis on 1,200 online customer reviews on 12 products from four categories to measure the relationships between online review dimensions and the helpfulness of reviews. The regression analysis and analysis of variance (ANOVA) were used to test the hypotheses.

The findings indicate that the effectiveness of length of a review is moderated by product type; for think products, longer reviews yield higher helpfulness. Furthermore, the level of consistency between individual review ratings and overall product ratings is associated with review helpfulness. The length of product descriptions and product ratings is moderated by the level of involvement. For products with high involvement, longer descriptions yield higher helpfulness.

A conceptual connection to customer interaction is proposed by online customer reviews that vary by product type. The findings provide implications for online retailers to better manage online customer reviews and increase the value of product ratings.

The purpose of this study is to investigate how the interplay of valences (positive or negative) between review texts and ratings affects consumers' reactions to the reviews and the product being assessed.

An experimental design with hypothetical movie reviews was used to investigate how inconsistent text‐rating reviews affect people's intention to consume a particular product and their perceptions of the review itself.

It was found that text valences (positive or negative) significantly influence how subjects perceive the interestingness and trustworthiness of reviews. The texts also have an influence on the subjects' movie‐attendance intention compatible with their valence. In addition, a cross‐over interaction was found between texts and ratings that affects a review's trustworthiness.

The study enriches understanding of consumer decision making when different formats of information about the same object are presented.

Marketers can benefit by incorporating review texts and rating valences to enhance the prediction accuracy of their products' sales performances. Review publishers can get a better understanding of how to present their reviews to enhance their perceived interestingness and trustworthiness.

Product reviews are commonly found in the mass media. These reviews use ratings as evaluative summaries of the texts. However, little research has been conducted regarding the communication effects that the ratings have in relation to the texts. The study seeks to fill this gap.

Prior literature on online customer reviews (OCRs) suggests that individuals are socially influenced by information shared by others. Given that the online environment brings together users from different cultures, understanding how users differ in their processing and generation of OCRs across cultures is imperative. Specifically, this paper explores how cross-cultural differences influence OCR generation when there are inconsistencies between recent and overall review ratings.

The authors employ an empirical study and an experimental approach to test the predictions. For the empirical study (Study 1), the authors collected and analyzed actual review data from an online hotel review platform, Booking.com. This was followed by an experimental study (Study 2) in which the authors manipulated the thinking style represented by each cultural orientation to further explain how and why cross-cultural differences exist.

The results show that compared with the review ratings of users from collectivist cultures, those of users from individualistic cultures are more likely to follow recent review ratings. Based on the experimental study, the authors further find that such cross-cultural differences in OCR generation are driven by differences in thinking style.

This research extends the literature by demonstrating the cross-cultural differences in individuals' herding tendencies in OCR generation. The authors also add to the literature by showing in which direction OCR herding occurs when there is a discrepancy between overall and recent review ratings. From a managerial perspective, the findings provide guidelines for online platforms serving the global market on predicting customers' OCR generation and constructing appropriate response strategies.

Consumers have pervasively relied on mobile reviews in digital economy. However, little knowledge exists regarding how customers adopt several mobile reviews to make purchasing decisions. With the assistance of reader-response theory, this study investigates how the consistency of product reviews, in terms of their adherence to both other reviews and the prior experience of the customer, affect perceived quality, confirmation of the customer's expectations, the customer's level of trust in the seller and the consequent purchase intention.

Based on a scenario simulation and an online experiment to collect data, the authors employed AMOS to test the proposed hypotheses using survey data collected from 314 customers in Study 1 and 420 consumers in Study 2.

The results indicate that global consistency positively and significantly contributes to confirmation, perceived quality and trust in sellers while sequential inconsistency positively and significantly influences perceived quality. Meanwhile, purchase intention is positively and significantly promoted by confirmation, perceived quality and trust in sellers, and initial valence has some moderating effects on these relationships.

This study contributes to the understanding of how customers apply product reviews to make purchasing decisions from a new angle. It also elucidates the way in which the perceived consistency of product reviews affects how reviewers are perceived and the consequent effect of these perceptions on a customer's purchase intentions.

The purpose of this paper is to provide a comprehensive review on electronic word-of-mouth (eWOM) credibility. Further, the authors propose a comprehensive and integrated model on eWOM credibility.

The authors conducted a systematic review of the extant literature on marketing, sociology and psychology to identify the factors that affect eWOM credibility. Further, the authors developed themes and identified factors which lead to eWOM credibility.

Four factors were identified, i.e. content, communicator, context and consumer, which affect eWOM credibility. Several variables associated with these four factors were identified, which result in eWOM credibility. Further, the authors developed 22 propositions to explain the causal relationship between these variables and eWOM credibility.

The conceptual model needs empirical validation across various eWOM platforms, i.e. social networking websites, e-commerce websites, etc.

Managers and e-commerce vendors can use these inputs to develop specific design elements and assessment tools which can help consumers to identify credible eWOM messages. Credible eWOM messages, in turn, will increase the “trust” and “loyalty” of the customers on e-commerce vendors.

This paper provides a conclusive takeaway of eWOM credibility literature by integrating multiple perspectives and arguments from the extant literature. This study also presents an integrated model, which provides a theoretical framework for researchers to further examine the interaction effect of various variables, which results in eWOM credibility.

The peer review history for this article is available at: https://publons.com/publon/10.1108/OIR-06-2020-0263

The purpose of this paper is to examine what influence travelers’ adoption of online reviews, and whether the online reviews will influence their travel planning decisions.

Data were collected from 193 respondents from eWOM websites and analyzed using structural equation modeling.

The results revealed that eWOM has a significant influence on travel decisions. Furthermore, travelers were willing to adopt information from eWOM and this information was useful in their travel planning and decisions. Gender and time spent on online reviews were found to affect travel planning and decisions. Travelers also found that the reviews and issues raised in eWOM had credibility and were of good quality.

The study was not able to incorporate all factors which may be relevant to this study and so further theoretical development may be necessary to develop the conceptual model. The sample size, while adequate, can be expanded further.

Operators and administrators of eWOM can use these findings to develop more user-friendly interfaces so that more positive reviews and sales can be generated.

The results showed that travelers who adopt the information in eWOM will, in turn, use eWOM in their travel planning. This confirms the importance of eWOM and travelers in general will translate their pre-travel decisions into actual travel planning.

This research extended existing eWOM and information system adoption studies and focused on the travel planning context. This research validated the significant roles of eWOM argument quality and credibility in predicting the information usefulness of eWOM.

Online ratings (review valence) have been found to exert a strong influence on hotel room prices. This study aims to systematically synthesize research estimating the impact of online ratings on room rates using a meta-analytical method.

From major academic databases, a total of 163 estimates of the effects of online ratings on room rates were coded from 22 studies across different countries through a systematic review of relevant literature. All estimates were converted into elasticity-type effect sizes, and a hierarchical linear meta-regression was used to investigate factors explaining variations in the effect sizes.

The median elasticity of online ratings on hotel room rates was estimated to be 0.851. Meta-regression results highlighted four categories of factors moderating the size of this elasticity: data characteristics, research settings, variable measures and publication outlet. Among sub-ratings, results revealed value rating and room rating to exert the largest impact on room rates, whereas staff and cleanliness ratings demonstrated non-significant impacts.

This study provides practical implications on the relative importance of different types of online ratings for online reputation and revenue management.

This study represents the first research effort to understand factors moderating the effects of online ratings on hotel room rates based on a quantitative review of the literature. Moreover, this study provides beneficial insights into the specification of empirical hedonic pricing models and data-collection strategies, such as the selection of price variables and choices of model functional forms.