Helpfulness Of Online Reviews Research Articles

To Tell, Not to Yell: The Effect of Writer's Intents on Readers’ Perceived Helpfulness of Online Product Reviews

Perceived review helpfulness has been investigated extensively; however, the influence of the review writer's intents behind a review has not been considered. This study investigates the effect of the writer's intents to provide information, express emotions, or call for action on reviews’ helpfulness, thereby bridging the gap between the writer's perspective and the reader's perspective. The writer's intents are operationalized through speech acts, an implicit, yet systematic, approach in which the intents of the writer are captured at the linguistic level, that is, how things are said. A unique parallel approach based on both readers’ perceptions and linguistic theory is used to operationalize the speech acts. This approach allows multiple speech acts per sentence, and it can estimate the influence of each speech act separately. The results show that assertive acts positively impact review helpfulness, whereas expressive and call-for-action acts have a negative effect. This confirms previous literature showing that the major drivers of review helpfulness are often related to information delivery. In this research, the idea that consumers like reviews with accurate information is detected at the level of the writer's intent, which is new in the context of text analysis to assess online review helpfulness.

Consumer-brand heuristics in luxury hotel reviews

Purpose This paper aims to evaluate and structure the basic heuristics consumers use in evaluating word-of-mouth (WOM) about luxury hotel brands while analyzing the impact of deception in online consumer reviews. Design/methodology/approach The research used a two-study mixed-methods approach, using interpersonal deception theory and social proof theory as lenses to conduct our analysis. For the first study, a qualitative conceptual mapping analysis was conducted, examining online consumer reviews to identify key concepts and their relationships in the context of luxury hotel brands. In the second study, the themes were further examined using a fuzzy-set qualitative comparative analysis to analyze their causal complexity and association between variables to determine how they influence the perceived helpfulness of online reviews for luxury hotel brands. Findings The results underline the importance of functional, objective variables, such as the number of reviews and stars, as social proof heuristics and other factors, including clout, authenticity and analytic tone, as interpersonal communication heuristics. Therefore, consumers use a combination of social and interpersonal communication heuristics to extract information from reviews and manage deception risk. Research limitations/implications The paper contributes to the consumer–brand relationship literature by assessing the heuristics consumers use in evaluating online reviews and provides additional information for research in online reputation management. Practical implications This study’s results can help marketing practitioners and brand managers manage their online reputations better. It can also aid managers in improving their messaging on hotel websites to entice consumers to complete bookings. Heuristics play an essential role in such messaging and understanding them can help marketers appeal directly to their target market. Originality/value This study contributes to the literature on consumer–brand relationships by providing a framework of heuristics that consumers use when evaluating luxury service brands and contributes to WOM and online reputation research by highlighting factors that may make online reviews more helpful.

How consumers process online review types in familiar versus unfamiliar destinations. A self-reported and neuroscientific study

The purpose of this research is to assess how digitalized information posted on social media about tourism destinations (TDs) impacts on consumers, with a particular focus on the role of brand familiarity and the source type and content style of online reviews. Two studies were conducted. First, using an online survey-based 2 × 2 × 2 experimental design with a sample of 548 consumers, we examined the interaction effects of review content style, brand familiarity and source of the post on the perceived informativeness and persuasiveness of online reviews about TDs. The findings showed that, when reviews referred to specific features of TDs (brand), firm-generated content (FGC) was more persuasive and informative than user-generated content (UGC) but, in general reviews, UGC was more persuasive and informative than FGC. A second study, using a sample of 136 consumers, assessed: (i) the impact of review content style and destination familiarity on the perceived diagnosticity (credibility and helpfulness) of online reviews; and (ii) the effects of review content style on visual attention paid, and customer engagement, using eye-tracking and electroencephalography. The findings showed that specific reviews are more diagnostic than general reviews, the positive effect of specific reviews on credibility being stronger for familiar destinations.

Text Mining Online Reviews: What Makes a Helpful Online Review?

One of the main strategies to facilitate innovation diffusion of a technology product is displaying positive customer reviews. However, not all reviews matter to people. We examine more than 100,000 smartphone reviews on Amazon.com to investigate how sentiment, emotions, and length of online reviews might influence online review helpfulness. We perform a lexicon-based sentiment analysis to extract sentimental and emotional polarity. The findings of this study indicate certain patterns in online reviews that receive the most ‘helpful’ votes. Firstly, negative reviews leave a bigger impact on people in terms of ‘helpful’ votes. Secondly, long reviews might leave a bigger impact on people compared to short reviews, however, the effect is moderated by both positive and negative sentiment polarity. People find long reviews helpful when they are ‘unsentimental.’ Emotional words in general are not helpful for improving the impact of online reviews, however, trust-triggering words improve online review helpfulness. To best introduce a technology, give practical information, let people be the judge, and use many trust-triggering words such as ‘assure’ and ‘believe.’

Understanding online review helpfulness: a pleasure-arousal-dominance (PAD) model perspective

PurposeDrawing on the pleasure-arousal-dominance (PAD) emotion model, the emotional states of consumers embedded in online reviews can be described through three dimensions, that is, pleasure, arousal and dominance, rather than only the one-dimensional positive and negative polarity, as in previous studies. Therefore, this study aims to explore the effect of online review emotion on perceived review helpfulness based on these three basic emotional dimensions.Design/methodology/approachA lexicon-based method is developed to analyze PAD emotions of online reviews from JD.com. The zero-inflated negative binomial regression is utilized to empirically validate the study hypothesis. The authors examine the influence of pleasure, arousal, dominance, emotion diversity and emotion deviation on review helpfulness, as well as the moderating effect of product type on the relationship between all independent variables and online review helpfulness.FindingsThe study results show that the pleasure emotion impairs the helpfulness of online reviews, while the arousal and dominance emotions have a positive impact. Moreover, the authors find that compared with search products, the effects of pleasure, arousal and dominance on perceived helpfulness are strengthened for experience products. However, the emotional diversity and emotional deviation have opposite effects on the helpfulness of search products and experience products. Additionally, the results show that dominance emotion plays a more important role in the interaction effect.Originality/valueThe empirical findings confirm the applicability of PAD in the online review context and extend the existing knowledge of the influence of review emotion on helpfulness. A feasible scheme for extracting PAD variables from Chinese text is developed. The study findings also have significant implications for reviewers, merchants and platform managers of e-commerce websites.

The effects of size referents in user‐generated photos on online review helpfulness

AbstractProduct size misperceptions are a common problem for consumers when shopping online. To help their peers estimate a product's size, customers often include a size referent next to the focal product when adding photos to their online reviews. The purpose of this article is to investigate the effects of user‐provided review photos (with and without a size referent) on online review helpfulness. From two online experiments and one eye‐tracking lab experiment, this article finds that photo‐enhanced reviews that include a size referent object can improve review helpfulness. In study 1, eye‐tracking data shows that consumers pay greater attention to the photo and less to the text when the size referent is included. Study 2 shows that size referents are more helpful when consumers have limited experience with the product and the size referent is familiar. The role of perceived reviewer effort is investigated as a mediator to explain this result. Study 3 shows that the effects of a familiar size referent are robust when the seller provides detailed size information but vary depending on review valence.

Helpfulness of online customer reviews for shopping goods and services: The perspective of signalling theory

AbstractTo help customers get quality feedback from the huge number of online reviews, online retailers attempt to generate evaluations from consumers of how helpful product reviews are. Drawing on signalling theory, this study proposes a comprehensive model to examine the influence of factors related to the reviewer (signaller), the review (signal) and the users (readers of the signal) on online review helpfulness via e‐commerce sites and apps. This study also includes the moderating role of product categories (goods/services) in the model. Data were collected from 466 Vietnamese respondents. Using a Smart Partial Lease Square 3.0, the outcomes indicate that online review information quality, online reviewer information credibility and two dimensions of receivers (e.g. users’ attitudes towards information and information task‐fit) all have a significant influence on online review helpfulness; however, users’ need for information has no impact on the helpfulness of online reviews. Interestingly, the product type (goods vs services) has a moderating effect between online review information quality/attitude towards information and online review helpfulness. This study contributes significantly to signalling theory and online review helpfulness by comprehensively incorporating three attributes that are paramount to online reviews: the review, the reviewer and the users. Additionally, the information is enriched by the investigation of the moderating effect of product categories (goods vs services) on the context of online review helpfulness.

Risk attitude toward epidemic and impulsive travel intention: influences on perceived helpfulness of online review

ABSTRACT To comprehend the unclear mechanisms through which risk attitude toward epidemics affects perceived helpfulness of online review, this study proposes a theoretical framework based on Dual Process Theory and Construal Level Theory to observe the mechanisms. Through empirical analysis based on 456 valid questionnaires, from review readers’ information processing manner perspective, risk-averse tourists prefer a systematic manner, whereas risk-appetite ones prefer heuristics manner, which leads to significantly higher perceived helpfulness for risk-averse than for risk-appetite ones. The mechanisms through which risk attitude directly negatively affects perceived helpfulness and also indirectly attenuates perceived helpfulness through inhibiting impulsive travel intention also are unveiled. As well as the moderating effects of two temporal distances that are defined as recovering from epidemic infection and near-future travel plans are underscored. These results indicate that risk attitude and impulsive travel intention are both antecedents of perceived helpfulness and provide new theoretical insights and feasible practical implications.

The effect of consumption goals on review helpfulness: Behavioral and eye-tracking research

Through utilizing behavioral methodology and integrating eye-tracking technology, we investigate the effect of consumption goals on review helpfulness during online shopping. Behaviorally, consumption goals (hedonic vs. utilitarian) can influence relative review (attribute-based vs. experience-based) helpfulness and this effect is mediated by review-goal congruity. Compared with those with hedonic consumption goals, consumers with utilitarian consumption goals perceive attribute-based reviews (vs. experience-based reviews) as more consistent with expectations, which makes the former of reviews more helpful for shopping decisions. In the study of eye-tracking, we found the attention-based cognitive process underlying the behavioral effect of consumption goals. Consumers have longer fixation duration and more fixation counts for attribute-based reviews when they have utilitarian consumption goals rather than hedonic consumption goals. These results show that by different types of consumption goals, consumers evaluate the review helpfulness and meantime allocate attentional resources. This research extends our current understanding of the relationship between consumption goals and online review helpfulness, and provides guidelines on the utilization of online reviews to facilitate the development of the marketing strategy for online retailers.

The role of explained actions and reactions in the helpfulness of online reviews

The role of explained actions and reactions in the helpfulness of online reviews

A novel method based on knowledge adoption model and non-kernel SVM for predicting the helpfulness of online reviews

In this paper, a novel method is proposed to predict online review helpfulness based on the knowledge adoption model (KAM) theory and non-kernel weighted quadratic surface support vector machine (WQSSVM). The KAM theory develops helpfulness-predicting features from two perspectives: the quality of online review content and the credibility of online review sources. Content quality is measured by calculating the percentage of domain words in a review, as well as the percentage of stop words. This method effectively addresses the issue of knowledge barriers existing in the online reviews in specific domains. Source credibility is measured by looking at the number of fans an author has, as well as the number of likes he has received. The WQSSVM significantly reduces the computational time in selecting a suitable kernel and related parameters. To investigate the performance of the proposed method, computational experiments were conducted on three crawled online review datasets of traditional Chinese medicine, takeouts, and mobile phone products. The numerical results not only indicate the superior capability of the proposed method in identifying the optimal combination of features and forecasting accuracy but also indicate the greater importance of source credibility over quality of content, for predicting the online review helpfulness.

What leads to online review helpfulness on e-commerce platform? Examining the moderating effect of high and low prices

Using signaling theory, this study unpacked the mechanisms through which online review information quality and reviewer information credibility influence online review helpfulness, in e-commerce context. Conducting a survey with users in Danang having read online reviews on Shopee.vn, 244 valid responses were used to evaluate the research model via PLS-SEM software. The study found that the information quality of online reviews strongly influences the helpfulness of online reviews compared to the information credibility of previous reviewers. Additionally, priced goods have a moderating effect on the relationship between online information credibility and online reviews helpfulness; and that relationship will be significant in high-priced products. This study makes a theoretical contribution to online reviews by elucidating the mechanisms of impact of review/reviewer information on reviews helpfulness and the moderating effect of product prices.

When trust cues help helpfulness: investigating the effect of trust cues on online review helpfulness using big data survey based on the amazon platform

When trust cues help helpfulness: investigating the effect of trust cues on online review helpfulness using big data survey based on the amazon platform

Effect of construal level on the drivers of online-review-helpfulness

AbstractThe purpose of this study is to explore the effect of purchase context on the relative importance of the drivers of online review helpfulness and also to explore the psychological process behind it. This will help the review management team of online retailers to create a better review ranking system that considers purchase context. Based on the theory of helpful behavior, we propose that review-helpfulness is driven by review characteristics such as review diagnosticity and vicarious expression in the review, and reviewer characteristics such as reviewer-trustworthiness. Moreover, following construal level theory, we explain the relative importance of the review and reviewer characteristics. Using four experimental studies, we find that, at low construal, review characteristics have higher relative importance than high construal, while reviewer characteristics have lower relative importance. The current study contributes to the literature on online review-helpfulness and the application of construal level theory in consumer behavior. The study proposes a unique method of ranking the reviews based on consumers’ purchase context and predicted psychological state while displaying them on the platforms. This is the first paper that explores the impact of construal level on review and reviewer characteristics on review-helpfulness.

Machine Learning Based Product Comparison for E-Commerce Websites

Online shopping through e-commerce has gained widespread popularity among consumers, revolutionizing the operations of businesses in the global market. This paper examines the benefits of e-commerce, such as its convenience and the ease of comparing prices and products, as well as the difficulty customers may encounter when selecting the optimal product. To overcome this difficulty, the paper suggests a real-time online consumer behavior prediction system that anticipates a visitor's purchasing intent using session and visitor data and assesses the effectiveness of Continuous Learning with the Naive Bayes strategy. The article also focuses on developing a recommendation system that strikes a balance between increasing precision and safeguarding users' privacy, utilizing the Prize dataset to assess the system's accuracy. Additionally, the paper delves into the domain of opinion mining, outlining its objectives and responsibilities, such as anticipating sentiment, summarizing aspect-based sentiment, and predicting the helpfulness of online feedback and reviews.

DMFN: A disentangled multi-level fusion network for review helpfulness prediction

Predicting the online review helpfulness is of great significance to consumers due to it affects consumers’ purchase decisions to a large extent. To generate accurate helpfulness predictions for online reviews, we need to consider all multi-modal messages including texts and images. Although extensive works have acquired great advances in the Review Helpfulness Prediction (RHP) task, there remain challenges in achieving a fine-grained fusion of the two heterogeneous modalities. Therefore, we propose a novel RHP method DMFN, which exploits the multi-level information of both texts and images to enhance the fine-detail representation of multimodal data. We further employ a weighted token-wise interaction to preserve finer-grained information in a lightweight way. Architecturally, DMFN consists of three parts, a multi-granularity feature generation part, a weighted token-wise interaction part, and a gated multi-level fully fusion part. The first part is built to fully extract the visual and linguistic features from multimodal reviews. The second part is designed to decouple the multi-granularity content and then sufficiently utilize the pair-wise correlations to obtain a disentangled manifold for multimodal inputs. The last part is constructed to adaptively integrate cross-modal features, and it controls the fusion of different features at multiple scales. Extensive experiment results on our collected RHP datasets from Yelp.com and Amazon.com show that our proposed approach is significantly superior to other existing benchmark RHP methods.

Revisiting online reviews: signals of latent reviewer traits mediate the review length-helpfulness relationship

ABSTRACT Consumers evaluate the helpfulness of online reviews using both characteristics of the review and the reviewer. We explore whether online review length signals latent information about the reviewer’s credibility, reflected by the reviewer’s honesty and social status. Analyzing a dataset of online reviews, we show that review length has opposing curvilinear relationships with reviewer honesty and social status. Taken together, long and short reviews communicate that the reviewer is less honest, but has higher social status, while medium length reviews signal greater reviewer honesty, but lower social status. Further, reviewer honesty and social status each mediate the review lengthhelpfulness relationship.

Exploring the antecedents for hospitality reviewers’ trustworthiness and its impact on business patronage

Although extant literature on online reviews in the hospitality industry has adequately covered the area of helpfulness, trustworthiness, and quality of online reviews, the trustworthiness of the reviewer has remained largely ignored. In particular, the antecedents of the reviewer’s trustworthiness and its influence on business outcomes have not been researched in detail. Using social credibility theory and social information theory, a framework for reviewers’ trustworthiness was developed and validated with the profile and review information of 143812 reviewers. Further, with data from 11776 businesses, we explore how reviewer trustworthiness interacts with business reputation while contributing to business patronage. A mixed method approach containing the text mining of qualitative analysis followed by PLS-SEM has been used in the analysis. The paper contributes to source-credibility literature in the context of online reviews and helps review managers identify trustworthy reviewers and measure their business impact. The paper is the first one that considered both qualitative and quantitative information while finding the antecedents and consequences of reviewer trustworthiness.

Content-Rating Consistency of Online Product Review and Its Impact on Helpfulness: A Fine-Grained Level Sentiment Analysis

Aim/Purpose: The objective of this research is to investigate the effect of review consistency between textual content and rating on review helpfulness. A measure of review consistency is introduced to determine the degree to which the review sentiment of textual content conforms with the review rating score. A theoretical model grounded in signaling theory is adopted to explore how different variables (review sentiment, review rating, review length, and review rating variance) affect review consistency and the relationship between review consistency and review helpfulness. Background: Online reviews vary in their characteristics and hence their different quality features and degrees of helpfulness. High-quality online reviews offer consumers the ability to make informed purchase decisions and improve trust in e-commerce websites. The helpfulness of online reviews continues to be a focal research issue regardless of the independent or joint effects of different factors. This research posits that the consistency between review content and review rating is an important quality indicator affecting the helpfulness of online reviews. The review consistency of online reviews is another important requirement for maintaining the significance and perceived value of online reviews. Incidentally, this parameter is inadequately discussed in the literature. A possible reason is that review consistency is not a review feature that can be readily monitored on e-commerce websites. Methodology: More than 100,000 product reviews were collected from Amazon.com and preprocessed using natural language processing tools. Then, the quality reviews were identified, and relevant features were extracted for model training. Machine learning and sentiment analysis techniques were implemented, and each review was assigned a consistency score between 0 (not consistent) and 1 (fully consistent). Finally, signaling theory was employed, and the derived data were analyzed to determine the effect of review consistency on review helpfulness, the effect of several factors on review consistency, and their relationship with review helpfulness. Contribution: This research contributes to the literature by introducing a mathematical measure to determine the consistency between the textual content of online reviews and their associated ratings. Furthermore, a theoretical model grounded in signaling theory was developed to investigate the effect on review helpfulness. This work can considerably extend the body of knowledge on the helpfulness of online reviews, with notable implications for research and practice. Findings: Empirical results have shown that review consistency significantly affects the perceived helpfulness of online reviews. The study similarly finds that review rating is an important factor affecting review consistency; it also confirms a moderating effect of review sentiment, review rating, review length, and review rating variance on the relationship between review consistency and review helpfulness. Overall, the findings reveal the following: (1) online reviews with textual content that correctly explains the associated rating tend to be more helpful; (2) reviews with extreme ratings are more likely to be consistent with their textual content; and (3) comparatively, review consistency more strongly affects the helpfulness of reviews with short textual content, positive polarity textual content, and lower rating scores and variance. Recommendations for Practitioners: E-commerce systems should incorporate a review consistency measure to rank consumer reviews and provide customers with quick and accurate access to the most helpful reviews. Impact on Society: Incorporating a score of review consistency for online reviews can help consumers access the best reviews and make better purchase decisions, and e-commerce systems improve their business, ultimately leading to more effective e-commerce. Future Research: Additional research should be conducted to test the impact of review consistency on helpfulness in different datasets, product types, and different moderating variables.

A Meta-Analysis on the Determinants of Online Product Reviews with Moderating Effect of Product Type

The technological advances in digital space have provided a renewed impetus for business to expand their footprint across digital modes. The growth of the internet and the ease of its access to the masses has encouraged many businesses to go online. Online e-commerce platforms make it easy to search, locate and place orders. Technology-assisted supply chains and fast delivery mechanisms ensure that users don't have to go elsewhere to fulfill their needs. To earn loyalty and customer satisfaction, e-commerce platforms have evolved into a sophisticated recommender system. It has evolved from just an informational source to a participative mode where users can share their experiences about their purchases. Customer values other user experiences more than the information provided by the seller. The presence of many conflicting and contradicting reviews can make the task of making rational decisions difficult for many users. Many studies were performed to understand what constitutes a review helpful and came up with different or mixed outcomes. The present study reviews the factors that influence online customer reviews helpful. Meta-analysis was performed to reconcile the mixed findings of different factors of online review helpfulness. The meta-analysis found that with the moderating effect of product type, factors like review length, readability, rating, reputation, and expertise positively correlate with helpfulness. Further, the customer finds moderate reviews more helpful in terms of polarity. Meta-analysis has a mix of findings for the selected data points in the study. The mixed findings include product type (search, experience, or other) and helpfulness measurement criteria.
 
 Received: 30 August 2022 / Accepted: 24 October 2022 / Published: 5 November 2022