Search results

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.

Online reviews in online health communities (OHCs) have been a vital information source for patients. The extant literature on the bias effects of helpful reviews mainly concentrates on traditional e-commerce, whereas research on OHCs is still rare. Thus, based on the heuristic-systematic model (HSM), this research explores how two unique reviewer characteristics in OHCs, which may induce attribution bias and confirmation bias, affect review helpfulness and how review length moderates these relationships.

This research analyzed 130,279 reviews collected from haodf.com (one of the representative OHCs in China) by adopting the negative binomial regression to test our research model.

The results indicate that reviewer cured status positively influences review helpfulness, whereas reviewer recommendation source negatively affects review helpfulness. Moreover, the effects of the two reviewer cues on review helpfulness will be weaker for longer reviews.

First, as one of the initial attempts, the current study investigates the effects of confirmation bias and attribution bias of online reviews in OHCs by exploring the effects of two unique reviewer characteristics on review helpfulness. Second, the weakening moderating effects of review length on the two bias effects provide empirical support for the theoretical arguments of the HSM in OHCs.

Big data analytics allows researchers and industry practitioners to extract hidden patterns or discover new information and knowledge from big data. Although artificial intelligence (AI) is one of the emerging big data analytics techniques, hospitality and tourism literature has shown minimal efforts to process and analyze big hospitality data through AI. Thus, this study aims to develop and compare prediction models for review helpfulness using machine learning (ML) algorithms to analyze big restaurant data.

The study analyzed 1,483,858 restaurant reviews collected from Yelp.com. After a thorough literature review, the study identified and added to the prediction model 4 attributes containing 11 key determinants of review helpfulness. Four ML algorithms, namely, multivariate linear regression, random forest, support vector machine regression and extreme gradient boosting (XGBoost), were used to find a better prediction model for customer decision-making.

By comparing the performance metrics, the current study found that XGBoost was the best model to predict review helpfulness among selected popular ML algorithms. Results revealed that attributes regarding a reviewer’s credibility were fundamental factors determining a review’s helpfulness. Review helpfulness even valued credibility over ratings or linguistic contents such as sentiment and subjectivity.

The current study helps restaurant operators to attract customers by predicting review helpfulness through ML-based predictive modeling and presenting potential helpful reviews based on critical attributes including review, reviewer, restaurant and linguistic content. Using AI, online review platforms and restaurant websites can enhance customers’ attitude and purchase decision-making by reducing information overload and search cost and highlighting the most crucial review helpfulness features and user-friendly automated search results.

To the best of the authors’ knowledge, the current study is the first to develop a prediction model of review helpfulness and reveal essential factors for helpful reviews. Furthermore, the study presents a state-of-the-art ML model that surpasses the conventional models’ prediction accuracy. The findings will improve practitioners’ marketing strategies by focusing on factors that influence customers’ decision-making.

Consumers’ evaluation of online review helpfulness has been widely examined. The extant literature suggests that the attributes of review content (e.g. review length and extremity) influence review helpfulness. However, review length cannot fully reflect the richness of the review content. Anchoring on information diagnosticity and extremity bias, this study aims to explore the effect of review comprehensiveness on its helpfulness.

Field observations were obtained from 11,812 online restaurant reviews on a popular restaurant review platform. A controlled experiment was conducted to further delineate the effect of review comprehensiveness.

Review comprehensiveness moderates the effects of review length and an extremely negative review on helpfulness. It also confirms that for reviews of the same length, one covering more aspects is perceived by consumers as more helpful.

Different aspects of information in a review can efficiently assist decision-making. The results suggest that review platforms can better design their interface by providing separate areas for different product aspects. The platform can then receive more comprehensive and helpful reviews and increase the diagnosticity of these.

The study enriches the literature by introducing review comprehensiveness and examining the joint effects of review length and comprehensiveness on helpfulness. It also contributes to the literature by indicating how to reduce the effect of review extremity.

With the increasing importance of e-commerce to the economy and people's lives, user-generated content, such as electronic word-of-mouth (eWOM) represented by online reviews, has exploded. On one hand, it is of great significance for review consumers (readers) to identify high-quality ones from a large number of existing reviews to assist their purchase decision. On the other hand, how to use appropriate strategies to make their published reviews more concerned by others is also important to review generators (reviewers). The purpose of this study is to understand the comprehensive relationship among review characteristics, review helpfulness and receiver attention.

This study uses the online movie reviews obtained from the most popular review platform in China to conduct multiple empirical analyses.

The results show that the review helpfulness plays a mediating role between the emotional characteristics of online reviews and the receiver attention, and such a mediating role is more significant among reviewers with rich review expertise. The reviewer's expertise also moderates the impact of review emotions on review helpfulness.

This work studies eWOM receiver involvement, which can ultimately impact product sales, but seldom be investigated in eWOM domain. Therefore, this research can enrich studies on eWOM and provide valuable practical implications as well.

As retailers have increasingly embraced an omnichannel retailing strategy, explaining and predicting the helpfulness of online review should consider both online-based and offline-based reviews. The paper aims to discuss this issue.

Based on the signaling theory, this study intends to examine the impacts of review-related and reviewer-related signals on review helpfulness in the context of omnichannel retailing. The proposed research model and corresponding hypotheses were tested by using negative binomial regression.

The results shown that both review-related (review rating and review sentiment strength) and reviewer-related (reviewer real name and reviewer expertise) signals positively affect review helpfulness. Contrary to the authors’ expectations, review length negatively affects review helpfulness. Specifically, when the review submitted from an omnichannel retailer’s offline channel, the positive impacts of reviewer real name on review helpfulness will be stronger, and the positive impacts of reviewer expertise on review helpfulness will be weaker.

Unlike many previous studies tend to explore the antecedents of review helpfulness in a single-channel setting, the study examined the factors that affect review helpfulness in an omnichannel retailing context.

This paper aims to intend to study the effect of movie production efficiency on eWOM and the moderating effect of efficiency on the relationship between eWOM and review helpfulness for movies.

Production efficiency is suggested by comparing the power of movie resources (e.g. the power of actors, directors, distributors, production companies) against box-office revenue through a data envelopment analysis (DEA).

The study results present that the number of reviews, the number of reviews by reviewers and review extremity are greater in an efficient subsample than in an inefficient subsample. For efficient movies, the review depth and the strength of the sentiments in the reviews are more positively related to review helpfulness. The prediction results for review helpfulness using the k-nearest neighbor method and automatic neural networks show that the efficient subsample provides a significantly lower prediction error rate than the inefficient subsample. The study results can support the effective facilitation of helpful online movie reviews.

As the numbers of online reviews are increasingly used to provide purchase decision support, it becomes crucial to understand which attributes represent average helpful reviews for movies. While previous studies have examined eWOM (online word-of-mouth) variables as predictors of helpfulness on movie websites, the role of the production efficiency of movies has not been examined considering the relationship between eWOM and review helpfulness for movies.

Identifying and predicting the most helpful reviews has been a focal interest in the fields including information management, e-commerce and marketing, etc. Though many factors are found correlated to the helpfulness of reviews, they may suffer endogeneity problems, as normally the data is observed in the same time window. This paper aims to tackle such a problem by examining the predictive power of different factors on the future increment of review helpfulness.

Adopting a longitudinal data of 443 K empirical business reviews from Yelp.com collected at two different time points, six groups of predictors are extracted from the first snapshot of data to predict the helpfulness increment of old and recent reviews, respectively, between the two snapshots.

It is found that these factors in general are with moderate accuracy predicting the helpfulness increment. A different group of features shows quite different predictive power. The reviewer disclosure information is the most significant factor, while the review readability does not significantly improve the accuracy of prediction.

Instead of the total number of helpful votes observed in the same time window with the explanatory variables, this paper focuses on the future increment of helpful votes observed in the following time window. With such a two-wave data set, the endogeneity problem can be avoided and the explanatory factors for review helpfulness can, thus, be further tested in the prediction scenario.

This study aims to uncover how heuristic information cues (HIC) and systematic information cues (SIC) of online reviews influence review helpfulness and examine a moderating role of social influence in the process of assessing review helpfulness. In particular, this study conceptualizes a theoretical framework based on dual-process and social influence theory (SIT) and empirically tests the proposed hypotheses by analyzing a broad set of actual customer review data.

For 4,177,377 online reviews posted on Yelp.com from 2004 to 2018, this study used data mining and text analysis to extract independent variables. Zero-inflated negative binomial regression analysis was conducted to test the hypothesized model.

The present study demonstrates that both HIC and SIC have a significant relationship with review helpfulness. Normative social influence cue (NSIC) strengthened the relationship between HIC and review helpfulness. However, the moderating effect of NSIC was not valid in the relationship between SIC and review helpfulness.

This study contributes to the extant research on review helpfulness by providing a conceptual framework underpinned by dual-process theory and SIT. The study not only identifies determinants of review helpfulness but also reveals how social influences can impact individuals’ judgment on review helpfulness. By offering a state-of-the-art analysis with a vast amount of online reviews, this study contributes to the methodological improvement of further empirical research.

本论文旨在揭示网络评论的启发性信息源和系统性信息源对于评论有用性的影响, 以及检验社会影响在评论有用性的调节作用。其中, 本论文基于双重历程理论和社会影响理论来构建理论模型, 并且利用实际数据来验证假设, 通过分析一系列实际客户评论数据。

本论文样本数据为2004年至2018年Yelp.com上面的4,177,377网络评论。本论文采用数据挖掘和文本分析的方法来提取自变量。本论文采用零膨胀负二项回归模型来验证假设。

研究结果表明, 启发性和系统性信息源都对网络评论有用性有着显著作用。规范性社会影响加强了启发性信息源对评论有用性的作用。然而, 规范性社会影响对系统性信息源与评论有用性的关系并未起到有效的调节作用。

本论文对现有评论有用性的文献有着补充贡献, 其采用双重历程理论和社会影响理论来构建理论模型。本论文不仅指出评论有用性的影响因素, 而且展示了社会影响如何影响个人对评论有用性的判断。本论文的样本数据庞大, 数据分析夯实, 这对于进一步的实际测量研究有着方法改进方面的贡献。

The prevalence of online review websites and the ever-growing difficulty of judging review quality result in the increasing need for consumers to reduce cognitive costs. Thus, the purpose of this study is to find out the determinants of review helpfulness based on a comprehensive theoretical framework and empirical model.

This study applied a comprehensive framework, including both review content quality and reviewer background, to investigate the determinants of review helpfulness. It also presents empirical models to further control factors around product features.

Consumers are more likely to give helpful votes to those informative and readable reviews accompanied by extreme ratings. Reviewers who disclose information, have a high reputation and report a poor experience are always identified as helpful. Consumers also tend to signal suggestions from users with a local cultural background as subjective and useless.

This study focuses on upscale hotels in China. Information registered on TripAdvisor was used presenting a residential address not nationality. Only few controlling factors available because of the limited information are shown on online review websites.

Managers of both hotels and online review websites need to focus on reviews and/or reviewers as KOLs who attract consumers’ attention and affect their subsequent decisions. A dialogue with those KOLs can be by focusing on responding to reviews with certain characteristics. A reward system for reviews and KOLs may benefit review quality on online review websites and reduce cognition costs.

This positivistic research design, with multilevel approach, presenting a comprehensive conceptual framework and empirical model not only considering review- and reviewer-related factors but also controlled factors in product or service level (hotel-related characteristics).