Design Preference Centered Review Recommendation: A Similarity Learning Approach

2011 ◽  
Author(s):  
Jian Jin ◽  
Ying Liu ◽  
Ping Ji ◽  
Richard Fung
Keyword(s):  
Classification Problem ◽  
Online Reviews ◽  
Learning Approach ◽  
Similarity Learning ◽  
Customer Reviews ◽  
Customer Preferences ◽  
Review Helpfulness ◽  
Review Recommendation ◽  
Design Preference ◽  
The Relationship

The rise of e-commerce websites like Amazon and Alibaba is changing the way how designers seek information to identify customer preferences in product design. From the feedbacks posted by consumers, either positive or negative, product designers can monitor the trend of consumers’ perception with respect to their product offerings and make efforts to improve accordingly. Starting from feature extraction from review documents, existing methods in identifying helpful online reviews regard the helpfulness prediction problem as a regression or classification problem and have not considered the relationship between customer reviews. Also, these approaches only consider the online helpfulness voting ratio or a unified helpfulness rating as the gold criteria for helpfulness evaluation and neglect various personal preferences from product designers. Therefore, in this paper, the focus is on how to predict reviews’ helpfulness by taking into account the personal preferences from both reviewers and designers. We start to analyze review helpfulness from both a generic aspect and a personal preference aspect. Classification methods and the proposed review similarity learning approach are utilized to estimate from the generic angle of helpfulness, while nearest neighbourhood based methods are adopted to reflect concerns from personal perspectives. Finally, a regression algorithm is called upon to predict review helpfulness based on the inputs from both aspects. Our experimental study, using a large quantity of review data crawled from Amazon and real ratings from product designers demonstrates the effectiveness of our approach and it opens a possibility for customized helpful review routing.

Customer Online Feedback with an Identity Versus No Identity: The Influence on Review Comments

2021 ◽  
pp. 109634802098888
Author(s):  
Dan Jin ◽  
Robin B. DiPietro ◽  
Nicholas M. Watanabe
Keyword(s):  
Online Community ◽  
Online Reviews ◽  
Purchasing Behavior ◽  
Mediation Effects ◽  
Driven Systems ◽  
Customer Reviews ◽  
Significant Difference ◽  
Online Feedback ◽  
The Relationship

As customers’ consumption is increasingly dominated by technology-driven systems, online self-verification becomes an important aspect of customers’ online purchasing behavior and plays a significant role in shaping social interactions in the online community. Across two studies, we examine whether online self-verification with an identity versus without an identity will lead to the different quality of online reviews. Study 1 used topic modeling with actual data stripped from Facebook and TripAdvisor customer online review sites and showed no difference between customer reviews underpinned with an identity or without. Likewise, Study 2 used an experimental design and found no significant difference between customer reviews with or without an identity. However, significant mediation effects of social ties and social capital were found when measuring the relationship between online self-verification and customer reviews. The findings build on the literature of user-generated online reviews and have important implications for academics and hospitality practitioners.

scholarly journals Extremely Negative Ratings and Online Consumer Review Helpfulness: The Moderating Role of Product Quality Signals

2020 ◽  
pp. 004728752091678 ◽  
Author(s):  
Raffaele Filieri ◽  
Claudio Vitari ◽  
Elisabetta Raguseo
Keyword(s):  
Product Quality ◽  
Online Reviews ◽  
Average Score ◽  
Consumer Review ◽  
Quality Signals ◽  
Review Helpfulness ◽  
Online Consumer Review ◽  
The Relationship ◽  
Moderating Role

Contrasting findings about the role of extremely negative ratings (ENRR) are found in the literature, thus suggesting that not all ENRR are perceived as helpful by consumers. In order to shed light on the most helpful ENRR, we have drawn on negativity bias and signaling theory, and we have analyzed the moderating role of product quality signals in the relationship between ENRR and review helpfulness. The study is based on the analysis of 9,479 online reviews, posted on TripAdvisor.com, pertaining to 220 French hotels. The findings highlight that ENRR is judged as being more helpful when the hotel has been awarded a certificate of excellence, and when the average rating score and the hotel classification are higher. On the basis of these results, we recommend that managers of higher category hotels, with a certificate of excellence and with higher average score ratings, pay more attention to extremely negative judgments.

The Relationship between Online Reviews, Brand Trust, and Willingness to Buy

2015 ◽  
pp. 139-161 ◽  
Author(s):  
V. Cheng ◽  
J. Rhodes ◽  
P. Lok
Keyword(s):  
Decision Making ◽  
Online Reviews ◽  
Mediating Effect ◽  
Consumer Decision Making ◽  
Brand Trust ◽  
Purchasing Decisions ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Willingness To Buy ◽  
The Relationship

This chapter investigates how online customer reviews affect consumer decision-making (willingness to buy) during their first purchase of services or products using brand trust as a mediating variable. A brief literature review, rationale and significance, and methodology are discussed, and a conceptual framework based on the relationships between the stated variables is adopted in this empirical study to demonstrate linkages and insights. The findings demonstrate that the “reliability dimension” of brand trust had a mediating effect on online customer reviews' valence to willingness to buy, while the “intentionality dimension” of brand trust had little effect. Furthermore, the findings demonstrate that online customer reviews generated by in-group and out-group reviewers have little effect on purchasing decisions (willingness to buy). These results suggest that marketers should focus more on managing negative online customer reviews that have a damaging effect on brand trust.

Finding eWOM customers from customer reviews

2019 ◽  
Vol 119 (1) ◽  
pp. 129-147 ◽  
Author(s):  
Pengfei Zhao ◽  
Ji Wu ◽  
Zhongsheng Hua ◽  
Shijian Fang
Keyword(s):  
Evaluation Method ◽  
Online Reviews ◽  
Customer Segmentation ◽  
Product Reviews ◽  
Data Set ◽  
Content Type ◽  
Customer Reviews ◽  
Product Sales ◽  
The Relationship ◽  
New Framework

PurposeThe purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales.Design/methodology/approachThis research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors’ proposed method.FindingsThe results corroborate that online product reviews that are made by the eWOM customers identified by the authors’ proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers.Research limitations/implicationsThe proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment.Practical implicationsBy combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers’ value specifically.Originality/valueThis study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales.

The moderating effect of movie production efficiency on the relationship between eWOM and review helpfulness

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sangjae Lee ◽  
Joon Yeon Choeh
Keyword(s):  
Production Efficiency ◽  
Nearest Neighbor ◽  
Online Reviews ◽  
Moderating Effect ◽  
K Nearest Neighbor ◽  
Content Type ◽  
Online Word Of Mouth ◽  
Study Results ◽  
Review Helpfulness ◽  
The Relationship

Purpose 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. Design/methodology/approach 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). Findings 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. Originality/value 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.

scholarly journals Determinants of online clothing review helpfulness: the roles of review concreteness, variance and valence

2021 ◽  
Vol 72 (06) ◽  
pp. 639-644
Author(s):  
YIBING SHAO ◽  
XIAOFEN JI ◽  
LILIN CAI ◽  
SONIA AKTER
Keyword(s):  
Information Source ◽  
Online Reviews ◽  
Purchasing Decisions ◽  
Essential Information ◽  
Factors Affecting ◽  
Review Helpfulness ◽  
Purchasing Behaviour ◽  
Textual Content ◽  
The Relationship ◽  
Significant Factors

Online reviews have emerged as an essential information source for online clothing purchasing behaviour. It is thus paramount for marketers to understand what makes online clothing review helpful to consumers. This research primarily aims to examine the relationship between review textual content factors and review helpfulness in the context of online clothing purchasing. Experiments on review concreteness (concrete or abstract), review variance (consistent or inconsistent) and review valence (positive or negative), between participants were conducted to explore the interaction effect. The findings suggest that online clothing review concreteness, variance and valence are significant factors affecting review helpfulness. Additionally, this study’s findings show that abstract review, negatively review and inconsistent review has a stronger effect on online clothing review helpfulness than concrete review, positively review and consistent review. The findings will help customers to write better clothing reviews, help retailers to manage their websites intelligently and aid customers in their product purchasing decisions.

Does the length of a review matter in perceived helpfulness? The moderating role of product experience

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Han Jia ◽  
Sumin Shin ◽  
Jinfeng Jiao
Keyword(s):  
Online Reviews ◽  
Product Type ◽  
Online Review ◽  
Content Type ◽  
Level Of Involvement ◽  
Customer Reviews ◽  
Product Experience ◽  
Online Customer Reviews ◽  
Review Helpfulness ◽  
Product Ratings

PurposeThis 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.Design/methodology/approachData 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.FindingsThe 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.Originality/valueA 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.

An analysis of Airbnb online reviews: user experience in 16 U.S. cities

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Chuhan (Renee) Thomsen ◽  
Miyoung Jeong
Keyword(s):  
User Experience ◽  
Online Reviews ◽  
Complex Nature ◽  
Content Type ◽  
Future Studies ◽  
Customer Reviews ◽  
The Usa ◽  
The Relationship ◽  
Us Cities ◽  
Practical Implications

Purpose This study aims to provide an in-depth understanding of the complex nature of Airbnb user experience by analyzing the pattern and sentiment of online reviews and assessing the relationships among review scores. Design/methodology/approach Big data analysis is conducted using Airbnb users’ online reviews of 16 US cities; correlation is run on review scores. Findings The key themes of Airbnb users’ online reviews are “clean,” “location,” “stay,” “home,” “place,” “host,” “neighborhood” and “recommend” and users have positive Airbnb experiences in general. The score of “cleanliness” significantly affects the “overall review” score. Research limitations/implications This study is exploratory in nature; mixed methods should be used in the future to measure the relationship between user experience and extracted themes. As the context is in the USA in the current study, comparisons of review patterns across different countries and regions are necessary for later studies. Furthermore, future studies should consider Airbnb users’ demographics, personality and lodging preferences. Practical implications It is important for Airbnb hosts to maintain a clean and accessible property. Both Airbnb hosts and hoteliers should enhance the attributes that generate positive customer reviews. Each city should develop different strategies based on the performance of “cleanliness” and “overall review.” Originality/value This study supplements the existing literature in Airbnb user experience by analyzing online reviews in 16 US cities via Leximancer 4.0.

Predicting the helpfulness of online customer reviews: The role of title features

2018 ◽  
Vol 62 (3) ◽  
pp. 272-287 ◽  
Author(s):  
Mina Akbarabadi ◽  
Monireh Hosseini
Keyword(s):  
Prediction Model ◽  
Real Life ◽  
Online Reviews ◽  
Machine Learning Techniques ◽  
Customer Reviews ◽  
Learning Techniques ◽  
Online Customer Reviews ◽  
Importance Analysis ◽  
Review Helpfulness

Nowadays, many people refer to online customer reviews that are available on most shopping websites to make a better purchase decision. An automated review helpfulness prediction model can help the websites to rank reviews based on their level of helpfulness. This study examines the effect of review title features on predicting the helpfulness of online reviews. Moreover, a new method is proposed to categorize action verbs in a review text. Text, reviewer, readability, and title features are the four main categories that are used in this article. We examine our proposed prediction model on two real-life Amazon datasets using machine learning techniques. The results show a promising performance of the model. However, feature importance analysis reveals the low importance of title features in the predictive model. It means that the title characteristics cannot be a powerful determinant of online review helpfulness. The results of this study can be beneficial to both buyers and website owners to have a deep insight into online reviews helpfulness.

scholarly journals The carrot and the stick in online reviews: determinants of un-/helpfulness voting choices

2021 ◽  
Author(s):  
Filipe Sengo Furtado ◽  
Thomas Reutterer ◽  
Nadine Schröder
Keyword(s):  
Voting Behavior ◽  
Information Overload ◽  
Management Practice ◽  
Decision Aids ◽  
Confirmation Bias ◽  
Online Reviews ◽  
Differential Effects ◽  
Customer Reviews ◽  
Review Helpfulness ◽  
To Receive

AbstractWith increasing volumes of customer reviews, ‘helpfulness’ features have been established by many online platforms as decision-aids for consumers to cope with potential information overload. In this study, we offer a differentiated perspective on the drivers of review helpfulness. Using a hurdle regression setup for both helpfulness and unhelpfulness voting behavior, we aim to disentangle the differential effects of what drives reviews to receive any votes, how many votes they receive and whether these effects differ for helpful against unhelpful review voting behavior. As potential driving factors we include reviews’ star rating deviations from the average rating (as a proxy for confirmation bias), the level of controversy among reviews and review sentiment (consistency of review content), as well as pricing information in our analysis. Albeit with opposite effect signs, we find that revealed review un-/helpfulness is consistently guided by the tonality (i.e., the sentiment of review texts) and that reviewers tend to be less critical for lower priced products. However, we find only partial support for a confirmation bias with differential effects for the level of controversy on helpfulness versus unhelpfulness review votings. We conclude that the effects of voting disagreement are more complex than previous literature suggests and discuss implications for research and management practice.

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