scholarly journals An Economic Analysis of Rebates Conditional on Positive Reviews

2021 ◽  
Author(s):  
Jianqing Chen ◽  
Zhiling Guo ◽  
Jian Huang
Keyword(s):  
Economic Analysis ◽  
Consumer Surplus ◽  
Online Reviews ◽  
Business Practice ◽  
Online Review ◽  
Policy Debate ◽  
Online Platforms ◽  
Moral Cost ◽  
Heterogeneous Consumers ◽  
Fake Reviews

In the prevailing e-commerce environment, conditional rebates have emerged as a common business practice on leading online platforms such as Taobao. Because rebates are only offered to purchasing consumers who post positive online reviews, a key concern is that it can easily induce fake reviews that might harm consumers. We theoretically analyze the seller’s optimal conditional-rebate strategies based on heterogeneous consumers’ online-review-posting behavior and derive three practically important findings. First, it is not always profitable for strategic sellers to pursue the conditional-rebate strategy. Blindly offering incentives may not help achieve the goal of review manipulation. Second, the conditional-rebate strategy does not necessarily result in fake reviews. Fake reviews occur only if consumers’ moral cost is low and the review-posting cost is not too high. Third, under certain conditions, offering conditional rebates can even increase consumer surplus and social welfare. Platform owners or policy designers can help reduce social losses by offering transparent sales information and by appropriately controlling the platform review-posting cost to induce quality reviews. Our study offers new insights into the fake-review phenomenon induced by conditional rebates and sheds new light on the policy debate about whether platforms should completely ban incentivized reviews.

scholarly journals “Worse Than What I Read?” The External Effect of Review Ratings on the Online Review Generation Process: An Empirical Analysis of Multiple Product Categories Using Amazon.com Review Data

2021 ◽  
Vol 13 (19) ◽  
pp. 10912
Author(s):  
Young Joon Park ◽  
Jaewoo Joo ◽  
Charin Polpanumas ◽  
Yeujun Yoon
Keyword(s):  
Online Reviews ◽  
Online Review ◽  
Generation Process ◽  
Product Categories ◽  
Consumer Review ◽  
Online Consumer ◽  
Multiple Product ◽  
Online Consumer Review ◽  
Fake Reviews ◽  
Better Than

In this paper, we study the online consumer review generation process by analyzing 37.12 million online reviews across nineteen product categories obtained from Amazon.com. This study revealed that the discrepancy between ratings by others and consumers’ post-purchasing evaluations significantly influenced both the valence and quantity of the reviews that consumers generated. Specifically, a negative discrepancy (‘worse than what I read’) significantly accelerates consumers to write negative reviews (19/19 categories supported), while a positive discrepancy (‘better than what I read’) accelerates consumers to write positive reviews (16/19 categories supported). This implies that others’ ratings play an important role in influencing the review generation process by consumers. More interestingly, we found that this discrepancy significantly influences consumers’ neutral review generation, which is known to amplify the effect of positive or negative reviews by affecting consumers’ search behavior or the credibility of the information. However, this effect is asymmetric. While negative discrepancies lead consumers to write more neutral reviews, positive discrepancies help reduce neutral review generation. Furthermore, our findings provide important implications for marketers who tend to generate fake reviews or selectively generate reviews favorable to their products to increase sales. Doing so may backfire on firms because negative discrepancies can accelerate the generation of objective or negative reviews.

How does review disconfirmation influence customer online review behavior? A mixed-method investigation

2020 ◽  
Vol 32 (11) ◽  
pp. 3685-3703
Author(s):  
Hengyun Li ◽  
Fang Meng ◽  
Bing Pan
Keyword(s):  
Mixed Method ◽  
Online Reviews ◽  
Service Evaluation ◽  
Online Review ◽  
Content Type ◽  
Consumption Behavior ◽  
The Social ◽  
Managerial Implications ◽  
Fake Reviews ◽  
The Relationship

Purpose With the growing online review manipulation and fake reviews in the hospitality industry, it is not uncommon that a consumer encounters disconfirmation when comparing the existing online reviews with his/her own product or service evaluation. The purpose of this study is to investigate the influence of review disconfirmation on customer online review writing behavior. Design/methodology/approach This study used a mixed-method combining online secondary big data modeling and experimental design. Findings Review disconfirmation influences customers’ emotional responses embedded in the review; a customer who encounters review disconfirmation tends to exert more reviewing effort, manifested by writing longer reviews; negativity bias exists in disconfirmation effects, in that negative review disconfirmation shows more significant and stronger effects than positive review disconfirmation. Practical implications Findings from this study provide important managerial implications for business owners and marketers who attempt to influence online reviews. The study suggests that fictitious online review manipulation might be detrimental to the business. Originality/value This research contributes to two literature streams, including research on the social influence of online consumer reviews, and the relationship between disconfirmation and consumers’ post-consumption behavior, by extending the influence of disconfirmation from the offline context to the online context.

The Polarity of Online Reviews: Prevalence, Drivers and Implications

2020 ◽  
Vol 57 (5) ◽  
pp. 853-877 ◽  
Author(s):  
Verena Schoenmueller ◽  
Oded Netzer ◽  
Florian Stahl
Keyword(s):  
Rating Scale ◽  
Secondary Data ◽  
Large Data ◽  
Online Reviews ◽  
Online Review ◽  
Data Set ◽  
Self Selection ◽  
Online Platforms ◽  
Cross Platform ◽  
High Degree

In this research, the authors investigate the prevalence, robustness, and possible reasons underlying the polarity of online review distributions, with the majority of the reviews at the positive end of the rating scale, a few reviews in the midrange, and some reviews at the negative end of the scale. Compiling a large data set of online reviews—over 280 million reviews from 25 major online platforms—the authors find that most reviews on most platforms exhibit a high degree of polarity, but the platforms vary in the degree of polarity on the basis of how selective customers are in reviewing products on the platform. Using cross-platform and multimethod analyses, including secondary data, experiments, and survey data, the authors empirically confirm polarity self-selection, described as the higher tendency of consumers with extreme evaluations to provide a review as an important driver of the polarity of review distributions. In addition, they describe and demonstrate that polarity self-selection and the polarity of the review distribution reduce the informativeness of online reviews.

Do Online Friends Bring Out the Best in Us? The Effect of Friend Contributions on Online Review Provision

2020 ◽  
Vol 31 (4) ◽  
pp. 1322-1336
Author(s):  
Zhihong Ke ◽  
De Liu ◽  
Daniel J. Brass
Keyword(s):  
Social Networking ◽  
Financial Incentives ◽  
Online Community ◽  
Online Reviews ◽  
Online Review ◽  
Consumer Decision Making ◽  
Online Platforms ◽  
Source Of Information ◽  
Willingness To Contribute

Online reviews are a crucial source of information for consumer decision making. Many businesses, companies, and platforms are interested in encouraging more consumers to review their products but are dubious about using financial incentives to buy online reviews. Our research describes a social way of growing online reviews. We show that cultivating an online community for reviewing by showing members reviews written by their online friends cannot only increasing their willingness to contribute but also the quality of the resulting reviews. The takeaways of this study include (1) unlike using financial rewards to incentivize review contribution, the studied approach can motivate review contributions without compromising the quality of reviews contributed; (2) the effect of exposing consumers to their friends’ reviews is comparable to that of Yelp’s weekly newsletters (thus this can be a powerful way of motivating consumer reviews); and (3) to effectively leverage friend reviews, online platforms should facilitate social networking among users and build an online community that recognizes and rewards members who make frequent, high-quality contributions to online reviews.

scholarly journals Influence of MOOC learners discussion forum social interactions on online reviews of MOOC

2021 ◽  
Author(s):  
Bing Wu
Keyword(s):  
Social Interactions ◽  
Regression Models ◽  
Online Courses ◽  
Online Reviews ◽  
Discussion Forum ◽  
Massive Open Online Courses ◽  
Online Review ◽  
Discussion Forums ◽  
Processing Data ◽  
Learning Progress

AbstractAlthough some studies have explored massive open online courses (MOOCs) discussion forums and MOOC online reviews separately, studies of both aspects are insufficient. Based on the theory of self-determination, this paper proposes research hypotheses that MOOC learning progress has a direct impact on MOOC online reviews and an indirect influence on MOOC online reviews through social interactions in discussion forums, as well. Coursera the largest MOOC platform, is selected as the empirical research object, and data from learners who participated in the MOOC discussion forum and provided MOOC online reviews from August 2016 to December 2019 are obtained from the most popular course, “Machine Learning”. After processing, data from 4376 learners are obtained. Then, according to research hypotheses, multi regression models are constructed accordingly. The results show that the length of MOOC online review text is affected by the MOOC learning progress, the number of discussion forum posts, the number of follow, the online review sentiment and MOOC rating. This study highlights the main factors that affect MOOC online reviews. As a result, some suggestions are put forward for the construction of MOOC.

Are Hotel Guests Altruistic? How Positive Review Disconfirmation Affects Consumers’ Online Review Behavior

2021 ◽  
pp. 109634802110303
Author(s):  
Hengyun Li ◽  
Fang Meng ◽  
Simon Hudson
Keyword(s):  
Social Influence ◽  
Experimental Research ◽  
Research Method ◽  
Online Reviews ◽  
Positive Deviance ◽  
Online Review ◽  
Altruistic Motivation ◽  
Prior Review ◽  
Concern For Others ◽  
Post Online

The research aims to examine how positive review disconfirmation (i.e., a positive deviance between a hotel consumer’s poststay evaluation and the average review rating by prior consumers) affects subsequent consumers’ willingness to post online reviews and their own review ratings. By employing an experimental research method, this study reveals that positive review disconfirmation increases hotel guests’ willingness to post online reviews, and increases their online review ratings through the mechanism of concern for others, demonstrating an act of altruism. In addition, comparatively the positive review disconfirmation effects are stronger when the variance of prior review ratings is smaller. This study enhances the online review social influence literature, and the consumer’s altruistic motivation of posting online reviews.

Experience

2021 ◽  
Vol 13 (1) ◽  
pp. 1-16
Author(s):  
Michela Fazzolari ◽  
Francesco Buccafurri ◽  
Gianluca Lax ◽  
Marinella Petrocchi
Keyword(s):  
Relative Frequency ◽  
Service Providers ◽  
Real Data ◽  
Online Reviews ◽  
Purchase Decision ◽  
The Past ◽  
Supervised Classifiers ◽  
Relative Frequency Distribution ◽  
Opinion Spam ◽  
Fake Reviews

Over the past few years, online reviews have become very important, since they can influence the purchase decision of consumers and the reputation of businesses. Therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.

THE SOCIAL TV PHENOMENON AND FAKE ONLINE RESTAURANT REVIEWS

2021 ◽  
Vol 27 (1) ◽  
pp. 25-42
Author(s):  
Breno de Paula Andrade Cruz ◽  
Susana C. Silva ◽  
Steven Dutt Ross
Keyword(s):  
Online Reviews ◽  
Assessment System ◽  
Reality Show ◽  
Social Tv ◽  
The Social ◽  
Restaurant Reviews ◽  
Brazilian Culture ◽  
Fake Reviews ◽  
Tv Show ◽  
Practical Implications

Purpose – The social TV phenomenon has raised the interest of some researchers in studying the production of online reviews. However, little is known about the characteristics of reviewers that, without having had indeed a real experience of consumption, still dare to assess the service. The purpose of this research is to understand these reviewers better, using an experiment conducted in Brazil. Design/methodology/approach – Through a cluster analysis with 2547 reviewers of 7 restaurants that participated in a reality show in Brazil, we were able to create 4 fours. Using Spearman Correlation and Kruskal-Wallis Test, differences among groups were analysed in the search of behavioural changes among different types of reviewers. Findings – We conclude that social TV influence fake online reviews of restaurants that were involved in a tv show. Furthermore, we were able to verify that some reviewers indeed assess the service without indeed having tried the service, which strongly bias the influence they are going to cause in potential consumers. Four types of reviewers were identified: the real expert, the amateur reviewer, the speculator and the pseudo expert. The 2 latter types are analyzed through the anthropologic lens of the popular Brazilian culture and the TV influence in that country. Research limitations/implications – we were able to understand how TV can influence the construction of fake online reviews for restaurants. Practical implications – It is important for the restaurant and hospitality industry in general, to be able to be attentive to the phenomenon of fake reviews that can totally biased the advantages of this assessment system that was created to produce trust among consumers, but that can act exactly the other way around. Originality/value – This study highlights the relevance of taking into account cultural background of the country where the restaurant is located, as well as emphasizing the relevance of conducting a previous analysis of the decision of embarking on a reality show that it has high chances to biasedly influence consumers’ decisions.

The importance of user-generated photos in restaurant selection

2019 ◽  
Vol 10 (1) ◽  
pp. 2-14 ◽  
Author(s):  
Bruno Oliveira ◽  
Beatriz Casais
Keyword(s):  
Design Methodology ◽  
Online Reviews ◽  
User Generated Content ◽  
Content Type ◽  
Online Platforms ◽  
Photo Sharing ◽  
Restaurant Managers ◽  
Marketing Activities ◽  
Hospitality Sector ◽  
Practical Implications

Purpose User-generated content and online reviews are highly relevant in purchase decision in the hospitality sector, including restaurants, but there is a lack of knowledge about the effect of sharing pictures in this context. This study aims to focus on the relevance of user-generated photos in online platforms for restaurants’ selection. Design/methodology/approach A research was conducted with a sample of 319 residents of Porto region, who had at least one meal in a restaurant over the 30 days before the answer of the survey and had searched online to select the restaurant. Findings The results show that while doing online research about restaurants, it is important for potential consumers to find pictures of food and physical evidences of restaurants generated by other users. Findings also show that consumers find user-generated photos especially at websites of reviews, although the importance of restaurant owned platforms, such as official social media pages and websites. Practical implications The research results appeal restaurant managers to understand the importance of user-generated photos in online platforms by promoting photo sharing in their restaurants with appropriate marketing activities for that purpose. Originality/value This paper expands the state-of-the-art about the importance of user-generated content, focusing on the importance of photos from restaurants shared by consumers in online platforms.

scholarly journals Analisis Pengaruh E-WOM, Online Review, dan Kualitas Informasi terhadap Minat Beli di Market Place Tokopedia

Prologia ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 127
Author(s):  
Julian Andrew ◽  
Rezi Erdiansyah
Keyword(s):  
Information Quality ◽  
Word Of Mouth ◽  
Online Reviews ◽  
Online Review ◽  
Electronic Word Of Mouth ◽  
Market Place ◽  
Purposive Sampling ◽  
Additional Information ◽  
Data Collection Technique

As the people's shopping habits via online starts to emerge, the e-commerce industry in Indonesia has also developed. In 2018, it was noted that 11.9% of Indonesian people were shopping online. However, in the midst of the vastness of online platforms with millions of items found in online storefronts, consumers need more information as their reference to arousing buying interest. As one of the biggest e-commerce players in Indonesia, Tokopedia provides features that enable sellers and consumers to exchange information regarding the items. In Tokopedia, prospective buyers can see electronic word of mouth messages, online reviews, and other additional information about the items that are known to be very influential in generating buying interest. This study uses a quantitative approach with an explanatory type in which the research seeks to find the effect of electronic word of mouth, online review, and the quality of information on buying interest of Jakarta students in Tokopedia e-commerce. The data collection technique used was purposive sampling by distributing questionnaires to 100 samples via online. Based on this research’s results, it was found that electronic word of mouth, online review, and information quality affect buying interest of students in Jakarta by 46% while the other 54% were influenced by other factors not examined in this study.Seiring dengan munculnya kebiasaan berbelanja masyarakat melalui online, industri e-commerce di Indonesia pun turut berkembang. Pada tahun 2018, tercatat bahwa sebanyak 11,9% orang di Indonesia melakukan kegiatan belanja secara online. Namun, di tengah luasnya platform online dengan jutaan barang yang terdapat di etalase online membuat konsumen membutuhkan informasi yang lebih sebagai bahan referensi dalam menimbulkan minat beli. Tokopedia merupakan pelaku e-commerce terbesar di Indonesia menyediakan fitur-fitur yang memungkinkan penjual dan konsumen untuk menulis dan bertukar informasi seputar barang tersebut. Di dalam Tokopedia, para calon pembeli dapat melihat pesan electronic word of mouth, online review, dan informasi-informasi tambahan lainnya seputar barang-barang yang dijual yang diketahui sangat berpengaruh dalam memunculkan minat beli. Penelitian ini menggunakan pendekatan kuantitatif dengan jenis eksplanatif dimana penelitian berusaha menemukan pengaruh e-WOM, kualitas informasi, dan online review terhadap minat beli mahasiswa Jakarta pada e-commerce Tokopedia. Teknik pengumpulan data yang digunakan adalah purposive sampling dengan menyebarkan kuesioner kepada 100 sampel secara online. Penelitian menemukan bahwa electronic word of mouth, online review, dan kualitas informasi berpengaruh terhadap minat beli pada mahasiswa di Jakarta sebanyak 46%, sedangkan 54% dipengaruhi oleh faktor-faktor lain yang tidak diteliti pada penelitian ini.

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