Do Human Faces Matter? Evidence from User-Generated Photos in Online Reviews

The importance of online reviews in e-commerce cannot be overstated, but few studies have focused on user-generated photos (UGPs) in reviews, especially human faces in UGPs. In this study, using Amazon online review data, we divide online reviews into text with UGPs, UGPs with faces, and UGPs with multiple faces based on the presence and number of faces, and discuss their effects on review helpfulness. Drawing on media richness theory and emotional contagion effects, we argue that faces provide a richness of information that can increase the effectiveness of photos as information mediators. Moreover, we argue that facial expressions and emotional states, as read-in and read-out devices that convey individual emotions, affect other consumers\u27 perceived review helpfulness. This study contributes to the literature on online reviews, media richness theory, and emotional contagion effects, while providing practical insights for e-commerce sites and consumers seeking to write effective online reviews

Estimating the Socio-Economic Impact of Product Reviews: Mining Text and Reviewer Characteristics

With the rapid growth of the Internet, the ability of users to create and publish content has created active electronic communities that provide a wealth of product information. However, the high volume of reviews that are typically published for a single product makes harder for individuals as well as manufacturers to locate the best reviews and understand the true underlying quality of a product. In this paper, we re-examine the impact of reviews on economic outcomes like product sales and see how different factors affect social outcomes like the extent of their perceived usefulness. Our approach explores multiple aspects of review text, such as lexical, grammatical, semantic, and stylistic levels to identify important text-based features. In addition, we also examine multiple reviewer-level features such as average usefulness of past reviews and the self-disclosed identity measures of reviewers that are displayed next to a review. Our econometric analysis reveals that the extent of subjectivity, informativeness, readability, and linguistic correctness in reviews matters in influencing sales and perceived usefulness. Reviews that have a mixture of objective, and highly subjective sentences have a negative effect on product sales, compared to reviews that tend to include only subjective or only objective information. However, such reviews are considered more informative (or helpful) by the users. By using Random Forest based classifiers, we show that we can accurately predict the impact of reviews on sales and their perceived usefulness. Reviews for products that have received widely fluctuating reviews, also have reviews of widely fluctuating helpfulness. In particular, we find that highly detailed and readable reviews can have low helpfulness votes in cases when users tend to vote negatively not because they disapprove of the review quality but rather to convey their disapproval of the review polarity. We examine the relative importance of the three broad feature categories: `reviewer-related' features, `review subjectivity' features, and `review readability' features, and find that using any of the three feature sets results in a statistically equivalent performance as in the case of using all available features. This paper is the first study that integrates econometric, text mining, and predictive modeling techniques toward a more complete analysis of the information captured by user-generated online reviews in order to estimate their socio-economic impact. Our results can have implications for judicious design of opinion forums

What Makes Consumer Perception of Online Review Helpfulness: Synthesizing the Past to Guide Future Research

A growing body of academic research has aimed to investigate the helpfulness of online customer reviews (OCRs) given their prevalence and the need to better understand their appraisal mechanisms. However, past studies have applied varied methods and reported conflicting findings. This study aims to improve the understanding of the contributors to OCR helpfulness by synthesizing past studies on the topic. Based on a systematic literature review, a summary of the precursors to OCR helpfulness is provided. We decipher both the consistent and conflicting results and discuss the possible explanations for these mixed findings. By summarizing past studies, the review also points out possible directions for future research

When More is More and Less is More: Depth and Breadth of Product Reviews and Their Effects on Review Helpfulness

With the growth of online shopping coupled with mobile technology, user-generated product reviews have become an important source of information for product diagnosticity. A significant academic endeavor has been made to comprehend what information factors of reviews help prospective customers better diagnose products. One such factor is review depth that is estimated by the number of a review’s words. We propose review breadth as an additional factor based on a review’s number of topics—the more review breadth, the more diverse information. By conducting the statistical and predictive analyses, we demonstrate that review breadth reliably measures a review’s information. This study makes academic and practical contributions. For academic researchers, review breadth is worth considering as a factor to estimate a review’s information over and above review depth. Based on the two information factors of review breadth and review depth, practitioners can recommend more helpful product reviews to their prospective customers

The Relationship Between Disclosing Purchase Information and Reputation Systems in Electronic Markets

In this work we investigate how the introduction of the Verified Purchase (VP) badge on Amazon.com affected both the review helpfulness and the product ratings. We first conduct a propensity score matching study and find that all else equal, camera reviews are on average ranked 7 positions higher than non-VP reviews, while book VP reviews are on average ranked 11 positions higher than non-VP reviews. Next, we use a natural experiment setting to identify whether the entry of the VP feature had an effect on the (1) overall review helpfulness (both VP and non-VP reviews), and (2) average product rating. Our results show that the introduction of VP caused an increase in review helpfulness of 7.7% for books, and 1.7% for electronics. Furthermore, it caused on average an increase of 20 and 18 positions in the ranks on book and electronic products respectively

Mechanisms of Negativity Bias: An Empirical Exploration of App Reviews In Apple’s App Store

Researchers in many diverse areas have consistently found that we are unduly influenced by negative information. In electronic commerce, this negativity bias is evident in the effect of product reviews on consumer behavior in the information systems literature. While the negativity bias is well documented, there has been little systematic and empirical research on its underlying causes. Utilizing a novel data set collected from Apple’s App Store, we examine three probable causes of the negativity bias: that negative reviews are more specific, that they have higher surprise value, and that they increase our ability to avoid losses. The empirical analysis revealed that while all three mechanisms contribute to the negativity bias, the ‘surprise’ factor and the ability to avoid losses play a more prominent role when consumers process and integrate positive and negative review information. Our findings also carry important practical implications for review platforms and online companies

How is the review helpfulness evaluated?

A user-generated review that is perceived as helpful is valuable for both customer and the retailer, and that is why online markets such as Amazon.com collect public opinion on reviews that are perceived more helpful. Review platforms allow customers to vote for reviews they deem helpful. While prior literature has examined what drives the helpfulness of reviews, many of these studies have looked at drivers of perceived helpfulness of reviews in isolation. Using the lens of dual process theory, this research examines how consumers evaluate the helpfulness of a review. We propose a framework and provide empirical evidence for the evaluation of the review helpfulness process. We find that extreme reviews have a higher effect on review helpfulness compared to moderate reviews, and this effect is mediated by the depth and sentiment of the review content

Exploring determinants of attraction and helpfulness of online product review:a consumer behaviour perspective

To assist filtering and sorting massive review messages, this paper attempts to examine the determinants of review attraction and helpfulness. Our analysis divides consumers’ reading process into “notice stage” and “comprehend stage” and considers the impact of “explicit information” and “implicit information” of review attraction and review helpfulness. 633 online product reviews were collected from Amazon China. A mixed-method approach is employed to test the conceptual model proposed for examining the influencing factors of review attraction and helpfulness. The empirical results show that reviews with negative extremity, more words, and higher reviewer rank easily gain more attraction and reviews with negative extremity, higher reviewer rank, mixed subjective property, and mixed sentiment seem to be more helpful. The research findings provide some important insights, which will help online businesses to encourage consumers to write good quality reviews and take more active actions to maximise the value of online reviews