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The star rating summarises the review content and conveys the message faster than other review components. Star ratings influence helpfulness of the reviews, and extreme reviews are considered as less helpful in the decision process. However, literature has rarely addressed variations in star ratings across product categories and variations between two online retailers. In this paper, the authors have compared the distribution of star ratings across 11 products and among the retailers.

Online reviews for 11 product categories have collected, and the authors compared the distribution of star ratings across 11 products and retailers. Correspondence analysis has been applied to show the association between star ratings and product categories for the e-retail firms.

The Amazon site contains proportionately more number of 1-star rated reviews than Flipkart. In Amazon reviews, few product categories are closely associated with 1-star and 2-star reviews, whereas no product categories are closely associated with 1-star and 2-star reviews in Flipkart reviews. The results indicate two distinct communication strategies followed by the firms in managing online consumer reviews.

The authors did not analyse data across demographic details because of access restriction policies of the websites.

Understanding the distribution of review characteristics will improve the consumer’s decision-making ability and using online review content judiciously.

This study’s results show significant insights on online retailing by providing cues in using shopping sites and online review characteristics of two prominent retailers.

This paper has brought out a distinct distribution pattern of online review between Amazon and Flipkart. Amazon allows a higher degree of negative contents, whereas Flipkart allows more number of positive reviews.

Online product ratings play an important role in the decision-making process of consumers, which are not only sources of information used by consumers to understand the function and quality of a product or service but also sources of information used to find desirable products. The purpose of this paper is to develop a decision-based method for supporting the purchase decisions of consumers based on not only the online product ratings but also the actual product attributes.

First, two types of utility values are designed to measure the preference of the consumer based on either online ratings or actual product attributes. Then, the traditional TOPSIS method is adopted to achieve a comprehensive value by integrating the two types of utility values so that all of the alternative products can be ranked. Further, a product selection support system prototype is designed and developed to support the purchase decisions of consumers.

To help consumers select desirable products efficiently, it is necessary to develop a product selection method based on the online ratings of alternative products and consumer expectations.

The research shows that the proposed method can not only support consumers’ purchase decisions based on a large number of online product ratings but also help manufacturers to find out consumers’ demands or requirements on products so as to facilitate the design of new products or the improvement of products. On the basis of the proposed method, the developed system prototype is helpful for consumers to select desirable products.

To support the purchase decisions of consumers, a new decision-based method for selecting desirable online products is proposed.

The purpose of this paper is to provide an empirical approach to the understanding of the potential interplay between influencer opinions and consumer-generated information on consumer decision-making. Given the growth of influencer marketing and the pervasive nature of consumer star-ratings, it becomes critical to understand how and why these information sources influence consumers’ shopping decisions.

Drawing from the literature on source credibility, this paper proposes that influencer opinion interacts with the influencer’s reach on influencer credibility, skepticism towards the product and purchase intentions. Boundary conditions of consumer-generated information are also tested.

Convergent results across three studies indicate that the effect of influencer opinion is contingent upon both valence of the opinion and reach of the influencer. Consumer-generated information (i.e. star-ratings and the volume of ratings) moderates the effect of influencer opinion on purchase intentions. These effects are mediated by the credibility of the influencer and skepticism towards the product.

Understanding the relative impact of influencer opinions in the presence of other consumer-generated information provides managers with a framework to effectively manage online communications.

To the best of the authors’ knowledge, this paper provides a theoretically grounded first look at the potential interplay between two extremely powerful factors, influencer opinion and consumer-generated information. This paper provides a better understanding of the psychological mechanism behind the intricate workings of consumer-generated information.

The aim of this research is to theorize and demonstrate that analyzing consumers’ text product reviews using text mining can enhance the explanatory power of a product sales model, particularly for hedonic products, which tend to generate emotional and subjective product evaluations. Previous research in this area has been more focused on utilitarian products.

Our text clustering-based procedure segments text reviews into multiple clusters in association with consumers’ numeric ratings to address consumer heterogeneity in taste preferences and quality valuations and the J-distribution of numeric product ratings. This approach is novel in terms of combining text clustering with numeric product ratings to address consumers’ subjective product evaluations.

Using the movie industry as our empirical application, we find that our approach of making use of product text reviews can improve the explanatory power and predictive validity of the box-office sales model.

Marketing scholars have actively investigated the impact of consumers’ online product reviews on product sales, primarily focusing on consumers’ numeric product ratings. Recently, studies have also examined user-generated content. Similarly, this study looks into users’ textual product reviews to explain product sales. It remains to be seen how generalizable our empirical results are beyond our movie application.

Whereas numeric ratings can indicate how much viewers liked products, consumers’ reviews can convey why viewers liked or disliked them. Therefore, our review analysis can help marketers understand what factors make new products succeed or fail.

Primarily our approach is suitable to products subjectively evaluated, mostly, hedonic products. In doing so, we consider consumer heterogeneity contained in reviews through our review clusters based on their divergent impacts on sales.

Product ratings and reviews are popular tools to support buying decisions of consumers. Many e-commerce platforms now offer product ratings and reviews as ratings and reviews are valuable for online retailers. However, luxury goods industry is somewhat slow to adapt to the digital terrain. The purpose of this paper is to answer “how luxury consumers see user-generated product ratings and reviews for their online shopping experience and what important factors or values are perceived by the luxury consumers when they shop online?”

To understand how luxury consumers use product ratings and reviews before buying online, a survey with a situational set up of variations of rating, review and price options in association with a number of hypothetical luxury goods was conducted among 421 global luxury consumers out of over 6,000 people. The study was carried out from September to October 2018 for six weeks in the form of online and mobile survey. User population is high net-worth individuals or luxury consumers derived from the author’s various professional and social networks and communities. Their geographical coverage would be global, but concentrated around the major cities.

The survey shows that ratings and reviews can be important source of information for luxury consumers. Online ratings and reviews are rated as helpful by 76.01% of the participants. People who chose the highly rated one (4.8/5) over the poorly rated (3.7/5) was 86.94%, while all else such as product category, star rating and price range are about the same. Feedback from the open question survey indicates that the perceived helpfulness of rating and review systems could vary. Comparing user reviews is time-consuming because of unstructured nature of contextual reviews and the relative nature of human perception on the rating scale.

There are two aspects of ratings and reviews playing an important role for luxury consumers’ buying decision. First, it is about helpfulness of collective rating score. Luxury consumers see a user-generated rating score and use the score when they make a choice even if the rating is not an absolute, but relative figure, not exactly like the star rating system in the hotel industry. Second, there is discrepancy between the status of the brand in association with its price position and perceived value as the industry does not cope with classifying their brands in any official star rating system.

Consumers need compact and concise information about the products they need. When there are only a few potential products left in their short wish-list, full user reviews can be helpful to get more details and general opinions about the products on the short list before making a final decision. In that regard, a primary indicator that will guide through the decision-making process of the luxury consumers would be the trustworthiness of user rating of each product in an aggregated score along with a potential use of sub-ratings, which has to be visible from the product landing page.

Even if there is a wide use and ubiquitous nature of product ratings and reviews in other consumer products, the author is curious about how luxury consumers use ratings and reviews for their buying decision because there are not that many researches done previously in spite of the importance of this issue. Luxury goods industry has hit €320bn in 2017 according to Bain and Co., and 25% of the trading volume will be replaced by the digital commerce by 2025.

Two valuable pieces of information – reviews and their corresponding numerical ratings – are accessible to potential customers before they make a purchasing decision. An extensive body of marketing literature has scrutinized the influence of customers’ reviews by linking such aspects as the volume and valance of reviews with product sales and customers’ purchase intention. The aim of this study, for which dual coding theory was used, was to understand the relationship between reviews and their corresponding numerical ratings.

The authors used the latent Dirichlet allocation technique to categorize customers’ reviews. The present findings contribute to the literature by showing the underlying mechanisms that customers use to interpret reviews and associate them with numerical ratings.

The gradient boosted decision tree model demonstrates that non-abstract-dominant reviews (reviews mainly consist of tangible objects, actions, events or affective words) are significant predictors of their corresponding numerical ratings. However, abstract-dominant reviews (i.e. those consisting primarily of intangible objects, events or actions) cannot predict their associated numerical ratings.

The present findings contribute to the literature by showing the underlying mechanisms that customers use to interpret reviews and associate them with numerical ratings.

The purpose of this paper is to apply firm aspiration theory to explore how firms respond to government product ratings.

Longitudinal examination of nine automobile manufacturers during National Highway Traffic and Safety Administration crash tests in the USA.

Firms take specific external actions to influence the political mechanisms that support ranking schemes when product ratings are below those of rivals and when previously highly rated products decline. In addition, firms receiving rankings above those of their competitors are found to be less likely to take such action, even when their overall ratings declined. Similarly, firms seeing improvements in previously low-rated products will take fewer actions aimed at influencing the political mechanisms that support rating schemes.

The primary contribution of this research is in establishing when firm product ratings will result in actions to influence external ratings criteria. Previous research has shown that firms respond to organizational ratings by taking action aimed at improving subsequent performance. The current research builds on such work by applying aspiration theory in an effort to predict and explain when and why certain ratings will attract firm attention to the external mechanisms that support such ratings.

This 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.

Data 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.

The 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.

A 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.

Return policies are major risk-allaying cues for customers, yet they are a critical cost/lost-sales for retailers. Despite their importance in the retailing industry, few studies have examined the interplay of return policies with other cues that customers use to make a purchase decision. Toward this end, this study aims to investigate the interaction effects certain salient high-scope and low-scope cues, such as consumer ratings and brand image, and retailers’ return policies have on consumer purchase decisions.

Building on literature from signaling theory and cue scope literature (high-scope and low-scope cues), the authors develop a research model that hypothesizes the interrelationships between return policies, price discounts, customer product ratings and brand image. Three experimental studies investigate the potential interplay between return policies (lenient vs stringent), price discounts (low vs high), customer product ratings (low vs high) and brand image (high vs low) on quality certainty perceptions and purchase intentions. The mediating effect of quality certainty perceptions on the interplay of various factors (return policy, price promotions, consumer ratings and brand image) and customer purchase intentions is also investigated.

Results indicate that a lenient return policy will have a positive effect when consumers encounter high scope cues that signal undesirable aspects of the product (i.e. low consumer ratings, low brand image). In contrast, when high scope cues signal desirable aspects of the product (i.e. high consumer ratings, high brand image), it attenuates the effects of return policy. The findings suggest that quality certainty acts as a psychological process.

Service researchers should seek to examine the role of return policies in a more comprehensive manner.

Return policies are important cues for consumers while making purchase decision. Thus, retailers need to realize that these policies may need to be more dynamic or tiered, rather than one-size-fits-all.

This study provides a more comprehensive view of how consumers consider multiple cues simultaneously in decision-making. Literature has mainly examined the interactions between different high-scope and low-scope cues, but there has been limited research directed toward the interplay between multiple high-scope cues.

The purpose of this paper is to look at product reviews from a cross‐cultural perspective. Product reviews are potentially an influential form of marketing communication, as well as a predictor of sales performance. With Hofstede's typology as a backdrop, the basic proposition of this study is that collectivistic cultural values place more emphasis on giving face to others, even in impersonal social environments, resulting in them giving more positive product reviews than their individualistic counterparts.

The paper uses content analysis of Chinese and American computer game reviews to test the hypotheses.

The results showed that Chinese reviews use fewer negative comments and give higher final ratings for the same set of products than their American counterparts. In addition, Chinese reviews showed a lower consistency between their evaluative comments and their final ratings. Also confirmed is a common belief that final ratings are a summary of the evaluative comments of the review‐texts.

Future research is invited.

Theoretically, the study refines the understanding of differences between individualistic and collectivistic cultures. Practically, it is suggested that if consumers' cultural values are reflected in product reviews, international marketers should weigh and balance possible cultural bias when they decode evaluations of their products from reviews published in other countries' media.

Product reviews are a rich but ignored resource with high marketing value. It is hoped that the study can stimulate both marketing researchers and practitioners to make better use of product reviews to further understand marketing phenomena and make better marketing decisions.