Measuring Product Type and Purchase Uncertainty with Online Product Ratings: A Theoretical Model and Empirical Application

2021 ◽  
Author(s):  
Peiyu Chen ◽  
Lorin M. Hitt ◽  
Yili Hong ◽  
Shinyi Wu
Keyword(s):  
Product Category ◽  
Product Type ◽  
Horizontal Differentiation ◽  
Review System ◽  
Product Positioning ◽  
Rating Data ◽  
Business Operations ◽  
Online Product Ratings ◽  
Product Ratings ◽  
Product Rating

Search and experience goods, as well as vertical and horizontal differentiation, are fundamental concepts of great importance to business operations and strategy. In our paper, we propose a set of theory-grounded data-driven measures that allow us to measure not only product type (search vs. experience and horizontal vs. vertical differentiation) but also sources of uncertainty and to what extent consumer reviews help resolve uncertainty. We used product rating data from Amazon.com to illustrate the relative importance of fit in driving product utility and the importance of search for determining fit for each product category at Amazon. Our results also show that, whereas ratings based on verified purchasers are informative of objective product values, the current Amazon review system appears to have limited ability to resolve fit uncertainty. Industry practitioners could utilize our approaches to quantitatively measure product positioning to support marketing strategy for retailers and manufacturers, covering an expanded group of products.

scholarly journals Modeling the Assimilation-Contrast Effects in Online Product Rating Systems: Debiasing and Recommendations

2018 ◽  
Author(s):  
Xiaoying Zhang ◽  
Hong Xie ◽  
Junzhou Zhao ◽  
John C.S. Lui
Keyword(s):  
Product Quality ◽  
Factor Model ◽  
Experimental Studies ◽  
Purchase Decisions ◽  
Contrast Effects ◽  
Rating Systems ◽  
Micro Level ◽  
Online Product Ratings ◽  
Product Ratings ◽  
Product Rating

The unbiasedness of online product ratings, an important property to ensure that users’ ratings indeed reflect their true evaluations to products, is vital both in shaping consumer purchase decisions and providing reliable recommendations. Recent experimental studies showed that distortions from historical ratings would ruin the unbiasedness of subsequent ratings. How to “discover” the distortions from historical ratings in each single rating (or at the micro-level), and perform the “debiasing operations” in real rating systems are the main objectives of this work. Using 42 million real customer ratings, we first show that users either “assimilate” or “contrast” to historical ratings under different scenarios: users conform to historical ratings if historical ratings are not far from the product quality (assimilation), while users deviate from historical ratings if historical ratings are significantly different from the product quality (contrast). This phenomenon can be explained by the well-known psychological argument: the “Assimilate-Contrast” theory. However, none of the existing works on modeling historical ratings’ influence have taken this into account, and this motivates us to propose the Histori- cal Influence Aware Latent Factor Model (HIALF), the first model for real rating systems to capture and mitigate historical distortions in each single rating. HIALF also allows us to study the influence patterns of historical ratings from a modeling perspective, and it perfectly matches the assimilation and contrast effects we previously observed. Also, HIALF achieves significant improvements in predicting subsequent ratings, and accurately predicts the relationships revealed in previous empirical measurements on real ratings. Finally, we show that HIALF can contribute to better recommendations by decoupling users’ real preference from distorted ratings, and reveal the intrinsic product quality for wiser consumer purchase decisions.

Mitigating Online Product Rating Biases Through the Discovery of Optimistic, Pessimistic, and Realistic Reviewers

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Sunghoon Lim ◽  
Conrad S. Tucker
Keyword(s):  
Large Scale ◽  
Training Data ◽  
Purchasing Decisions ◽  
Analysis Model ◽  
Rating Data ◽  
Star Rating ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Product Ratings ◽  
Product Rating

The authors of this work present a model that reduces product rating biases that are a result of varying degrees of customers' optimism/pessimism. Recently, large-scale customer reviews and numerical product ratings have served as substantial criteria for new customers who make their purchasing decisions through electronic word-of-mouth. However, due to differences among reviewers' rating criteria, customer ratings are often biased. For example, a three-star rating can be considered low for an optimistic reviewer. On the other hand, the same three-star rating can be considered high for a pessimistic reviewer. Many existing studies of online customer reviews overlook the significance of reviewers' rating histories and tendencies. Considering reviewers' rating histories and tendencies is significant for identifying unbiased customer ratings and true product quality, because each reviewer has different criteria for buying and rating products. The proposed customer rating analysis model adjusts product ratings in order to provide customers with more objective and accurate feedback. The authors propose an unsupervised model aimed at mitigating customer ratings based on rating histories and tendencies, instead of human-labeled training data. A case study involving real-world customer rating data from an electronic commerce company is used to validate the method.

Dynamic trends in online product ratings: A diagnostic utility explanation

2018 ◽  
Vol 87 ◽  
pp. 80-89 ◽  
Author(s):  
Fang Wang ◽  
Kalyani Menon ◽  
Chatura Ranaweera
Keyword(s):  
Diagnostic Utility ◽  
Online Product Ratings ◽  
Product Ratings

scholarly journals The Match-up Between Celebrity Associations and Product Type

2019 ◽  
Vol 15 (1) ◽  
pp. 65-89 ◽  
Author(s):  
Yadvinder Parmar ◽  
Mandeep Kaur Ghuman ◽  
Bikram Jit Singh Mann
Keyword(s):  
Product Category ◽  
Product Type ◽  
Celebrity Endorsements ◽  
Product Categories ◽  
Generic Framework

This research develops a generic framework that matches celebrity associations with various product categories and finds an ideal set of celebrity associations for each product category. Three studies have been conducted for achieving the purpose of the study. Study one identifies associations that consumers link with celebrities and classifies them into thirteen different categories. It also finds a total of 30 products and services that consumers associate with celebrity endorsements. In study two, the respondents are asked if each of the three celebrities is appropriate for endorsing each of the 30 identified products and services. The results support the match-up hypothesis notion that different celebrities are considered appropriate for different product categories. In study three, the respondents were asked to identify the associations that a celebrity should possess for endorsing various product categories. The results reveal that the celebrity associations can be classified into two broad categories—universal associations and product specific associations. Universal associations include the associations which the respondents consider to be essential for all types of products. Product specific associations include the associations that vary in their importance depending on the type of product category. The findings have significant implications for academicians, brand managers and celebrity management companies.

An eye tracking study of minimally branded products: hedonism and branding as predictors of purchase intentions

2018 ◽  
Vol 27 (2) ◽  
pp. 146-157 ◽  
Author(s):  
Juan Mundel ◽  
Patricia Huddleston ◽  
Bridget Behe ◽  
Lynnell Sage ◽  
Caroline Latona
Keyword(s):  
Eye Tracking ◽  
Online Survey ◽  
Product Category ◽  
Purchase Intentions ◽  
Product Type ◽  
Content Type ◽  
Attentional Processes ◽  
Potted Plants ◽  
Total Fixation ◽  
The Relationship

Purpose This study aims to test the relationship between consumers’ perceptions of product type (utilitarian vs hedonic) and the attentional processes that underlie decision-making among minimally branded products. Design/methodology/approach This study uses eye-tracking measures (i.e. total fixation duration) and data collected through an online survey. Findings The study shows that consumers spend more time looking at hedonic (vs utilitarian) and branded (vs unbranded) products, which influences perceptions of quality. Practical implications The findings of this research provide guidelines for marketing minimally branded products. Originality/value The authors showed that the product type influences the time consumers spend looking at an item. Previous findings about effects of branding are extended to an understudied product category (i.e. live potted plants).

Social Influence Effects in Online Product Ratings

2012 ◽  
Vol 76 (5) ◽  
pp. 70-88 ◽  
Author(s):  
Shrihari Sridhar ◽  
Raji Srinivasan
Keyword(s):  
Social Influence ◽  
Online Product Ratings ◽  
Product Ratings

scholarly journals Understanding Persuasion Cascades in Online Product Rating Systems

2019 ◽  
Vol 33 ◽  
pp. 5490-5497
Author(s):  
Hong Xie ◽  
Yongkun Li ◽  
John C.S. Lui
Keyword(s):  
Product Quality ◽  
Estimation Error ◽  
Opinion Dynamics ◽  
Estimation Accuracy ◽  
Matrix Perturbation ◽  
Rating Systems ◽  
Quality Estimation ◽  
Scoring Rule ◽  
Product Ratings ◽  
Product Rating

Online product rating systems have become an indispensable component for numerous web services such as Amazon, eBay, Google play store and TripAdvisor. One functionality of such systems is to uncover the product quality via product ratings (or reviews) contributed by consumers. However, a well-known psychological phenomenon called “messagebased persuasion” lead to “biased” product ratings in a cascading manner (we call this the persuasion cascade). This paper investigates: (1) How does the persuasion cascade influence the product quality estimation accuracy? (2) Given a real-world product rating dataset, how to infer the persuasion cascade and analyze it to draw practical insights? We first develop a mathematical model to capture key factors of a persuasion cascade. We formulate a high-order Markov chain to characterize the opinion dynamics of a persuasion cascade and prove the convergence of opinions. We further bound the product quality estimation error for a class of rating aggregation rules including the averaging scoring rule, via the matrix perturbation theory and the Chernoff bound. We also design a maximum likelihood algorithm to infer parameters of the persuasion cascade. We conduct experiments on the data from Amazon and TripAdvisor, and show that persuasion cascades notably exist, but the average scoring rule has a small product quality estimation error under practical scenarios.

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