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 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.

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

The Value of Social Dynamics in Online Product Ratings Forums

2011 ◽  
Vol 48 (3) ◽  
pp. 444-456 ◽  
Author(s):  
Wendy W. Moe ◽  
Michael Trusov
Keyword(s):  
Social Dynamics ◽  
Online Product Ratings ◽  
Product Ratings

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.

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