scholarly journals What Factors Influence Online Product Sales? Online Reviews, Review System Curation, Online Promotional Marketing and Seller Guarantees Analysis

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 3920-3931 ◽  
Author(s):  
Zhijie Zhao ◽  
Jiaying Wang ◽  
Huadong Sun ◽  
Yang Liu ◽  
Zhipeng Fan ◽  
...  
Keyword(s):  
Online Reviews ◽  
Review System ◽  
Product Sales ◽  
Promotional Marketing

scholarly journals Online Reviews and Product Sales: The Role of Review Visibility

2021 ◽  
Vol 16 (4) ◽  
pp. 638-669
Author(s):  
Miriam Alzate ◽  
Marta Arce-Urriza ◽  
Javier Cebollada
Keyword(s):  
Decision Making ◽  
Dynamic Models ◽  
Generalized Method Of Moments ◽  
Online Reviews ◽  
Consumer Decision Making ◽  
Product Sales ◽  
The Impact ◽  
The Relationship ◽  
The Empirical Analysis

When studying the impact of online reviews on product sales, previous scholars have usually assumed that every review for a product has the same probability of being viewed by consumers. However, decision-making and information processing theories underline that the accessibility of information plays a role in consumer decision-making. We incorporate the notion of review visibility to study the relationship between online reviews and product sales, which is proxied by sales rank information, studying three different cases: (1) when every online review is assumed to have the same probability of being viewed; (2) when we assume that consumers sort online reviews by the most helpful mechanism; and (3) when we assume that consumers sort online reviews by the most recent mechanism. Review non-textual and textual variables are analyzed. The empirical analysis is conducted using a panel of 119 cosmetic products over a period of nine weeks. Using the system generalized method of moments (system GMM) method for dynamic models of panel data, our findings reveal that review variables influence product sales, but the magnitude, and even the direction of the effect, vary amongst visibility cases. Overall, the characteristics of the most helpful reviews have a higher impact on sales.

Culturally Contingent Electronic Word-of-Mouth Signaling and Screening: A Comparative Study of Product Reviews in the United States and Japan

2018 ◽  
Vol 26 (2) ◽  
pp. 80-102 ◽  
Author(s):  
Hsin-Chen Lin ◽  
Manohar U. Kalwani
Keyword(s):  
Cultural Differences ◽  
Word Of Mouth ◽  
Online Reviews ◽  
Cross Cultural ◽  
Electronic Word Of Mouth ◽  
National Differences ◽  
Relative Product ◽  
Product Sales ◽  
The Relationship ◽  
Over Time

Electronic word of mouth (eWOM) is an important source of influence on consumer decision making, yet little is known about cross-cultural differences in both the occurrence of eWOM and the relationship between eWOM and sales. The authors draw on signaling theory to develop a conceptual model and assess the relationships between country and the occurrence of eWOM, as well as between online ratings and relative product sales according to country. Online reviews and sales rank data for books, CDs, and DVDs were collected from Amazon U.S. and Amazon Japan in 2009 and 2017. Results suggest cross-national differences in both the occurrence of eWOM (eWOM signaling) and the relationship between eWOM and relative product sales (eWOM screening). These national differences appear to change over time: some remain stable, some disappear, and others emerge. The proposed culturally contingent signaling and screening model may be adopted as a framework for future research on cross-cultural eWOM. The results also inform the literature on cultural change by suggesting that cultural differences in eWOM change in nuanced patterns over time.

scholarly journals Understanding and Encouraging Online Reviewing With a Selection-Based Review System

2019 ◽  
Vol 31 (5) ◽  
pp. 446-464
Author(s):  
Rowanne Fleck ◽  
Benjamin R Cowan ◽  
Eirini Darmanin ◽  
Yixin Wang
Keyword(s):  
Cost Benefit ◽  
Online Reviews ◽  
Review System ◽  
Personal View ◽  
Trade Off ◽  
Consumer Reviews ◽  
Online Consumer Reviews ◽  
Mobile Interface ◽  
Online Consumer ◽  
The Cost

Abstract Online consumer reviews are important for people wishing to make purchases online. However, not everyone contributes online reviews. This paper looks at consumer motivations of reviewing and rating behaviour in order to motivate the design of a mobile interface for online reviewing. An interview study found that people tend to contribute reviews and ratings based on their perception of whether they would be helpful or not to others as well as their own personal view of the usefulness of reviews and ratings when buying products. There also seems to be a cost-benefit trade-off that influences people’s decisions to review and rate: people tend to make a decision based on the perceived value of that review or rating to the community against the effort and costs of contributing. A mobile interface was designed that was intended both to reduce the cost of leaving reviews and to increase the perception of the usefulness of the reviews to others. An initial evaluation of this reviewing interface suggests that it could encourage more people to leave reviews.

scholarly journals Spammer group detection and diversification of customers’ reviews

2021 ◽  
Vol 7 ◽  
pp. e472
Author(s):  
Naveed Hussain ◽  
Hamid Turab Mirza ◽  
Abid Ali ◽  
Faiza Iqbal ◽  
Ibrar Hussain ◽  
...  
Keyword(s):  
Real World ◽  
Online Reviews ◽  
Direct Impact ◽  
Review System ◽  
Detection Problem ◽  
Review Time ◽  
Public Opinions ◽  
Customer Reviews ◽  
Product Features ◽  
Group Detection

Online reviews regarding different products or services have become the main source to determine public opinions. Consequently, manufacturers and sellers are extremely concerned with customer reviews as these have a direct impact on their businesses. Unfortunately, to gain profit or fame, spam reviews are written to promote or demote targeted products or services. This practice is known as review spamming. In recent years, Spam Review Detection problem (SRD) has gained much attention from researchers, but still there is a need to identify review spammers who often work collaboratively to promote or demote targeted products. It can severely harm the review system. This work presents the Spammer Group Detection (SGD) method which identifies suspicious spammer groups based on the similarity of all reviewer’s activities considering their review time and review ratings. After removing these identified spammer groups and spam reviews, the resulting non-spam reviews are displayed using diversification technique. For the diversification, this study proposed Diversified Set of Reviews (DSR) method which selects diversified set of top-k reviews having positive, negative, and neutral reviews/feedback covering all possible product features. Experimental evaluations are conducted on Roman Urdu and English real-world review datasets. The results show that the proposed methods outperformed the existing approaches when compared in terms of accuracy.

Finding eWOM customers from customer reviews

2019 ◽  
Vol 119 (1) ◽  
pp. 129-147 ◽  
Author(s):  
Pengfei Zhao ◽  
Ji Wu ◽  
Zhongsheng Hua ◽  
Shijian Fang
Keyword(s):  
Evaluation Method ◽  
Online Reviews ◽  
Customer Segmentation ◽  
Product Reviews ◽  
Data Set ◽  
Content Type ◽  
Customer Reviews ◽  
Product Sales ◽  
The Relationship ◽  
New Framework

PurposeThe purpose of this paper is to identify electronic word-of-mouth (eWOM) customers from customer reviews. Thus, firms can precisely leverage eWOM customers to increase their product sales.Design/methodology/approachThis research proposed a framework to analyze the content of consumer-generated product reviews. Specific algorithms were used to identify potential eWOM reviewers, and then an evaluation method was used to validate the relationship between product sales and the eWOM reviewers identified by the authors’ proposed method.FindingsThe results corroborate that online product reviews that are made by the eWOM customers identified by the authors’ proposed method are more related to product sales than customer reviews that are made by non-eWOM customers and that the predictive power of the reviews generated by eWOM customers are significantly higher than the reviews generated by non-eWOM customers.Research limitations/implicationsThe proposed method is useful in the data set, which is based on one type of products. However, for other products, the validity must be tested. Previous eWOM customers may have no significant influence on product sales in the future. Therefore, the proposed method should be tested in the new market environment.Practical implicationsBy combining the method with the previous customer segmentation method, a new framework of customer segmentation is proposed to help firms understand customers’ value specifically.Originality/valueThis study is the first to identify eWOM customers from online reviews and to evaluate the relationship between reviewers and product sales.

scholarly journals Predicting consumer product demands via Big Data: the roles of online promotional marketing and online reviews

2015 ◽  
Vol 55 (17) ◽  
pp. 5142-5156 ◽  
Author(s):  
Alain Yee Loong Chong ◽  
Eugene Ch’ng ◽  
Martin J. Liu ◽  
Boying Li
Keyword(s):  
Big Data ◽  
Online Reviews ◽  
Consumer Product ◽  
Promotional Marketing

scholarly journals Improving Product Sales on E-Commerce Websites Based on Reviewers Opinions

2020 ◽  
pp. 361-366
Author(s):  
S. Kayalvili ◽  
Muthu Priyadharshini. A
Keyword(s):  
Early Stage ◽  
Online Reviews ◽  
Page Rank ◽  
Product Lifetime ◽  
Three Stages ◽  
Product Sales ◽  
Behavior Characteristics ◽  
Source Of Information

Online reviews are the important source of information for users before selecting a product for making a decision. Reviews of product particularly early reviews have impact on the product sales. Study the behavior characteristics of early reviewers through their posted early reviews. At first divide the product lifetime into three stages- Early, majority and laggards. A person who posts reviews in early stage is considered as early reviewers. The Early reviewers are the first one who responds to the product at the beginning stage. Rating behaviors of early reviewers are predicted based on k-means with Page Rank.

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