Effect of eWOM Valence on Online Retail Sales

2017 ◽  
Vol 18 (1) ◽  
pp. 198-209 ◽  
Author(s):  
Gobinda Roy ◽  
Biplab Datta ◽  
Rituparna Basu
Keyword(s):  
Real Time ◽  
Product Information ◽  
Online Reviews ◽  
Purchase Decision ◽  
Kappa Statistics ◽  
Retail Sector ◽  
Online Data ◽  
Online Retail ◽  
Online Sales ◽  
Likelihood Model

Online retail sector in India has witnessed a phenomenal growth in recent times. Online shopping has also become a popular trend among the younger generation in India. Increasing number of shoppers visit online retailer websites and read online reviews before making their purchase decision. The online reviews or electronic word of mouth (eWOM) becomes an important guiding tool for the online shoppers with its intrinsic product information and evaluation characteristics. The present study aims to analyze the effects of various eWOM antecedents on online sales by considering the effects of positive and mixed-neutral eWOM (MNWOM) valence (stimuli) on sales. It also explores the role of market-level eWOM factors, such as price, on online sales of security products like antivirus software. The confirmatory bias of these factors was noted, while the elaboration likelihood model (ELL) has been used to understand the relative importance of these factors in influencing customers’ purchase decision and sales. Further, a content analysis method supplemented by a multiple regression method was used to analyze 205 real-time online sales (reviews from verified purchasers) data pertaining to popular and top-selling antivirus products taken from two leading e-commerce websites. The study contributes as a pioneering effort in the domain with the use of innovative methodology of capturing real-time online data with a subsequent kappa statistics validation. The results showed a new insightful perspective of eWOM valence and price on sales, and provided further research directions.

scholarly journals AN AUTOMATED METHOD TO CONDUCT IMPORTANCE-PERFORMANCE ANALYSIS OF PRODUCT ATTRIBUTES FROM ONLINE REVIEWS - AN EXTENSION WITH A CASE STUDY

2021 ◽  
Vol 1 ◽  
pp. 417-426
Author(s):  
Kangcheng Lin ◽  
Harrison Kim
Keyword(s):  
Performance Analysis ◽  
Latent Dirichlet Allocation ◽  
Product Information ◽  
Online Reviews ◽  
Product Attributes ◽  
Attribute Importance ◽  
Online Data ◽  
Automated Method ◽  
Ibm Watson

AbstractWith the growth of online marketplaces and social media, product designers have been seeing an exponential growth of data available, which can serve as an extremely valuable source of information communicated from customers without geographical limitations. The data will reveal customers’ preferences, which can be expensive and slow to obtain via traditional methods such as survey and questionnaires. While existing methods in the literature have been proposed to extract product information and make inference from online data, they have limitations, especially in providing reliable results and in dealing with data sparsity. Therefore, this paper proposes a method to conduct an Important-performance analysis from online reviews. The major steps of this method involve using latent Dirichlet allocation (LDA) to identify product attributes, using IBM Watson Natural Language Understanding tool to perform aspect-based sentiment analysis, and using XGBoost model to infer product attribute importance from the collected dataset. In our case study, we have collected over 150,000 text reviews of more than 3,000 laptops from Amazon.

E-WOM (Word of Mouth): A Technical Communication Tool

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Jyoti Kumari ◽  
Rinki Verma
Keyword(s):  
Product Information ◽  
Online Reviews ◽  
Purchase Decision ◽  
Purchase Decisions ◽  
Marketing Communication ◽  
Communication Tool ◽  
Management Perspective ◽  
Shopping Experience ◽  
Causal Impact ◽  
Use Of Internet

Internet has reformed the behavior of customers as earlier they behaved. Traditionally customers were not aware about the products or services and awareness level of them was totally depending on companies’ perspectives but now-a-days customers are more informed and more curious. The widespread use of internet has extended customer’s options for collecting product information by considering others comments and reviews posted on online shopping portals. E-WOM has made online shopper’s purchase decisions easier. E-WOM reviews are seen by perspective customers of related products and services who want to know more information from those customers who have purchased and used the interested product or service. EWOM empowers customers by giving information, experience of other who are not known to One another. Consumers perceive online reviews are more trustworthy than other marketing communication element. E-WOM /Online reviews act like a salient salesman for those customers who shop through e-retailers. The wider acceptance and popularity of e-wom has received substantial attention from researchers and e-retailers. A questionnaire with variables taken from prior studies was framed to measure the responses of online customers. The questionnaire was distributed among customers from different demographics for collecting the data. The results show that consumer reviews have a causal impact on consumer purchase decision and they play a crucial role in selecting and evaluating the appropriate product. Finally, the conclusion and their implications are discussed. In management perspective, E-WOM helps the e-retailers to better capture consumer purchase decision, shopping experience and their expectations so that they can generate more revenue.

The effects of online reviews on purchasing intention: The moderating role of need for cognition

2011 ◽  
Vol 39 (1) ◽  
pp. 71-81 ◽  
Author(s):  
Chin-Lung Lin ◽  
Sheng-Hsien Lee ◽  
Der-Juinn Horng
Keyword(s):  
Attitude Change ◽  
Product Information ◽  
Need For Cognition ◽  
Online Reviews ◽  
Online Marketing ◽  
Elaboration Likelihood ◽  
Purchasing Intention ◽  
Online Shoppers ◽  
Likelihood Model

The Internet has provided a competitive platform for online marketing, and online shopping has become an important part of daily life for consumers who view online reviews as an effective channel of acquiring product information before making purchase decisions. Based on the elaboration likelihood model (ELM; Petty & Cacioppo, 1981, 1986), in the present study the effects of online reviews on purchasing intention are explored using need for cognition as a moderator. Findings that emerge from the results are: Firstly, when online reviews are high quality this has a positive effect on the purchasing intention of online shoppers. Secondly, when there are a high number of online reviews this positively affects the purchasing intention of online shoppers. Finally, shoppers with a high need for cognition take the central route in attitude change, but shoppers with a low need for cognition tend to adopt the peripheral route in forming attitude. Marketing implications are suggested.

scholarly journals E-WOM (Word of Mouth): A Technical Communication Tool

2018 ◽  
Vol 10 (01) ◽  
pp. 16-24
Author(s):  
Jyoti Kumari ◽  
Rinki Verma ◽  
S. H. Mehdi
Keyword(s):  
Product Information ◽  
Online Reviews ◽  
Purchase Decision ◽  
Purchase Decisions ◽  
Marketing Communication ◽  
Communication Tool ◽  
Management Perspective ◽  
Shopping Experience ◽  
Causal Impact ◽  
Use Of Internet

Internet has reformed the behavior of customers as earlier they behaved. Traditionally customers were not aware about the products or services and awareness level of them was totally depending on companies’ perspectives but now-a-days customers are more informed and more curious. The widespread use of internet has extended customer’s options for collecting product information by considering others comments and reviews posted on online shopping portals. E-WOM has made online shopper’s purchase decisions easier. E-WOM reviews are seen by perspective customers of related products and services who want to know more information from those customers who have purchased and used the interested product or service. EWOM empowers customers by giving information, experience of other who are not known to One another. Consumers perceive online reviews are more trustworthy than other marketing communication element. E-WOM /Online reviews act like a salient salesman for those customers who shop through e-retailers. The wider acceptance and popularity of e-wom has received substantial attention from researchers and e-retailers. A questionnaire with variables taken from prior studies was framed to measure the responses of online customers. The questionnaire was distributed among customers from different demographics for collecting the data. The results show that consumer reviews have a causal impact on consumer purchase decision and they play a crucial role in selecting and evaluating the appropriate product. Finally, the conclusion and their implications are discussed. In management perspective, E-WOM helps the e-retailers to better capture consumer purchase decision, shopping experience and their expectations so that they can generate more revenue.

scholarly journals Tracking inflation on a daily basis

2020 ◽  
Vol 156 (1) ◽  
Author(s):  
Santiago E. Alvarez ◽  
Sarah M. Lein
Keyword(s):  
Real Time ◽  
Price Index ◽  
Daily Basis ◽  
Relative Importance ◽  
Online Data ◽  
Demand And Supply ◽  
Supply Shocks ◽  
Policy Measures ◽  
Real Time Information ◽  
Time Information

Abstract Using online data for prices and real-time debit card transaction data on changes in expenditures for Switzerland allows us to track inflation on a daily basis. While the daily price index fluctuates around the official price index in normal times, it drops immediately after the lockdown related to the COVID19 pandemic. Official statistics reflect this drop only with a lag, specifically because data collection takes time and is impeded by lockdown conditions. Such daily real-time information can be useful to gauge the relative importance of demand and supply shocks and thus inform policymakers who need to determine appropriate policy measures.

Experience

2021 ◽  
Vol 13 (1) ◽  
pp. 1-16
Author(s):  
Michela Fazzolari ◽  
Francesco Buccafurri ◽  
Gianluca Lax ◽  
Marinella Petrocchi
Keyword(s):  
Relative Frequency ◽  
Service Providers ◽  
Real Data ◽  
Online Reviews ◽  
Purchase Decision ◽  
The Past ◽  
Supervised Classifiers ◽  
Relative Frequency Distribution ◽  
Opinion Spam ◽  
Fake Reviews

Over the past few years, online reviews have become very important, since they can influence the purchase decision of consumers and the reputation of businesses. Therefore, the practice of writing fake reviews can have severe consequences on customers and service providers. Various approaches have been proposed for detecting opinion spam in online reviews, especially based on supervised classifiers. In this contribution, we start from a set of effective features used for classifying opinion spam and we re-engineered them by considering the Cumulative Relative Frequency Distribution of each feature. By an experimental evaluation carried out on real data from Yelp.com, we show that the use of the distributional features is able to improve the performances of classifiers.

scholarly journals flipkart.com’s Strategy on Amazon.com’s Entry into India :A Case Study

2013 ◽  
Vol 12 (4) ◽  
pp. 131-143
Author(s):  
Padmanabh B
Keyword(s):  
Foreign Direct Investment ◽  
Government Policy ◽  
Direct Investment ◽  
Online Shopping ◽  
Foreign Investments ◽  
Online Retail ◽  
Online Sales ◽  
Internet Users ◽  
The Government

The online retail industry in India is expected to grow to Rs. 7000 crores by 2015. Its size in 2013 is Rs. 2500 crores. By 2014 India is expected to become the 3rd largest nation of Internet users and this would provide huge potential to the online retail Industry1.Among the major cities in India, consumers in Mumbai topped the chart in doing online shopping followed by Ahmedabad and Delhi2. As per Google study conducted in 2012, 51 percent of the traffic for its Great online shopping festival (GOSF) was due to customers from cities other than the four metros. Referring to the growth in online sales, Nitin Bawankule, industry director, e-commerce, online classifieds and media/entertainment at Google India said, “Top motivators for shopping online include cash back guarantee, cash on delivery, fast delivery, substantial discounts compared to retail, and access to branded products”3.  The E –commerce space in India has seen a lot of action and there are many online players like flipkart.com, Myntra.com, Fabmart, Indiaplaza and Indiatimesshopping. Amazon.com made an indirect entry through Junglee.com. The reason for this indirect entry is the result of government policy towards foreign direct investment.  The Government of India announced in September 2012 the revised foreign direct investment policy in retail. As per this announcement foreign investments are blocked in e-commerce sector while allowing 51 percent FDI in multi-brand retail stores and 100 percent FDI in single brand retail. Amazon has been eyeing the Indian E commerce market which is estimated around $2 billion4.

scholarly journals Explaining and Predicting Helpfulness & Funniness of Online Reviews on the Steam Platform

2021 ◽  
Vol 29 (6) ◽  
pp. 0-0
Keyword(s):  
Applied Research ◽  
Online Shopping ◽  
Product Information ◽  
Online Reviews ◽  
Online Review ◽  
Gradient Boosting ◽  
Related Data ◽  
Review Helpfulness ◽  
Display Content ◽  
The Impact

Online review is a crucial display content of many online shopping platforms and an essential source of product information for consumers. Low-quality reviews often cause inconvenience to the platform and review readers. This article aims to help Steam, one of the largest digital distribution platforms, predict the review helpfulness and funniness. Via Python, 480,000 game reviews related data for 20 games were captured for analysis. This article analyzed the impact of three categories of influencing factors on the usefulness and funniness of game reviews, which are characteristics of review, reviewer and game. Additionally, by using the Random Forest-based classifier, the usefulness of reviews could be accurately predicted, while for funniness, Gradient Boosting Decision Tree was the better choice. This article applied research on the usefulness of reviews to game products and proposed research on the funniness of reviews.

scholarly journals Analysis and Design of Distribution Systems Information Flow in the Sugar Supply Chain in Indonesia

2021 ◽  
Vol 2 (1) ◽  
pp. 47
Author(s):  
Ratna Ekawati ◽  
Yandra Arkeman ◽  
Suprihatin Suprihatin ◽  
Titi Candra Sunarti
Keyword(s):  
Supply Chain ◽  
Real Time ◽  
Information Flow ◽  
Value Chain ◽  
Distribution Systems ◽  
Product Information ◽  
Information Support ◽  
Main Process ◽  
Time Data ◽  
Analysis And Design

Today's modern supply chain represents a complex and real-time, organization, resource, activity, information, and data source that is involved in the distribution of products and services ranging from upstream to downstream of the supply chain. In the past 4.0 supply chain technology was not just a linear business function, but as the center of the main process of ecosystems that are in a blind spot chained by value. With information as a foundation in the decision-making process so that information can create integrated and efficiently coordinated supply chains. So that it can show continuity from planning to production, inventory, quality, and price control in each chain. An inefficient distribution that results in mistrust among stakeholders, because it has an impact on the decline and loss of value chain in quality and quantity. Integrity problems from the data collected were found in this study. These findings include the identification of various stakeholders, including farmers, importers to customers, and regulators, as well as their needs, which will be described through the use case, and BPMN. The results obtained are that the main actors (stakeholders) of the system are divided into farmers, importers, processing factories, headquarters, hauling services, and markets (customers) in the distribution of product information flow systems. Suggests tracking and tracing based on real-time data flow of product information coming from each actor in the sugar supply chain that is equipped with an accurate data distribution information support system.

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