Positive emotion bias: Role of emotional content from online customer reviews in purchase decisions

2020 ◽  
Vol 52 ◽  
pp. 101891 ◽  
Author(s):  
Junpeng Guo ◽  
Xiaopan Wang ◽  
Yi Wu
Keyword(s):  
Positive Emotion ◽  
Emotional Content ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
Online Customer Reviews

Fraud abroad

2019 ◽  
Vol 13 (2) ◽  
pp. 249-275
Author(s):  
Jake David Hoskins ◽  
Ryan Leick
Keyword(s):  
Sharing Economy ◽  
Gdp Per Capita ◽  
Content Type ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Level Of Economic Development ◽  
Per Capita ◽  
Using Data ◽  
Developed Nations

Purpose This study aims to investigate a sharing economy context, where vacation rental units that are owned and operated by individuals throughout the world are rented out through a common website: vrbo.com. It is posited that gross domestic product (GDP) per capita, a common indicator of the level of economic development of a nation, will impact the likelihood that prospective travelers will choose to book accommodations in the sharing economy channel (vs traditional hotels). The role of online customer reviews in this process is investigated as well, building upon a significant body of extant research which shows their level of customer decision influence. Design/methodology/approach An empirical analysis is conducted using data from the website Vacation Rentals By Owner on 1,940 rental listings across 97 countries. Findings GDP per capita serves as risk deterrent to prospective travelers, making the sharing economy an acceptable alternative to traditional hotels for the average traveler. It is also found that the total number of online customer reviews (OCR volume) is a signal of popularity to prospective travelers, while the average star rating of those online customer reviews (OCR valence) is instead a signal of accommodation quality. Originality/value This study adds to a growing agenda of research investigating the effect of online customer reviews on consumer decisions, with a particularly focus on the burgeoning sharing economy. The findings help to explain when the sharing economy may serve as a stronger disruptive threat to incumbent offerings. It also provides the following key insights for managers: sharing economy rental units in developed nations are more successful in driving booking activity, managers should look to promote volume of online customer reviews and positive online customer reviews are particularly influential for sharing economy rental booking rates in less developed nations.

Exploring Customers’ Luxury Consumption in Restaurants: A Combined Method of Topic Modeling and Three-Factor Theory

2021 ◽  
pp. 193896552110376
Author(s):  
Wooseok Kwon ◽  
Minwoo Lee ◽  
John T. Bowen
Keyword(s):  
Topic Modeling ◽  
Service Management ◽  
Essential Elements ◽  
Luxury Consumption ◽  
Customer Reviews ◽  
Customer Perceptions ◽  
Asymmetry Analysis ◽  
Online Customer Reviews ◽  
Three Factor

This study explores customers’ perceptions and underlying factors related to luxury consumption in restaurants. Although many studies have explored customers’ consumption of luxury goods, very few of these studies involved luxury hospitality services. Furthermore, hospitality literature has rarely discussed the emerging identification of inconspicuous consumption in luxury. By applying topic modeling to analyze online customer reviews, the current study identifies the essential elements of visiting luxury restaurants. Moreover, it elicits the asymmetric role of the identified factors in accelerating overall customer satisfaction or dissatisfaction through impact-asymmetry analysis, which adopts the three-factor theory. Findings suggest that many inconspicuous factors exist in luxury consumption and that the mechanisms that affect satisfaction differ among a satisfier, a dissatisfier, and a hybrid. The acknowledgment of the asymmetric effects will help practitioners in luxury restaurants enhance their understandings of customer perceptions and efficiently improve service management and marketing.

scholarly journals Avatar Design of Virtual Salespeople: Mitigation of Recommendation Conflicts

2020 ◽  
pp. 109467052096487
Author(s):  
Yu-Ting Lin ◽  
Her-Sen Doong ◽  
Andreas B. Eisingerich
Keyword(s):  
Social Presence ◽  
Negative Impact ◽  
Negative Influence ◽  
Purchase Decisions ◽  
Negative Effects ◽  
Marketing Practice ◽  
Managerial Implication ◽  
Customer Reviews ◽  
Online Purchase

The role of virtual salesperson (VS) and the importance of customer reviews in facilitating online purchase decisions and sales have recently received much attention from technology companies, marketing practice professionals, and academics. However, customers’ willingness to follow the purchase advice of the VS when there is a conflict between these recommendations and those of other online customers is less understood. This research theorizes and investigates the extent to which customers’ relationship satisfaction with, and trust in, the VS helps explain customer willingness to follow VS advice in the context of recommendation conflict. Using four studies, our research explores how and when the VS’s avatar design mitigates the negative influence of conflict. An important theoretical and managerial implication of this research is that VS avatar designs that are high in automated social presence (ASP) help reduce the negative impact of conflict. However, we find that ASP mitigates the negative effects of conflict only for avatars that score low (vs. high) on cuteness.

Online customer reviews and consumer evaluation: The role of review font

2018 ◽  
Vol 55 (4) ◽  
pp. 430-440 ◽  
Author(s):  
Yunhui Huang ◽  
Changxin Li ◽  
Jiang Wu ◽  
Zhijie Lin
Keyword(s):  
Consumer Evaluation ◽  
Customer Reviews ◽  
Online Customer Reviews

Predicting the helpfulness of online customer reviews: The role of title features

2018 ◽  
Vol 62 (3) ◽  
pp. 272-287 ◽  
Author(s):  
Mina Akbarabadi ◽  
Monireh Hosseini
Keyword(s):  
Prediction Model ◽  
Real Life ◽  
Online Reviews ◽  
Machine Learning Techniques ◽  
Customer Reviews ◽  
Learning Techniques ◽  
Online Customer Reviews ◽  
Importance Analysis ◽  
Review Helpfulness

Nowadays, many people refer to online customer reviews that are available on most shopping websites to make a better purchase decision. An automated review helpfulness prediction model can help the websites to rank reviews based on their level of helpfulness. This study examines the effect of review title features on predicting the helpfulness of online reviews. Moreover, a new method is proposed to categorize action verbs in a review text. Text, reviewer, readability, and title features are the four main categories that are used in this article. We examine our proposed prediction model on two real-life Amazon datasets using machine learning techniques. The results show a promising performance of the model. However, feature importance analysis reveals the low importance of title features in the predictive model. It means that the title characteristics cannot be a powerful determinant of online review helpfulness. The results of this study can be beneficial to both buyers and website owners to have a deep insight into online reviews helpfulness.

Sentimental Analysis of Online Reviews Using Fuzzy Sets and Rough Sets

2017 ◽  
pp. 376-394
Author(s):  
Anuradha Jagadeesan ◽  
Amit Patil
Keyword(s):  
Fuzzy Sets ◽  
Sentiment Analysis ◽  
Rough Sets ◽  
Opinion Mining ◽  
Online Reviews ◽  
Purchase Decisions ◽  
Customer Reviews ◽  
Online Customer Reviews ◽  
Excellent Source ◽  
The Web

With the increased interest of online users in E-commerce, the web has become an excellent source for buying and selling of products online. Customer reviews on the web help potential customers to make purchase decisions, and for manufacturers to incorporate improvements in their product or develop new marketing strategies. The increase in customer reviews of a product influence the popularity and the sale rate of the product. This lead to a very important question about the analysis of the sentiments (opinions) expressed in the reviews. As such internet does not have any quality control over customer reviews and it could vary in terms of its quality. Also the trustworthiness of the online reviews is debatable. Sentiment Analysis (SA) or Opinion Mining is the computational analysis of opinions, sentiments, emotions and subjectivity of text. In this chapter, we take a look at the various research challenges and a new dimension involved in sentiment analysis using fuzzy sets and rough sets.

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