Reading Between the Stars: Understanding the Effects of Online Customer Reviews on Product Demand

情绪分析 相关性(法律) 产品(数学) 质量(理念) 阅读(过程) 营销 计算机科学 业务 人工智能 语言学 哲学 几何学 数学 认识论 政治学 法学
作者
Hallie S. Cho,Manuel E. Sosa,Sameer Hasija
出处
期刊:Manufacturing & Service Operations Management [Institute for Operations Research and the Management Sciences]
卷期号:24 (4): 1977-1996 被引量:29
标识
DOI:10.1287/msom.2021.1048
摘要

Problem definition: Many studies have examined quantitative customer reviews (i.e., star ratings) and found them to be a reliable source of information that has a positive effect on product demand. Yet the effect of qualitative customer reviews (i.e., text reviews) on demand has been less thoroughly studied, and it is not known whether (or how) the sentiment expressed in text reviews moderates the influence of star ratings on product demand. We are therefore led to examine how the interplay between review sentiment and star ratings affects product demand. Academic/practical relevance: Consumer perceptions of product quality and how they are shared via customer reviews are of extreme relevance to the firm, but we still do not understand how product demand is affected by the quantitative and qualitative aspects of customer reviews. Our paper seeks to fill this critical gap in the literature by analyzing star ratings, the sentiment of customer reviews, and their interaction. Methodology: Using 2002–2013 data for the U.S. automobile market, we investigate empirically the impact of star ratings and review sentiment on product demand. Thus, we estimate an aggregated multinomial choice model after performing a machine learning–based sentiment analysis on the entire corpus of customer reviews included in our sample. We take advantage of a quasi-exogenous shock to establish a causal link between online reviews and product demand. Results: We find robust empirical evidence that (i) review sentiment and star ratings both have a decreasingly positive effect on product demand and (ii) the effect (on demand) of their interaction suggests that the two components of reviews are complements. Positive sentiments in text reviews increase the positive effect of ratings when the effect of ratings is decidedly positive while they also compensate for the tendency of consumers to discount extremely high star ratings. Managerial implications: The firm should pay greater attention to quantitative and qualitative customer reviews to better understand how consumers perceive the quality of its offerings.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
蓝莓橘子酱应助wxb采纳,获得10
刚刚
酷炫依白发布了新的文献求助10
1秒前
半秋发布了新的文献求助30
1秒前
科研通AI6.1应助phy采纳,获得10
1秒前
胡子西瓜发布了新的文献求助10
1秒前
2秒前
2秒前
大力蚂蚁完成签到,获得积分10
2秒前
3秒前
上官若男应助binxman采纳,获得10
3秒前
北北贝贝发布了新的文献求助10
3秒前
Jsssds发布了新的文献求助10
3秒前
4秒前
maox1aoxin完成签到,获得积分0
4秒前
4秒前
科研通AI2S应助温婉的篮球采纳,获得10
5秒前
5度转角应助紧张的安梦采纳,获得10
6秒前
Su完成签到,获得积分10
6秒前
6秒前
6秒前
7秒前
炙热灵发布了新的文献求助10
7秒前
7秒前
酷炫依白完成签到,获得积分10
8秒前
buzhidao发布了新的文献求助10
8秒前
9秒前
小蘑菇应助二二采纳,获得10
10秒前
善学以致用应助肖子瑶采纳,获得10
10秒前
11秒前
yy完成签到,获得积分10
11秒前
11秒前
葡萄柚绿茶完成签到,获得积分10
12秒前
看不懂发布了新的文献求助10
12秒前
小蘑菇应助大宝君采纳,获得10
12秒前
ahosre发布了新的文献求助10
13秒前
wanci应助懒洋洋采纳,获得30
13秒前
13秒前
隐形的蓝天完成签到 ,获得积分20
13秒前
andrew12399完成签到,获得积分10
13秒前
打打应助依依采纳,获得10
14秒前
高分求助中
Modern Epidemiology, Fourth Edition 5000
Kinesiophobia : a new view of chronic pain behavior 5000
Molecular Biology of Cancer: Mechanisms, Targets, and Therapeutics 3000
Digital Twins of Advanced Materials Processing 2000
Propeller Design 2000
Weaponeering, Fourth Edition – Two Volume SET 2000
Handbook of pharmaceutical excipients, Ninth edition 1500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 纳米技术 化学工程 生物化学 物理 计算机科学 内科学 复合材料 催化作用 物理化学 光电子学 电极 冶金 细胞生物学 基因
热门帖子
关注 科研通微信公众号,转发送积分 6010243
求助须知:如何正确求助?哪些是违规求助? 7554159
关于积分的说明 16132890
捐赠科研通 5156869
什么是DOI,文献DOI怎么找? 2762080
邀请新用户注册赠送积分活动 1740633
关于科研通互助平台的介绍 1633366