亲爱的研友该休息了!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您度过漫漫科研夜!身体可是革命的本钱,早点休息,好梦!

Quality Disclosure Strategy under Customer Learning Opportunities

质量(理念) 采购 灵活性(工程) 人气 业务 营销 产品(数学) 经济 几何学 心理学 数学 社会心理学 认识论 哲学 管理
作者
Zhu Han,Yimin Yu,Saibal Ray
出处
期刊:Production and Operations Management [Wiley]
卷期号:30 (4): 1136-1153 被引量:24
标识
DOI:10.1111/poms.13295
摘要

For experience goods (products or services), given the uncertainty about their actual quality and the growing popularity of social media, potential customers nowadays depend on experiences of peers who have used the goods previously to learn about their quality. In this paper, we study how such customer learning affects a firm's (credible) quality disclosure strategy as well as other relevant decisions. To model such learning, we adopt the anecdotal reasoning framework, which we show to be rational and a special case of the Bayesian framework. There are two main insights that we glean from this study. First, we find that the incorporation of the learning behavior significantly alters the optimal disclosure strategy from its single threshold structure in the extant literature to a multi‐threshold policy. Specifically, firms with high‐ or low‐quality goods prefer not disclosing quality information in order to utilize the pricing flexibility that such a strategy affords; on the other hand, a medium‐quality firm might disclose its quality level, even though this hinders its pricing flexibility, so that customers are confident about it when purchasing the product. Second, we show that the change in the disclosure strategy impacts the optimal pricing decision, which can be non‐monotone in the quality level. Our results suggest that when disclosure is expensive, high‐quality firms are better off educating potential customers through advertising or social media, rather than disclosing their quality levels. They also suggest to policymakers that mandatory quality disclosure may not be socially optimal as more customers obtain quality information through peer learning. Our findings are robust and hold true under quite general customer valuation distributions, in capacitated settings and even when price can be used as a signal of quality level by firms.
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
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

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
杨怂怂完成签到 ,获得积分10
7秒前
11秒前
17秒前
朱朱子完成签到 ,获得积分10
1分钟前
Akim应助科研通管家采纳,获得10
1分钟前
GingerF应助科研通管家采纳,获得100
1分钟前
传奇3应助科研通管家采纳,获得10
1分钟前
1分钟前
1分钟前
1分钟前
Plum22发布了新的文献求助20
1分钟前
HAG完成签到,获得积分20
1分钟前
Perry完成签到,获得积分10
2分钟前
江枫渔火完成签到 ,获得积分10
2分钟前
MchemG应助科研通管家采纳,获得10
3分钟前
闫雪完成签到,获得积分10
3分钟前
3分钟前
闫雪发布了新的文献求助10
3分钟前
Plum22发布了新的文献求助20
3分钟前
直觉应助闫雪采纳,获得10
3分钟前
蚂蚁踢大象完成签到 ,获得积分10
4分钟前
hgsgeospan完成签到,获得积分10
4分钟前
直率的笑翠完成签到 ,获得积分10
5分钟前
hgs完成签到,获得积分10
5分钟前
5分钟前
MchemG应助科研通管家采纳,获得10
5分钟前
JamesPei应助科研通管家采纳,获得10
5分钟前
隐形曼青应助科研通管家采纳,获得10
5分钟前
Kevin完成签到,获得积分10
6分钟前
6分钟前
辉哥发布了新的文献求助10
6分钟前
6分钟前
6分钟前
董可以发布了新的文献求助10
6分钟前
英俊的铭应助董可以采纳,获得10
6分钟前
curtain完成签到,获得积分10
7分钟前
大个应助科研通管家采纳,获得10
7分钟前
MchemG应助科研通管家采纳,获得10
7分钟前
所所应助科研通管家采纳,获得10
7分钟前
科研通AI2S应助科研通管家采纳,获得10
7分钟前
高分求助中
The Mother of All Tableaux: Order, Equivalence, and Geometry in the Large-scale Structure of Optimality Theory 3000
A new approach to the extrapolation of accelerated life test data 1000
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
基于可调谐半导体激光吸收光谱技术泄漏气体检测系统的研究 350
Robot-supported joining of reinforcement textiles with one-sided sewing heads 320
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 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3990105
求助须知:如何正确求助?哪些是违规求助? 3532119
关于积分的说明 11256456
捐赠科研通 3271016
什么是DOI,文献DOI怎么找? 1805171
邀请新用户注册赠送积分活动 882288
科研通“疑难数据库(出版商)”最低求助积分说明 809228