Quality Disclosure Strategy under Customer Learning Opportunities

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.

• 저널Production and Operations Management · 30(4) · 1136–1153
• 토픽Consumer Market Behavior and Pricing · Marketing
• DOI10.1111/poms.13295
• 저자Zhu Han, Yimin Yu, Saibal Ray