Online Shopping Intermediaries: The Strategic Design of Search Environments

Dukes, Anthony J.; Liu, Lin

doi:10.1287/mnsc.2015.2176

Cited by 92 publications

(42 citation statements)

References 19 publications

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“…The ad-slot model is related to the sponsored-search literature (Athey and Ellison, 2011;De Corniere, 2016;De Corniere and De Nijs, 2016). Dukes and Liu (2015) also model search design by letting the platform choose a search cost in a setting where consumers have to search in two dimensions, both between and within products. However, unlike our study, the authors do not explicit model targeted search, nor do they model the revenue models of the platform.…”

Section: Literature Reviewmentioning

confidence: 99%

Targeted Search and Platform Design

Zhong

2016

SSRN Journal

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Major online platforms such as Amazon and eBay have invested significantly in search technologies to direct consumer searches to relevant products. These technologies lead to targeted search, implying consumers are visiting more relevant sellers first. For example, consumers may directly enter their desirable attributes into search queries, and the platform will retrieve relevant sellers accordingly. The platform may also let consumers refine the search outcomes by various criteria. This

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Section: Literature Reviewmentioning

confidence: 99%

Targeted Search and Platform Design

Zhong

2016

SSRN Journal

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“…In these studies, consumers are assumed to either learn perfectly or receive an informative signal with exogenous precision. In addition, there is a stream of literature on partial product evaluation by consumers (e.g., Branco, Sun, and Villas-Boas 2012, 2016; Dukes and Liu 2015; Ke, Shen, and Villas-Boas 2016; Liu and Dukes 2016; Zhu and Dukes 2017). Branco, Sun, and Villas-Boas (2012, 2016) and Ke, Shen, and Villas-Boas (2016) consider a gradual learning model in which consumers incur search costs to learn product attribute information sequentially and characterize optimal stopping rules for either purchase or no purchase.…”

Section: Related Literaturementioning

confidence: 99%

Consumer Rational (In)Attention to Favorable and Unfavorable Product Information, and Firm Information Design

Jerath¹,

Ren²

2021

Journal of Marketing Research

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We study how a consumer optimally allocates attention to favorable and unfavorable information related to a product before purchasing it, when information processing is costly. We find that attention allocation depends on the consumer’s prior belief about whether the product matches her needs or not, and on her unit information processing cost. A consumer processes both “confirmatory” and “disconfirmatory” information to her prior belief, but to different degrees under different conditions. Generally speaking, if the consumer has an extreme prior, or if the unit cost of processing information is high such that only a small amount of information is optimally processed, she processes more confirmatory than disconfirmatory information; this offers a rational explanation for the phenomenon known as “confirmation bias.” We also find that a seller can benefit by influencing the consumer’s attention allocation by strategically choosing how much favorable and unfavorable information to make available for the consumer to process and by influencing the information processing cost, where the optimal strategy depends on the ability to adjust product price. Surprisingly, a seller has a lower incentive to suppress unfavorable information when the consumer has a worse prior belief about product fit. We illustrate our model with an application to information provision in product reviews.

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“…Janssen and Ke (2020) show that when consumers sequentially search sellers, in equilibrium some sellers may provide service to consumers despite the free riding from other sellers. Dukes and Liu (2016) examine a platform’s optimal choice for the consumers’ search cost when consumers decide on the number of sellers to simultaneously search and how deeply to evaluate each seller.…”

Section: Literature Reviewmentioning

confidence: 99%

Consumer Search and Filtering on Online Retail Platforms

Jiang¹,

Zou²

2020

Journal of Marketing Research

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This article examines how the consumer’s search cost and filtering on a retail platform affect the platform, the third-party sellers, and the consumers. The authors show that, given the platform’s percentage referral fee, a lower search cost can either increase or decrease the platform’s profit. By contrast, if the platform optimally adjusts its referral fee, a lower search cost will increase the platform’s profit. As the consumer’s search cost decreases, if the platform’s demand elasticity increases significantly, the platform should reduce its fee, potentially resulting in an all-win outcome for the platform, the sellers, and the consumers; otherwise, a lower search cost will increase the platform’s optimal fee percentage, potentially leading to higher equilibrium retail prices. Furthermore, the availability of filtering on the platform will in expectation induce consumers to search fewer products but buy products with higher match values, and filtering can either increase or decrease equilibrium retail prices. When filtering reveals only a small amount of the products’ match-value variations, it will benefit the platform, the sellers, and the consumers. This article shows that the effects of filtering and those of a decrease in search cost are qualitatively different.

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Online Shopping Intermediaries: The Strategic Design of Search Environments

Cited by 92 publications

References 19 publications

Targeted Search and Platform Design

Targeted Search and Platform Design

Consumer Rational (In)Attention to Favorable and Unfavorable Product Information, and Firm Information Design

Consumer Search and Filtering on Online Retail Platforms

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