Consumer Search and Optimal Information

2018 ◽  
Author(s):  
Ju Hu ◽  
Mustafa Dogan
Keyword(s):  
Consumer Search ◽  
Optimal Information

Consumer Online Search with Partially Revealed Information

2021 ◽  
Author(s):  
Chris Gu ◽  
Yike Wang
Keyword(s):  
Outer Layer ◽  
Consumer Welfare ◽  
Search Costs ◽  
Consumer Search ◽  
Complexity Measure ◽  
Travel Agency ◽  
Information Complexity ◽  
Optimal Information ◽  
Online Travel Agency ◽  
Cognitive Cost

Modern-day search platforms generally have two layers of information presentation. The outer layer displays the collection of search results with attributes selected by platforms, and consumers click on a product to reveal all its attributes in the inner layer. The information revealed in the outer layer affects the search costs and the probability of finding a match. To address the managerial question of optimal information layout, we create an information complexity measure of the outer layer, namely orderedness entropy, and study the consumer search process for information at the expense of time and cognitive costs. We first conduct online random experiments to show that consumers respond to and actively reduce cognitive cost for which our information complexity measure provides a representation. Then, using a unique and rich panel tracking consumer search behaviors at a large online travel agency (OTA), we specify a novel sequential search model that jointly describes the refinement search and product clicking decisions. We find that cognitive cost is a major component of search cost, while loading time cost has a much smaller share. By varying the information revealed in the outer layer, we propose information layouts that Pareto-improve both revenue and consumer welfare for our OTA. This paper was accepted by Juanjuan Zhang, marketing.

Optimal Information Disclosure in Advance Selling

2016 ◽  
Author(s):  
Daniel Z. Li
Keyword(s):  
Information Disclosure ◽  
Advance Selling ◽  
Optimal Information

Consumer Search and Purchase: An Empirical Investigation of Search-Based Retargeting

2017 ◽  
Author(s):  
Zhenling Jiang ◽  
Tat Chan ◽  
Hai Che ◽  
Youwei Wang
Keyword(s):  
Empirical Investigation ◽  
Consumer Search

The Costly Quest for a Better Price - A Model of Consumer Search Beyond the Supermarket

2018 ◽  
Author(s):  
Chun-Hui mname Miao
Keyword(s):  
Consumer Search

Consumer Search with Limited Product Evaluation

2015 ◽  
Vol 25 (1) ◽  
pp. 32-55 ◽  
Author(s):  
Lin Liu ◽  
Anthony Dukes
Keyword(s):  
Product Evaluation ◽  
Consumer Search

scholarly journals Information Acquisition and Its Incentives in an E-Commerce Supply Chain under the Offline Showroom Model

2021 ◽  
Vol 16 (5) ◽  
pp. 1791-1804
Author(s):  
Mengli Li ◽  
Xumei Zhang
Keyword(s):  
Supply Chain ◽  
Information Acquisition ◽  
Incentive Contracts ◽  
Demand Forecast ◽  
Competition Intensity ◽  
Demand Information ◽  
Optimal Information ◽  
Acquisition Strategy ◽  
The One ◽  
The Impact

Recently, the showroom model has developed fast for allowing consumers to evaluate a product offline and then buy it online. This paper aims at exploring the optimal information acquisition strategy and its incentive contracts in an e-commerce supply chain with two competing e-tailers and an offline showroom. Based on signaling game theory, we build a mathematical model by considering the impact of experience service and competition intensity on consumers’ demand. We find that, on the one hand, information acquisition promotes supply chain members to obtain demand information directly or indirectly, which leads to forecast revenue. On the other hand, information acquisition promotes supply chain members to distort optimal decisions, which results in signal cost. The optimal information acquisition strategy depends on the joint impact of forecast revenue, signal cost and demand forecast cost. Notably, in some conditions, the offline showroom will not acquire demand information even when its cost is equal to zero. We also design two different information acquisition incentive contracts to obtain Pareto improvement for all supply chain members.

Consumer search with blind buying

2021 ◽  
Vol 126 ◽  
pp. 402-427
Author(s):  
Yanbin Chen ◽  
Sanxi Li ◽  
Kai Lin ◽  
Jun Yu
Keyword(s):  
Consumer Search
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