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Essays on Consumer Search and Decisi...

Xu, Ella Jiaming,

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Essays on Consumer Search and Decision-Making Process /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Essays on Consumer Search and Decision-Making Process // Ella Jiaming Xu.
Author:	Xu, Ella Jiaming,
Description:	1 electronic resource (103 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-11, Section: B.
Contained By:	Dissertations Abstracts International86-11B.
Subject:	Home economics. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31847368
ISBN:	9798314897065

Essays on Consumer Search and Decision-Making Process /
Xu, Ella Jiaming,

Essays on Consumer Search and Decision-Making Process /Ella Jiaming Xu. - 1 electronic resource (103 pages)

Source: Dissertations Abstracts International, Volume: 86-11, Section: B.

This dissertation contains two chapters that study consumer search and decision-making process. In the first essay, joint with Runshan Fu, Tulin Erdem, Raluca Ursu, and Bryan Bollinger, we develop an explainable deep learning model (Recurrent Neural Networks) based on theories of consumer search to capture the complexity of search behavior at the consideration stage. One feature of our model is that it flexibly allows learning about both product attributes and preference weights through search, rather than learning about only one of these primitives as assumed in prior work. We apply this model to a dataset of consumers searching for smartphones, which includes attribute-level eye-tracking data and search refinement tool usage. We find that our model improves the predictions of more flexible but atheoretical deep learning models and maintains high accuracy even for small data samples. In addition, we show that consumers mainly search to learn their preference weights early on and switch to learning product attributes about 40% into their search process. Finally, we derive a number of other managerially valuable insights into the formation of consideration sets. In the second essay, joint with Bryan Bollinger, Raluca Ursu, and Gavan Fitzsimons, we examine how changing product locations (as part of an exogenously-timed aisle reset) can disrupt habitual buying. Retailers have substantial ability to influence consumer purchase decisions through the placement of products in shopping aisles. Such influence can be amplified as a result of habitual buying behavior that leads to state dependence in choice (Thomadsen and Seetharaman, 2018). We pair household sales data with individual in-store eye-tracking measures, to show that 1) consumers search longer, are more likely to buy previously unpurchased products, and spend less time on each searched product in response to product location changes; 2) changing the locations of consumers' previously purchased products has spillover effects: consumers are more likely to search and buy products that are close to the old and the new location of their previously purchased products; 3) other in-store marketing strategies, e.g., price promotions, can moderate the effect of product location changes.

English

ISBN: 9798314897065Subjects--Topical Terms:

551902
Home economics.
Subjects--Index Terms:

Consumer decision-making process

Essays on Consumer Search and Decision-Making Process /
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520 $a This dissertation contains two chapters that study consumer search and decision-making process. In the first essay, joint with Runshan Fu, Tulin Erdem, Raluca Ursu, and Bryan Bollinger, we develop an explainable deep learning model (Recurrent Neural Networks) based on theories of consumer search to capture the complexity of search behavior at the consideration stage. One feature of our model is that it flexibly allows learning about both product attributes and preference weights through search, rather than learning about only one of these primitives as assumed in prior work. We apply this model to a dataset of consumers searching for smartphones, which includes attribute-level eye-tracking data and search refinement tool usage. We find that our model improves the predictions of more flexible but atheoretical deep learning models and maintains high accuracy even for small data samples. In addition, we show that consumers mainly search to learn their preference weights early on and switch to learning product attributes about 40% into their search process. Finally, we derive a number of other managerially valuable insights into the formation of consideration sets. In the second essay, joint with Bryan Bollinger, Raluca Ursu, and Gavan Fitzsimons, we examine how changing product locations (as part of an exogenously-timed aisle reset) can disrupt habitual buying. Retailers have substantial ability to influence consumer purchase decisions through the placement of products in shopping aisles. Such influence can be amplified as a result of habitual buying behavior that leads to state dependence in choice (Thomadsen and Seetharaman, 2018). We pair household sales data with individual in-store eye-tracking measures, to show that 1) consumers search longer, are more likely to buy previously unpurchased products, and spend less time on each searched product in response to product location changes; 2) changing the locations of consumers' previously purchased products has spillover effects: consumers are more likely to search and buy products that are close to the old and the new location of their previously purchased products; 3) other in-store marketing strategies, e.g., price promotions, can moderate the effect of product location changes.
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653 $a Consumer decision-making process
653 $a Consumer search
653 $a Deep learning
653 $a Machine learning
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720 1 $a Erdem, Tulin $e degree supervisor.
720 1 $a Fu, Runshan $e degree supervisor.
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