國立虎尾科技大學 |

Essays on Dynamic Updating of Consumer Preferences.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Essays on Dynamic Updating of Consumer Preferences./
作者:	Lu, Tong.
面頁冊數:	1 online resource (154 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-10(E), Section: A.
Contained By:	Dissertation Abstracts International79-10A(E).
標題:	Marketing. -
電子資源:	click for full text (PQDT)
ISBN:	9780438036840

Essays on Dynamic Updating of Consumer Preferences.
Lu, Tong.

Essays on Dynamic Updating of Consumer Preferences. - 1 online resource (154 pages)

Source: Dissertation Abstracts International, Volume: 79-10(E), Section: A.

Thesis (Ph.D.)--University of Pennsylvania, 2018.

Includes bibliographical references

Consumers dynamically update their preferences over time based on information learned through product search and consumption experiences, particularly in online media. Using three unique datasets from different domains, we address specific ways in which firms can use rich information about their customers' behaviors to improve (1) the visual display of products on a webpage in online shopping, (2) predictions of new product adoption in online gaming, and (3) the timing of product release in online learning. First, we explore how consumers visually search through product options using eye-tracking data from two experiments conducted on the websites of two online clothing stores, which can inform retailers on how to position products on a virtual webpage. Second, we examine how consumers' variety-seeking preferences change depending on past consumption outcomes within the context of an online multi-player video game, which can be used to improve predictions of new product adoption. Third, we use clickstream data from an online education platform to test theories of goal progress, knowledge accumulation, and boundedly rational forward-looking behavior, which can be used to explain binge consumption patterns and inform content providers on the best way to structure and release content. In each of these three projects, we build a mathematical model of individual decisions, with the parameterization grounded in theories of consumer behavior, and we demonstrate through in-sample prediction that our model is able to capture specific heterogeneous patterns within the data. We then test that our model is able to make out-of-sample predictions related to managerial interventions, and empirically verify our predictions using either lab experiments or new field data following a natural experiment policy change.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438036840Subjects--Topical Terms:

557931
Marketing.
Index Terms--Genre/Form:

554714
Electronic books.

Essays on Dynamic Updating of Consumer Preferences.
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245 1 0 $a Essays on Dynamic Updating of Consumer Preferences.
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500 $a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: A.
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502 $a Thesis (Ph.D.)--University of Pennsylvania, 2018.
504 $a Includes bibliographical references
520 $a Consumers dynamically update their preferences over time based on information learned through product search and consumption experiences, particularly in online media. Using three unique datasets from different domains, we address specific ways in which firms can use rich information about their customers' behaviors to improve (1) the visual display of products on a webpage in online shopping, (2) predictions of new product adoption in online gaming, and (3) the timing of product release in online learning. First, we explore how consumers visually search through product options using eye-tracking data from two experiments conducted on the websites of two online clothing stores, which can inform retailers on how to position products on a virtual webpage. Second, we examine how consumers' variety-seeking preferences change depending on past consumption outcomes within the context of an online multi-player video game, which can be used to improve predictions of new product adoption. Third, we use clickstream data from an online education platform to test theories of goal progress, knowledge accumulation, and boundedly rational forward-looking behavior, which can be used to explain binge consumption patterns and inform content providers on the best way to structure and release content. In each of these three projects, we build a mathematical model of individual decisions, with the parameterization grounded in theories of consumer behavior, and we demonstrate through in-sample prediction that our model is able to capture specific heterogeneous patterns within the data. We then test that our model is able to make out-of-sample predictions related to managerial interventions, and empirically verify our predictions using either lab experiments or new field data following a natural experiment policy change.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
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650 4 $a Statistics. $3 556824
650 4 $a Education. $3 555912
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