國立虎尾科技大學 |

Theoretical and Empirical Explorations of Influencer Marketing.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Theoretical and Empirical Explorations of Influencer Marketing./
作者:	Tian, Zijun.
面頁冊數:	1 online resource (147 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
Contained By:	Dissertations Abstracts International84-12B.
標題:	Web studies. -
電子資源:	click for full text (PQDT)
ISBN:	9798379755775

Theoretical and Empirical Explorations of Influencer Marketing.
Tian, Zijun.

Theoretical and Empirical Explorations of Influencer Marketing. - 1 online resource (147 pages)

Source: Dissertations Abstracts International, Volume: 84-12, Section: B.

Thesis (Ph.D.)--University of Pennsylvania, 2023.

Includes bibliographical references

In my dissertation, I explore different aspects of influencer marketing and provide managerial implications on how influencer marketing can become more effective. In particular, which influencers should firms sponsor and what influencer-generated content can better leverage the viral dynamics on social media? In the first chapter, I strategically model the firm's cooperation with honest influencers, analyze when the firm benefits from such cooperation and its optimal "design" of the influencer's credible message through quality revelation and product selection. I show that the firm can asymmetrically gain from the uncertainties introduced through the influencer's message that shift the consumers' beliefs in its preferred way. Noisier message increases the firm's profit when sponsoring mediocre products. In the second chapter, I study the recently hot debate between sponsoring mega vs. micro influencers, and in particular, offer firms an important metric to consider for optimizing their influencer selection strategies: the follower elasticity of impressions (FEI). Computing FEI involves estimating the causal effect of an influencer's popularity on the view counts of their videos, which I achieve through a combination of a unique dataset collected from TikTok, a representation learning model for quantifying video content, and a machine learning-based causal inference method. I find that on average, FEI is always positive, but often nonlinear with respect to the number of followers. Then, I examine the factors that predict variation in these FEI curves, and show how firms can use these heterogeneous FEIs to better determine influencer partnerships. In general, one the one hand, my results challenge common firm strategies of sponsoring very popular influencers, and offer them alternative, data-driven strategies for optimizing their influencer selection based on the advertised content. On the other hand, I also highlight the similarities as well as the differences between influencer marketing and traditional advertising (mostly TV).

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379755775Subjects--Topical Terms:

1148502
Web studies.
Subjects--Index Terms:

Bayesian persuasionIndex Terms--Genre/Form:

554714
Electronic books.

Theoretical and Empirical Explorations of Influencer Marketing.
LDR:03495ntm a22004217 4500 001 1145249
005 20240618081759.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798379755775
035 $a (MiAaPQ)AAI30424851
035 $a AAI30424851
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Tian, Zijun. $3 1470504
245 1 0 $a Theoretical and Empirical Explorations of Influencer Marketing.
264 0 $c 2023
300 $a 1 online resource (147 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 84-12, Section: B.
500 $a Advisor: Dew, Ryan;Iyengar, Raghuram.
502 $a Thesis (Ph.D.)--University of Pennsylvania, 2023.
504 $a Includes bibliographical references
520 $a In my dissertation, I explore different aspects of influencer marketing and provide managerial implications on how influencer marketing can become more effective. In particular, which influencers should firms sponsor and what influencer-generated content can better leverage the viral dynamics on social media? In the first chapter, I strategically model the firm's cooperation with honest influencers, analyze when the firm benefits from such cooperation and its optimal "design" of the influencer's credible message through quality revelation and product selection. I show that the firm can asymmetrically gain from the uncertainties introduced through the influencer's message that shift the consumers' beliefs in its preferred way. Noisier message increases the firm's profit when sponsoring mediocre products. In the second chapter, I study the recently hot debate between sponsoring mega vs. micro influencers, and in particular, offer firms an important metric to consider for optimizing their influencer selection strategies: the follower elasticity of impressions (FEI). Computing FEI involves estimating the causal effect of an influencer's popularity on the view counts of their videos, which I achieve through a combination of a unique dataset collected from TikTok, a representation learning model for quantifying video content, and a machine learning-based causal inference method. I find that on average, FEI is always positive, but often nonlinear with respect to the number of followers. Then, I examine the factors that predict variation in these FEI curves, and show how firms can use these heterogeneous FEIs to better determine influencer partnerships. In general, one the one hand, my results challenge common firm strategies of sponsoring very popular influencers, and offer them alternative, data-driven strategies for optimizing their influencer selection based on the advertised content. On the other hand, I also highlight the similarities as well as the differences between influencer marketing and traditional advertising (mostly TV).
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Web studies. $3 1148502
650 4 $a Multimedia communications. $3 655342
653 $a Bayesian persuasion
653 $a Causal inference
653 $a Deep learning
653 $a Influencer marketing
653 $a Information design
653 $a Video data
655 7 $a Electronic books. $2 local $3 554714
690 $a 0338
690 $a 0558
690 $a 0646
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of Pennsylvania. $b Economics. $3 1179499
773 0 $t Dissertations Abstracts International $g 84-12B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30424851 $z click for full text (PQDT)