國立虎尾科技大學 |

Strategic Decisions at the Intersection of AI and Marketing /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Strategic Decisions at the Intersection of AI and Marketing // Evelyn Olivia Smith.
Author:	Smith, Evelyn Olivia,
Description:	1 electronic resource (96 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-01, Section: B.
Contained By:	Dissertations Abstracts International86-01B.
Subject:	Statistics. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31298731
ISBN:	9798383227206

Strategic Decisions at the Intersection of AI and Marketing /
Smith, Evelyn Olivia,

Strategic Decisions at the Intersection of AI and Marketing /Evelyn Olivia Smith. - 1 electronic resource (96 pages)

Source: Dissertations Abstracts International, Volume: 86-01, Section: B.

Artificial Intelligence (AI) systems are widely adopted by firms to automate tasks such as recommending content, creating advertisements, and conducting marketing research. The primary objective of this research is to broaden our understanding of the interactions among firms, consumers, and AI. This objective is achieved through the development and analysis of a game theory model of firm and consumer reactions to an algorithmic policy, and through the analysis of portrait graphics created by generative AI. Modeling the strategic behaviors of content firms and consumers, I show that an algorithmic constraint requiring recommendation equality between groups can benefit consumers from both groups, increase AI learning investment, or inadvertently harm the consumers it aims to protect. Using generative AI as an example, I empirically show how AI exhibits single-identity and intersectional biases in the depiction of portraits for different occupations. The research extends the marketing literature through theoretical contributions and provides implications of marketing decisions for firms and users interacting with AI systems.

English

ISBN: 9798383227206Subjects--Topical Terms:

556824
Statistics.
Subjects--Index Terms:

Content recommendation

Strategic Decisions at the Intersection of AI and Marketing /
LDR:02616nam a22004453i 4500 001 1157799
005 20250603111417.5
006 m o d
007 cr|nu||||||||
008 250804s2024 miu||||||m |||||||eng d
020 $a 9798383227206
035 $a (MiAaPQD)AAI31298731
035 $a AAI31298731
040 $a MiAaPQD $b eng $c MiAaPQD $e rda
100 1 $a Smith, Evelyn Olivia, $e author. $3 1484073
245 1 0 $a Strategic Decisions at the Intersection of AI and Marketing / $c Evelyn Olivia Smith.
264 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 1 electronic resource (96 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: 86-01, Section: B.
500 $a Advisors: Shulman, Jeffrey D. Committee members: Sayedi, Amin; Mummalaneni, Simha.
502 $b Ph.D. $c University of Washington $d 2024.
520 $a Artificial Intelligence (AI) systems are widely adopted by firms to automate tasks such as recommending content, creating advertisements, and conducting marketing research. The primary objective of this research is to broaden our understanding of the interactions among firms, consumers, and AI. This objective is achieved through the development and analysis of a game theory model of firm and consumer reactions to an algorithmic policy, and through the analysis of portrait graphics created by generative AI. Modeling the strategic behaviors of content firms and consumers, I show that an algorithmic constraint requiring recommendation equality between groups can benefit consumers from both groups, increase AI learning investment, or inadvertently harm the consumers it aims to protect. Using generative AI as an example, I empirically show how AI exhibits single-identity and intersectional biases in the depiction of portraits for different occupations. The research extends the marketing literature through theoretical contributions and provides implications of marketing decisions for firms and users interacting with AI systems.
546 $a English
590 $a School code: 0250
650 4 $a Statistics. $3 556824
653 $a Content recommendation
653 $a Empirical analysis
653 $a Game theory
653 $a Generative AI
653 $a Quantitative
690 $a 0338
690 $a 0501
690 $a 0463
690 $a 0800
710 2 $a University of Washington. $b Business - Foster School of. $3 1437687
720 1 $a Shulman, Jeffrey D. $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-01B.
790 $a 0250
791 $a Ph.D.
792 $a 2024
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31298731