Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/177872
Full metadata record
DC FieldValueLanguage
dc.contributor.authorKoh, Jing Hanen_US
dc.date.accessioned2024-06-03T00:59:03Z-
dc.date.available2024-06-03T00:59:03Z-
dc.date.issued2024-
dc.identifier.citationKoh, J. H. (2024). Faces in advertising: exploring the interplay of attractiveness, realness categorisation, and purchase intentions. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177872en_US
dc.identifier.urihttps://hdl.handle.net/10356/177872-
dc.description.abstractWhile digital ad spending is projected to soar in Singapore, a new player emerges – artificial intelligence. Powerful AI tools like Generative Adversarial Networks (GANs) promise significant cost savings in content creation. Particularly, the emergence of hyper realistic AI-generated faces may have the potential to upend the marketing industry. Past research has shown that faces are influential in advertising, with attractive faces increasing purchase intentions (PI), especially with paired with a related product. But can AI replicate this effect? To understand this, our study examined how FA, stimuli type (AI/Real faces), and participant categorisation (perceived AI/Real) affect PI. Employing a combination of Pixlr-generated AI faces and faces from an online research database, we presented these stimuli to 174 Singaporean participants (primarily Chinese undergraduates from NTU). PI was measured using a 4-item Purchase Intentions measure (PIMA). Our analyses (multiple regression and repeated-measures ANOVA) produced a surprising outcome: no significant difference in PI between AI and human faces in ads. Interestingly, even awareness of the manipulation (AI/Real) did not alter this effect. No interaction effects were found. However, FA remained significant: more attractive faces – whether AI or real – increased PI. Demographic factors, such as race and gender, subtly influenced PI, adding additional complexity to ad outcomes. Through this exploration, we hope to inform marketers on the suitability of cost-effective generative strategies as advertising alternatives. Future research may explore demographic influences on PI towards AI-aided ads and work towards validating these findings in real-world applications, such as through A/B testing.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.subjectComputer and Information Scienceen_US
dc.subjectSocial Sciencesen_US
dc.titleFaces in advertising: exploring the interplay of attractiveness, realness categorisation, and purchase intentionsen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorXu Hongen_US
dc.contributor.schoolSchool of Social Sciencesen_US
dc.description.degreeBachelor's degreeen_US
dc.contributor.supervisoremailXUHONG@ntu.edu.sgen_US
dc.subject.keywordsAdvertisementsen_US
dc.subject.keywordsAI facesen_US
dc.subject.keywordsFacial attractivenessen_US
dc.subject.keywordsPurchase intentionsen_US
dc.subject.keywordsMarketingen_US
dc.subject.keywordsAIen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
Appears in Collections:SSS Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
Lib-KJH_FYP Final.pdf
  Restricted Access
8.96 MBAdobe PDFView/Open

Page view(s)

37
Updated on Jul 24, 2024

Download(s)

1
Updated on Jul 24, 2024

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.