Predicting Advertising Persuasiveness: A Decision Tree Method for Understanding Emotional (In)Congruence of Ad Placement on YouTube

Taylor Jing Wen, Ching Hua Chuan, Jing Yang, Wanhsiu Tsai

Research output: Contribution to journalArticlepeer-review

Abstract

By applying the computational method of decision trees, this research identifies the most decisive attributes enhancing ad persuasiveness by examining the contextual effects of emotional (in)congruence on ad placement for music videos on YouTube. Findings of this interdisciplinary research not only evaluated key psychological constructs via a computational approach to predict persuasiveness but also extended the theoretical consideration of contextual (in)congruence into the domain of emotion. Methodologically, this study demonstrates the effectiveness of decision trees in exploratory theory testing. Practically, the predictive results from the decision tree model provide much needed strategic guidance to inform advertising design and evaluation for video-sharing websites.

Original languageEnglish (US)
JournalJournal of Current Issues and Research in Advertising
DOIs
StateAccepted/In press - 2021

ASJC Scopus subject areas

  • Marketing

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