Authors: J Mundel, J Yang
Year: 2025
Published in: Journal of Interactive Advertising, 2021 - Taylor & Francis
Institution: Arizona State University, Loyola University
Research Area: Consumer Behavior, Social Media Marketing, COVID-19 Studies
Discipline: Marketing, Social Science
Brands with strong perceived fit between their products and COVID-19 messaging showed higher consumer engagement and positive attitudes, while those with lower perceived fit faced negative evaluations due to perceptions of opportunism.
Methods: Analyzed consumer responses to Instagram ads using perceived brand-social issue fit as a determinant of ad evaluations, brand attitudes, and engagement intentions.
Key Findings: Consumer responses, ad evaluations, brand attitudes, engagement intentions, and perceived brand opportunism based on fit between product type and COVID-19 messaging.
DOI: https://www.tandfonline.com/doi/abs/10.1080/15252019.2021.1958274#
Citations: 29
Authors: B Lebrun, S Temtsin, A Vonasch
Year: 2024
Published in: Frontiers in Robotics and ..., 2024 - frontiersin.org
Institution: University of Lausanne, University of California Berkeley, University of Massachusetts Amherst, Arizona State University
Research Area: AI in Social Science Research, Survey Methodology, Data Quality
Discipline: Artificial Intelligence
The study examines the integrity of online questionnaire responses and concludes that humans can identify AI-generated text with 76% accuracy, but current AI detection systems are ineffective, raising concerns about data quality in online surveys.
Methods: Human participants and automatic AI detection systems were tested on their ability to differentiate AI-generated text from human-generated text in the context of online questionnaires.
Key Findings: The study measured the ability of humans and AI detection tools to correctly identify whether text was generated by a human or an AI system for online questionnaire responses.
DOI: https://doi.org/10.3389/frobt.2023.1277635
Citations: 26
