Arizona State University: Research Institution — Prolific Citations Library

Browse 2 peer-reviewed papers from Arizona State University spanning Consumer Behavior, Social Media Marketing (2024–2025). Research powered by Prolific's high-quality participant data.

This page lists 2 peer-reviewed papers from researchers at Arizona State University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (2 of 2)

Consumer engagement with brands' COVID-19 messaging on social media: the role of perceived brand-social issue fit and brand opportunism

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 Sciences

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
Detecting the corruption of online questionnaires by artificial intelligence

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