Browse 6 peer-reviewed papers from Monash University spanning Human-AI Interaction, Consumer Behavior (2023–2025). Research powered by Prolific's high-quality participant data.
This page lists 6 peer-reviewed papers from researchers at Monash University in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.
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Authors: A Misra, TD Dinh, SY Ewe
Year: 2025
Published in: British Food Journal, 2024 - emerald.com
Institution: Monash University
Research Area: Consumer Behavior, Social Media Marketing
Discipline: Marketing
The study found that the number of followers and content type of food influencers significantly shape consumer behavior in the social media context, highlighting their role in effective marketing strategies for the food industry.
Methods: Quantitative analysis examining the relationship between influencers' follower counts, content type, and consumer reactions using social media data.
Key Findings: Influencer follower count, type of content communicated by influencers, consumer behavior influenced by these variables.
DOI: https://doi.org/10.1108/BFJ-01-2024-0096
Citations: 30
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Authors: S de Jong, V Paananen, B Tag
Year: 2025
Published in: Proceedings of the ACM on ..., 2025 - dl.acm.org
Institution: Niels van Berkel: Aalborg University, Sander de Jong, Ville Paananen, Benjamin Tag: Monash University
Research Area: Cognitive Forcing, Human-AI Interaction, AI Explainability (XAI), Decision-Making in AI Systems.
Discipline: Human-Computer Interaction (HCI), Artificial Intelligence
Partial explanations encourage critical thinking and reduce user overreliance on incorrect AI suggestions, with performance varying based on individual need for cognition and task difficulty.
Methods: Two experiments were conducted: (1) participants identified shortest paths in weighted graphs, and (2) participants corrected spelling and grammar errors in text, with AI suggestions accompanied by no, partial, or full explanations.
Key Findings: Effectiveness of partial explanations in reducing overreliance on incorrect AI suggestions, and interaction of explanation type with task difficulty and user need for cognition.
DOI: https://doi.org/10.1145/3710946
Citations: 14
Sample Size: 474
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Authors: Y Ba, MV Mancenido, EK Chiou, R Pan
Year: 2025
Published in: Behavior Research Methods, 2025 - Springer
Institution: University of Delaware, National Taiwan University, University of British Columbia, Monash University
Research Area: Crowdsourcing, Data Quality, Spamming Behavior Detection, LLM Applications in Behavioral Research
Discipline: Computer Science, Artificial Intelligence, LLM
The paper introduces a systematic method to evaluate crowdsourced data quality and detect spam behaviors through variance decomposition, proposing a spammer index and credibility metrics to improve consistency and reliability in labeling tasks.
Methods: Variance decomposition, Markov chain models, and generalized random effects models were used to assess annotator consistency and credibility; metrics were applied to both simulated and real-world data from two crowdsourcing platforms.
Key Findings: Quality of crowdsourced data, spammer behaviors, annotators’ consistency, and credibility.
Citations: 2
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Authors: M Zhuang, E Deschrijver, R Ramsey, O Turel
Year: 2025
Published in: Scientific Reports, 2025 - nature.com
Institution: Monash University, The University of Melbourne, KU Leuven, California State University Fullerton
Research Area: Human-AI Interaction, Social Bias, Decision-Making
Discipline: Social Science, Human-AI Interaction
The study found that humans exhibit similar discriminatory behavior toward both AI and human agents, with resource allocation being influenced more by decision alignment than the recipient's identity.
Methods: A preregistered experiment was conducted where participants distributed resources between themselves and either human or AI agents based on dot estimation decisions.
Key Findings: Discriminatory behavior and resource allocation preferences toward AI and human agents as influenced by decision congruency.
DOI: https://doi.org/10.1038/s41598-025-94631-9
Sample Size: 500
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Authors: F Zanartu, J Cook, M Wagner, J Garcia
Year: 2024
Published in: ArXiv
Institution: Monash University, University of Melbourne
Research Area: Artificial Intelligence, Computational Social Science, Misinformation Detection, Fallacy Analysis in Climate Communication.
Discipline: Artificial Intelligence, Computational Social Science
Citations: 6
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Authors: S Zorowitz, J Solis, Y Niv, D Bennett
Year: 2023
Published in: Nature human behaviour, 2023 - nature.com
Institution: Princeton University, Rutgers University, Monash University
Research Area: Research Methodology, Behavioral Research, Experimental Psychology (focus on data quality and spurious correlations)
Discipline: Behavioral Science
DOI: https://doi.org/10.1038/s41562-023-01640-7
Citations: 110