Information Science: Academic Discipline — Prolific Citations Library

Explore 6 peer-reviewed papers in Information Science (2021–2025). Academic research using Prolific for high-quality human data collection.

This page lists 6 peer-reviewed papers in the discipline of Information Science in the Prolific Citations Library, a curated collection of research powered by high-quality human data from Prolific.

Papers (6 of 6)

The effect of popularity cues and peer endorsements on assertive social media ads

Authors: A Agarwal, SY Lee

Year: 2025

Published in: Information Systems ..., 2025 - pubsonline.informs.org

Institution: University of Texas

Research Area: Information Systems, Behavioral Economics, Social Media Marketing, Advertising

Discipline: Information Systems Research, Marketing, Behavioral Science

This academic article explores a specific topic within the field of Information Systems Research.

Citations: 14
What's in the black box? How algorithmic knowledge promotes corrective and restrictive actions to counter misinformation in the USA, the UK, South Korea and Mexico

Authors: M Chung

Year: 2025

Published in: Internet Research, 2023 - emerald.com

Institution: University of Washington, Emory University

Research Area: Algorithmic Knowledge, Misinformation Countermeasures, Comparative Media Studies, Information Science

Discipline: Information Science

The study examines how algorithmic knowledge influences attitudes and actions against misinformation, revealing that perceptions of media influence on self and others predict corrective actions and support for regulation differently across four countries.

Methods: Four national surveys were conducted in the USA, UK, South Korea, and Mexico, with data analyzed through multigroup structural equation modeling (SEM).

Key Findings: Algorithmic knowledge, perceived influence of misinformation on self and others, intention to correct misinformation, support for regulation and content moderation.

DOI: https://doi.org/10.1108/INTR-07-2022-0578

Citations: 14

Sample Size: 5432
When Content is Goliath and Algorithm is David: The Style and Semantic Effects of Generative Search Engine

Authors: L Ma, J Qin, X Xu, Y Tan

Year: 2025

Published in: arXiv preprint arXiv:2509.14436, 2025•arxiv.org

Institution: University of North Carolina Charlotte, University of Science and Technology of China, University of Washington

Research Area: LLM behavior, Algorithmic content preference, Human–AI interaction

Discipline: Computer Science, Information Retrieval, Artificial Intelligence

This paper studies how generative search engines that use large language models (LLMs)—like Google’s AI overviews—select and cite web content, showing that these engines prefer content that is more predictable and semantically coherent for the model, and that LLM-based content polishing can increase the diversity and usefulness of AI summaries for users.

DOI: https://doi.org/10.48550/arXiv.2509.14436
Fairness in algorithmic management: Bringing platform-workers into the fold

Authors: N Jabagi, AM Croteau, L Audebrand, J Marsan

Year: 2024

Published in: 2024 - aisel.aisnet.org

Institution: FSA ULaval, JMSB, Concordia

Research Area: Algorithmic management, Perceived fairness, Platform work, Information Systems

Discipline: Information Systems, Social Science

DOI: https://aisel.aisnet.org/hicss-57/cl/ai_and_future_work/5

Citations: 8
Examining the moderating role of online celebrity trustworthiness and risk propensity in UTAUT2 framework: A mixed-method approach

Authors: Ruturaj Baber, Prerana Baber, Sumit Narula

Year: 2024

Published in: Science Direct

Institution: Amity University Haryana, Christ University, Jiwaji University

Research Area: Online Celebrity Influence, Technology Acceptance (UTAUT2), Consumer Behavior

Discipline: Information Systems, Behavioral Science, Marketing
Large language models and the wisdom of small crowds

Authors: S Trott

Year: 2021

Published in: Open Mind, 2024 - direct.mit.edu

Institution: Stanford University, Microsoft Research

Research Area: LLMs in Social Science Research, Crowdworking, Human Behavior Simulation

Discipline: Artificial Intelligence, Social Science, Information Systems

Citations: 22