University Of St Gallen: Research Institution — Prolific Citations Library

Browse 2 peer-reviewed papers from University Of St Gallen spanning LLMs in Persuasion, Meta-Analysis (2024–2025). Research powered by Prolific's high-quality participant data.

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

Papers (2 of 2)

A meta-analysis of the persuasive power of large language models

Authors: L Hölbling, S Maier, S Feuerriegel

Year: 2025

Published in: Scientific Reports, 2025 - nature.com

Institution: University of Lausanne, University of Zurich, University of St. Gallen

Research Area: LLMs in Persuasion, Meta-Analysis, Artificial Intelligence, Human-Computer Interaction (HCI)

Discipline: Artificial Intelligence

Large language models (LLMs) demonstrate similar persuasive performance to humans overall, but their effectiveness varies widely based on contextual factors such as model type, conversation design, and domain.

Methods: Systematic review and meta-analysis using Hedges' g to compute standardized effect sizes, with exploratory moderator analyses and publication bias checks (Egger's test, trim-and-fill analysis).

Key Findings: The persuasive effectiveness of LLMs compared to humans across various contexts and studies.

Sample Size: 17422
Perceived algorithmic fairness: An empirical study of transparency and anthropomorphism in algorithmic recruiting

Authors: J Ochmann, L Michels, V Tiefenbeck

Year: 2024

Published in: Information Systems ..., 2024 - Wiley Online Library

Institution: University of St. Gallen, Technische Universität München, ETH Zürich

Research Area: Algorithmic Fairness in Recruiting, Human-Algorithm Interaction, Transparency, Anthropomorphism.

Discipline: Information Systems

The study explores how transparency and anthropomorphism influence applicants' perceptions of algorithmic fairness in recruiting, revealing justice dimensions that shape these perceptions.

Methods: An online application scenario with eight experimental groups analyzing fairness perceptions using a stimulus-organism-response framework and organizational justice theory.

Key Findings: Perceptions of algorithmic fairness based on justice dimensions (procedural, distributive, interpersonal, and informational justice) and the impact of transparency and anthropomorphism interventions.

DOI: https://doi.org/10.1111/isj.12482

Citations: 65

Sample Size: 801