University Of Bern: Research Institution — Prolific Citations Library

Browse 2 peer-reviewed papers from University Of Bern spanning Advertising, Persuasion Strategies (2024–2025). Research powered by Prolific's high-quality participant data.

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

Papers (2 of 2)

LLM-Generated Ads: From Personalization Parity to Persuasion Superiority

Authors: E Meguellati, S Civelli, L Han, A Bernstein

Year: 2025

Published in: arXiv preprint arXiv ..., 2025 - arxiv.org

Institution: Oregon Health Sciences University, Oregon University of California, Irvine, Han Institute, NYU School of Law, Bernstein Research

Research Area: Advertising, Persuasion Strategies, Human-AI Interaction in Content Generation

Discipline: Artificial Intelligence

LLM-generated advertisements achieved parity with human-written ads in personalization and demonstrated superiority in persuasion using psychological principles, outperforming human ads even when AI-origin detection impacted results.

Methods: Two-part study: First examined LLM personalization based on personality traits; second tested psychological persuasion principles using universal messages across authority, consensus, cognition, and scarcity.

Key Findings: Effectiveness of LLM-generated ads in personalization and persuasive storytelling compared to human-created ads.

Sample Size: 1200
Employees adhere more to unethical instructions from human than AI supervisors: Complementing experimental evidence with machine learning

Authors: L Lanz, R Briker, FH Gerpott

Year: 2024

Published in: Journal of Business Ethics, 2024 - Springer

Institution: University of Lausanne, University of Neuchâtel, University of Bern

Research Area: AI Ethics, Organizational Behavior, Supervisory Influence in the Workplace

Discipline: Business Ethics, Organizational Behavior, Artificial Intelligence Ethics

Employees are less likely to adhere to unethical instructions from AI supervisors compared to human supervisors, partly due to perceived differences in 'mind' and individual characteristics like compliance tendency and age.

Methods: The study employed four experiments using causal forest and transformer-based machine learning algorithms, as well as pre-registered experimental manipulations to evaluate employee behavior towards unethical instructions from AI and human supervisors.

Key Findings: Adherence to unethical instructions from AI versus human supervisors; mediating role of perceived mind and moderating factors like compliance tendency and age.

DOI: https://doi.org/10.1007/s10551-023-05393-1

Citations: 72

Sample Size: 1701