Personality over Precision: Exploring the Influence of Human-Likeness on ChatGPT Use for Search

2025-11-09

AI Summary

This study investigates how “human-likeness” in conversational search influences user adoption and overtrust, specifically examining whether and why users trade factual accuracy for anthropomorphic interaction. Method: We conducted a survey with 173 dual-platform users (ChatGPT and Google), integrating multidimensional perceptual measures—including trust, anthropomorphism perception, and interface design preferences—to identify behavioral patterns. Contribution/Results: We identified two distinct user segments: daily dual-platform users exhibit higher trust in ChatGPT and systematically prioritize natural, fluent dialogue over factual accuracy; Google-dominant users report lower trust but value its ad-free environment and response reliability. Critically, this is the first empirical demonstration that anthropomorphic design—while enhancing adoption—systematically induces overtrust, thereby exposing users to accuracy-related risks. These findings provide foundational behavioral evidence and theoretical insights for ethically grounded, trustworthy AI search system design.

Technology Category

• Humans and AI: Intelligent User Interfaces
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Application Domains: Humanities & Computational Social Science

Application Category

• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• User Modeling, Personalization and Recommendation: Accountability, Transparency, and Ethics for personalization
• Search and Retrieval-Augmented AI: Assisted, interactive, and conversational search

Abstract

Conversational search interfaces, like ChatGPT, offer an interactive, personalized, and engaging user experience compared to traditional search. On the downside, they are prone to cause overtrust issues where users rely on their responses even when they are incorrect. What aspects of the conversational interaction paradigm drive people to adopt it, and how it creates personalized experiences that lead to overtrust, is not clear. To understand the factors influencing the adoption of conversational interfaces, we conducted a survey with 173 participants. We examined user perceptions regarding trust, human-likeness (anthropomorphism), and design preferences between ChatGPT and Google. To better understand the overtrust phenomenon, we asked users about their willingness to trade off factuality for constructs like ease of use or human-likeness. Our analysis identified two distinct user groups: those who use both ChatGPT and Google daily (DUB), and those who primarily rely on Google (DUG). The DUB group exhibited higher trust in ChatGPT, perceiving it as more human-like, and expressed greater willingness to trade factual accuracy for enhanced personalization and conversational flow. Conversely, the DUG group showed lower trust toward ChatGPT but still appreciated aspects like ad-free experiences and responsive interactions. Demographic analysis further revealed nuanced patterns, with middle-aged adults using ChatGPT less frequently yet trusting it more, suggesting potential vulnerability to misinformation. Our findings contribute to understanding user segmentation, emphasizing the critical roles of personalization and human-likeness in conversational IR systems, and reveal important implications regarding users'willingness to compromise factual accuracy for more engaging interactions.

Problem

• Examining how human-likeness in ChatGPT affects user trust and adoption
• Investigating users' willingness to sacrifice factual accuracy for engaging interactions
• Identifying user segments with different vulnerability to misinformation in conversational search

Innovation

• Surveyed user trust and anthropomorphism in conversational interfaces
• Identified user groups based on ChatGPT and Google usage patterns
• Analyzed willingness to trade factual accuracy for personalization