Scaling Technology Acceptance Analysis with Large Language Model (LLM) Annotation Systems

TL;DR

This paper demonstrates that large language models can effectively annotate user-generated content to predict technology acceptance, providing a scalable alternative to traditional surveys with high consistency and accuracy.

Contribution

The study introduces a novel LLM-based annotation system for technology acceptance analysis, validated through studies showing its reliability and agreement with human experts.

Findings

LLM annotations show moderate-to-strong consistency.

Lowering model temperature improves annotation accuracy.

LLM annotations outperform expert agreement for UTAUT variables.

Abstract

Technology acceptance models effectively predict how users will adopt new technology products. Traditional surveys, often expensive and cumbersome, are commonly used for this assessment. As an alternative to surveys, we explore the use of large language models for annotating online user-generated content, like digital reviews and comments. Our research involved designing an LLM annotation system that transform reviews into structured data based on the Unified Theory of Acceptance and Use of Technology model. We conducted two studies to validate the consistency and accuracy of the annotations. Results showed moderate-to-strong consistency of LLM annotation systems, improving further by lowering the model temperature. LLM annotations achieved close agreement with human expert annotations and outperformed the agreement between experts for UTAUT variables. These results suggest that LLMs can be an effective tool for analyzing user sentiment, offering a practical alternative to traditional survey methods and enabling deeper insights into technology design and adoption.