Operationalizing the technology acceptance model with large language models: A framework for strategic insights from user reviews

Abstract

In the competitive digital service sector, leveraging user-generated data for strategic operational improvements is a critical engineering and management challenge. This study presents a business intelligence framework that integrates Artificial Intelligence (AI) with established management theory to operationalize technology acceptance drivers from unstructured text. We develop a systematic methodology that employs ChatGPT for theory-guided keyword generation to identify and measure the core constructs of the Technology Acceptance Model (TAM)—perceived ease of use, perceived usefulness, and Behavioral intention to use—within a massive dataset of 1,694,581 user reviews from leading US food delivery apps. Through a robust data processing pipeline incorporating sentiment analysis (VADER, AFINN) and Ordinary Least Squares (OLS) regression, we validate the framework’s efficacy, demonstrating that the AI-measured constructs explain 85.4% of the variance in users’ intention to use (R2 = 0.854, p < 0.001). The results indicate that user perceptions of ease of use (β = 0.29, p < 0.001) and usefulness (β = 0.51, p < 0.001) are significant predictors of adoption intention. This research provides a tangible, data-driven framework for managers and engineers to systematically diagnose user experience, prioritize feature development, and formulate product strategies. The proposed methodology offers a replicable, theory-AI integrated analytics pipeline for transforming unstructured textual data into actionable engineering and business intelligence, offering a pathway to connect large-scale data analytics with strategic management decision-making.