Personality-Driven Innovation Adoption: Modeling ChatGPT Diffusion with BERT and Random Forest

Abstract

In innovation adoption, individuals' decision-making process is shaped by innovation’s attributes and is primarily affected by their unique personality traits, which have a bearing on their perception levels. This current study introduces a novel personality-driven innovatin adoption model that combines Rogers’ diffusion theory with the OCEAN (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) personality model. Our approach accounts for the significante influence of individual differences in adoption decisions by using personalized features. Our approach accounts for the significante influence of individual differences in adoption decisions by using personalized features. We analyze ChatGPT adoption using a dataset of 38,939 tweets and user metadata after preprocessing. A BERT-Random Forest model predicts users' Big Five traits, which help refine innovation attributes such as relative advantage, uncertainty, and acceptability, ensuring the adoption process aligns with psychological factors. This proposed framework holds significant potential for forecasting individual adoption behaviors by acknowledging the relevance of individual psychological traits in the decision-making process. Furthermore, We compare our personality prediction approach with baseline models including LSTM + Random Forest, BiLSTM + Random Forest, and RoBERTa + Random Forest, and show that BERT + RF achieves the most realistic personality feature estimates. Incorporating personality-driven and recalculated perceived innovation attributes enhances the model's ability to simulate real-world adoption patterns, providing a deeper understanding of how innovations diffuse in social networks.