Objective of the Study: To understand the variables influencing the adoption of ChatGPT for content creation by analyzing 227 user observations.
Methodology/Approach: The study used a quantitative approach, employing the Random Forest algorithm to analyze demographic and usage variables. Data were collected via an online survey and processed to develop a predictive model.
Originality/Relevance: This research addresses the adoption of generative AI in content creation, contributing to the literature on digital transformation and the ethical and reliability challenges associated with these technologies.
Main Results: The predictive model achieved 75% accuracy, 81.82% precision, and an area under the ROC curve of 0.85. Age was identified as the most important variable, followed by usage frequency and educational level.
Theoretical/Methodological Contributions: The study offers an innovative predictive analysis of the use of generative AI in content creation, utilizing Random Forest to reveal behavioral patterns in user adoption.
Social/Managerial Contributions: The findings provide insights for guiding marketing strategies and product development, enabling companies to tailor interfaces for users more likely to adopt ChatGPT, while addressing ethical concerns in AI-generated content.
Objective of the Study: To understand the variables influencing the adoption of ChatGPT for content creation by analyzing 227 user observations. Methodology/Approach: The study used a quantitative approach, employing the Random Forest algorithm to analyze demographic and usage variables. Data were collected via an online survey and processed to develop a predictive model. Originality/Relevance: This research addresses the adoption of generative AI in content creation, contributing to the literature on digital transformation and the ethical and reliability challenges associated with these technologies. Main Results: The predictive model achieved 75% accuracy, 81.82% precision, and an area under the ROC curve of 0.85. Age was identified as the most important variable, followed by usage frequency and educational level. Theoretical/Methodological Contributions: The study offers an innovative predictive analysis of the use of generative AI in content creation, utilizing Random Forest to reveal behavioral patterns in user adoption. Social/Managerial Contributions: The findings provide insights for guiding marketing strategies and product development, enabling companies to tailor interfaces for users more likely to adopt ChatGPT, while addressing ethical concerns in AI-generated content. Read More