Decoding the hype about generative AI using text-mining and thematic analysis: a task-technology fit perspective

Purpose – This study aims to understand the hype behind Generative AI (GenAI) adoption using data from popular media, such as newspapers, magazines, expert opinions and podcasts. A robust text mining method analyzes data and maps results using the Task-Technology Fit (TTF) theory. Design/methodology/approach – The research uses a multi-method approach (text mining and thematic analysis) to analyze textual data from 703 articles retrieved from ProQuest using the netnography approach. Findings – The topics identified using structural topic modeling and thematic analysis were mapped onto the TTF theory. This led to the development of Generative AI Task-Technology Fit (GATTF), which extends TTF theory by two additional factors: consequences and external factors. Furthermore, sentiment analysis shows that users consider information generated by GenAI credible and positive. Research limitations/implications – The study uses secondary data limited to only English. GenAI has critical implications for policymakers in developing guidelines for controlling misuse and respecting copyright data. Originality/value – This study contributes to the growing literature on GenAI by analyzing a substantial amount of online textual data and extending the framework of the TTF theory.