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.