Abstract
This study examines how Generative AI discourse diffuses across knowledge-based industries during the early adoption phase following ChatGPT's release, using Diffusion of Innovation theory and collective intelligence perspectives. The study analysed six months of social media data and found sector level differences in discussion activity, use cases, and concerns. Media and marketing exhibit high engagement with positive sentiment, prioritizing content generation capabilities, while healthcare and finance demonstrate cautious engagement with neutral sentiment, focusing on analytical applications. Misinformation emerges as the dominant concern overall, though sectors prioritize different risks based on their institutional contexts. Statistical modeling reveals that sentiment is the strongest driver of information propagation velocity, with sector-concern interactions demonstrating that controversy amplifies rather than inhibits diffusion in content-oriented industries. The findings also demonstrate how collective intelligence mechanisms shape early-stage technology evaluation through sector-specific interpretive frames, with different industries constructing divergent perceptions of identical capabilities based on compatibility with existing practices and regulatory environments. The study contributes empirical evidence of cross-sectoral diffusion dynamics and offers practical insights for sector-specific adoption strategies and regulatory approaches.
Recommended Citation
Shrivastava, S., Bag, S., & Routray, S. (In press). Generative Artificial Intelligence for Knowledge-Driven Industries: Leveraging Collective Intelligence to Address Discourse Patterns and Sectoral Diffusion. Communications of the Association for Information Systems, 58, pp-pp. Retrieved from https://aisel.aisnet.org/cais/vol58/iss1/32
When commenting on articles, please be friendly, welcoming, respectful and abide by the AIS eLibrary Discussion Thread Code of Conduct posted here.
