Abstract

Generative AI (GenAI) technologies such as ChatGPT, Gemini, Alpha Code, CoPilot, Diffusion, Dall-E, etc., have made headlines since late 2022 due to their ability to easily generate content, from text to multimedia, in various domains (Stokel-Walker and Noorden 2023). These technologies are improving the productivity of workers in multiple industries, especially those that are based in the knowledge economy (Alavi and Westerman 2023). Moreover, they are consistently reducing factual-content errors and making information easily accessible through multiple information formats including audio, images, and text. Large language models are at the core of this GenAI revolution. They are trained using publicly available information from platforms such as Wikipedia, Stack Overflow, GitHub, etc. Although GenAI tools have made information search more efficient, recent research shows they are undermining and degrading engagement with online question and answer (Q&A)-based knowledge communities like Stack Overflow and Reddit (Burtch et al. 2023). We extend this stream of research by examining the impact of GenAI on the market value and quality of peer-produced content using the world’s largest open-source knowledge repository i.e., Wikipedia, which is different from Q&A-based communities mentioned above. We follow an approach like Burtch et al. (2023) and extend empirical analyses focusing on ChatGPT’s release on November 30, 2022. We collect monthly Wikipedia page views and content (text) data for six months before and after the release date as the treatment group. We then collect data for same months a year before as the control group. The difference-in-difference (DID) analyses demonstrate significant decrease in Wikipedia page views (market value) after the release of ChatGPT. However, we found an increase in the quality of Wikipedia articles as evidenced by a significant increase in verbosity and readability of the articles after ChatGPT release. Our analyses have controlled for betweenness and closeness centrality of the articles, and article, year-month, and article category fixed-effects. We will extend this research by finding the mechanisms underlying the impact of GenAI on online knowledge repositories. Further, we plan to conduct detailed analyses to examine the impact of GenAI on knowledge contributors.

Paper Number

tpp1279

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