The consequences of generative AI for online knowledge communities

Generative artificial intelligence technologies, especially large language models (LLMs) like ChatGPT, are revolutionizing information acquisition and content production across a variety of domains. These technologies have a significant potential to impact participation and content production in online knowledge communities. We provide initial evidence of this, analyzing data from Stack Overflow and Reddit developer communities between October 2021 and March 2023, documenting ChatGPT's influence on user activity in the former. We observe significant declines in both website visits and question volumes at Stack Overflow, particularly around topics where ChatGPT excels. By contrast, activity in Reddit communities shows no evidence of decline, suggesting the importance of social fabric as a buffer against the community-degrading effects of LLMs. Finally, the decline in participation on Stack Overflow is found to be concentrated among newer users, indicating that more junior, less socially embedded users are particularly likely to exit.

Knowledge Transfer Within Organizations: Mechanisms, Motivation, and Consideration

Knowledge transfer-the extent to which one unit learns from or is affected by the experience of another-has the potential to improve the performance of organizations. Through knowledge transfer, developments made in one unit of an organization can benefit others. Studies have found, however, considerable variation in the extent to which knowledge transfers across organizational units. In some cases, knowledge transfers seamlessly, whereas in others, knowledge transfer is far from complete. This article reviews research with the aim of explaining the variation observed in knowledge transfer. Key factors identified as explaining the variation include knowledge transfer opportunities, knowledge characteristics, mechanisms for knowledge transfer, motivation for transfer, and the depth of consideration of knowledge. These factors are integrated into a theoretical framework that predicts when knowledge transfer will be successful. The article concludes with a discussion of directions for future research to increase our understanding of knowledge transfer in organizations.

Does Social Influence Change with Other Information Sources? A Large-Scale Randomized Experiment in Medical Crowdfunding

We examine how social influence interacts with other information sources to affect user behaviors in the context of medical crowdfunding. We conduct a large-scale randomized field experiment on a leading medical crowdfunding platform, showing friends’ donation information to donors in the treatment group and not showing such information in the control group, and examine how the likelihood to donate differs. In addition, we conduct a survey on Amazon Mechanical Turk to evaluate the informational value of different case attributes in conveying the patients’ need for help to donors. We find that for cases containing attributes with high informational value (e.g., minor patient, severe conditions), social influence is insignificant. In contrast, for cases lacking attributes with high informational value, social influence significantly increases donors’ likelihood to donate. Overall, our results show that the impact of social influence depends on the informational value of other information sources, suggesting that the social influence in our context is primarily informational. Our findings indicate that rather than generating an entrenchment effect, where cases with attributes of high informational value attract disproportionate benefits, social influence can increase donation likelihood to cases that lack such attributes, promoting more equal access to resources overall.

History: Olivia Liu Sheng, Senior Editor; Yuliang (Oliver) Yao, Associate Editor.

Supplemental Material: The online appendices are available at https://doi.org/10.1287/isre.2022.1189 .

The Mechanisms and Components of Knowledge Transfer: The Virtual Special Issue on Knowledge Transfer Within Organizations

Knowledge transfer within organizations has important implications for organizational performance and competitive advantage. In this virtual special issue, we review articles on this topic published in Organization Science between 2014 and 2020 and identify 53 articles for their theoretical and empirical contributions. These articles examine knowledge transfer through five transfer mechanisms: social networks, routines, personnel mobility, organizational design, and search. We consider the intersection of each transfer mechanism with important components of knowledge transfer (characteristics of sources/recipients, characteristics of knowledge, and characteristics of contexts). We present 15 exemplar articles, each of which reflects the intersection of a mechanism and a component of knowledge transfer. We also present an overview of the methodological approaches and empirical contexts that are utilized. We conclude our article with a discussion of future research opportunities. The articles published in Organization Science have advanced understanding of both the mechanisms through which knowledge transfer occurs and the conditions under which it is most likely.