价值(数学)
人工神经网络
计算机科学
人工智能
知识管理
管理
数据科学
机器学习
经济
作者
Robert Wayne Gregory,Ola Henfridsson,Evgeny Káganer,Harris Kyriakou
标识
DOI:10.5465/amr.2019.0178
摘要
Some of the world’s most profitable firms own platforms that exhibit network effects. A platform exhibits network effects if the more people that use it, the more valuable that it becomes to each user. Theorizing about the value perceived by users of a platform that exhibits network effects has traditionally focused on direct and indirect network effects. In this paper, we theorize about a third type of network effects—data network effects—that has emerged from advances in artificial intelligence (AI) and the growing availability of data. A platform exhibits data network effects if the more that the platform learns from the data it collects on users, the more valuable the platform becomes to each user. We argue that there is a positive direct relationship between the AI capability of a platform and the value perceived in the platform by its users—a relationship that is moderated by platform legitimation, data stewardship and user-centric design.
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