Technical maturity and network effects of XF artificial intelligence open platform - JIANG, Tao (Institutional Knowledge at Singapore Management University, 2023)
Studying the impact mechanism of the commercial value of artificial intelligence open technology platforms has theoretical and practical significance. This article aims to enrich and expand the theoretical research on technology maturity, value co creation, and network effects on open technology platforms at home and abroad through empirical research on artificial intelligence open technology platforms and ecology. This study takes XF's open technology platform case as the research object, and based on technology maturity theory, value co creation, and network effects theory, examines the network effect value creation mechanism of open technology platforms driven by technology maturity in three development stages: "capacity building period", "business model exploration period", and "ecosystem cultivation phase", and conducts empirical testing on some of the hypotheses. The theoretical contributions of this study are mainly reflected in: firstly, this article compares open technology platforms with other platforms, especially clarifies the differences with software platforms, and supplements and improves the existing research on platform classification in platform economy. The artificial intelligence open technology platform is accompanied by an increase in technological maturity, changes in developer heterogeneity, and continuous deepening of platform network effects, driving the continuous evolution of platform form. Secondly, this article verifies the relationship between the technological maturity of artificial intelligence open technology platforms and network effects (user activity, user stickiness). After research, it was found that technology platforms exhibit differentiated technological maturity at different stages, and developer heterogeneity also varies due to this. This has a positive effect on stimulating, deepening, and expanding network effects. This means that there are two paths for the impact of technology platforms on network effects: firstly, the maturity of technology reflects the quality of platform development, and its improvement brings about an increase in user base and activity; Secondly, as complements, the heterogeneity of developers at different stages of platform development determines significant differences in risk preferences and behavioral patterns of technology adoption, which can also have a profound impact on network effects. The research conclusions of this article enrich the research on platform economy, especially network effects.