Yi Zhao

Mr. Yi Zhao


Research Focus & Metrics

Academic Career Length

1Year (2026-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
14 August 2026 in Administrative Sciences
Verified
Journal Article
Open access
PDF file
YZYi Zhao
Yi Zhao
张文张 文松
张 文松
YAYuxiang An
Yuxiang An
JWJiayuan Wang
Jiayuan Wang
BCBaolian Chen
Baolian Chen
Currently, intelligent agent applications generally face the bottleneck of “strong single-agent intelligence but weak group collaboration,” making it difficult to form systemic ecological advantages through network effects. Addressing this practical dilemma, this paper adopts the platform ecosystem perspective, defines three dimensions of intelligent agent network ecological advantage—resource complementarity, governance coordination, and dynamic adaptability—and systematically reveals its nonlinear formation mechanism based on the coupling of self-organization, emergence, and governance. Taking 2024 Chinese listed platform enterprises as the sample and employing fuzzy-set qualitative comparative analysis (fsQCA), this study identifies three multi-factor equivalent configurational paths that drive high ecological advantage: the resource-open collaborative type achieves synergistic amplification and leap through deep fusion of resource openness; the algorithm-incentive-regulation type drives asymmetric leadership through technological depth and governance response; and the resource-governance-adaptive type constructs resilience barriers in highly uncertain environments through comprehensive and balanced configurations. This paper constructs an active symbiosis theory of intelligent agent ecosystems, advancing ecosystem theory from the traditional “passive complementarity” paradigm to a new “active symbiosis” paradigm, revealing multiple concurrent causality and asymmetric causality, clarifying the dual role of regulatory sensitivity as both a “bridge” and an “amplifier,” and providing a novel theoretical framework and analytical paradigm for platform ecosystem governance in the GenAI and multi-agent era.