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| An Evaluation Model for the Resilience of Digital Publishing Industry Chain from the Perspective of AIGC Empowerment |
| MA Wenhuan1,WANG Ziwei2,*,CHEN Lei2,WANG Jikun2,CAO Kai2 |
1. Metallurgical Industry Press Co., Ltd., 100009, Beijing, China 2. Beijing Institute of Graphic Communication, 102600, Beijing, China |
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Abstract Based on the practical progress and internal logic of artificial intelligence generated content (AIGC) empowering digital publishing, this study constructs a digital publishing industry chain resilience evaluation model encompassing five key elements: structural links, resilience attributes, resilience sources, institutional guarantees, and resilience levels. The model aims to provide theoretical support and analytical tools for the scientific identification, precise assessment, and systematic enhancement of industry chain resilience in the context of deep AIGC integration and accelerated industry restructuring. It emphasizes the dynamic collaborative evolution among these elements, addressing limitations of current resilience research that often rely excessively on physical boundaries, focuses on a single capital-driven approach, and neglects institutional factors. As such, the model provides a more integrated explanatory perspective for current industry chain resilience research. AIGC is profoundly reshaping the links in the digital publishing chain by overcoming temporal and multi-party constraints, enabling individual entities to complete publishing process anytime and anywhere. This promotes efficient coordination and integration across different links, thereby enhancing the resilience of the industry chain. AIGC also introduces cross-link risks such as copyright disputes, information silos, and user data breaches—that, if accumulated and propagated, may disrupt the operational rhythm of the chain and weaken its resilience. Therefore, a dedicated evaluation model is urgently needed to assess AIGC's dual impact on industrial chain resilience. Existing resilience models are primarily based on either the ecological dynamics-inspired four attributes (4R: robustness, redundancy, adaptability, and rapidity) framework or the sustainable livelihoods-oriented five capitals (5C: human, social, natural, physical, and financial capitals) system, both of which are better suited to traditional industrial chains with clear physical boundaries. Even studies that attempt to integrate 4R and 5C commonly overlook the critical role of legal and policy-related institutional factors. In contrast, digital publishing centers on virtual information space while relying on physical infrastructure for support, resulting in highly blurred boundaries between dynamic and static elements. Moreover, as a key vehicle for ideological dissemination, it carries distinct political attributes and cultural security responsibilities. Consequently, legal and policy-based institutional safeguards are not merely external environmental variables but have become structural elements embedded within the operation of the digital publishing industry chain as well as endogenous variables directly shaping its resilience. To address these gaps, this study proposes a "3L+4R+5C+G+3Le" resilience evaluation model for the digital publishing industry chain. It defines three links (3L): content production, channel distribution, and user consumption, and analyzes the influence of the 5C on resilience. Legal, policy, and other institutional safeguards (G) serve as an endogenous foundation spanning the entire chain. Resilience performance is characterized through 4R. Finally, through weighting and comprehensive measurement, resilience is classified into three levels (3Le: high, medium, and low). This model represents a conceptual shift from "structural analysis" to "state identification, " transforming industrial chain resilience from an abstract notion into an assessable and manageable strategic capability. Future research should focus on developing resilience indicators and exploring pathways to enhance resilience in the digital publishing industry chain.
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Published: 09 January 2026
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Corresponding Authors:
Ziwei WANG
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