| 周文杰,周晓英,闫慧.信息资源管理的“双链互构”理论框架:演绎归纳与应用场景[J].中国图书馆学报,2026,52(1):79~99 |
| 信息资源管理的“双链互构”理论框架:演绎归纳与应用场景 |
| The “Dual chain Interconstruct” Theoretical Framework for Information Resources Management:Deductive Induction and Application Context |
| 投稿时间：2025-06-08 修订日期：2025-08-15 |
| DOI： |
| 中文关键词: 信息资源管理 DIKW模型 信息生命周期理论 “双链互构” |
| 英文关键词: Information resources management DIKW model Information lifecycle theory “Dual chain interconstruct” |
| 基金项目: |
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| 摘要点击次数: 220 |
| 全文下载次数: 217 |
| 中文摘要: |
本文旨在构建信息资源管理的“双链互构”理论框架,以应对信息生态系统变化带来的理论创新需求,并适应信息资源管理学科更名后的基础理论建设和职业实践发展新形势。基于DIKW模型和信息生命周期理论的二维向度,通过逻辑演绎和归纳分析,构建了涵盖前信息、信息、后信息、“概化”四个层次的理论框架,并在此基础上提出信息资源管理的“双链互构”理论框架。该框架有效揭示了从信息产生到智慧生成的动态过程,强调信息价值随着管理过程而逐步升级的内核,在新闻资讯、图书/档案管理、知识管理、科技创新等多类应用场景中具有较强的解释力。“双链互构”理论框架有望为信息资源管理提供新的理论视角和实践指导,助力学科发展,提升信息资源管理效率,实现信息价值的最大化。图1。表3。参考文献23。
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| 英文摘要: |
The rapid evolution of information technology and ecosystem environments has created opportunities for theoretical innovation in Information Resources Management （IRM）. Following the renaming of the IRM discipline,it is urgent to establish a logically coherent academic discourse system. Based on the two dimensions of the DIKW （Data-Information-Knowledge-Wisdom） model and the information lifecycle theory,we employ logical deduction and inductive analysis to construct a “dual chain interconstruct” theoretical framework that spans four levels:pre information,information,post information,and “generalization.”
The “dual chain interconstruct” theoretical framework consists of a horizontal information lifecycle chain and a vertical DIKW value enhancement chain. The horizontal chain describes the full process management of information from generation （pre information）,organization （information）,and application （post information） to wisdom generation （“generalization”）,while the vertical chain reflects the hierarchical progression from data to wisdom. These two chains interact iteratively to form a dynamic system:the outputs of each stage of the information lifecycle provide material for DIKW value advancement,while the advancement of value levels indicates the direction of lifecycle management. This interactive relationship,grounded in the “time axis” of IRM,demonstrates the evolutionary path of information from a disordered state to an ordered state and from low value to high value.
The “dual chain interconstruct” theoretical framework is applicable to the following scenarios:Internet data collection corresponds to data integration in the pre information stage；library collection management reflects the organization and preservation in the information stage；institutional knowledge management focuses on the application of knowledge in the post information stage；and scientific and technological innovation highlights wisdom generation in the “generalization” stage. Analysis shows that the framework effectively explains the patterns of IRM across diverse contexts and demonstrates strong adaptability.
The innovations of the present study are reflected in three aspects:first,it introduces a “dual chain interconstruct” theoretical framework integrating the information lifecycle and DIKW,constructing a systematic dynamic evolution mechanism for IRM；second,it introduces the concept of the “generalization” stage,elucidating the core role of knowledge transfer and cross domain application in wisdom generation；and third,it combines practical scenarios to reveal the logic and feedback loops of dynamic interaction between the information lifecycle and DIKW at the micro level. In terms of application,the framework exhibits explanatory power in scenarios such as news data acquisition （pre information stage）,library collection management （information stage）,organizational knowledge repositories （post information stage）,and cross domain integration in S&T innovation （“generalization” stage）.
The “dual chain interconstruct” theoretical framework which combines extensibility and adaptability is expected to provide a unified discourse system for IRM discipline development,professional practice,and information governance in the era of artificial intelligence. 1 fig. 3 tabs. 23 refs.
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