考虑用户特征的企业赞助虚拟社区知识共享演化博弈分析

IF 1.7 4区 工程技术 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS
Complexity Pub Date : 2025-05-08 DOI:10.1155/cplx/7549833
Zhihui Ding, Zuoming Lu, Wenlong Zhu
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引用次数: 0

摘要

企业赞助的虚拟社区(ESVC)是用户知识共享的重要平台。用户特征作为这些社区的影响因素,影响着知识共享过程的演变。本文构建了一个考虑用户特征影响的知识共享进化博弈模型。建立收益矩阵,确定稳定点进行进一步分析。通过仿真分析,探讨用户特征变化对双方知识共享策略的影响。研究发现:第一,ESVC用户的知识储备影响其知识分享意愿,随着知识储备的增加,普通用户的知识分享意愿先增加后降低;其次,随着知识储备的扩大,领导用户分享知识的意愿先降低后增加。第三,领先用户和普通用户的知识吸收和转化能力的提高都促进了知识共享行为,尽管这种影响的程度在两个群体之间有所不同。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Evolutionary Game Analysis of Knowledge Sharing in Enterprise Sponsored Virtual Communities Considering User Characteristics

Evolutionary Game Analysis of Knowledge Sharing in Enterprise Sponsored Virtual Communities Considering User Characteristics

Enterprise sponsored virtual communities (ESVC) are important platforms for user knowledge sharing. User characteristics, as influential factors in these communities, impact the evolution of knowledge sharing processes. In this study, a knowledge sharing evolutionary game model that considers the influence of user characteristics was constructed. A payoff matrix was established, and stable points were identified for further analysis. Simulation analysis was conducted to explore the impact of changes in user characteristics on the knowledge sharing strategies of both parties. This study revealed the following findings: First, the knowledge reservoir of ESVC users affects their willingness to share knowledge, with ordinary users’ willingness initially increasing and then decreasing as their knowledge reservoirs grow. Second, the willingness of leading users to share knowledge initially decreases and then increases as their knowledge reservoirs expand. Third, improvements in the knowledge absorption and transformation capabilities of both leading and ordinary users promote knowledge sharing behaviour, although the extent of this impact differs between the two groups.

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来源期刊
Complexity
Complexity 综合性期刊-数学跨学科应用
CiteScore
5.80
自引率
4.30%
发文量
595
审稿时长
>12 weeks
期刊介绍: Complexity is a cross-disciplinary journal focusing on the rapidly expanding science of complex adaptive systems. The purpose of the journal is to advance the science of complexity. Articles may deal with such methodological themes as chaos, genetic algorithms, cellular automata, neural networks, and evolutionary game theory. Papers treating applications in any area of natural science or human endeavor are welcome, and especially encouraged are papers integrating conceptual themes and applications that cross traditional disciplinary boundaries. Complexity is not meant to serve as a forum for speculation and vague analogies between words like “chaos,” “self-organization,” and “emergence” that are often used in completely different ways in science and in daily life.
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