答辩时间：2025年11月27日 13：30

答辩地点：启铸恭温楼 C113

答辩论文信息：

详情：https://cba.cueb.edu.cn/xzsy/129a7497fb014c1e9ca3763f5eb0ffab.htm

学位论文简介

王若兰

This dissertation examines the factors of knowledge-sharing behavior in VKC’s like Stack Overflow, one of the world’s largest programming Q&A communities. The topic was selected due to the interest on researcher to study the knowledge sharing dynamics in digital spaces and later reinforced due to the platform’s declining activity pre-AI-assisted context and the lack of empirical research explaining why developers choose or not to contribute knowledge before the raising and widespread of other artificially intelligent alternatives. 

Grounded in Social Exchange Theory (SET) and Social Capital Theory (SCT) and complemented by Individual Motivation and the Theory of Planned Behavior (TPB), the study integrates motivations (Enjoyment of Helping, Reputation, Social Affiliation, Reciprocity), costs (Knowledge Sharing Effort, Knowledge Sharing Power and Opportunity Cost) and social relational factors (Trust and Pro-sharing norms) into a comprehensive structural equation model. This model investigates not only intention to share knowledge but also its translation into actual knowledge contribution, incorporating Expertise Level as a moderating factor.

The application significance lies in offering evidence-based insights for platform managers, community designers, and organizations seeking to sustain user participation, enhance knowledge quality, and counteract declining engagement in digital knowledge ecosystems impacted soon by new technologies. Methodologically, the study provides practical implications for measuring and incentivizing user behavior in other VKC that function as electronic networks of practice. The dissertation’s innovation points include (1) The study uniquely combines SET, SCT, Individual Motivations, and TPB—an approach not previously applied collectively to Stack Overflow. (2) Most of the studies examine only positive drivers; this thesis models both benefits and costs (a simultaneously analysis motivation) producing a more realistic and comprehensive explanation of user behavior. (3) The thesis tests the moderation effects of trust, pro-sharing norms and expertise level influence the mechanisms through individual motivations and intention translate into actual contributions. (4) It is a contemporary study because it addresses how community participation behavior is driven pre-AI widespread of LLMM assistance tools, offering timely insights for platform sustainability. (5) Uses a first hand dataset of 364 Stack Overflow contributors, enriched through a tailored survey design that captures motivations, constraints, and behavioral outcomes not measured in official Stack Overflow survey.