图书情报工作

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2026 , Vol. 70 >Issue 17: 50 - 63

DOI: https://doi.org/10.13266/j.issn.0252-3116.2026.17.004

研究论文

用户异质性视角下以DeepSeek为代表的开源生成式AI技术采纳意愿影响机制研究

  • 马续补 ,
  • 周冰芮 ,
  • 秦春秀 ,
  • 林强
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  • 1 西安电子科技大学经济与管理学院 西安 710126;
    2 西安电子科技大学图书馆 西安 710126
马续补，教授，博士，博士生导师，E-mail：xbma@xidian.edu.cn；周冰芮，硕士研究生；秦春秀，教授，博士，博士生导师；林强，馆员。

收稿日期: 2025-07-02

  修回日期: 2025-10-10

  网络出版日期: 2026-08-20

基金资助

本文系国家社会科学基金项目“疫情防控常态化背景下突发公共卫生事件信息组织方法与信息公开策略研究”（项目编号：22BTQ053）研究成果之一。

收起

Research on the Influencing Mechanism of the Adoption Intention for Open-Source Generative AI Technology from the Perspective of User Heterogeneity: A Case Study of DeepSeek*

  • Ma Xubu ,
  • Zhou Bingrui ,
  • Qin Chunxiu ,
  • Lin Qiang
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  • 1 School of Economics and Management, Xidian University, Xi'an 710126;
    2 Library of Xidian University, Xi'an 710126
Ma Xubu, Professor, PhD, Doctoral Supervisor, E-mail: xbma@xidian.edu.cn; Zhou Bingrui, Master’s Candidate; Qin Chunxiu, Professor, PhD, Doctoral Supervisor; Lin Qiang, Librarian.

Received date: 2025-07-02

  Revised date: 2025-10-10

  Online published: 2026-08-20

Supported by

This work is supported by the National Social Science Fund of China (NSSFC) project titled “Research on Information Organization Methods and Information Disclosure Strategies for Public Health Emergencies Against the Background of Normalized Epidemic Prevention and Control” (Grant No. 22BTQ053).

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摘要

[目的/意义]以DeepSeek为代表的开源生成式AI技术正加速技术普惠进程，然而用户异质性导致其技术采纳路径出现显著分化，因此亟需克服传统模型的同质性用户假设。以用户异质性作为核心理论视角与创新点，能够系统解构开发者、企业用户、普通用户及探索型用户的技术采纳驱动机制。[方法/过程]整合技术接受模型（TAM）、统一技术接受与使用理论（UTAUT）及信息生态理论，基于用户在“角色—能力—需求”三个维度的差异，构建开源生成式AI的“场景—技术—生态”分析框架。共收集391份有效问卷数据，采用结构方程模型（SEM）分析指标间的因果关系，并结合模糊集定性比较分析方法（fsQCA）挖掘四类用户的组态路径差异。[结果/结论]研究发现，开发者的技术采纳意愿以技术效率与隐私权衡驱动，呈现“全维驱动”与“风险回避”两条路径；企业用户接受高度场景化，追求“社群赋能”“合规优先”等四类需求；普通用户依赖自身体验，追求轻量化体验；探索用户关注轻量化体验与新颖性。结果表明，用户异质性显著影响技术采纳路径，需通过差异化治理策略，推动开源生成式AI的普惠化应用。

关键词： 开源生成式AI DeepSeek; 用户异质性; 场景化治理; 组态路径

本文引用格式

马续补 , 周冰芮 , 秦春秀 , 林强 . 用户异质性视角下以DeepSeek为代表的开源生成式AI技术采纳意愿影响机制研究[J]. 图书情报工作, 2026 , 70(17) : 50 -63 . DOI: 10.13266/j.issn.0252-3116.2026.17.004

Abstract

[Purpose/Significance] Open-source generative AI technologies, represented by DeepSeek, are accelerating the process of technological inclusiveness. However, user heterogeneity has led to significant differentiation in technology adoption paths. Therefore, it is urgent to abandon the homogeneity assumption of users in traditional models. Taking user heterogeneity as the core theoretical perspective and innovation, this study systematically deconstructs the technology adoption driving mechanisms for developers, enterprise users, ordinary users, and exploratory users. [Method/Process] The Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Information Ecology Theory are integrated. Based on users’ differences in the three dimensions of “role-capability-demand”, a “scenario-technology-ecology” analytical framework for open-source generative AI is constructed. A total of 391 valid questionnaire data were collected. Structural Equation Modeling (SEM) was employed to analyze the causal relationships between indicators, and fuzzy-set Qualitative Comparative Analysis (fsQCA) was utilized to explore the configurational path differences across the four user groups. [Result/Conclusion] The findings indicate that developers’ technology adoption intention is driven by the trade-off between technical efficiency and privacy, presenting two paths: “full-dimensional drive” and “risk avoidance”. Enterprise users’ acceptance is highly scenario-specific, pursuing four types of needs including “community empowerment” and “compliance priority”. Ordinary users rely on personal experience and pursue lightweight experiences. Exploratory users focus on lightweight experiences and novelty. The results show that user heterogeneity significantly influences technology adoption paths, and differentiated governance strategies are required to advance the inclusive application of open-source generative AI.

Key words： open-source generative AI DeepSeek; user heterogeneity; scenario-based governance; configurational paths

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