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

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

RESEARCH PAPERS

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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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

Cite this article

Ma Xubu , Zhou Bingrui , Qin Chunxiu , Lin Qiang . 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*[J]. Library and Information Service, 2026 , 70(17) : 50 -63 . DOI: 10.13266/j.issn.0252-3116.2026.17.004

References

[1] 段玉聪. 抢占AI话语权： DeepSeek的技术优势、战略布局与未来生态图景[J]. 新疆师范大学学报(哲学社会科学版), 2025, 46(4): 109-125. (DUAN Y C. Competing for AI leadership： DeepSeek’s algorithmic edge and vision for the future ecosystem[J]. Journal of Xinjiang Normal University (Philosophy and social sciences), 2025, 46(4): 109-125.)
[2] 蒋艳. 人工 智能 发展 报告(2023～ 2024)[M]. 北京 : 社会 科学 文献 出版 社, 2024. (JIANG Y. Artificial intelligence development report (2023-2024)[M]. Beijing: Social Sciences Academic Press, 2024.)
[3] 郭顺利, 张雪宁. 科研人员生成式人工智能应用中知识采纳行为关键影响因素识别研究[J]. 情报科学, 2025, 43(5): 58-69. (GUO S L, ZHANG X N. Identification of key influencing factors of knowledge adoption behavior in generative artificial intelligence applications for researchers[J]. Information science, 2025, 43(5): 58-69.)
[4] DAVIS F D. Perceived usefulness, perceived ease of use, and user acceptance of information technology [J]. MIS quarterly, 1989, 13(3): 319-340.
[5] VENKATESH V, MORRIS M G, DAVIS G B, et al. User acceptance of information technology: toward a unified view[J]. MIS quarterly, 2003, 27(3): 425-478.
[6] MOGAJI E, VIGLIA G, SRIVASTAVA P, et al. Is it the end of the technology acceptance model in the era of generative artificial intelligence?[J]. International journal of contemporary hospitality management, 2024, 36(10): 3324-3339.
[7] RAPP A, DI LODOVICO C, TORRIELLI F, et al. How do people experience the images created by generative artificial intelligence? An exploration of people’s perceptions, appraisals, and emotions related to a Gen-AI text-to-image model and its creations[J]. International journal of human-computer studies, 2025, 193: 103375.
[8] JONES J, JIANG W, SYNOVIC N, et al. What do we know about Hugging Face? A systematic literature review and quantitative validation of qualitative claims[C] //Proceedings of the 18th ACM/IEEE international symposium on empirical software engineering and measurement. Barcelona: ACM, 2024: 13-24.
[9] AIT A, IZQUIERDO J L C, CABOT J. Hfcommunity: a tool to analyze the hugging face hub community[C] //2023 IEEE international conference on software analysis, evolution and reengineering. Piscataway: IEEE, 2023: 728-732.
[10] ISMATULLAEV U V U, KIM S H. Review of the factors affecting acceptance of AI-infused systems[J]. Human factors, 2024, 66(1): 126-144.
[11] SCHWESIG R, BRICH I, BUDER J, et al. Using artificial intelligence (AI)? Risk and opportunity perception of AI predict people’s willingness to use AI[J]. Journal of risk research, 2023, 26(10): 1053-1084.
[12] REDDY S. Generative AI in healthcare: an implementation science informed translational path on application, integration and governance[J]. Implementation science, 2024, 19(1): 27.
[13] PARK J S, O’BRIEN J, CAI C J, et al. Generative agents: interactive simulacra of human behavior[C] //Proceedings of the 36th annual ACM symposium on user interface software and technology. San Francisco: ACM, 2023: 1-22.
[14] WANG R Y, STRONG D M. Beyond accuracy: what data quality means to data consumers [J]. Journal of management information systems, 1996, 12(4): 5-33.
[15] 耿荣娜, 张向先, 郭顺利. 社会化电子商务用户信息采纳行为影响因素研究[J]. 情报科学, 2017, 35(10): 160-166. (GENG R N, ZHANG X X, GUO S L. The study of influence factors on social e-commerce users’ information adoption behavior[J]. Information science, 2017, 35(10): 160-166.)
[16] BEYAN E V P, ROSSY A G C. A review of AI image generator: influences, challenges, and future prospects for architectural field[J]. Journal of artificial intelligence in architecture, 2023, 2(1): 53-65.
[17] AGARWAL R, PRASAD J. Are individual differences germane to the acceptance of new information technologies?[J]. Decision sciences, 1999, 30(2): 361-391.
[18] LIMAYEM M, HIRT S G, CHEUNG C M K. How habit limits the predictive power of intention: the case of information systems continuance[J]. MIS quarterly, 2007: 705-737.
[19] VON HIPPEL E. Democratizing innovation[M]. Cambridge: The MIT Press, 2006.
[20] SUBRAMANIAM M, YOUNDT M A. The influence of intellectual capital on the types of innovative capabilities[J]. Academy of management journal, 2005, 48(3): 450-463.
[21] FISS P C. Building better causal theories: a fuzzy set approach to typologies in organization research[J]. Academy of management journal, 2011, 54(2): 393-420.
[22] 刘建准, 黄金凡, 石密. 信息生态视角下在线医疗社区用户信息采纳行为影响因素研究[J]. 文献与数据学报, 2023, 5(3): 72-86. (LIU J Z, HUANG J F, SHI M. A study on the factors influencing information adoption behavior of online medical community users from the perspective of information ecology[J]. Journal of library and data, 2023, 5(3): 72-86.)
[23] RYAN S P, TUCKER C. Heterogeneity and the dynamics of technology adoption[J]. Quantitative marketing and economics, 2012, 10: 63-109.
[24] TAHERDOOST H. A review of technology acceptance and adoption models and theories[J]. Procedia manufacturing, 2018, 22: 960-967.
[25] WISDOM J P, CHOR K H B, HOAGWOOD K E, et al. Innovation adoption: a review of theories and constructs[J]. Administration and policy in mental health and mental health services research, 2014, 41: 480-502.
[26] BENKLER Y. Coase’s penguin, or, Linux and “the nature of the firm” [J]. Yale law journal, 2002, 112(3): 369-446.
[27] 孙建军, 成颖, 柯青. TAM模型研究进展—— 模型演化[J]. 情报 科学, 2007(8): 1121-1127. (SUN J J, CHENG Y, KE Q. Advances of research on technology: acceptance model[J]. Information science, 2007(8): 1121-1127.)
[28] 王艳玲, 张广胜, 李全海. 基于技术接受模型的电商平台采纳行为及影响因素[J]. 企业经济, 2020(3): 132-137. (WANG Y L, ZHANG G S, LI Q H. Adoption intention and influencing factors of e-commerce platforms based on the technology acceptance model[J]. Enterprise economy, 2020(3): 132-137.)
[29] 刘婷艳, 王晰巍, 张雨. 基于TAM模型的直播带货用户信息交互行为影响因素研究[J]. 现代情报, 2022, 42(11): 27- 39. (LIU T Y, WANG X W, ZHANG Y. Research on influencing factors of user information interaction behavior in live streaming commerce based on TAM model[J]. Journal of modern information, 2022, 42(11): 27-39.)
[30] 甘春梅, 宋常林. 基于TAM的移动图书馆采纳意愿的实证分析 [J]. 图书情报知识, 2015(3): 66-71. (GAN C M, SONG C L. Empirical analysis on intention to adopt mobile library based on TAM[J]. Documentation, information & knowledge, 2015(3): 66- 71.)
[31] 王伟军, 王阳, 王玉珠, 等. 移动商务用户个性化推荐采纳行为影响因素的实证研究[J]. 系统管理学报, 2017, 26(5): 816-823. (WANG W J, WANG Y, WANG Y Z, et al. Influencing factors of mobile commerce users’ adoption on personalized recommendation[J]. Journal of systems & management, 2017, 26(5): 816-823.)
[32] 刘炜. 基于扩展TTF和UTAUT模型的老年用户社会化网络服务采纳行为研究[J]. 软科学, 2015, 29(3): 120-124. (LIU W. SNS acceptance of older adults based the extended TTF and UTAUT model[J]. Soft science, 2015, 29(3): 120-124.)
[33] 赵静, 倪明扬, 张倩, 等. AIGC重构研究生学术实践：持续 使用 意愿 影响 因素 研究 [J]. 现代 情报, 2024, 44(7): 34- 46. (ZHAO J, NI M Y, ZHANG Q, et al. AIGC reconfiguring graduate student academic practice: study on factors influencing continuous usage intention[J]. Journal of modern information, 2024, 44(7): 34-46.)
[34] 李普聪, 张珂, 董洪据, 等. 多场景移动医疗用户持续采纳行为的影响因素[J]. 图书馆论坛, 2023, 43(5): 111-118. (LI P C, ZHANG K, DONG H J, et al. Influencing factors of mobile health users’ continuous adoption based on multiple contexts[J]. Library tribune, 2023, 43(5): 111-118.)
[35] STRONG D M, LEE Y W, WANG R Y. Data quality in context[J]. Communications of the ACM, 1997, 40(5): 103-110.
[36] TAYLOR S, TODD P. Assessing IT usage: the role of prior experience[J]. MIS quarterly, 1995, 19(4): 561-570.
[37] FEATHERMAN M S, PAVLOU P A. Predicting e-services adoption: a perceived risk facets perspective[J]. International journal of human-computer studies, 2003, 59(4): 451-474.
[38] MORRIS N S, MACLEAN C D, CHEW L D, et al. The single item literacy screener: evaluation of a brief instrument to identify limited reading ability[J]. BMC family practice, 2006, 7: 1-7.
[39] 周浩, 龙立荣. 共同方法偏差的统计检验与控制方法[J]. 心理 科学 进展, 2004(6): 942-950. (ZHOU H, LONG L R. Statistical remedies for common method Biases[J]. Advances in psychological science, 2004(6): 942-950.)
[40] MISANGYI V F, GRECKHAMER T, FURNARI S, et al. Embracing causal complexity: the emergence of a neoconfigurational perspective[J]. Journal of management, 2017, 43(1): 255-282.
[41] RAGIN C C. Redesigning social inquiry: fuzzy sets and beyond[M]. Chicago: University of Chicago Press, 2008: 44-68.
[42] A N D R E W S R, B E Y N O N M J, M C D E R M O T T A F. Organizational capability in the public sector: a configurational approach[J]. Journal of public administration research and theory, 2016, 26(2): 239-258.
[43] 杜运周, 贾良定. 组态视角与定性比较分析(QCA): 管理学研究一条新道路[J]. 管理世界, 2017(6): 155-167. (DU Y Z, JIA L D. Configuration perspective and qualitative comparative analysis (QCA): a new path for management research[J]. Journal of management world, 2017(6): 155-167.)
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