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Journal of library and information science in agriculture ›› 2026, Vol. 38 ›› Issue (7): 59-69.doi: 10.13998/j.cnki.issn1002-1248.26-0070

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Factors Influencing Users' Intentions to Adopt AI Intelligent Services in Public Libraries: An Empirical Study Based on TAM and PLS-SEM

ZHUANG Jiayu   

  1. School of Information, The University of Texas at Austin, Austin 78701
  • Received:2026-02-06 Online:2026-07-05 Published:2026-07-13

Abstract:

[Purpose/Significance] This study aims to reveal the influencing factors that affect users' behavioral intention to adopt Artificial Intelligence (AI) smart services in public libraries. As public cultural institutions transition toward intelligent service paradigms, the integration of generative AI offers unprecedented opportunities to enhance knowledge accessibility and operational efficiency. By exploring users' actual needs for AI-driven tools - such as intelligent reference desks, personalized reading recommendations, and automated retrieval systems - this research seeks to provide robust theoretical and practical guidance. Ultimately, it aims to promote the deep integration of AI technologies within the broader framework of smart library construction, ensuring that these innovations align with user expectations and the public interest. [Method/Process] Drawing upon the Technology Acceptance Model (TAM) as the foundational theoretical framework, this study introduces Trust and Perceived Risk as critical external variables to accurately reflect the current technological climate, which is increasingly characterized by data privacy concerns and algorithmic opacity. Data were collected through a structured online questionnaire survey targeting a diverse demographic of public library users, resulting in 257 valid responses. To empirically test the proposed research model and hypotheses, Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed. The rigorous analytical process included a comprehensive assessment of the measurement model to confirm internal consistency, convergent validity, and discriminant validity, followed by the evaluation of the structural model to determine the statistical significance of the path coefficients and the overall explanatory power of the integrated framework. [Results/Conclusions] The empirical evaluation of the structural model yielded several key findings. First, both Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) exert a significant positive impact on user satisfaction, highlighting that functional utility and intuitive interfaces are baseline requirements for AI adoption. Second, Trust, Satisfaction, PU, and PEOU are all identified as strong, direct positive predictors of users' Behavioral Intention (BI) to use AI smart services. Third, Perceived Risk (PR) significantly and negatively influences BI, acting as a major barrier to adoption. Interestingly, the influence of PR on PU was found to be statistically insignificant, suggesting that users evaluate the functional benefits of AI independently of its potential risks. Finally, Trust was shown to effectively mitigate user concerns, exerting a significant negative impact on PR. Based on these insights, it is recommended that public libraries prioritize enhancing the algorithmic transparency of their AI applications to systematically build user trust. Furthermore, libraries should integrate regional cultural elements to develop localized and distinctive AI services, diversify AI application scenarios to meet multifaceted user demands, and actively implement educational workshops and lectures focused on improving public AI literacy.

Key words: public libraries, artificial intelligence, user behavioral intention, TAM model, PLS-SEM, perceived risk, trust

CLC Number: 

  • G250.7

Table 1

Research findings on AI technology adoption intentions and behaviour (excerpt)"

研究者研究对象理论框架研究成果
YANG等[26]AI在知识服务行业的应用“科技-组织-环境”框架(TOE)企业的创新指数、AI技术就绪度与竞争和监管环境影响AI在知识密集型行业的应用
BIANCHINI等[27]AI在科学研究中实现应用的驱动力与阻力科技人力资源与成本理论框架(STHC)具备丰富AI使用经验的科研人员是推动AI工具在科学研究领域成功应用的关键要素
ANDREWS等[28]图书馆员对于AI应用及其余相关新兴技术的接受度影响因素统一技术接受模型(UTAUT)绩效期望与技术使用态度显著影响图书馆员对AI及相关技术的采纳意愿
李子[29]媒介素养视域下老年群体AI智能问诊接受度研究:基于TAM模型的实证调查技术接受模型(TAM)银发人群对AI问诊医疗服务的易用性和有用性感知程度较低,进而影响采纳意愿

Fig.1

Classical TAM model"

Fig.2

Research model"

Table 2

Measurement items"

变量题项内容内容来源

信任

(T)

T1我认为公共图书馆的AI工具或服务能够提供真实、准确且可靠的信息CHOUDHURY等[50]
T2我认为在使用公共图书馆AI工具或服务的过程中是安全的,无需担心个人隐私泄露问题
T3我认为公共图书馆AI工具或服务的运行机制是透明的

感知风险

(PR)

PR1我担心公共图书馆中的AI工具或服务可能生成不实信息YUSUF等[51]
PR2我担心公共图书馆AI服务或应用在实际使用过程中可能无法达到预期效果
PR3我担心在使用公共图书馆中的AI工具或服务过程中存在个人隐私泄露的风险

感知易用性

(PEOU)

PEOU1我认为公共图书馆中的AI工具或服务操作简便、易于使用CHATTERJEE[52]
PEOU2我认为公共图书馆中的AI工具或服务应具有友好的用户界面
PEOU3我能够较为轻松地掌握并使用公共图书馆中的AI工具或服务
PEOU4我认为其他用户能够在较短时间内上手公共图书馆中的AI工具或服务

感知有用性

(PU)

PU1我认为公共图书馆中的AI工具或服务能够提高我查找和获取信息的效率

DAVIS[31]

崔宇红等[53]

PU2我认为公共图书馆中的AI工具或服务能够为用户提供更加智能化的信息服务体验
PU3我认为公共图书馆中的AI工具或服务有助于提升用户的阅读与学习体验
PU4我认为通过使用公共图书馆中的AI工具或服务能够提升自身的AI素养

用户满意度

(S)

S1我对使用公共图书馆中的AI工具或服务持积极态度HUSSAIN等[25]
S2我赞成公共图书馆引入和应用AI

使用意愿

(BI)

BI1我愿意尝试使用公共图书馆中的AI工具或服务

曹晶等[54]

李子[29]

BI2我愿意在未来持续使用公共图书馆提供的AI工具或服务
BI3我愿意向亲朋好友或同事推荐公共图书馆中的AI工具或服务

Table 3

Demographic information"

变量类目数量/人占比/%
性别11645.14
14154.86
年龄18岁以下259.73
18~25岁8633.46
26~40岁7930.74
41~50岁3714.4
50岁以上3011.67
学历高中及以下207.78
专科5722.18
本科11243.58
硕士及以上6826.46
职业学生8332.3
机关事业单位人员6826.46
民营企业员工4919.07
其他5722.17

Table 4

Structural reliability and convergent validity"

变量题项标准化因子载荷Cronbach's α系数CRAVE
感知有用性(PU)PU10.8040.8250.8010.612
PU20.702
PU30.745
PU40.757

感知易用性

(PEOU)

PEOU10.8460.7410.7490.518
PEOU20.765
PEOU30.720
PEOU40.827

感知风险

(PR)

PR10.8350.7890.7240.557
PR20.792
PR30.731

信任

(T)

T10.7160.7930.7650.501
T20.764
T30.815

满意度

(S)

S10.7900.7010.7240.555
S20.755

使用意愿

(BI)

BI10.8560.8030.7750.613
BI20.708
BI30.805

Table 5

Discriminant validity: AVE square root values"

变量感知有用性感知易用性信任感知风险满意度使用意愿
感知有用性0.782
感知易用性0.6950.720
信任0.5800.5590.708
感知风险0.6370.6100.5070.746
满意度0.6260.6500.5430.4770.745
使用意愿0.6670.5240.6310.5510.5820.783

Table 6

Model fit indices"

模型拟合指数标准值实际值
X²/df1X2/df 31.083
GFI0.90.946
AGFI0.850.926
RMSEA0.050.018
CFI0.90.994
NFI0.80.933
TLI0.90.993
SRMR0.080.035

Table 7

Hypothesis testing"

假设标准路径系数S.E.C.R.结论
H1:T→BI0.243***0.0683.556支持
H2:T→PR0.382***0.0884.335支持
H3:PR→BI0.174*0.0792.200支持
H4:PR→PU0.0720.3190.226不支持
H5:PU→BI0.340***0.0764.464支持
H6:PU→S0.651***0.1753.725支持
H7:PEOU→BI0.288***0.0486.059支持
H8:PEOU→S0.350***0.1053.323支持
H9:S→BI0.648**0.2053.167支持

Fig.3

Path coefficients and significance"

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