基于TAM模型与PLS-SEM的公共图书馆AI智慧服务用户使用意愿影响因素研究

doi:10.13998/j.cnki.issn1002-1248.26-0070

Abstract

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

ZHUANG Jiayu. Factors Influencing Users' Intentions to Adopt AI Intelligent Services in Public Libraries: An Empirical Study Based on TAM and PLS-SEM[J].Journal of library and information science in agriculture, 2026, (): 1-11.

Figures/Tables 10

Table 1

Fig.1

Fig.2

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]等
	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]
用户满意度（S）	S2	我赞成公共图书馆引入和应用AI	HUSSAIN等^[25]
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 2

Table 3

Table 4

Table 5

Table 6

Table 7

Fig.3

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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

变量	类目	数量/人	占比/%
性别	男	116	45.14
性别	女	141	54.86
年龄	18岁以下	25	9.73
	18~25岁	86	33.46
	26~40岁	79	30.74
	41~50岁	37	14.4
	50岁以上	30	11.67
学历	高中及以下	20	7.78
	专科	57	22.18
	本科	112	43.58
	硕士及以上	68	26.46
职业	学生	83	32.3
	机关事业单位人员	68	26.46
	民营企业员工	49	19.07
	其他	57	22.17

变量	题项	标准化因子载荷	Cronbach's α系数	CR	AVE
感知有用性（PU）	PU1	0.804	0.825	0.801	0.612
	PU2	0.702
	PU3	0.745
	PU4	0.757
感知易用性（PEOU）	PEOU1	0.846	0.741	0.749	0.518
	PEOU2	0.765
	PEOU3	0.720
	PEOU4	0.827
感知风险（PR）	PR1	0.835	0.789	0.724	0.557
	PR2	0.792
	PR3	0.731
信任（T）	T1	0.716	0.793	0.765	0.501
	T2	0.764
	T3	0.815
满意度（S）	S1	0.790	0.701	0.724	0.555
满意度（S）	S2	0.755	0.701	0.724	0.555
使用意愿（BI）	BI1	0.856	0.803	0.775	0.613
	BI2	0.708
	BI3	0.805

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	0.695	0.720
信任	0.580	0.559	0.708
感知风险	0.637	0.610	0.507	0.746
满意度	0.626	0.650	0.543	0.477	0.745
使用意愿	0.667	0.524	0.631	0.551	0.582	0.783

模型拟合指数	标准值	实际值
X²/df	1<X²/df <3	1.083
GFI	>0.9	0.946
AGFI	>0.85	0.926
RMSEA	<0.05	0.018
CFI	>0.9	0.994
NFI	>0.8	0.933
TLI	>0.9	0.993
SRMR	<0.08	0.035

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

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假设	标准路径系数	S.E.	C.R.	结论
H1：T→BI	0.243***	0.068	3.556	支持
H2：T→PR	0.382***	0.088	4.335	支持
H3：PR→BI	0.174*	0.079	2.200	支持
H4：PR→PU	0.072	0.319	0.226	不支持
H5：PU→BI	0.340***	0.076	4.464	支持
H6：PU→S	0.651***	0.175	3.725	支持
H7：PEOU→BI	0.288***	0.048	6.059	支持
H8：PEOU→S	0.350***	0.105	3.323	支持
H9：S→BI	0.648**	0.205	3.167	支持

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