期望一致与技术接受：公众对政务数字人接纳意愿研究

doi:10.13998/j.cnki.issn1002-1248.24-0408

Abstract

Abstract:

[Purpose/Significance] In the digital age, government digital avatars represent a significant innovative application of the integration of generative artificial intelligence and digital government. These digital avatars aim to enhance the efficiency, accessibility, and responsiveness of public services. This study aims to explore the factors and pathways that influence public acceptance of government digital avatars, providing a theoretical basis and practical insights for improving these services and enhancing the user experience. Unlike previous studies that have focused primarily on technological and functional aspects, this research emphasizes users' perceptions and expectations, filling the gap in existing research on user experience. This innovation of this paper lies in the integration of the Expectation Confirmation Theory (ECT) and the Technology Acceptance Model (TAM) and the extension of other user perception factors to systematically analyze how these factors together influence public acceptance. [Method/Process] The study adopts a comprehensive approach, integrating the Expectation Confirmation Theory (ECT) and the Technology Acceptance Model (TAM), and extends the framework to include additional user perception factors such as perceived information quality, perceived intelligence, perceived convenience, perceived attractiveness, perceived usefulness, and AI trust. Structured questionnaires were used to collect data from a diverse sample, measuring constructs such as expectation confirmation, satisfaction, and various user perception factors. The data were analyzed using structural equation modeling (SEM), which provides robust statistical insights into the relationships between these constructs. In addition, mediation effect models were used to examine indirect effects, providing a comprehensive understanding of how these factors influence public acceptance. Data were collected from a diverse group of respondents to ensure the findings are broadly applicable and representative. [Results/Conclusions] The results suggest that expectation confirmation significantly increases public satisfaction with government digital avatars, which in turn positively affects their acceptance. Perceived information quality, perceived intelligence, perceived convenience, perceived attractiveness, perceived usefulness, and AI trust serve as critical mediators in this relationship. In particular, high levels of perceived quality and intelligence significantly increase satisfaction and acceptance, while convenience and attractiveness also play an important role. AI trust emerges as a critical factor, mediating the impact of user perceptions on acceptance. However, the study does have some limitations. First, the lack of understanding of the professional backgrounds of the research population may lead to differences in acceptance between different professional groups. Future research should look more closely at different occupational groups to gain a fuller understanding. Second, the sample consisted mainly of respondents from younger demographic groups, which may affect the generalizability of the conclusions. Future research should broaden the geographical and demographic coverage of the sample to increase diversity and representativeness. In addition, the lack of qualitative research limits the depth of understanding of users' deep-seated views and needs about government digital avatars. Future research should include qualitative components, such as in-depth interviews and focus group discussions, to explore the actual experiences and specific needs of users and to complement the quantitative findings. This study provides practical recommendations for improving user satisfaction and acceptance, and for supporting the development of effective digital governance solutions. By specifically optimizing government digital avatar services, public satisfaction and trust in digital government services can be increased, further promoting the application and development of digital avatar technology in digital government.

Key words: government digital human avatar, digital government, public acceptance intention, ECT, TAM

CLC Number:

G203

Jinghao CHEN, Feng JIA, Qianxi LIU. Expectation Confirmation and Technology Acceptance: A Study of the Public's Attention to Accept the Government's Digital Human Avatar[J].Journal of Library and Information Science in Agriculture, 2024, 36(6): 16-33.

Figures/Tables 8

Fig.1

Table 1

Measurement items and sources of variables"

变量	题项	问题	来源
感知信息质量（IQ）	IQ1	我认为政务数字人提供的信息是非常可信的	GHASEMAGHAEI和HASSANEIN^[31]
	IQ2	我认为政务数字人提供的信息是非常准确的
	IQ3	我认为政务数字人提供的信息是丰富、完善且全面的
	IQ4	我认为政务数字人提供的信息非常清晰明了
感知智能（PI）	PI1	我认为政务数字人的专业能力很强，能够胜任政务咨询服务的工作	BALAKRISHNAN^[30]
	PI2	我认为政务数字人可以以一种更高效、智能的方式提供政务咨询服务
	PI3	我认为政务数字人具备很好的政务服务能力
	PI4	我认为政务数字人具备提供政务咨询服务的专业技能
感知吸引力（PA）	PA1	我觉得政务数字人面部很有吸引力	FILIERIR等^[44]
	PA2	我觉得政务数字人被培养得很得体、很优雅
	PA3	我喜欢政务数字人的外观
	PA4	我认为政务数字人的形象对我而言是具有吸引力的
感知人格化（ANT）	ANT1	我认为政务数字人的外貌和人类很像	HUANG和YU^[16]
	ANT2	我认为政务数字人的声音很自然
	ANT3	我发现政务数字人在提供咨询服务时，肢体工作优雅大方，自然流畅
	ANT4	我觉得政务数字人在回答问题时，面部表情非常生动自然
感知便利（PC）	PC1	我觉得政务数字人的使用过程很方便	LIN^[45]
	PC2	我觉得政务数字人很有耐心，能让办事流程更为清晰，易懂
	PC3	我觉得政务数字人能帮我快速获取想要的信息
人工智能信任（TR）	TR1	我认为政务数字人与我的互动是有效且具有胜任力的	计纬等^[34]
	TR2	我认为政务数字人可以很好地发挥其角色作用
	TR3	我认为政务数字人有能力，且业务熟练
	TR4	我认为政务数字人对我的回答是诚实的
	TR5	我认为政务数字人的服务真诚且真实
	TR6	我认为政务数字人会以我的最大利益行事
变量	题项	问题	来源
感知有用性（PU）	PU1	我觉得使用政务数字人有助于提高政务服务绩效或表现	BHATTACHERJEE^[8]、DAVIS^[39]
	PU2	我觉得使用政务数字人有助于提高政务服务的办事效率
	PU3	我觉得使用政务数字人能使得办理业务变得更快速迅捷
	PU4	我觉得数字政务人能满足我的办事需求，对我帮助很大
	PU5	总的来说，对于居民办事，我感觉政务数字人非常有用
感知易用性（PE）	PE1	我感觉政务数字人的操作很简单	BHATTACHERJEE^[8]、LANKTON^[46]
	PE2	我觉得学习使用政务数字人对我来说非常容易
	PE3	我很自信能够快速学会并熟练使用政务数字人
	PE4	总的来说，我认为政务数字人使用起来很容易
期望一致（CE）	CE1	我觉得政务数字人比我想象的要自然流畅	BHATTACHERJEE^[8]、HUANG和YU^[16]
	CE2	我觉得政务数字人的外貌形象比我想象的要逼真
	CE3	我觉得政务数字人的工作表现比我预期的要好
	CE4	总的来说，我对政务数字人的大部分期望都得到了证实
满意度（SAT）	SAT1	我认为在政府政务大厅中使用政务数字人是一个明智的选择。	徐孝军等^[47]、LI等^[48]
	SAT2	与政务数字人进行沟通交流时会让我感到满意
	SAT3	我对政务数字人的工作表现感到满意
	SAT4	比起人工服务，我更喜欢政务数字人为我提供咨询服务
	SAT5	总的来说，政务数字人的服务是令人满意的
接纳意愿（CI）	CI1	未来我继续使用政务数字人为我提供政务咨询服务的概率很高	ASHFAQ等^[49]
	CI2	如果有机会，我会向别人推荐使用政务数字人
	CI3	未来我会尝试使用政务数字人获取更多相关政务信息

Table 1

Table 2

Table 3

Table 4

Table 5

Fig.2

Table 6

References 50

1	中华人民共和国中央人民政府. 中华人民共和国国民经济和社会发展第十四个五年规划和2035年远景目标纲要[EB/OL]. [2021-03-13].
2	中华人民共和国中央人民政府. 数字中国建设整体布局规划[EB/OL]. [2023-02-27].
3	刘玮. ChatGPT类生成式人工智能嵌入数字政府建设: 可供、限制与优化——基于技术可供性视角[J]. 情报理论与实践, 2023, 46(10): 69-76.
	LIU W. Embedding ChatGPT-like generative artificial intelligence in digital government construction: Affordances, constraints, and optimization - A perspective based on technological affordance[J]. Information studies: Theory & application, 2023, 46(10): 69-76.
4	SESTINO A, D'ANGELO A. My doctor is an avatar! The effect of anthropomorphism and emotional receptivity on individuals' intention to use digital-based healthcare services[J]. Technological forecasting and social change, 2023, 191: 122505.
5	NI M Q. Digital participation for inclusive growth: A case study of Singapore's collaborative digital governance model[M]//The Urban Book Series. Cham: Springer International Publishing, 2022: 323-337.
6	KATTEL R, MERGEL I. Estonia's digital transformation: Mission mystique and the hiding hand[M]//Great Policy Successes. Cambridge, UK: Oxford University Press, 2019: 143-160.
7	TWEED A. From Eliza to Ellie: the evolution of the AI therapist[EB/OL]. [2019-09-12].
8	BHATTACHERJEE A. An empirical analysis of the antecedents of electronic commerce service continuance[J]. Decision support systems, 2001, 32(2): 201-214.
9	BHATTACHERJEE A. Understanding information systems continuance: An expectation-confirmation model[J]. MIS quarterly, 2001, 25(3): 351.
10	LEE M C. Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation–confirmation model[J]. Computers & education, 2010, 54(2): 506-516.
11	GUPTA A, DHIMAN N, YOUSAF A, et al. Social comparison and continuance intention of smart fitness wearables: An extended expectation confirmation theory perspective[J]. Behaviour & information technology, 2021, 40(13): 1341-1354.
12	YUEN K F, CAI L H, QI G Q, et al. Factors influencing autonomous vehicle adoption: An application of the technology acceptance model and innovation diffusion theory[J]. Technology analysis & strategic management, 2021, 33(5): 505-519.
13	HUARNG K H, YU T H K, LEE C F. Adoption model of healthcare wearable devices[J]. Technological forecasting and social change, 2022, 174: 121286.
14	OENTORO W. Mobile payment adoption process: A serial of multiple mediation and moderation analysis[J]. The bottom line, 2021, 34(3/4): 225-244.
15	YE J H, LEE Y S, WANG C L, et al. The continuous use intention for the online learning of Chinese vocational students in the post-epidemic era: The extended technology acceptance model and expectation confirmation theory[J]. Sustainability, 2023, 15(3): 1819.
16	HUANG Y K, YU Z Y. Understanding the continuance intention for artificial intelligence news anchor: Based on the expectation confirmation theory[J]. Systems, 2023, 11(9): 438.
17	赵雪芹, 刘雅宁. 在线视频用户关闭弹幕的影响因素研究[J]. 新闻与传播评论, 2018, 71(5): 16-26.
	ZHAO X Q, LIU Y N. A study on the factors influencing online video users to turn off Danmaku[J]. Journalism & communication review, 2018, 71(5): 16-26.
18	STONE R W, BAKER-EVELETH L. Students' expectation, confirmation, and continuance intention to use electronic textbooks[J]. Computers in human behavior, 2013, 29(3): 984-990.
19	OGHUMA A P, LIBAQUE-SAENZ C F, WONG S F, et al. An expectation-confirmation model of continuance intention to use mobile instant messaging[J]. Telematics and informatics, 2016, 33(1): 34-47.
20	李望平, 苏冬燕, 陈晓星, 等. 基于技术接受模型和计划行为理论的内陆核电站重启公众接受度研究[J]. 科技管理研究, 2023, 43(1): 171-180.
	LI W P, SU D Y, CHEN X X, et al. Study on public acceptance of restart of inland nuclear power plants based on technology acceptance model and theory of planned behavior[J]. Science and technology management research, 2023, 43(1): 171-180.
21	TAM C, SANTOS D, OLIVEIRA T. Exploring the influential factors of continuance intention to use mobile Apps: Extending the expectation confirmation model[J]. Information systems frontiers, 2020, 22(1): 243-257.
22	吴晓波, 张伟齐, 李思涵, 等. 基于技术接受模型(TAM)的智能投资顾问用户采纳意愿链式影响机制研究[J]. 浙江大学学报(人文社会科学版), 2023, 53(7): 5-19.
	WU X B, ZHANG W Q, LI S H, et al. A sequential mediation model of the factors influencing the user adoption of robo-advisor: A technology acceptance model (TAM) perspective[J]. Journal of Zhejiang University (humanities and social sciences), 2023, 53(7): 5-19.
23	KAMAL S A, SHAFIQ M, KAKRIA P. Investigating acceptance of telemedicine services through an extended technology acceptance model (TAM)[J]. Technology in society, 2020, 60: 101212.
24	RAHI S, GHANI M ABD. Integration of expectation confirmation theory and self-determination theory in Internet banking continuance intention[J]. Journal of science and technology policy management, 2019, 10(3): 533-550.
25	庞洋. 生成式人工智能嵌入数字政府建设的系统性分析[J]. 学术交流, 2023(8): 148-162.
	PANG Y. Systematic analysis of generative artificial intelligence embedded in digital government construction[J]. Academic exchange, 2023(8): 148-162.
26	杨舒文, 包林. 数字媒体艺术中的虚拟性身体形象建构[J]. 中国高校社会科学, 2023(4): 133-141.
	YANG S W, BAO L. Construction of virtual body image in digital media art[J]. Social sciences in Chinese higher education institutions, 2023(4): 133-141.
27	LI J, HUANG J S, LI Y Q. Examining the effects of authenticity fit and association fit: A digital human avatar endorsement model[J]. Journal of retailing and consumer services, 2023, 71: 103230.
28	刘斌, 崔茉琳. ChatGPT时代的虚拟数字人: 拟主体对人类认知与行为的中介[J]. 西南民族大学学报(人文社会科学版), 2023, 44(11): 126-132.
	LIU B, CUI M L. Virtual digital humans in the ChatGPT era: The mediating role of quasi-subjects on human cognition and behavior[J]. Journal of southwest Minzu university (humanities and social science), 2023, 44(11): 126-132.
29	BLUT M, WANG C, WÜNDERLICH N V, et al. Understanding anthropomorphism in service provision: A meta-analysis of physical robots, chatbots, and other AI[J]. Journal of the academy of marketing science, 2021, 49(4): 632-658.
30	BALAKRISHNAN J, ABED S S, JONES P. The role of meta-UTAUT factors, perceived anthropomorphism, perceived intelligence, and social self-efficacy in chatbot-based services?[J]. Technological forecasting and social change, 2022, 180: 121692.
31	GHASEMAGHAEI M, HASSANEIN K. Online information quality and consumer satisfaction: The moderating roles of contextual factors – A meta-analysis[J]. Information & management, 2015, 52(8): 965-981.
32	李桥. 高速铁路旅客感知便利性对旅客满意和忠诚的影响研究[J]. 铁道学报, 2021, 43(6): 1-7.
	LI Q. Effects of perceived service convenience of high-speed railway passengers on passenger satisfaction and loyalty[J]. Journal of the China railway society, 2021, 43(6): 1-7.
33	汤志伟, 龚泽鹏, 韩啸, 等. 公众对智能政务服务和人工政务服务的感知与选择——基于调查实验的研究发现[J]. 电子政务, 2023(9): 105-116.
	TANG Z W, GONG Z P, HAN X, et al. Public perception and choice between intelligent government services and human government services: Findings from a survey experiment[J]. E-Government, 2023(9): 105-116.
34	计纬, 戚海峰, 费鸿萍. 智能助手对消费者的推荐说服机制研究——基于社会角色视角[J/OL]. 南开管理评论, 2024: 1-23.
	JI W, QI H F, FEI H P. A study on the persuasive mechanism of recommendations by intelligent assistants: Based on the social role perspective[J/OL]. Nankai business review, 2024: 1-23.
35	BEDUÉ P, FRITZSCHE A. Can we trust AI? An empirical investigation of trust requirements and guide to successful AI adoption[J]. Journal of enterprise information management, 2022, 35(2): 530-549.
36	YIGITCANLAR T, DEGIRMENCI K, INKINEN T. Drivers behind the public perception of artificial intelligence: Insights from major Australian cities[J]. AI & society, 2022: 1-21.
37	CHEN Y, PRENTICE C, WEAVEN S, et al. The influence of customer trust and artificial intelligence on customer engagement and loyalty - The case of the home-sharing industry[J]. Frontiers in psychology, 2022, 13: 912339.
38	HSIAO K L, CHEN C C. What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction[J]. Library hi tech, 2022, 40(4): 929-946.
39	DAVIS F D, BAGOZZI R P, WARSHAW P R. User acceptance of computer technology: A comparison of two theoretical models[J]. Management science, 1989, 35(8): 982-1003.
40	HWANG J, CHOE J Y, KIM H M, et al. Human baristas and robot baristas: How does brand experience affect brand satisfaction, brand attitude, brand attachment, and brand loyalty?[J]. International journal of hospitality management, 2021, 99: 103050.
41	NA S, HEO S, CHOI W, et al. Firm size and artificial intelligence (AI)-based technology adoption: The role of corporate size in South Korean construction companies[J]. Buildings, 2023, 13(4): 1066.
42	刘春明, 王丹, 周杨. 高校教师移动学习平台信息采纳行为研究[J]. 情报科学, 2024, 42(2): 109-118.
	LIU C M, WANG D, ZHOU Y. A study on university teachers' adoption behavior of mobile learning platform information[J]. Information science, 2024, 42(2): 109-118.
43	朱哲慧, 袁勤俭. 技术接受模型及其在信息系统研究中的应用与展望[J]. 情报科学, 2018, 36(12): 168-176.
	ZHU Z H, YUAN Q J. Technology acceptance model and its application and prospect in the field of information system[J]. Information science, 2018, 36(12): 168-176.
44	FILIERI R, ACIKGOZ F, LI C Y, et al. Influencers' “organic” persuasion through electronic word of mouth: A case of sincerity over brains and beauty[J]. Psychology & marketing, 2023, 40(2): 347-364.
45	LIN C Y. Perceived convenience retailer innovativeness: How does it affect consumers?[J]. Management decision, 2016, 54(4): 946-964.
46	LANKTON N, MCKNIGHT D H, THATCHER J B. Incorporating trust-in-technology into expectation disconfirmation theory[J]. The journal of strategic information systems, 2014, 23(2): 128-145.
47	许孝君, 程光辉, 钱丹丹, 等. 数字经济下旅游商务网站信息生态链运行机制对用户感知的影响研究[J]. 情报科学, 2023, 41(9): 78-86.
	XU X J, CHENG G H, QIAN D D, et al. The influence of the operation mechanism of the information ecological chain of tourism business websites on user perception under the digital economy[J]. Information science, 2023, 41(9): 78-86.
48	LI L, LEE K Y, EMOKPAE E, et al. What makes you continuously use chatbot services? Evidence from Chinese online travel agencies[J]. Electronic markets, 2021, 31(3): 575-599.
49	ASHFAQ M, YUN J, YU S B, et al. I, Chatbot: Modeling the determinants of users' satisfaction and continuance intention of AI-powered service agents[J]. Telematics and informatics, 2020, 54: 101473.
50	毕达天, 张雪, 米艳霖, 等. 算法感知视角下跨平台信息推荐用户接纳意愿研究[J]. 图书情报工作, 2023, 67(21): 100-110.
	BI D T, ZHANG X, MI Y L, et al. A study on user acceptance intention of cross-platform information recommendations from the perspective of algorithm perception[J]. Library and information service, 2023, 67(21): 100-110.

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变量	类别	频率/人	百分比/%
性别	男	187	37.3
性别	女	314	62.7
年龄	18岁以下	1	0.2
	18~25岁	288	57.5
	26~35岁	163	32.5
	36~45岁	36	7.2
	46~55岁	12	2.4
	55岁以上	1	0.2
文化程度	高中及以下	18	3.6
	大专	41	8.2
	本科	350	69.9
	硕士	89	17.8
	博士	3	0.6
对政务数字人的了解程度	从来没有听说过	94	18.8
	听说过，但不了解	130	25.9
	有些了解	163	32.5
	较为了解	93	18.6
	非常了解	21	4.2

变量	题项	标准化因子载荷	Cronbach's α	CR	AVE
感知信息质量（IQ）	IQ1	0.848	0.796	0.872	0.630
	IQ2	0.777
	IQ3	0.735
	IQ4	0.810
感知智能（PI）	PI1	0.810	0.803	0.873	0.632
	PI2	0.743
	PI3	0.831
	PI4	0.794
感知吸引力（PA）	PA1	0.863	0.865	0.910	0.716
	PA2	0.786
	PA3	0.876
	PA4	0.857
感知人格化（ANT）	ANT1	0.723	0.835	0.880	0.648
	ANT2	0.841
	ANT3	0.809
	ANT4	0.840
感知便利（PC）	PC1	0.806	0.740	0.841	0.638
	PC2	0.790
	PC3	0.800
人工智能信任（TR）	TR1	0.720	0.788	0.911	0.631
	TR2	0.819
	TR3	0.758
	TR4	0.827
	TR5	0.792
	TR6	0.845
感知有用性（PU）	PU1	0.702	0.850	0.880	0.722
	PU2	0.760
	PU3	0.809
	PU4	0.799
	PU5	0.788
感知易用性（PE）	PE1	0.778	0.836	0.891	0.673
	PE2	0.856
	PE3	0.773
	PE4	0.870
期望一致（CE）	CE1	0.801	0.781	0.864	0.613
	CE2	0.745
	CE3	0.802
	CE4	0.782
满意度（SAT）	SAT1	0.742	0.811	0.877	0.588
	SAT2	0.770
	SAT3	0.801
	SAT4	0.703
	SAT5	0.814
接纳意愿（CI）	CI1	0.820	0.753	0.860	0.672
	CI2	0.821
	CI3	0.819

变量	AVE	IQ	PI	PA	ANT	PC	TR	PU	PE	CE	SAT	CI
IQ	0.630	0.794
PI	0.632	0.704	0.795
PA	0.716	0.539	0.506	0.846
ANT	0.648	0.569	0.556	0.707	0.805
PC	0.638	0.632	0.686	0.514	0.498	0.799
TR	0.631	0.697	0.726	0.574	0.628	0.717	0.794
PU	0.722	0.637	0.632	0.483	0.478	0.711	0.706	0.850
PE	0.673	0.544	0.502	0.337	0.390	0.608	0.606	0.592	0.820
CE	0.613	0.602	0.629	0.652	0.703	0.635	0.729	0.645	0.501	0.783
SAT	0.588	0.630	0.692	0.562	0.607	0.702	0.704	0.712	0.576	0.737	0.767
CI	0.672	0.592	0.595	0.488	0.521	0.703	0.731	0.713	0.560	0.621	0.736	0.820

假设	假设路径	估计值	标准误	显著性	检验结果
H1a	期望一致→满意度	0.961	0.451	***	成立
H1b	满意度→接纳意愿	0.403	0.151	**	成立
H2a	感知有用性→满意度	0.413	0.071	***	成立
H2b	感知有用性→接纳意愿	0.520	0.143	***	成立
H2c	期望一致→感知有用性	0.728	0.054	***	成立
H3a	感知吸引力→满意度	0.074	0.031	*	成立
H3b	期望一致→感知吸引力	0.960	0.075	***	成立
H3c	感知人格化→满意度	0.066	0.024	*	成立
H3d	期望一致→感知人格化	0.954	0.032	*	成立
H4a	感知智能→满意度	0.179	0.091	*	成立
H4b	期望一致→感知智能	0.207	0.082	***	成立
H4c	感知信息质量→满意度	0.324	0.091	***	成立
H4d	期望一致→感知信息质量	0.854	0.059	***	成立
H4e	感知便利→满意度	0.406	0.190	*	成立
H4f	期望一致→感知便利	0.902	0.061	***	成立
H5a	人工智能信任→满意度	0.549	0.275	*	成立
H5b	期望一致→人工智能信任	0.854	0.054	***	成立
H6a	感知易用性→感知有用性	0.195	0.034	***	成立
H6b	感知易用性→满意度	0.073	0.028	**	成立

序号	中介变量	路径	间接效应	Boot S.E.	95%CI		中介效应
序号	中介变量	路径	间接效应	Boot S.E.	下限	上限	中介效应
1	感知信息质量、公众满意度	CE→IQ→CI	0.109	0.033	0.050	0.177	是
		CE→SAT→CI	0.302	0.042	0.221	0.385	是
		CE→IQ→SAT→CI	0.081	0.020	0.046	0.125	是
2	感知智能、公众满意度	CE→PI→CI	0.072	0.030	0.013	0.131	是
		CE→SAT→CI	0.249	0.038	0.177	0.328	是
		CE→PI→SAT→CI	0.101	0.023	0.061	0.149	是
3	感知便利、公众满意度	CE→PC→CI	0.191	0.041	0.111	0.271	是
		CE→SAT→CI	0.215	0.032	0.154	0.278	是
		CE→PC→SAT→CI	0.116	0.025	0.070	0.168	是
4	感知吸引力、公众满意度	CE→PA→CI	0.035	0.031	-0.025	0.096	否
		CE→SAT→CI	0.344	0.048	0.252	0.440	是
		CE→PA→SAT→CI	0.045	0.021	0.008	0.088	是
5	感知人格化、公众满意度	CE→ANT→CI	0.033	0.033	-0.031	0.098	否
		CE→SAT→CI	0.359	0.051	0.264	0.463	是
		CE→ANT→SAT→CI	0.033	0.022	-0.009	0.077	否
6	人工智能信任、公众满意度	CE→TR→CI	0.222	0.040	0.144	0.302	是
		CE→SAT→CI	0.167	0.030	0.110	0.228	是
		CE→TR→SAT→CI	0.146	0.029	0.092	0.205	是
7	感知有用性、公众满意度	CE→PU→CI	0.312	0.051	0.211	0.410	是
		CE→SAT→CI	0.100	0.025	0.055	0.152	是
		CE→PU→SAT→CI	0.101	0.028	0.051	0.160	是
8	感知有用性、公众满意度	PE→PU→CI	0.247	0.046	0.159	0.340	是
		PE→SAT→CI	0.043	0.014	0.020	0.072	是
		PE→PU→SAT→CI	0.112	0.029	0.059	0.173	是

Expectation Confirmation and Technology Acceptance: A Study of the Public's Attention to Accept the Government's Digital Human Avatar

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