图书馆AI聊天机器人持续使用意愿研究——基于情感体验的中介

doi:10.13998/j.cnki.issn1002-1248.24-0390

Abstract

Abstract:

[Purpose/Significance] In the era of digital intelligence, robots technology is playing an increasingly important role in the field of education. The applying of AI chatbots in library scenarios is an important lever for the future construction of learning ecosystems in universities. This study aims to explain the influencing factors of users' willingness to continue using library AI chatbots, and provide a new perspective beyond the IT perspective to understand the impact of the basic characteristics of AI chatbots on human behavioral intentions, in order to better understand the sustainability thinking of interpreters and provide some inspiration for the further development of library AI chatbots in the future learning ecosystem. [Method/Process] Based on U&G theory and the SOR framework, we developed a conceptual model of library AI chatbot users's willingness to continue using the chatbot. Data were collected using a questionnaire survey method, with teachers and students as the main respondents. The variables of the AI chatbot user's continuous usage intention model were set to 8, each consisting of 3-6 options, and then measured using a 7-point Likert scale. Finally, the variables and hypotheses in the model were validated using a mixed research method of PLS-SEM and fsQCA. [Results/Conclusions] The research results indicate that three types of satisfaction, hedonic (entertainment and avoidance), social (social presence), and utilitarian (convenience and information consultation), have a significant positive impact on emotional experience (awe experience and emotional participation), with avoidance having the greatest impact on awe experience and social presence having the greatest impact on emotional participation. Emotional experience has a significant positive impact on the intention to continue using, with awe experience having the greatest impact on the intention to continue using. Emotional experience, as a mechanism of action, affects user satisfaction and willingness to use. Based on the data analysis, four suggestions are proposed from the perspective of future learning ecology design and user psychology. When designing library AI chatbots, the usage scenarios should be enriched, and the healing function, immersive experiences and immersive experiences should be emphasized. The limitation of this study is that the use of first-hand cross-sectional data cannot prove whether the influencing mechanism changes over time. In the future, a combination of first-hand and second-hand data can be used to improve the explanatory power. In addition, although this study ensures the validity and reliability of cross-sectional data, there may be geographic and cultural differences in users' behavioral intentions. In the future, a comparative study of the intention to continue using library AI chatbots in different regions and levels can be considered.

Key words: library, AI chatbot, awe experience, emotional involvement, future learning ecosystem

CLC Number:

G258.6

Haiting MA, Chuansheng CHENG. Continuance Intention to Use AI Chatbots in Libraries: Mediation Based on Emotional Experience[J].Journal of library and information science in agriculture, 2024, 36(6): 34-49.

Figures/Tables 12

Fig.1

Table 1

Factor measurement options of continuance intention to use AI chatbots"

研究变量	测量选项	来源
娱乐	EN1.我喜欢使用AI聊天机器人，因为它给我带来了很多乐趣	[34]
	EN2.我通常会花时间和AI聊天机器人在一起，因为它们很有趣
	EN3.我无聊的时候会用AI聊天机器人打发时间
逃避	ES1.AI聊天机器人帮助我忘记烦恼和压力	[35]
	ES2.AI聊天机器人帮助我避免孤独
	ES3.AI聊天机器人帮助我逃离现实世界
社交存在	SP1.与AI聊天机器人的互动让我感受到了社交生活	[36]
	SP2.我对AI聊天机器人有一种人类敏感性
	SP3.在我与AI聊天机器人的互动中，我能够做自己，展示我到底是什么样的人
	SP4.与AI聊天机器人的互动让我感觉很舒服，就像和朋友在一起一样
信息咨询	IS1.我使用AI聊天机器人来获取我需要的信息	[17]
	IS2.我确实使用AI聊天机器人作为信息来源
	IS3.使用AI聊天机器人可以提高我对所提问题的理解
	IS4.使用AI聊天机器人可以解决我的疑虑
便利性	CON1.我认为AI聊天机器人使用起来很方便	[37]
	CON2.我可以在电脑或智能手机上随时随地使用AI聊天机器人
	CON3.使用AI聊天机器人时，我可以用更少的努力得到我想要的东西
敬畏体验	当我使用AI聊天机器人产品时	[30]
	AE1.我总是有一种与它联系在一起的感觉
	AE2.我总是和它有一种合一的感觉
	AE3.有时，我感到浩瀚无垠，惊得下巴都掉下来了
	AE4.有时，我发现很难完全理解这段经历
	AE5.有时，我觉得自己置身于某种宏伟的事物中，并试图理解我所经历的
	AE6.有时，我会起鸡皮疙瘩，喘不过气来
情感参与	EI1.当我使用AI聊天机器人产品时，我会深深地感受到它	[21]
	EI2.在我结束与AI聊天机器人的对话后，我可能会保留一段时间的使用记忆
	EI3.当我使用AI聊天机器人产品时，我会沉浸于聊天机器人
持续使用	CIU1.我想继续使用AI聊天机器人，而不是停止使用	[38]
	CIU2.如果可能的话，我打算继续使用AI聊天机器人
	CIU3.我将来会继续使用AI聊天机器人

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References 47

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项目	变量	数量	占比/%
性别	男	175	44.4
	女	219	55.6
	总计	394	100.0
年龄	19岁及以下	45	11.4
	20~29岁	157	39.8
	30~39岁	120	30.5
	40~49岁	63	16.0
	50岁及以上	9	2.3
	总计	394	100.0
受教育程度	本科	246	62.4
	硕士及以上	148	37.6
	总计	394	100.0
聊天机器人的使用频率（h/天）	0.5h及以下	153	38.8
	0.5~1h	105	26.6
	1~2h	89	22.6
	2~5h	37	9.4
	5h及以上	10	2.5
	总计	394	100.0

Construct	Cronbach's α	VIF	AVE	CR	MSV	ASV
EN	0.839	1.575	0.755	0.902	0.346	0.510
ES	0.845	1.852	0.763	0.906	0.346	0.540
SP	0.864	1.665	0.710	0.907	0.403	0.573
IS	0.868	1.733	0.717	0.910	0.403	0.557
CON	0.829	1.601	0.744	0.897	0.337	0.547
AE	0.899	1.599	0.664	0.922	0.380	0.534
EI	0.837	1.627	0.754	0.902	0.360	0.545
CIU	0.820	1.634	0.736	0.893	0.380	0.552

项目	EN	ES	SP	IS	CON	AE	EI	CIU
EN	0.869
ES	0.497	0.873
SP	0.486	0.483	0.843
IS	0.410	0.437	0.550	0.847
CON	0.417	0.451	0.465	0.491	0.863
AE	0.422	0.473	0.472	0.480	0.460	0.815
EI	0.442	0.449	0.515	0.474	0.457	0.427	0.868
CIU	0.361	0.421	0.471	0.509	0.479	0.533	0.476	0.858

项目	EN	ES	SP	IS	CON	AE	EI	CIU
EN	-
ES	0.590	-
SP	0.570	0.565	-
IS	0.480	0.511	0.635	-
CON	0.496	0.538	0.549	0.575	-
AE	0.481	0.543	0.534	0.541	0.528	-
EI	0.525	0.533	0.604	0.554	0.547	0.492	-
CIU	0.434	0.505	0.559	0.606	0.578	0.619	0.575	-

项目	假设	路径系数	t统计量	p值	是否支持假设
EN→AE	H1a	0.109	2.032	0.042	Yes*
EN→EI	H1b	0.135	2.619	0.009	Yes**
ES→AE	H2a	0.190	3.685	<.001	Yes***
ES→EI	H2b	0.132	2.491	0.013	Yes*
SP→AE	H3a	0.145	2.498	0.013	Yes*
SP→EI	H3b	0.224	3.927	<.001	Yes***
IS→AE	H4a	0.190	3.159	0.002	Yes**
IS→EI	H4b	0.159	3.000	0.003	Yes**
CON→AE	H5a	0.168	3.413	0.001	Yes**
CON→EI	H5b	0.159	2.985	0.003	Yes**
AE→CIU	H6	0.404	9.228	<.001	Yes***
EI→CIU	H7	0.304	6.548	<.001	Yes***

Continuance Intention to Use AI Chatbots in Libraries: Mediation Based on Emotional Experience

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项目	直接效应	p值	间接效应	95%置信区间	SE值	调节类型
EN→AE→CIU	0.066	0.143	0.152	(0.104, 0.205)	0.026	间接调节
EN→EI→CIU	0.066	0.143	0.119	(0.075, 0.166)	0.024	间接调节
ES→AE→CIU	0.125	0.006	0.158	(0.109, 0.214)	0.027	补充调节
ES→EI→CIU	0.125	0.006	0.112	(0.067, 0.161)	0.024	补充调节
SP→AE→CIU	0.184	<.001	0.159	(0.110, 0.212)	0.026	补充调节
SP→EI→CIU	0.184	<.001	0.119	(0.031, 0.110)	0.027	补充调节
IS→AE→CIU	0.243	<.001	0.146	(0.026, 0.126)	0.025	补充调节
IS→EI→CIU	0.243	<.001	0.102	(0.098, 0.198)	0.025	补充调节
CON→AE→CIU	0.203	<.001	0.146	(0.100, 0.198)	0.025	补充调节
CON→EI→CIU	0.203	<.001	0.105	(0.061, 0.152)	0.023	补充调节

前因条件	一致性	覆盖度
娱乐	0.741	0.786
~娱乐	0.431	0.683
社交存在	0.777	0.815
~社交存在	0.396	0.638
逃避	0.760	0.803
~逃避	0.408	0.650
信息咨询	0.770	0.819
~信息咨询	0.399	0.629
便利性	0.789	0.807
~便利性	0.377	0.631
敬畏体验	0.810	0.831
~敬畏体验	0.373	0.621
情感参与	0.791	0.809
~情感参与	0.377	0.633

充分条件	持续使用意愿
充分条件	S1	S2	S3	S4	S5	S6
娱乐	●		⊗	◆	◆	⊗
逃避	●		●	◆	●	●
社交存在	●	◆	●	⊗	●	●
信息咨询		●		●	⊗	◆
便利性	●	●	◆	●
敬畏体验		●	●	●	●	●
情感参与	●	◆	⊗		◆	◆
一致性	0.948	0.960	0.950	0.953	0.936	0.960
原始覆盖度	0.524	0.532	0.149	0.175	0.180	0.189
唯一覆盖度	0.027	0.039	0.012	0.014	0.005	0.010
总体解的一致性	0.923
总体解的覆盖度	0.632

子样本模型	原始覆盖度	唯一覆盖度	一致性
M1: ENSPESCONAE*EI	0.424	0.032	0.946
M2: ENSPISCONAE*EI	0.419	0.026	0.952
M3: ENESISCONAE*EI	0.410	0.017	0.954
M4: ~ENSPESISCONAE~EI	0.141	0.018	0.940
M5: ~ENSPESIS~CONAEEI	0.141	0.025	0.943
总体解的一致性	0.925
总体解的覆盖度	0.513

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