AIGC用户中辍行为影响因素模型构建与实证研究

doi:10.13998/j.cnki.issn1002-1248.24-0314

摘要/Abstract

摘要：

[目的/意义] 为了厘清AIGC情境下用户心理韧性的驱动因素以及AIGC用户中辍行为的特征规律，有效缓解AIGC用户在初始采纳阶段后流失和中辍等消极行为造成的潜在风险，刺激AIGC用户持续使用，促进AIGC产业由技术驱动向用户驱动的高质量转变。 [方法/过程] 研究以韧性理论和S-O-R理论为基础，构建了AIGC用户中辍行为影响因素研究模型，通过发放问卷的形式，搜集了328份原始数据对所构建的模型进行实证和检验。 [结果/结论] 研究结果显示，心理韧性是有效缓解AIGC用户中辍行为的重要因素，技术韧性和信息质量是提升用户心理韧性的重要驱动因素，以此为基础，提出了提升用户心理韧性、防止用户中辍，促进用户持续使用的对策与建议。

关键词: 心理韧性, 中辍行为, AIGC, S-O-R理论, 累积性情感因素, 信息行为

Abstract:

[Purpose/Significance] In the context of the rapid development of the artificial intelligence generated content (AIGC), it is crucial to understand the driving factors of users' psychological resilience and the characteristics of AIGC users' dropout behavior. This research focuses on this area to address the lack of in-depth studies in the existing literature. It aims to contribute to the knowledge system by providing a more comprehensive understanding of user behavior in the context of the AIGC. This is significant for promoting the transformation of the AIGC industry, as it helps to reduce the negative impacts of user loss and transfer, and promotes the sustainable use of the AIGC. It also has practical value in addressing the challenges facing the industry. [Method/Process] This study is based on resilience theory and S-O-R theory, which provide a solid theoretical foundation for the research. A questionnaire survey method is used, which is an appropriate approach for collecting data directly from users. A total of 328 questionnaires were collected from a wide range of AIGC users, ensuring the representativeness and reliability of the data. The empirical analysis and testing of the constructed model helps to validate the research hypotheses and draw meaningful conclusions. [Results/Conclusions] The research shows that psychological resilience is indeed a key factor in reducing dropout among AIGC users. Technological resilience and information quality play an important role in enhancing the psychological resilience of users. Based on these results, specific strategies and suggestions are proposed, such as improving the technological stability and performance of the AIGC, enhancing the quality of the information provided, and providing personalized support and training for users. However, there are some limitations to this study. For example, the sample size may not be large enough to cover all types of AIGC users. Future research could increase the sample size and explore other potential factors that may influence user behavior. In addition, longitudinal studies could be conducted to better understand the dynamic changes in user behavior over time. In conclusion, this study provides valuable insights into the factors influencing AIGC user dropout behavior and offers practical suggestions for promoting user retention and sustainable use. It paves the way for further research in this field and contributes to the development of the AIGC industry.

Key words: psychological resilience, dropout behavior, AIGC users, S-O-R theory, accumulated emotional factors, information behavior

中图分类号: G203;G252

姚丽琴, 张海. AIGC用户中辍行为影响因素模型构建与实证研究[J]. 农业图书情报学报, 2024, 36(5): 79-92.

Liqin YAO, Hai ZHANG. Model Construction and Empirical Research on the Influencing Factors of AIGC User Dropout Behavior[J]. Journal of Library and Information Science in Agriculture, 2024, 36(5): 79-92.

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统计指标	类型	频次	比率/%	统计指标	类型	频次	比率/%
性别	男	176	53.66	年龄	25岁以下	98	29.88
	女	152	46.34		26~36	172	52.44
	合计	328	100.00		37~47	38	11.58
					48岁及以上	20	6.10
					合计	328	100.00
				使用平台	ChatGPT	168	51.22
学历	高中以下	34	10.37		文心一言	75	22.87
	本（专）科	156	47.56		讯飞星火	48	14.63
	硕士及硕士研究生	96	29.27		百川智能	33	10.06
	博士及博士研究生	42	12.80		其他平台	4	1.22
	合计	328	100.00		合计	328	100.00

变量	测度项	Cronbach' α系数	测度项删除后Cronbach' α系数	因子载荷	CR	AVE
技术韧性（TRE）	TRE_1	0.835	0.739	0.724	0.775	0.634
	TRE_2		0.812	0.698
	TRE_3		0.745	0.769
技术拟人性（TEP）	TEP_1	0.826	0.678	0.813	0.822	0.607
	TEP_2		0.782	0.767
	TEP_3		0.807	0.756
信息质量（INQ）	INQ_1	0.903	0.853	0.726	0.787	0.653
	INQ_2		0.794	0.803
	INQ_3		0.648	0.697
心理韧性（PYR）	PYR_1	0.796	0.658	0.835	0.816	0.597
	PYR_2		0.627	0.764
	PYR_3		0.732	0.714
满意度（SAT）	SAT_1	0.851	0.721	0.768	0.829	0.619
	SAT_2		0.797	0.753
	SAT_3		0.824	0.837
中辍行为（DPB）	DPB_1	0.894	0.852	0.864	0.859	0.669
	DPB_2		0.769	0.786
	DPB_3		0.824	0.803

变量	TRE	TEP	INQ	PYR	SAT	DPB
TRE	0.796
TEP	0.475	0.779
INQ	0.368	0.464	0.808
PYR	0.332	0.532	0.438	0.772
SAT	0.437	0.455	0.346	0.397	0.786
DPB	0.476	0.378	0.236	0.232	0.437	0.818

变量	测度项	测量内容	文献来源
技术韧性	TRE_1	我认为，AIGC技术是未来的发展趋势	LEE A R 倪士光陈智
	TRE_2	我认为，AIGC技术能够适应社会发展，不断更新
	TRE_3	我认为，AIGC技术能够克服技术瓶颈，不断演进
技术拟人性	TEP_1	我觉得AIGC的信息内容符合我的阅读习惯	唐文龙 PARK M
	TEP_2	我觉得AIGC服务比较了解我的信息需求
	TEP _3	我觉得AIGC技术较为智能，拟人程度较高
信息质量	INQ_1	我觉得AIGC提供的信息是可靠的	YUAN 王晰巍等
	INQ_2	AIGC为我提供了准确信息
	INQ_3	我从AIGC获得了足够的信息
心理韧性	PYR_1	面对困难时，我会集中自己的全部精力	HUANG 张秀娥
	PYR_2	面对困难时，我能够控制好自己的情绪
	PYR_3	经历困难和挫折后，我一般会比较成熟和有经验
满意度	SAT_1	AIGC服务平台总体上达到了我的预期	张海 YUAN
	SAT_2	AIGC服务平台总体上能够满足我的需求
	SAT_3	我对AIGC服务平台的交互体验感到满意
中辍行为	DPB_1	使用一段时间后，我会少用或者不用AIGC服务平台	张敏甘春梅
	DPB_2	我目前不会再用了，等技术有突破的时候，我再使用
	DPB_3	工作的时候经常使用，生活的时候很少使用

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