AIGC平台“Z世代”用户持续使用意愿影响因素研究

doi:10.13998/j.cnki.issn1002-1248.25-0032

Abstract

Abstract:

[Purpose/Significance] The proliferation of generative artificial intelligence (AIGC) platforms has ushered in a transformative era for content creation. However, the industry is facing significant challenges due to technological homogenization. Platforms struggle to retain users, particularly Generation Z (those born between 1995 and 2009), due to standardized architectures, overlapping data sources, and repetitive training methodologies. Generation Z exhibits low loyalty and high migration tendencies. They are digital natives whose behaviors are shaped by unique socio-technological traits. They prioritize immersive experiences, thrive in circle culture, and rely heavily on peer-driven decision-making. However, existing studies primarily focus on generic user groups, and the ways in which these distinct characteristics influence sustained engagement with AIGC tools. This research bridges the gap by integrating the Stimulus-Organism-Response (SOR) framework with Generation Z's behavioral patterns, creating a new theoretical model that explains their constant usage intentions. These findings advance the theoretical discourse on user behavior in AI-driven ecosystems. They also offer actionable strategies for platforms to differentiate themselves in a user-centric way. This addresses critical challenges in an increasingly saturated market. [Method/Procedure] Guided by the Stimulus-Organism-Response (SOR) theoretical framework, this study proposed a model to examine the impact of xternal stimuli, such as circle influence, online word-of-mouth, and platform quality on continuous usage intention, as mediated by satisfaction and immersive experience., Individual innovativeness is considered to be a moderating factor. Data were collected via an online questionnaire distributed to 356 Gen Z users with experience using AIGC platforms. A 7-point Likert scale was used to measure constructs. Structural equation modeling (SEM) was employed to test the hypothesized relationships, including reliability and validity checks, as well as mediation effect, and moderation analyses. [Results/Conclusions] The findings reveal that circle influence, online word-of-mouth, and platform quality have a significant and positive impact on user satisfaction and immersive experience, These factors then mediate the relationship with continuous usage intention. Among these factors, circle influence demonstrates the strongest effect on satisfaction, highlighting Gen Z's social identity and dependence on their peers. Although platform quality is less dominant than social and reputational factors, it remains a foundational driver of user experience. It was found that individual innovativeness positively moderates the relationship between immersive experience and continuous usage intention. This indicates that users with higher innovativeness derive greater satisfaction from interactive experiences, which enhances their loyalty. However, no significant moderating effect of individual innovativeness was observed between satisfaction and continuous usage intention. Accordingly, the following suggestions are put forward to promote users' continuous usage intention. These suggestions include optimizing online word-of-mouth, strengthening circle operation, and enhancing the guidance of innovativeness. Future research could focus on exploring the differences in continuous usage intention among different types of AIGC platforms for Generation Z. Additionally, the model of influencing factors could be further refined to consider more complex real-world scenarios.

Key words: Generation Z, generative artificial intelligence platform, stimulus-organic-response model, user willingness to continue using, information behavior

CLC Number:

G252.0

GOU Ruike, LUO Wei. Influencing Factors of Continuous Use Intention of "Generation Z" Users of an AIGC Platform[J].Journal of library and information science in agriculture, 2025, 37(3): 66-80.

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变量	测量题项	参考来源
网络口碑（IW）	IW1：网上对AIGC平台的关注度很高 IW2：近期我看到过很多针对AIGC平台的口碑信息 IW3：该网络口碑内容客观、公正地描述了AIGC平台的相关信息 IW4：AIGC平台有大量正面口碑时，我会选择继续使用该平台	刘筱婷^[58]
平台质量（PQ）	PQ1：AIGC平台提供的信息准确且全面 PQ2：AIGC平台提供的信息能满足我的需求 PQ3：AIGC平台的系统能可靠且稳定地运行 PQ4：在AIGC平台遇到使用问题时平台能及时响应并解决	PARASURAMAN等^[59] 李睿智等^[60] 姜文博等^[61]
圈层影响（SI）	SI1：我信任圈层中共享的信息 SI2：圈层中的好友们希望我使用AIGC平台 SI3：基于圈层中的见闻，我被鼓励去使用AIGC平台	张长亮^[53] 赵宇翔等^[62] 谭春辉等^[63]
沉浸体验（IE）	IE1：我认为在AIGC平台上与机器以自然语言交流互动很有趣 IE2：AIGC平台上依据我的想法生成的信息、图像等很有意思 IE3：我在AIGC平台上查找信息时，我感觉到了探索的兴奋	孙祺宇等^[56]
满意度（S）	S1：AIGC平台的体验使我感到愉悦 S2：我认为使用AIGC平台的服务是明智的选择 S3：AIGC平台基本满足我的需求，整体上感觉十分满意	BHATTACHERJEE^[9]
个体创新性（ID）	ID1：我很乐于接受新事物和新观点 ID2：我很乐意去尝试新的技术/产品/服务 ID3：我通常比周围的人更先尝试新技术/新产品 ID4：使用AIGC平台使我觉得好奇新鲜	石婷婷^[64]
持续使用意愿（CI）	CI1：未来我打算继续使用AIGC平台 CI2：未来我会保持目前频率甚至增加频率使用AIGC平台 CI3：我愿意向身边的人推荐使用AIGC平台	BHATTACHERJEE^[9] 谭春辉等^[63]

类别	选项	人数/人	比例/%
性别	男	170	47.75
性别	女	186	52.25
出生年份	1995年以前	15	4.21
	1995—2000年	75	21.07
	2001—2005年	129	36.24
	2006—2009年	128	35.96
	2009年以后	9	2.53
学历	高中及以下	144	40.45
	专科	60	16.85
	本科	91	25.56
	硕士	48	13.48
	博士	13	3.65
使用历史	3个月以内	87	24.44
	3个月到半年	135	37.92
	半年到一年	98	27.53
	一年到两年	36	10.11
使用频率	每周1次甚至更少	26	7.30
	每周2~3次	80	22.47
	每周4~6次	150	42.13
	每周6次以上	100	28.09
每次使用时长	少于30分钟	31	8.71
	0.5~1小时	120	33.71
	1~2小时	143	40.17
	2~3小时	36	10.11
	3小时以上	26	7.30

潜变量	题项	标准化载荷	克隆巴赫（α）	CR	AVE
圈层影响	SI1	0.816	0.877	0.877	0.640
	SI2	0.783
	SI3	0.795
	SI4	0.806
网络口碑	IW1	0.780	0.874	0.874	0.634
	IW2	0.802
	IW3	0.773
	IW4	0.829
平台质量	PQ1	0.789	0.870	0.870	0.626
	PQ2	0.809
	PQ3	0.751
	PQ4	0.815
个体创新性	PI1	0.797	0.861	0.861	0.608
	PI2	0.756
	PI3	0.781
	PI4	0.785
沉浸体验	IE1	0.793	0.875	0.832	0.623
	IE2	0.794
	IE3	0.780
满意度	S1	0.782	0.832	0.876	0.638
	S2	0.801
	S3	0.787
	S4	0.824
持续使用意愿	CI1	0.805	0.884	0.884	0.657
	CI2	0.808
	CI3	0.830
	CI4	0.798

变量	CI	S	IE	PI	PQ	IW	SI
CI	0.810
S	0.503	0.799
IE	0.500	0.466	0.789
PI	0.477	0.458	0.433	0.780
PQ	0.489	0.46	0.426	0.447	0.791
IW	0.501	0.498	0.447	0.465	0.538	0.796
SI	0.497	0.491	0.441	0.449	0.464	0.455	0.800

研究假设	作用关系	标准化影响因素	S.E.	C.R.	P	是否成立
H1a	圈层影响显著正向影响用户满意度	0.290	0.067	4.140	***	是
H1b	圈层影响显著正向影响用户的沉浸体验	0.260	0.068	3.461	***	是
H2a	网络口碑显著正向影响用户满意度	0.280	0.065	4.630	***	是
H2b	网络口碑显著正向影响用户的沉浸体验	0.250	0.066	3.894	***	是
H3a	平台质量显著正向影响用户满意度	0.200	0.070	2.866	0.004	是
H3b	平台质量显著正向影响用户的沉浸体验	0.200	0.072	2.70	0.007	是
H4	满意度显著正向影响用户的持续使用意愿	0.370	0.058	5.972	***	是
H6	沉浸体验显著正向影响用户的持续使用意愿	0.370	0.062	5.791	***	是

Influencing Factors of Continuous Use Intention of "Generation Z" Users of an AIGC Platform

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假设	中介路径	效应值	S.E.	Lower	Upper	P
H5	圈层影响-满意度-持续使用意愿	0.042	0.017	0.015	0.082	0.002
	网络口碑-满意度-持续使用意愿	0.039	0.016	0.013	0.079	0.002
	平台质量-满意度-持续使用意愿	0.030	0.013	0.010	0.063	0.003
H7	圈层影响-沉浸体验-持续使用意愿	0.040	0.015	0.016	0.077	***
	网络口碑-沉浸体验-持续使用意愿	0.038	0.015	0.015	0.075	***
	平台质量-沉浸体验-持续使用意愿	0.032	0.014	0.010	0.066	0.001

假设	路径描述	相关系数	S.E.	C.R.	P	是否支持
H8a	满意度*个体创新性->持续使用意愿	0.080	0.043	1.856	0.063	不支持
H8b	沉浸体验*个体创新性->持续使用意愿	0.136	0.043	3.164	0.002	支持

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