基于元分析的GAI用户持续使用意愿影响因素研究OACHSSCD

[Purpose/Significance]This study aims to address the critical transition from the initial use of Generative Artificial Intelligence(GAI)to its continued use,focusing on identifying and quantifying the key factors that influence users'continued usage intention.The importance of this research lies in its effort to overcome the fragmentation and non-comparability of previous studies,offering a unified framework that can provide actionable insights for future research,poli-cymaking,technological development,and product iteration.The findings aim to enhance understanding of user engage-ment with GAI,which has become a crucial tool in various industries such as education,healthcare,and manufacturing.This research highlights the necessity of investigating the long-term usage patterns of GAI to ensure its effective integration and continued value generation in real-world applications.[Method/Process]A meta-analysis was conducted on 34 stu-dies,both domestic and international,to examine the overall effects of various determinants on GAI users'continued usage intention.The analysis involved tests for heterogeneity,publication bias,and overall effect sizes.Meta-regression was employed to explore moderating effects,enabling a comprehensive understanding of how different factors interact.The study integrated diverse methodologies from existing literature to establish a holistic model that quantifies the strength of influence each factor has on users'continued intention to use GAI.This approach helped to provide empirical evidence and a clear framework for understanding the determinants of GAI adoption and continued use.[Result/Conclusion]The results demonstrate that most of the variables under study have a significant positive effect on GAI users'continued usage intention,with varying effect sizes.Performance expectancy and expectation confirmation show particularly strong effects,suggesting that users'perceptions of the usefulness and effectiveness of GAI,as well as the fulfillment of their expecta-tions,are critical drivers for their continued engagement with the technology.Furthermore,significant heterogeneity was observed across samples,indicating that the impact of these variables depends heavily on contextual factors such as user characteristics and application scenarios.The findings are statistically robust and offer practical insights for future research on GAI.This research also suggests that user satisfaction with GAI,driven by its perceived value and ease of use,plays a key role in enhancing continued engagement.The study's conclusions are of significant theoretical value,offering a solid empirical foundation for understanding user retention mechanisms of GAI.The paper also provides valuable implications for the design and optimization of GAI systems to improve user experience,increase adoption,and ensure long-term success.