Model Construction and Empirical Research on the Influencing Factors of AIGC User Dropout Behavior
[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.
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