Cross-sectional study links Douyin overload, fatigue and quitting intentions

A cross-sectional survey of Generation Z Douyin users aged 18–29 in Shenzhen, China, found that information, system-feature, communication and social overload were each positively associated with discontinuance intention—users’ stated intentions to stop using the platform. The study, published in BMC Psychology , found that fatigue statistically mediated three of those relationships but not the relationship involving social overload. Researchers distributed an online questionnaire through WeChat after ethics approval and received 583 responses, of which 568 were valid and analysed.

The participants were Douyin users aged 18 to 29 living in Shenzhen. Using partial least squares structural equation modelling, the authors examined associations among the four overload dimensions, fatigue, flow experience and discontinuance intention. All four forms of overload were positively associated with discontinuance intention in the statistical model.

Fatigue served as a statistically supported mediator for information overload, system-feature overload and communication overload, indicating that fatigue accounted for part of their association with intentions to stop using Douyin. By contrast, social overload was associated with discontinuance intention without a statistically supported fatigue pathway. The researchers also examined flow experience as a moderator of the relationship between fatigue and discontinuance intention.

The fatigue-by-flow interaction was positive, with a coefficient of β = 0.112 and p = 0.013. Specifically, the conditional slope linking fatigue to discontinuance intention increased from 0.247 at low flow to 0.471 at high flow. This result showed that the fatigue–discontinuance association was stronger at higher flow levels rather than weaker.

The authors interpreted the finding as indicating that immersive engagement may coexist with heightened withdrawal intentions and, under high fatigue, amplify the association between fatigue and the intention to disengage. The analysis did not establish that flow caused either fatigue or discontinuance intention. The model explained 28.8% of the variance in fatigue and 47.4% of the variance in discontinuance intention.

These values represent the variation accounted for by the variables included in the model; they are not percentages of users who experienced fatigue or intended to discontinue Douyin. They also do not demonstrate that the model would perform similarly in other cities, age groups or platforms. Overall, the findings point to distinct pathways linking overload with discontinuance intention.

Information, system-feature and communication overload were associated with discontinuation partly through fatigue, while social overload had a different pattern. Because these are associations in a cross-sectional model, the results cannot establish whether overload preceded fatigue, whether fatigue increased withdrawal intentions over time, or whether users subsequently stopped using the platform. The study’s reliance on self-reported data from young adults in one Chinese city further limits what can be inferred about behaviour over time or beyond the surveyed population.

Subject of Research: Structural equation modelling of social media overload, fatigue, flow and discontinuance intention among Generation Z Douyin users in Shenzhen

Article Title: Social media overload, fatigue, and discontinuance intention among Generation Z Douyin users in Shenzhen, China: A cross-sectional structural equation modelling study

Article References: Peng, W., & Ismail, F. R. (2026). Social media overload, fatigue, and discontinuance intention among Generation Z Douyin users in Shenzhen, China: A cross-sectional structural equation modelling study. BMC Psychology. https://doi.org/10.1186/s40359-026-05563-6

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05563-6

Keywords: Douyin, Generation Z, social media overload, digital fatigue, discontinuance intention, flow experience, structural equation modelling, cross-sectional survey, Shenzhen, short-video platforms, Social, media