ScholarWorks@중앙대학교

초록

This study employs the Stressor–Strain–Outcome(SSO) model to examine the mechanisms underlying algorithm fatigue and algorithm resistance behaviors among middle-aged Chinese TikTok users. Specifically, technical factors (algorithm opacity, algorithm bias, and algorithm hegemony) and personal factors (information overload and privacy concerns) were conceptualized as stressors, and their effects on algorithm fatigue were analyzed. Algorithm fatigue was treated as the strain variable, while algorithm resistance coping was considered the outcome variable. In addition, loneliness was examined as a moderator that conditions the relationship between stressors and algorithm fatigue. To test the proposed hypotheses and answer the research questions, we conducted an online survey of 437 middle-aged Chinese TikTok users. The results revealed that both technical and personal stressors had significant positive effects on algorithm fatigue, which, in turn, was a key driver of algorithm resistance coping. Furthermore, loneliness moderated the relationship between algorithm hegemony and algorithm fatigue. These findings theoretically contribute to the literature by elucidating the mechanisms through which algorithm fatigue and resistance occur.

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