Perceived algorithmic recommendation features and information fatigue among social media users

Abstract This study develops an integrated framework to examine how perceived recommendation accuracy, perceived algorithmic transparency, and perceived recommendation novelty are associated with information fatigue through perceived information overload and perceived information narrowing on social media platforms. Using survey data from 425 adult users, the study employed PLS-SEM, cIPMA, and fsQCA. The PLS-SEM results show that perceived recommendation accuracy is positively associated with both cognitive appraisals and is indirectly associated with information fatigue through them. Perceived algorithmic transparency is positively associated with perceived information overload and indirectly associated with information fatigue through this appraisal. Perceived recommendation novelty is negatively associated with perceived information overload, perceived information narrowing, and information fatigue. Both cognitive appraisals are positively associated with information fatigue. cIPMA identifies perceived recommendation novelty as having the largest absolute total association with information fatigue, followed by perceived information overload and perceived information narrowing, while no antecedent is a necessary condition for high information fatigue. fsQCA identifies two configurations associated with high information fatigue and four with non-high information fatigue. The findings clarify the appraisal and configurational mechanisms underlying information fatigue and offer empirically informed directions for future platform design and testing concerning relevance, novelty, diversity, and processing demands.