LAUSR

Social network sites (SNSs) provide new avenues for self-expression and connectivity, and they have considerable potential to strengthen social capital and psychological well-being. SNSs have consequently become deeply rooted in people’s daily lives. During the COVID-19 pandemic, e-learning has become a dominant learning modality to maintain social distancing. Because of the excellent connectivity provided by Internet platforms, SNSs can be leveraged as collaborative learning tools to enhance learning performance. However, conflicts may emerge when extending the socializing function to learning; thus, this topic merits in-depth investigation. One potential reason for the conflicts is the various types of overload caused by the system features, information, communication, and social aspects that users experience, leading to negative emotional responses, such as social network fatigue. Although SNS overloads have been extensively studied, most of these studies were conducted from the perspective of SNSs as platforms for socializing, and the overloads were treated as linear and independent. We apply multi-criteria decision-making tools to bridge the research gaps. Specifically, we recruited 15 active Facebook learning community members as an expert panel under the saturation principle. After extensive pairwise comparisons between the primary constructs and further matrix calculations, our significant research findings include antecedents to social network fatigue and their causal effects, representing a valuable complement to conventional structural equation modeling–approaches. We also discuss the theoretical and practical implications of the study.