Exploring user-generated content motivations: A systematic review of theoretical perspectives and empirical gaps in online learning

Abstract

Technological advancements, digital transformation, and the increasing prominence of web-based platforms have significantly expanded the pool of online content producers, particularly within the User-Generated Content (UGC) model. This study comprehensively reviews the literature on UGC- generative motivations published from January 2005 to December 2022. Using the Web of Science (WoS) and China National Knowledge Infrastructure (CNKI) databases, we updated retrieving English and Chinese literature in June and November 2024, respectively. We screened the identified studies based on specific inclusion and exclusion criteria, resulting in 63 and another 3 primary studies. These studies were analyzed to extract 13 distinct UGC-generative motivations, 46 motivation influence factors, and 22 most empirically supported theoretical perspectives. The relationship between motivations and motivation influence factors was classified into intrinsic, extrinsic, personal, and technical levels. Our findings indicate a notable gap in empirical research regarding UGC generation from the perspectives of knowledge ecosystems and cognitive surplus, particularly in the context of Technical and Vocational Education and Training (TVET) online learning. The study underscores the importance of leveraging cognitive surplus to enhance the UGC knowledge ecosystem, specifically recommending targeted strategies for educators and platform designers to motivate TVET teachers to contribute to UGC effectively.