A predictive model for online content virality: the case of UPOU's massive open online courses

Purpose This study aims to develop a predictive model for online content virality by identifying key drivers of virality and exploring their interactions. In doing so, the research addresses the lack of consensus on a universal model for predicting virality within today's interconnected digital landscape. Design/methodology/approach To achieve this goal, a quantitative methodology was used, featuring a cross-sectional survey of 380 respondents who registered for University of the Philippines Open University's (UPOU) Massive Open Online Courses (MOOCs). Data collection was conducted through an online survey to investigate factors initiating viral spread, utilizing descriptive statistics, correlation analyses and binary logistic regression to evaluate the interplay of external, intrapersonal and interpersonal factors. Findings The resulting analysis presents a predictive model highlighting the significance of both online and offline sharing. It demonstrates a “social sharing infinity loop,” where word of mouth contributes to a continuous cycle of virality, where relevant or useful content continues to spread, further amplifying its reach. These findings offer specific insights into the complex dynamics of content virality within educational settings like UPOU's MOOCs. Practical implications By understanding these factors, practitioners can optimize digital strategies to enhance the visibility and impact of online learning content. Originality/value The value of this study lies in its proactive approach; while understanding factors that initiate viral spread typically occurs after content has gained traction, this paper provides a predictive model to anticipate such behavior. It contributes a comprehensive framework for understanding how the “social sharing infinity loop,” driven by word of mouth and relevance, sustains extensive dissemination across social circles.