Click, watch, share: Unpacking what drives millennials to spread online video advertising

Abstract

The rapid evolution of digital media consumption has transformed advertising strategies, with online video advertisements (OVAs) emerging as a dominant medium. This study investigates the factors influencing millennials’ intention to share OVAs, focusing on the role of attitude, subjective norms, and the mediating effect of personal involvement. Grounded in the Theory of Reasoned Action (TRA), the research posits that both attitudinal and normative beliefs significantly shape behavioral intentions. Data were collected through a purposive sampling method targeting Malaysian millennials who actively engage with OVAs. Structural Equation Modeling using SmartPLS was employed to assess the hypothesized relationships. Findings reveal that attitude and subjective norms have a significant and positive influence on the intention to share OVAs. More importantly, personal involvement significantly mediates these relationships, indicating that when millennials perceive advertisements as personally relevant or engaging, their likelihood of sharing increases. This suggests that emotional and cognitive resonance with content amplifies the impact of internal evaluations and social influences. The study contributes theoretically by extending TRA with the inclusion of personal involvement as a mediating construct, offering a deeper understanding of the psychological mechanisms behind digital sharing behavior. Practically, it highlights the importance for marketers to craft engaging and personally relevant video content that aligns with millennials' interests and social values. By incorporating elements such as storytelling, personalization, and peer influence, advertisers can enhance the shareability and reach of OVAs. Overall, the research provides critical insights into designing more effective digital advertising strategies targeting the millennial segment. Future research may explore cross-generational comparisons or integrate other behavioral models to further enhance predictive validity and applicability across diverse demographic contexts.