Exploring Podcast And Videocast Consumption: A Construct Development And Validation Study Using TPB

Digital content consumption, particularly through podcasts and videocasts, is rapidly reshaping media engagement patterns globally. While existing research has explored general online behavior, there remains a gap in understanding the psychological drivers specific to digital audio and video content consumption, especially in emerging markets. This study aims developed and validated a Digital Content Consumption (DCC) scale within the Brazilian context, integrating it with the Theory of Planned Behavior (TPB) to enhance our understanding of consumption behavior. A cross-sectional survey was conducted among Brazilian consumers during thew month May of 2023. Participants were recruited through a convenience sample of adult internet users who had consumed podcasts or videocasts within the past month. The primary exposure was the consumption of digital audio and video content. The study employed Exploratory Factor Analysis, Confirmatory Factor Analysis, and Structural Equation Modeling (SEM) to analyze the data and validate the DCC scale. The final sample comprised 223 participants, with a mean age of 32 years and an even gender distribution. The SEM analysis demonstrated a strong model fit for the DCB scale within the TPB framework. Attitudes emerged as a significant predictor of both intention and behavior in digital content consumption. Utilitarianism was found to mediate the relationship between attitudes and consumption. The DCCB scale showed high internal consistency and construct validity across different demographic subgroups. The development and validation of the DCC scale provide a robust tool for measuring digital content consumption behavior. This study extends the application of the Theory of Planned Behavior to the specific domain of podcast and videocast consumption, offering valuable insights for content creators and marketers. Future research should explore the scale's applicability in diverse cultural contexts and its potential for predicting long-term engagement patterns with longitudinal studies