The shift toward fragmented and ubiquitous use of multimedia poses several challenges to our understanding and assessment of multimedia exposure and its effects. This article focuses on multimedia ...advertising exposure and its impact on consumer behavior. It presents the development of Multimedia Ad Exposure Scale (MMAES) – an instrument designed to measure short-term effects of online multimedia ad exposure in terms of engagement, psychological reactance, awareness and attitude, and purchase intention. The main research challenge has been to identify core dimensions that can reliably measure such exposure, particularly in the context of ad-supported video streaming. The development of MMAES is presented through its conceptualization, operationalization, and an observational study conducted via crowdsourcing. The target group is young adults (ages 18-24, N = 360), digital natives who engage with ad-supported video streaming more than any other user group. Exploratory factor analysis revealed a well-defined four-factor structure of MMAES. The results of the validity and reliability measures show good content and construct validity as well as good overall reliability and very good internal consistency of MMAES. Overall, the results show that MMAES is a reliable instrument for measuring the short-term effects of multimedia ad exposure and its weak ground truth.
The article addresses modelling of consumer engagement in video advertising based on automatically derived low-level video features. The focus is on a young consumer group (18–24 years old) that uses ...ad-supported online streaming more than any other group. The reference ground truth for consumer engagement was collected in an online crowdsourcing study (N = 150 participants) using the User Engagement Scale-Short Form (UES-SF). Several aspects of consumer engagement were modeled: focused attention, aesthetic appeal, perceived usability, and reward. The contribution of low-level video features was assessed using both the linear and nonlinear models. The best predictions were obtained for the UES-SF dimension Aesthetic Appeal (R2=0.35) using a nonlinear model. Overall, the results show that several video features are statistically significant in predicting consumer engagement with an ad. We have identified linear relations with Lighting Key and quadratic relations with Color Variance and Motion features (p<0.02). However, their explained variance is relatively low (up to 25%).
