# Low-Level Video Features as Predictors of Consumer Engagement in Multimedia Advertisement
## 2 Related Work
### Ad Exposure Effects and Consumer Engagement
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**Research Task** & **Method** & **Findings** \\ \hline Measuring engagement based on affective and social cues [7] & A survey and literature review based on affective and social cues of engagement & Conceptualisation of engagement based on various ways the engagement information can be collected and used. Focus is on the affective cues, social cues, and interactive media. \\ \hline Conceptualization of consumer brand engagement [12] & Conceptualization of CBE based on literature review and advertising practitioners’ perceptions of CBE. Focus is on cognitive, affective, conative, experimental, and social cues. & CBE “emerges as a multi-dimensional construct that beyond traditional cognitive, emotional and conative dimensions seems to be based on emerging experiential and social dimensions that appear as its central elements.” \\ \hline Development of scale [8] & Conceptualisation and operationalisation of CBE scale, focusing on brand engagement and social media advertising. \\ \hline Development of engagement (brand/community) [3] & Conceptualisation and operationalisation of consumer engagement in the context of online brand communities & A 22-item scale of consumer engagement focused on brand and community. \\ \hline Conceptualization of a flexible approach to measuring engagement [2] & A survey and literature review & A flexible approach to measuring engagement is needed, taking into account context-specific experiences that can vary across brands and products. \\ \hline Conceptualization of consumer engagement in digital ecosystems and computational advertising [4] & A survey and literature review & Conceptualisation of consumer engagement through meaningful and sustained interactions with consumers in digital ecosystems and computational advertising. \\ \hline Research designs that support repeated measures of consumer engagement [10] & A survey and literature review & Research designs that support repeated observations of individual attitudes and behavior should be well positioned to study media effects and processes in the digital age. \\ \hline Memory performance in ad exposure [9] & Recognition tests using retrospective confidence judgments & Exploratory study measuring the effects of creative ads and insight on memory performance. The findings suggest that insight makes advertisements more memorable, especially those that are creative. \\ \hline Prediction of engagement based on physiological responses to video ads using machine learning [13] & Classification of emotional arousal based on affect and physiological responses (GSR, ECG, EOG, EEG, and PPG). & Methods of affective computing, emotion recognition, classification were used. The study found that the patterns of physiological response to each multimedia stimuli were effective in the classification of stimulus type. \\ \hline Development of user engagement scale [5] & Validation of UES-SF: User Engagement Scale & A verified a four-factor structure (aesthetic appeal, focused attention, perceived usability, and reward) for the 12-item of User Engagement Scale short form was tested in various digital scenarios (e.g., e-shopping). \\ \hline Prediction of video engagement using machine learning [18] & Prediction of video engagement based on a large-scale measurement of video engagement from a collection of 5.3 million YouTube videos. & The study proposes a new metric, relative engagement that is calibrated against video properties and strongly correlates with recognized notions of quality. \\ \hline Prediction of video engagement using machine learning [15] & A graph-embedding model to predict video engagement of an advertisement based on viewing patterns over time, using only a few samples. \\ \hline Prediction of video engagement using machine learning [17] & Prediction of user engagement indicators based on the interpretable patterns of arousal, valence, and trustworthiness. The analysis was conducted on the emotion-annotated dataset of 600 YouTube videos. \\ \hline \hline \end{tabular}
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