Search results

This chapter aims to provide an overview and conceptualization of multiscreening in the field of advertising effectiveness.

By means of the multi-dimensions of media multitasking, it is possible to differentiate different forms of media multitasking. This framework is used to describe and explain the phenomenon of multiscreening. The framework consists of four categories each with its own dimensions: (1) task relations (e.g., task hierarchy, task switch, shared modality), (2) task inputs (e.g., information flow), (3) task outputs (e.g., behavioral responses), and (4) user differences. The description of multiscreening per dimensions is completed with a review of recent literature in the field of multiscreening, media multitasking and persuasion.

Literature in the field of media multitasking often assumes detrimental effects. Practical implications for advertisers are discussed by presenting an overview of the existing literature on multiscreening and advertising effectiveness. At the end of the chapter, a summary of the different dimensions is presented and an answer is formulated to the question: Is multiscreening a challenge or opportunity for advertisers?

In addition to practical implications, this chapter also offers an overview of the current research in the field of multiscreening and advertising effects. By presenting recent literature in this field, it becomes clear where knowledge is lacking. Directions for future research are discussed.

This chapter is the first to present a structured overview of the phenomenon of multiscreening. It will provide practitioners and researchers with the current status in the field of multiscreening and advertising effectiveness. In addition, the chapter can also be seen as a guide for future directions in the field of multiscreening.

The purpose of this paper is to explore the interrelationship between television (TV) consumption (viewing ratings), engagement behaviors of different actors on Twitter (TV programs, media, celebrities and viewers) and the content of engagement behaviors (affective, program-related and social content).

TV ratings and Twitter data were obtained. The content of tweets was analyzed by means of a sentiment analysis. A vector auto regression model was used to understand the interrelationship between tweets of different actors and TV consumption.

First, the results showed a negative interrelationship between TV viewing and viewers’ tweeting behavior. Second, tweets by celebrities and media exhibited similar patterns and were both affected mostly by the number of tweets by viewers. Finally, the content of tweets matters. Affective tweets positively relate to TV viewing, and program-related and social content positively relates to the number of tweets by viewers.

The findings help us understand the online engagement ecosystem and provide insights into drivers of TV consumption and online engagement of different actors.

The results indicate that content producers may want to focus on stimulating affective conversations on Twitter to trigger more online and offline engagement. The results also call for rethinking the meaning of TV metrics.

While some studies have explored viewer interactions on Twitter, only a few studies have looked at the effects of such interactions on variables outside of social media, such as TV consumption. Moreover, the authors study the interrelations between Twitter interactions with TV consumption, which allows us to examine the effect of online engagement on offline behaviors and vice versa. Finally, the authors take different actors into account when studying real-life online engagement.