Factors that Determine Advertising Evasion in Social Networks

Abstract

The present work is framed within the study of advertising evasion online and particularly in social networks. Social networks are a growing phenomenon, where users spend most of their time online and where companies are moving part of their advertising investment, as they are considered an ideal place for commercial campaigns. In order to deepen in the variables that precede advertising evasion in social networks, a relationship model was developed based on the theoretical framework of advertising evasion on the Internet, which was contrasted at an empirical level through a panel of users. For this purpose, a structural equation model was designed, which highlighted the relationships between the main antecedent variables of evasion, such as perceived control, advertising intrusion, and psychological reaction.

Artificial Intelligence And Its Applications In Service Industry – A Review

“…• Sentiment analysis models: In this model, people's opinion on the product is considered to identify potential shortcomings and opportunities. Twitter has used this model to know the realtime customer experience and their feedback, this helps to predict the development of Indian telecom companies (Ferreira, Gandomi, & Cardoso, 2021;Silva et al, 2021).…”

“…• Programmatic advertising models: In this model, biding in real-time happens monotonously and periodically for online ad spaces. This supported to develop the interest in the viewer and to make him end up buying whatever is being displayed (Silva et al, 2021).…”

“…The banks and financial services are mostly customized services which are directly dependent on customer service agents. The task of these agents is to do phone calls, emails and messages to the customers, in this process the agents will grab the information about the customers and their requirements (Silva et al, 2021). According to the survey report there are approximately 1 million calls per month which are being done through IVR, this is distributed among agents (Königstorfer & Thalmann, 2020).…”