Abstract
Experts highlight their concern about the excessive use of social media, with people systematically declaring to enter social networks for a specific purpose and leaving hours after diving into the “rabbit hole.” This overuse has been associated with the ability of platform recommendation algorithms to assign content of interest. The combination of machine learning algorithms with big data evolves the predictive power of these recommendation systems, leading to new perspectives for people to interact with the platforms. This article studied human-machine interaction mechanisms and motivators of this process. The authors integrated different perspectives found in the literature into a comprehensive model which tries to explain which factors would help to understand this mechanism of interaction. A survey was carried out with 297 YouTube users. The model presented different findings as the presence of two paths of interaction with different motivators. Escapism is a motivator for both routes. Both routes had impacts in terms of satisfaction and dependence, with exchanged forces.
TopBackground
In this section, the evolution of the interaction between men and machines is discussed, presenting the theories that have emerged. This evolution was constituted from three main cycles: starting the debate about mechanical models on decision making, the aversion to these models and the adoption boom. This literature review was structured based on articles that discussed the interaction between human and machines. The articles were selected from the Scopus database based on the queries “human-machine interaction” and “human-application interaction.” Only articles from peer-review journals were considered. The first cycle covers studies that show that machines are more accurate than humans. The second is characterized by studies that point to the emergence of people’s aversion to machines in the decision-making process. The latter features articles that show people incorporating algorithms as a part of their decision-making process.
Key Terms in this Chapter
Dependence: A non-chemical and behavioral addiction that involves human–machine interaction ( de Bérail, Guillon, &. Bungener, 2019 ).
Attitude: A positive or negative predisposition that a person has towards an object, product, brand, or person ( Ajzen & Fishbein, 1980 ; Davis, 1989 ).
Delegation: The transfer of the decision responsibility from people to technology ( Schneider & Leyer, 2019 ).
Satisfaction: How well a person’s expectations are met by a particular product or service ( Oliver, 2010 ).