Abstract
Phubbing affects an individual’s social life and well-being. It has been found to affect romantic relationships, communication and social skills, and emotional and behavioral problems. Some relationships that phubbing has with, for example, smartphone addiction, Internet addiction, social media addiction, FoMO, and neuroticism are well known and established in the literature. However, phubbing is not exclusively reducible to addiction or personality-driven dynamics. For this reason, this study is aimed at exploring the motivations behind phubbing behavior. Firstly, the research is aimed at confirming the relationships between phubbing and technology-related addictions (e.g., social media addiction and mobile phone addiction) and personality traits (e.g., neuroticism and conscientiousness). In addition, the study is aimed at examining the relationship between phubbing and three potential individual-level factors for possible phubbing modeling: intrinsic motivation, boredom state, and online vigilance. A total of 551 participants took part in the study (mean age = 32 years; SD = 14.15 ). After confirming the relationships that phubbing has with the abovementioned variables, a hierarchical regression model was produced in order to model the phubbing phenomenon as comprehensively as possible. The final model explained approximately 72% of the variance in phubbing. The primary contributors to the explained variance were variables related to the dependent use of new technologies, dimensions of online vigilance, boredom, and intrinsic motivation for using new technologies. Sociodemographic factors and personality traits accounted for a smaller portion of the variance (3.4% and 9.1%, respectively). These findings suggest that the individual-level factors driving phubbing behavior are related to intrinsic motivation, online vigilance, and boredom, rather than sociodemographic factors or personality traits. The study encourages further research to explore and expand upon the range of motivations underlying phubbing behavior, while considering factors related to dysfunctional or addictive technology use.
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