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Reinforcer pathology of internet-related behaviors among college students: Data from six countries.

Samuel F AcuffAngelina PilattiMegan CollinsLeanne M HidesNutankumar S ThingujamWen Jia ChaiWai Meng YapRuichong ShuaiLee HogarthAdrian J BravoJames G Murphy
Published in: Experimental and clinical psychopharmacology (2021)
Research has demonstrated that repeated engagement in low-effort behaviors that are associated with immediate reward, such as Internet use, can result in a pathological reinforcement process in which the behavior is increasingly selected over other activities due, in part, to a low availability of alternative activities and to a strong preference for immediate rather than delayed rewards (delay discounting). However, this reinforcer pathology model has not been generalized to other Internet-related behaviors, such as online gaming or smartphone use. Given the widespread availability of these technologies, it is also important to examine whether reinforcer pathology of Internet-related behaviors is culturally universal or culture-specific. The current study examines relations between behavioral economic constructs (Internet demand, delay discounting, and alternative reinforcement) and Internet-related addictive behaviors (harmful Internet use, smartphone use, online gaming, and Internet sexual behavior) in a cross-sectional sample of college students (N = 1,406) from six different countries (Argentina, Australia, India, Malaysia, the United Kingdom, and the United States). Using structural equation modeling, Internet demand was associated with harmful Internet use, smartphone use, and online gaming; delay discounting was associated with harmful smartphone use; and alternative reinforcement was associated with harmful Internet and smartphone use. The models were partially invariant across countries. However, mean levels of behavioral economic variables differed across countries, country-level gross domestic product, person-level income, and sex at birth. Results support behavioral economic theory and highlight the importance of considering both individual and country-level sociocultural contextual factors in models for understanding harmful engagement with Internet-related behaviors. (PsycInfo Database Record (c) 2021 APA, all rights reserved).
Keyphrases
  • health information
  • social media
  • healthcare
  • emergency department
  • mental health
  • machine learning
  • artificial intelligence
  • gestational age