Cognitive biases and excessive use of social media: The facebook implicit associations test (FIAT).


Journal

Addictive behaviors
ISSN: 1873-6327
Titre abrégé: Addict Behav
Pays: England
ID NLM: 7603486

Informations de publication

Date de publication:
06 2020
Historique:
received: 16 10 2019
revised: 21 01 2020
accepted: 22 01 2020
pubmed: 15 2 2020
medline: 27 2 2021
entrez: 15 2 2020
Statut: ppublish

Résumé

Many theoretical accounts of addictive behaviors, including models of Internet use disorders, implicate cognitive biases in the formation and maintenance of excessive behaviors. Yet, little empirical evidence regarding the role of such biases, including implicit attitude, in the development and maintenance of excessive use of social media exists. We seek to bridge this gap in this study. To this end, we present the development of the Facebook Implicit Association Test (FIAT) and employ it in a sample of 220 Facebook users. The results (1) confirm the validity of the concept of implicit attitude and its measure in the context of social media, (2) demonstrate that implicit attitude is significantly positively associated with excessive use scores, in a magnitude similar to that observed for associations with substance use, and (3) show that implicit attitude is sheltered against social desirability bias, unlike self-reported and explicit measures, such as excessive use. Overall, this study builds theoretical and methodological foundations for further inquiries into the role of implicit attitude in research on the excessive use of social media.

Identifiants

pubmed: 32058236
pii: S0306-4603(19)31256-0
doi: 10.1016/j.addbeh.2020.106328
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

106328

Informations de copyright

Copyright © 2020 Elsevier Ltd. All rights reserved.

Auteurs

Ofir Turel (O)

Information Systems and Decision Sciences, California State University Fullerton, 800 N. State College Blvd. SGMH-4160, Fullerton, CA 92834, USA. Electronic address: oturel@fullerton.edu.

Alexander Serenko (A)

Faculty of Business and IT, Ontario Tech University, 2000 Simcoe St. North, Office ERC 2082, Oshawa, Ontario L1G 0C5, Canada.

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Classifications MeSH