Exploring different technology addiction types in young adults
A latent profile analysis
Bibliographic Data
| ID | 21624969 |
|---|---|
| Authors | Murat Ağirkan (0000-0002-9695-8525, Erzincan Binali Yıldırım University, corresponding author), Namık Kemal Haspolat (0000-0003-1755-7393, Erzincan Binali Yıldırım University), Oğuzhan Çelik (0000-0002-6453-2618, Erzincan Binali Yıldırım University) |
| Year | 2024 |
| Publication date | 2024-08-06 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Current Psychology (JOURNAL) |
| Journal identifiers | ISSN: 1046-1310 • E-ISSN: 1936-4733 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s12144-024-06372-z |
| OpenAlex | W4401371399 |
| Language | EN |
| References cited | 47 |
This study aimed to examine the different latent profiles of technology addiction in young adults, and the criteria for belonging to these profiles, in relation to sociodemographic factors (gender and SES) and technology use status (daily social network usage time, daily instant messaging usage time, daily online gaming usage time, daily website usage time, and connection location). Data were collected from a sample of 1,431 young adults [988 (69.1%) female, 443 (30.9%) male]. Latent Profile Analysis (LPA) was used to identify the latent profiles, and Multinomial Regression Analysis was employed to examine the relationships between the latent profiles and sociodemographic and technology use. The LPA revealed four latent profiles of technology addiction in young adults: “Non-Addicts (NA)”, “Moderate Technology Addicts-Gamers (MTAG)”, “High Technology Addicts-Non-Gamers (HTANG)”, and “High Technology Addicts-Gamers (HTAG)”. The criteria for belonging to these four profiles were statistically significantly related to sociodemographic and technology use factors, except for connection location, and age. These findings provided further support for our knowledge about planning programs or multi-layered intervention models to prevent technology addiction in young adults
Addiction · Developmental psychology · Latent class model · Latent growth modeling · Multinomial logistic regression · Psychiatry · Structural equation modeling · Young adult · Clinical Psychology · Computer Science · Digital Marketing and Social Media · Impact of Technology on Adolescents · Psychology · Technology Adoption and User Behaviour
Theory of Addiction
Conducting Confirmatory Latent Class Analysis Using M plus
Disentangling Shape from Level Effects in Person-Centered Analyses
Factor Analysis and AIC
Integrating Technology Addiction and Use
User Acceptance of Information Technology
Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models
User Acceptance of Computer Technology
Testing the number of components in a normal mixture
Internet addiction and psychological distress among Chinese schoolchildren before and during the Covid-19 outbreak
Treatment for the concerned significant others of gamblers
A typology of adolescents’ technology use before and during the Covid-19 pandemic
Smartphone use, flow and wellbeing
A latent class analysis of adolescents' technology and interactive social media use
Gender and attitudes toward technology use
Prevalence of Internet Gaming Disorder and Its Association With Personality Traits and Gaming Characteristics Among Chinese Adolescent Gamers
Latent Profile of Internet and Internet Game Usage Among South Korean Adolescents During the Covid-19 Pandemic
Risk Factors for Internet Gaming Disorder
Screen-based media use clusters are related to other activity behaviours and health indicators in adolescents
Technological Addiction in Context
Mobile phone addiction and school performance among Chinese adolescents from low-income families
Bayesian Model Selection in Social Research
Multimodel Inference
An Evaluation of Two Characterizations of the Relationships Between Problematic Internet Use, Time Spent Using the Internet, and Psychosocial Problems
| Highly cited | No |
|---|