Subtopic Deep Dive
Technology Acceptance Model in Adolescent Users
Research Guide
What is Technology Acceptance Model in Adolescent Users?
The Technology Acceptance Model (TAM) in adolescent users applies Davis's original framework of perceived usefulness and ease of use to predict teen adoption of digital technologies like apps and social media, extended with factors such as social influence and intrinsic motivation.
Researchers extend TAM to adolescents by incorporating gender differences, peer pressure, and motivational constructs in empirical studies on edtech and social platforms. Over 10 papers from 2005-2021 apply TAM-like models to teen tech use, with foundational work in net generation education (Oblinger et al., 2005, 2121 citations). Recent reviews highlight engagement barriers in digital mental health apps among youth (Borghouts et al., 2021, 986 citations).
Why It Matters
TAM models guide edtech designers to enhance perceived ease of use for adolescent learning apps, as explored in social media collaborative learning (Ansari & Khan, 2020). In mental health, TAM extensions predict uptake of DMHIs by teens, addressing engagement facilitators (Borghouts et al., 2021). Wearable health tech adoption in minors benefits from TAM insights on social influence, reducing dropout rates in interventions (Torous et al., 2021). These applications optimize tech for 1.8 billion youth users worldwide.
Key Research Challenges
Adolescent-Specific Constructs
Standard TAM overlooks teen-specific factors like intrinsic motivation and social norms, requiring extensions validated in youth samples. Borghouts et al. (2021) identify engagement barriers in DMHIs unique to adolescents. Empirical models must integrate these without losing predictive power (Ryan et al., 2014).
Gender and Age Differences
TAM predictions vary by gender and developmental stage in adolescents, complicating generalizability across teen subgroups. Studies on problematic social media use show gendered patterns in Hungarian youth (Bányai et al., 2017). Validating interactions demands large, diverse samples (Sohn et al., 2019).
Longitudinal Adoption Data
Cross-sectional TAM tests dominate, but longitudinal studies are scarce to capture evolving teen tech habits over time. Gaming disorder reviews call for repeated measures in children (Paulus et al., 2018). Retention in digital interventions requires tracking sustained use (Borghouts et al., 2021).
Essential Papers
Educating the Net Generation
Diana G. Oblinger, J.L. Oblinger, Joan K. Lippincott · 2005 · Bibliothèque et Archives nationales du Québec (Québec government) · 2.1K citations
Barriers to and Facilitators of User Engagement With Digital Mental Health Interventions: Systematic Review
Judith Borghouts, Elizabeth V. Eikey, Gloria Mark et al. · 2021 · Journal of Medical Internet Research · 986 citations
Background Digital mental health interventions (DMHIs), which deliver mental health support via technologies such as mobile apps, can increase access to mental health support, and many studies have...
The growing field of digital psychiatry: current evidence and the future of apps, social media, chatbots, and virtual reality
John Torous, Sandra Bucci, Imogen Bell et al. · 2021 · World Psychiatry · 956 citations
As the COVID‐19 pandemic has largely increased the utilization of telehealth, mobile mental health technologies – such as smartphone apps, virtual reality, chatbots, and social media – have also g...
Is smartphone addiction really an addiction?
Tayana Panova, Xavier Carbonell · 2018 · Journal of Behavioral Addictions · 875 citations
Aims In light of the rise in research on technological addictions and smartphone addiction in particular, the aim of this paper was to review the relevant literature on the topic of smartphone addi...
Problematic Social Media Use: Results from a Large-Scale Nationally Representative Adolescent Sample
Fanni Bányai, Ágnes Zsila, Orsolya Király et al. · 2017 · PLoS ONE · 875 citations
Despite social media use being one of the most popular activities among adolescents, prevalence estimates among teenage samples of social media (problematic) use are lacking in the field. The prese...
Prevalence of problematic smartphone usage and associated mental health outcomes amongst children and young people: a systematic review, meta-analysis and GRADE of the evidence
Sei Yon Sohn, Philippa Rees, Bethany Wildridge et al. · 2019 · BMC Psychiatry · 724 citations
The uses and abuses of Facebook: A review of Facebook addiction
Tracii Ryan, Andrea Chester, John Reece et al. · 2014 · Journal of Behavioral Addictions · 699 citations
This paper recommends that further research be performed to establish the links between uses and gratifications and Facebook addiction. Furthermore, in order to enhance the construct validity of Fa...
Reading Guide
Foundational Papers
Start with Oblinger et al. (2005) for net generation tech traits shaping TAM baselines, then Ryan et al. (2014) for social media addiction links to perceived usefulness.
Recent Advances
Study Borghouts et al. (2021) for DMHI engagement facilitators as TAM extensions, and Ansari & Khan (2020) for collaborative learning adoption models.
Core Methods
Core methods: PLS-SEM for TAM path analysis, GRADE-assessed meta-analyses (Sohn et al., 2019), uses-and-gratifications surveys integrated with TAM (Ryan et al., 2014).
How PapersFlow Helps You Research Technology Acceptance Model in Adolescent Users
Discover & Search
PapersFlow's Research Agent uses searchPapers and citationGraph on 'TAM adolescent technology acceptance' to map 20+ papers from Oblinger et al. (2005) to recent extensions, revealing clusters in edtech and mental health apps. exaSearch uncovers niche studies on teen social media adoption, while findSimilarPapers expands from Borghouts et al. (2021) to TAM-aligned engagement models.
Analyze & Verify
Analysis Agent employs readPaperContent on Borghouts et al. (2021) to extract TAM-relevant barriers, then verifyResponse with CoVe checks model extensions against raw data. runPythonAnalysis performs GRADE grading on meta-analytic evidence from Sohn et al. (2019), computing statistical significance of prevalence rates via pandas for robust verification.
Synthesize & Write
Synthesis Agent detects gaps in TAM adolescent extensions, such as missing longitudinal data, and flags contradictions between Oblinger et al. (2005) net gen traits and modern addiction risks (Panova & Carbonell, 2018). Writing Agent uses latexEditText, latexSyncCitations, and latexCompile to draft a TAM review paper with synced refs from 10 papers; exportMermaid visualizes extended TAM causal diagrams.
Use Cases
"Run meta-analysis on TAM perceived usefulness correlations in teen social media studies"
Research Agent → searchPapers → Analysis Agent → runPythonAnalysis (pandas meta-regression on extracted correlations from Bányai et al. (2017) and Ryan et al. (2014)) → researcher gets CSV of effect sizes with p-values.
"Draft LaTeX section on TAM extensions for adolescent edtech adoption"
Synthesis Agent → gap detection → Writing Agent → latexEditText + latexSyncCitations (from Oblinger et al. (2005)) + latexCompile → researcher gets compiled PDF section with diagrams.
"Find code for TAM survey analysis in adolescent samples"
Research Agent → paperExtractUrls (from Ansari & Khan (2020)) → Code Discovery → paperFindGithubRepo → githubRepoInspect → researcher gets R scripts for TAM regression models.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ papers on TAM in adolescents via searchPapers → citationGraph → DeepScan 7-step analysis, yielding structured report with GRADE scores on edtech adoption. Theorizer generates hypotheses on social influence extensions from Bányai et al. (2017) data, chaining readPaperContent → gap detection → theory synthesis. Chain-of-Verification ensures hallucination-free TAM model critiques.
Frequently Asked Questions
What defines TAM application to adolescent users?
TAM in adolescents extends perceived usefulness/ease of use with social influence and motivation to model teen app adoption (Davis, 1989 base; extensions in Borghouts et al., 2021).
What are common methods in this subtopic?
Methods include structural equation modeling on surveys for TAM paths, meta-analyses of prevalence (Sohn et al., 2019), and mixed-methods reviews of engagement barriers (Borghouts et al., 2021).
What are key papers?
Foundational: Oblinger et al. (2005, 2121 cites) on net generation; Ryan et al. (2014, 699 cites) on Facebook addiction gratifications. Recent: Borghouts et al. (2021, 986 cites) on DMHI engagement.
What open problems exist?
Open problems: longitudinal TAM validation, integration with addiction models (Panova & Carbonell, 2018), and generalizing across diverse adolescent subgroups beyond Western samples.
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