Submitted:
06 July 2025
Posted:
07 July 2025
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Abstract
Keywords:
1. Introduction
1.1. Background
1.2. Problem Statement
1.3. Significance and Necessity of the Research
1.4. Theoretical Framework and Literature Review
1.5. Research Objectives and Questions
1.5.1. This Study Aims to:
- Analyze the current legal frameworks governing juvenile criminal responsibility in cyber offenses.
- Examine psychological factors influencing adolescents’ capacity for criminal intent and control in cyberspace.
- Propose an integrated model for assessing juvenile liability that incorporates developmental psychology.
1.5.2. Research Questions Include:
- How do existing laws address juvenile cybercrime, and what are their limitations?
- What psychological characteristics affect adolescents’ criminal responsibility in virtual environments?
- How can legal and psychological criteria be combined to improve juvenile justice outcomes in cybercrime cases?
2. Theoretical Foundations and Literature Review
2.1. Key Theories and Fundamental Concepts
2.2. Review of Previous Studies
2.3. Critical Analysis of Prior Research
2.4. Research Gaps
- Insufficient integration of psychological maturity assessments within juvenile cybercrime legal frameworks.
- Lack of empirical studies combining legal analysis with psychological evaluation of adolescent offenders in cyberspace.
- Limited models addressing how cyber-specific factors (anonymity, digital detachment) affect juvenile responsibility.
- Need for updated, culturally contextualized research on juvenile criminal responsibility in digital environments, especially in Iranian legal context.
2.5. Conceptual Model
- Legal criteria of age and mens rea,
- Psychological measures of cognitive and moral development,
- Cyber-environmental factors influencing behavior (e.g., anonymity, peer networks).
3. Methodology
3.1. Research Design
3.2. Population and Sampling
- Legal experts and practitioners involved in juvenile justice and cyber law (for qualitative legal analysis).
- Adolescents aged 12-18 with varying levels of internet usage, selected to assess psychological maturity related to cyber behavior.
3.3. Data Collection Instruments
- Legal analysis: Semi-structured interviews and document analysis of current juvenile cybercrime laws and court rulings.
- Psychological assessment: Standardized questionnaires measuring cognitive development, moral reasoning, and impulse control, adapted from validated scales such as the Moral Judgment Test (MJT) and the Adolescent Decision-Making Questionnaire (ADMQ).
3.4. Validity and Reliability
3.5. Data Analysis Methods
- Qualitative data from interviews and legal documents were analyzed using thematic content analysis supported by MAXQDA software to code and identify key legal themes and gaps.
- Quantitative data from psychological assessments were analyzed using SPSS (version 28). Descriptive statistics summarized participant characteristics. Inferential tests including Chi-square tests, t-tests, and logistic regression were applied to examine relationships between psychological maturity and legal responsibility indicators.
4. Findings
4.1. Descriptive Statistics
4.2. Results of Statistical Tests
4.3. Response to Research Questions and Hypothesis Testing
- RQ1: How do existing laws address juvenile cybercrime, and what are their limitations?Legal analysis confirms that current laws often apply adult standards, leading to inadequate deterrents and potential over-criminalization of minors who may not fully comprehend the gravity of their actions . The lack of specific, age-appropriate legal frameworks for cyber offenses remains a significant limitation.
- RQ2: What psychological characteristics affect adolescents’ criminal responsibility in virtual environments?Findings demonstrate that lower cognitive development, less mature moral reasoning, and a reduced perception of the consequences of online actions are key psychological characteristics influencing adolescents’ criminal responsibility in cyberspace. This is consistent with findings that highlight the impact of virtual spaces on juvenile delinquency and the need for social prevention strategies.
- RQ3: How can legal and psychological criteria be combined to improve juvenile justice outcomes in cybercrime cases?The results strongly suggest that integrating psychological assessments of cognitive and moral maturity into legal proceedings can lead to more nuanced and just outcomes. This integration would help differentiate between malicious intent and developmentally influenced poor judgment, aligning legal responses with the actual capacity of the adolescent. This aligns with calls for refined legal protection and a balanced approach.
4.4. Key Quotations from Qualitative Data
- "The law has a supportive approach towards adolescents, but because our responses to crimes committed by adolescents lack the necessary deterrence, and in cases where children commit these behaviors, the criminal reaction can be highly controversial and disproportionate because it can lead to the criminalization of children who are not aware of the impact of their actions."
- "Sending and exchanging sexual messages is an example of problematic user behavior. This behavior can create numerous problems (both social and legal). Typically, it is assumed that sending a message only poses a risk if an image is shared without the subject's consent, but this is not the case with children. Sometimes a child shares a message with their own consent, and in fact, they themselves become the victim."
5. Discussion
6. Conclusion
7. Recommendations
7.1. Practical Recommendations
- For Legislators and Policymakers: Revise juvenile cybercrime laws to incorporate psychological maturity assessments alongside chronological age, ensuring legal responsibility reflects adolescents’ developmental capacities. Establish specialized juvenile cybercrime courts or branches with trained judges, psychologists, and social workers to provide tailored and fair adjudication.
- For Judicial Authorities: Implement mandatory psychological evaluations in juvenile cybercrime cases to inform sentencing and rehabilitation decisions. Develop protocols for creating detailed personality and social background profiles of adolescent offenders to support individualized interventions.
- For Educators and Trainers: Integrate legal literacy and cyber ethics education into school curricula to raise awareness among children and adolescents about their rights, responsibilities, and the consequences of online behaviors. Promote digital citizenship programs that emphasize moral reasoning and decision-making skills relevant to cyberspace.
- For Social and Support Organizations: Expand counseling and support services for children and adolescents vulnerable to or involved in cyber offenses, including crisis hotlines and online reporting mechanisms. Strengthen family education and community awareness campaigns to foster protective environments around youth internet use.
- For Technology and Regulatory Bodies: Collaborate to develop safer online environments by enforcing content classification, age-appropriate access controls, and monitoring mechanisms to reduce exposure to harmful content and cyber risks.
7.2. Suggestions for Future Research
- Conduct longitudinal studies to track changes in adolescents’ cognitive and moral development and their impact on cyber behavior over time.
- Explore cultural, socio-economic, and regional variations in juvenile cybercrime and responsibility to develop context-sensitive legal and psychological frameworks.
- Investigate the effectiveness of multidisciplinary intervention programs combining legal, psychological, and educational strategies in reducing juvenile cyber offenses and recidivism.
- Examine the role of emerging technologies (e.g., AI, virtual reality) in shaping juvenile cybercrime patterns and legal responses.
References
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| Author(s) & Year | Focus Area | Key Findings | Limitations / Gaps |
| Wissink et al. (2023) | Psychological risk factors for juvenile cybercrime | Meta-analysis showing cognitive and emotional immaturity as predictors of offending | Limited integration with legal frameworks |
| Weulen Kranenbarg et al. (2022) | Social network influences on juvenile cyber delinquency | Peer influence and ICT skills strongly affect cyber offending patterns | Focused on social factors, less on legal aspects |
| Hosseini Akbarnezhad (2020) | International legal protections for juveniles in cyberspace | Analysis of international treaties shaping national policies | Lacks empirical data on psychological assessments |
| Sadeghi Dehsahraei et al. (2024) | Legal protections for children in cyberspace | Emphasizes need for differential legal treatment and privacy rights | Older study; limited focus on cyber-specific crimes |
| Nikbin et al., (2025) | Legal responses to risks faced by children online | Highlights challenges of criminalizing juvenile online behavior | Calls for balanced legal and psychological approach |
| Zarei (2024) | Jurisprudential views on juvenile criminal responsibility in Iran | Discusses age and maturity in legal responsibility | Limited empirical validation |
| Component | Description |
| Research Design | Mixed-methods (qualitative + quantitative) |
| Population | Legal experts (n=15), Adolescents (n=200) |
| Sampling Method | Purposive (experts), Stratified random (adolescents) |
| Data Collection Tools | Interviews, document analysis, standardized questionnaires |
| Validity & Reliability | Expert review, Cronbach’s alpha > 0.85, pilot test |
| Data Analysis | Thematic analysis (MAXQDA), Statistical tests (SPSS) |
| Digital Activity (Past 6 months) | Frequency (%) |
| Daily Internet Use | 85 |
| Social Media Engagement | 92 |
| Online Gaming | 70 |
| Cyberbullying Involvement (as perpetrator) | 15 |
| Sharing Risky Content | 20 |
| Perceived Lack of Awareness of Impact | 60 |
| Variable | Statistic | Significance (p-value) |
| Association between awareness and cyberbullying | χ²(1) = 18.5 | < 0.001 |
| Moral Judgment Test (MJT) score predicting risky behavior | OR = 0.78 (95% CI 0.65–0.93) |
0.005 |
| Adolescent Decision-Making Questionnaire (ADMQ) score predicting risky behavior | OR = 0.82 (95% CI 0.70–0.96) |
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