Enterprise AI Analysis: Psychology
A Validity and Reliability Study of the Artificial Intelligence Attitude Scale (AIAS-4) and its Relationship with Social Media Addiction and Eating Behaviors in Turkish Adults
This study focused on validating the Artificial Intelligence Attitude Scale (AIAS-4) in Turkish adults and exploring its relationship with social media addiction, eating behaviors, and life satisfaction. Conducted in two stages, the first involved adapting AIAS-4 with 172 adults, confirming its validity and reliability (Cronbach's alpha = 0.90, McDonald's omega = 0.89). The second stage evaluated relationships among AI attitude, social media addiction, eating behavior, and life satisfaction in 510 adults. Key findings indicate that BMI and social media addiction positively impact eating behaviors, while a positive AI attitude negatively impacts them. The study emphasizes multidisciplinary approaches and awareness programs for preventing and managing eating disorders.
Executive Impact: Key Findings for Enterprise AI
The study's metrics underscore the critical factors influencing AI adoption and its effects on public health, particularly in the context of digital engagement and eating behaviors.
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AIAS-4 Cronbach's Alpha
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AIAS-4 McDonald's Omega
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Avg. Social Media Use (hours/day)
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Total Participants (Stage 2)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
AI Attitude (AIAS-4)
Social Media Addiction (SMAS-AF)
Eating Behaviors (EDE-Q, SESMEB)
Life Satisfaction (CLAS)
AI Attitude (AIAS-4)
The Artificial Intelligence Attitude Scale (AIAS-4) was validated for Turkish adults, showing it to be a reliable and practical tool for assessing public perceptions of AI technology. A higher score indicates a more positive attitude towards AI.
Social Media Addiction (SMAS-AF)
Excessive social media use, measured by SMAS-AF, was found to positively correlate with increased time spent on social media and also negatively impact life satisfaction. It is linked to disordered eating behaviors and reduced quality of life.
Eating Behaviors (EDE-Q Total, SESMEB)
Eating behaviors were assessed using EDE-Q and SESMEB. BMI and social media addiction positively influenced eating disorder symptoms, while a positive AI attitude had a negative impact. This highlights the role of both digital and physical factors.
Life Satisfaction (CLAS)
Life satisfaction, measured by CLAS, was negatively correlated with social media addiction. This suggests that excessive social media use can reduce overall well-being, emphasizing the need for balanced digital engagement.
0.157
Decrease in EDE-Q Total Score for 1-point increase in AIAS-4
Study Methodology Phases
Stage 1: AIAS-4 Turkish Adaptation (172 Adults)
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Validity & Reliability Assessment
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Stage 2: Relationship Evaluation (510 Adults)
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AI Attitude, Social Media Addiction, Eating Behavior, Life Satisfaction Examined
| Factor | Influence on Eating Disorder Symptoms |
|---|---|
| BMI Increase (1 kg/m²) |
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| AIAS-4 Score Increase (1 point) |
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| SMAS-AF Score Increase (1 unit) |
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| SESMEB Score Increase (1 unit) |
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AI in Health Monitoring & Early Intervention
AI's potential for improving eating behaviors and early detection of eating disorders is significant. By analyzing large amounts of data, including social media posts and smartphone usage, AI algorithms can recognize behavioral patterns and language cues indicative of eating disorders. This allows for timely alerts to users and healthcare professionals, facilitating early intervention and improving treatment effectiveness. Collaborations between physicians, psychotherapists, and AI developers are crucial to ensure AI models provide accurate, harmful information-free guidance.
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Estimated Annual Savings
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Employee Hours Reclaimed Annually
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Implementation Roadmap: Strategic AI Deployment
A phased approach to integrate AI solutions effectively, ensuring successful adoption and maximum benefit.
Phase 1: AIAS-4 Integration & Data Collection
Integrate AIAS-4 into existing mental health assessment platforms. Begin collecting baseline data on AI attitudes, social media usage, eating behaviors, and life satisfaction across diverse user groups. Estimated Duration: 3-4 Weeks.
Phase 2: AI Model Development & Training
Develop and train AI models using collected data to identify correlations between AI attitude, social media addiction, and eating disorder risk. Focus on creating predictive analytics for early intervention. Estimated Duration: 6-8 Weeks.
Phase 3: Pilot Program & User Feedback
Launch a pilot program with AI-powered interventions (e.g., personalized diet plans, monitoring systems for eating behaviors). Collect user feedback to refine AI algorithms and ensure ethical, accurate, and supportive health guidance. Estimated Duration: 4-6 Weeks.
Phase 4: Scalable Deployment & Continuous Monitoring
Deploy refined AI solutions across broader user populations. Implement continuous monitoring of AI model performance and user outcomes, ensuring ongoing validity, reliability, and effectiveness in promoting healthy eating behaviors and improved life satisfaction. Estimated Duration: Ongoing.
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