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<p>This study examines how perceived ease of use (PEU), performance expectancy (PE), artificial intelligence (AI) perceptions, perceived vulnerability (PV), and awareness of cybersecurity (ACS) shape behavioral intention and secure information society behavior (ISB) in a developing-economy context. By integrating the three models such as technology acceptance model (TAM), protection motivation theory (PMT), and the unified theory of acceptance and use of technology (UTAUT), the research aligns individual adoption dynamics with SDGs 8, 9, 16, and 17. Adopting a quantitative non-probability convenience-purposive sampling, the research surveys 368 Bangladeshi digital users via an online questionnaires and analyzes the data through a hybrid PLS-SEM and Python-based ANN framework to cover both linear and non-linear effects within an integrated three models. This study provides the first TAM–PMT–UTAUT integration for AI-cybersecurity adoption in Bangladesh (n = 368), revealing performance expectancy (PE) as the dominant awareness of cybersecurity (ACS) predictor, followed by PEU and AI perception. Awareness of cybersecurity (ACS) drives Information Society Behavior (ISB), but perceived vulnerability (PV) unexpectedly weakens this path via threat overload—contrasting high-resource findings—while awareness of cybersecurity proves insignificant. Python-based artificial neural network (ANN) visualizations uncover non-linear patterns, outperforming in predictive validity. These findings offer critical implications for policymakers and developers designing user-centric, AI-enabled security solutions. Ultimately, this research highlights how targeted behavioral interventions and competence-focused training can safeguard critical infrastructure, thereby advancing sustainable development objectives in emerging information societies.</p>

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Modeling artificial intelligence enabled cybersecurity behavior in the information society using PLS-SEM and artificial neural network approach

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年7月4日

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  • Mahfujur Rahman Faraji,
  • Koushik Bandapadya,
  • Md. Mahfuzur Rahman,
  • Sharjil Bin Yousuf,
  • Mohammad Sohel,
  • Md Zainal Abedin,
  • Mohammad Rakibul Islam Bhuiyan

摘要

This study examines how perceived ease of use (PEU), performance expectancy (PE), artificial intelligence (AI) perceptions, perceived vulnerability (PV), and awareness of cybersecurity (ACS) shape behavioral intention and secure information society behavior (ISB) in a developing-economy context. By integrating the three models such as technology acceptance model (TAM), protection motivation theory (PMT), and the unified theory of acceptance and use of technology (UTAUT), the research aligns individual adoption dynamics with SDGs 8, 9, 16, and 17. Adopting a quantitative non-probability convenience-purposive sampling, the research surveys 368 Bangladeshi digital users via an online questionnaires and analyzes the data through a hybrid PLS-SEM and Python-based ANN framework to cover both linear and non-linear effects within an integrated three models. This study provides the first TAM–PMT–UTAUT integration for AI-cybersecurity adoption in Bangladesh (n = 368), revealing performance expectancy (PE) as the dominant awareness of cybersecurity (ACS) predictor, followed by PEU and AI perception. Awareness of cybersecurity (ACS) drives Information Society Behavior (ISB), but perceived vulnerability (PV) unexpectedly weakens this path via threat overload—contrasting high-resource findings—while awareness of cybersecurity proves insignificant. Python-based artificial neural network (ANN) visualizations uncover non-linear patterns, outperforming in predictive validity. These findings offer critical implications for policymakers and developers designing user-centric, AI-enabled security solutions. Ultimately, this research highlights how targeted behavioral interventions and competence-focused training can safeguard critical infrastructure, thereby advancing sustainable development objectives in emerging information societies.