An Empirical Framework for Evaluating Societal Perception and Acceptance of Artificial Intelligence Technologies in Greece: A Data-Driven Case Study Approach
Keywords:
Artificial Intelligence, Public Perception, Technology Acceptance, GreeceAbstract
The rapid integration of Artificial Intelligence (AI) across industrial, educational, and societal domains has intensified the need to understand public perception and acceptance of these technologies, particularly within culturally and economically diverse regions such as Greece. This study proposes an empirical, data-driven framework for evaluating societal attitudes toward AI technologies, focusing on trust formation, technological affinity, and lifelong learning orientation as key determinants of acceptance. Drawing upon established measurement models, including trust in automation, AI competency frameworks, and psychometric reliability theory, the study synthesizes multidisciplinary perspectives into a unified analytical structure.
The framework integrates survey-based constructs such as perceived usefulness, technological self-efficacy, emotional intelligence, and socio-technical imaginaries to assess how individuals interpret AI’s role in daily life, education, and employment. Prior research indicates that public understanding of AI is shaped by media representation, institutional trust, and perceived risk-benefit balance (Brauner et al., 2023; Sartori & Bocca, 2023). Additionally, AI adoption in engineering and healthcare contexts demonstrates that technical literacy significantly influences acceptance behavior (Khaleel et al., 2023).
Methodologically, the proposed model incorporates reliability testing using Cronbach’s alpha, structural trust assessment, and affinity scoring for human-technology interaction. The framework is designed for application in Greek socio-cultural contexts, where technological adoption is influenced by education systems, labor market expectations, and generational differences.
Findings from synthesized literature suggest that AI acceptance is strongly mediated by trust, perceived competence, and exposure to AI-related information. The study contributes a scalable empirical model that can be adapted for cross-national comparisons and policy design in AI governance and education reform.
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