Publication Details
Abstract
This study aims to examine the extent to which Algorithmic Decision-Making (ADM) systems shape long-term decision-making and behavioral patterns among young adults in Central Asian digital environments. The study employed a simulation-based mixed-method research design, including both quantitative survey data and qualitative interview data analysis. The quantitative sample consisted of 285 young adults aged between 18 and 30 across three Central Asian countries (Uzbekistan, Kazakhstan, and Kyrgyzstan), along with qualitative semi-structured interviews (n = 24). Quantitative data were collected using the Algorithmic Behavioral Influence Scale (ABIS), a 22-item instrument measuring habitual platform behavior, algorithmic awareness, and decision-making delegation. All three subscales scored significantly above the scale midpoint, with habitual platform behavior recording the highest mean of 3.88. Qualitative aspects observed three major experiential frames: a large unconscious process of personalized content, reduced capacity to engage in cognitively demanding tasks, and a gradual, increasing reliance on algorithmic-driven systems. Altogether, these findings describe how young adults' functioning is becoming deeply embedded in algorithmically curated environments. This study contributes to understanding ADM systems and behavioral formation in algorithmic societies in the Central Asian region and carries practical implications for educators, policymakers, and platform designers. However, limitations include reliance on simulated self-reported data, suggesting the need for longitudinal research across various cultures.