Нейроекономічні аспекти прийняття інноваційно-інвестиційних рішень при оновленні основного капіталу агропідприємств в умовах цифрової трансформації - Басюркіна, Наталія; Спаський, Ігор (Odesa National University of Technology, 2025)
This comprehensive research investigates the complex neuroeconomic dimensions of innovation and investment decision-making processes specifically related to fixed capital renewal in agricultural enterprises operating within the rapidly evolving landscape of digital transformation. The study systematically examines how cognitive biases, emotional factors, and neurophysiological mechanisms influence strategic investment choices made by agribusiness leaders in an increasingly digitalized economic environment. The work establishes a novel theoretical framework that integrates traditional financial analysis methods with cutting-edge neuroeconomic approaches, providing a multidisciplinary perspective on investment behavior in the agricultural sector. Through empirical research involving agricultural enterprise managers across diverse operational scales, the study identifies key neurological and psychological patterns that emerge during high-stakes investment decision-making, particularly when evaluating digital technology adoption for production modernization. Special attention is devoted to analyzing how uncertainty, information asymmetry, and risk perception affect investment decisions through both conscious and unconscious neural processes. The research reveals significant correlations between specific neurophysiological markers and investment efficiency outcomes, suggesting new avenues for optimizing decision-making protocols in agribusiness. Additionally, the study documents how digital transformation is fundamentally altering traditional investment evaluation paradigms in agriculture, necessitating new methodological approaches that account for both quantitative financial metrics and qualitative neuroeconomic factors. The findings contribute to the growing body of knowledge at the intersection of agricultural economics, neuroscience, and digital innovation management by proposing a structured methodological framework for enhancing investment decisions through neuroeconomic insights. This framework incorporates advanced technological tools for decision support, including AI-assisted analysis of decision-maker cognitive patterns and predictive modeling of investment outcomes based on neurological indicators. The practical applications of this research extend to agricultural enterprise management, offering evidence-based strategies for optimizing capital renewal decisions in the context of digital transformation. These strategies include specialized decision-support systems that account for cognitive biases, customized risk assessment protocols based on neurophysiological profiles, and training methodologies designed to enhance decision quality through improved neural processing of complex investment information. The proposed approaches have been validated through case studies with agricultural enterprises of varying sizes, demonstrating significant improvements in investment efficiency and technological adaptation outcomes.