Quantifying the Effects of AI-Driven Inventory Management on Operational Efficiency in Online Retail

This study aims to investigate the impact of various factors, including AI System Maturity, Data Integration Capabilities, Organizational Readiness, Top Management Support, Competitive Pressure, and Supplier Technological Sophistication, on Operational Efficiency in organizations adopting artificial intelligence. A quantitative research approach was employed, utilizing a structured questionnaire distributed to 300 respondents across various industries in Bangladesh, achieving a final sample size of 190. The data were analyzed using SmartPLS software to evaluate the measurement and structural models. The findings revealed that AI System Maturity and Data Integration Capabilities significantly enhance Operational Efficiency. Additionally, Top Management Support, Competitive Pressure, and Supplier Technological Sophistication also contributed positively, while Organizational Readiness did not show a significant relationship. Organizations seeking to improve their operational efficiency should prioritize developing their AI systems and data integration capabilities, alongside fostering top management support and addressing competitive pressures. By enhancing operational efficiency, organizations can potentially improve service delivery and innovation, positively impacting society. This research contributes to the literature on AI adoption by highlighting key determinants of operational efficiency, providing valuable insights for both academics and practitioners. The study's limitations include the reliance on self-reported data, which may introduce bias, and the focus on a single country context, limiting generalizability. Future studies could explore different industry contexts and incorporate longitudinal data for a comprehensive understanding of AI's long-term impacts.