Journal of Resource Management and Decision Engineering 4 Serial Number 12 2025 09 10 Providing a Digital Supply Chain Model in the Online Food Retail Industry Providing a Digital Supply Chain Model in the Online Food Retail Industry 1 12 10.61838/kman.jrmde.135 EN Journal Article 2025 05 01 <p class="Abstract" style="line-height: normal;"><span lang="EN-GB">This study was conducted with the aim of presenting a digital supply chain model in the online food retail industry. In terms of purpose, it is applied research, and in terms of data collection, it falls under mixed-method studies (qualitative–quantitative). The statistical population in the qualitative section consisted of professors, experts in the field of technology and supply chain, as well as senior managers of production and research and development in the online food retail industries. In the quantitative section, the population included all managers and employees of online supermarkets in Tehran. The sample size in the qualitative section was determined based on theoretical saturation (10 participants) using purposive sampling, while in the quantitative section, it was estimated at 380 participants based on structural equation modeling sampling requirements, using stratified random sampling. The data collection tool in the qualitative section was interviews, and in the quantitative section, a researcher-made questionnaire. The validity of the questionnaire was confirmed in terms of face and content validity by several experts; convergent validity was verified through the calculation of the Average Variance Extracted (AVE), and discriminant validity was confirmed using the square root of AVE. The reliability of the questionnaire was obtained through Cronbach’s alpha at 0.898 for the entire instrument. SmartPLS 3 software was used for data analysis. The findings led to the identification of 27 components and 106 indicators. The variables of smart customer relationship management, intelligent communication in the supply chain, smart inventory and warehouse management, smart production control, and smart maintenance were identified as the main effective categories. Economic, social, technological, environmental, managerial, organizational, and industrial factors were identified as the effective causal conditions. Employee knowledge and skills, employee intelligence and awareness, and appropriate infrastructure were recognized as effective contextual conditions. Intra-organizational factors, laws and regulations, and inter-organizational factors were considered as effective intervening conditions. Financing, employee empowerment, and cultural development for creating a smart supply chain were determined as effective strategies. Finally, the outcomes included cost reduction, quality improvement, productivity increase, performance enhancement, flexibility, and customer satisfaction improvement.</span></p> https://journalrmde.com/index.php/jrmde/article/download/135/123