Document Type : Original Article
Authors
1 Ph.D. Student, Department of Business Management, Khorramshahr International Branch, Islamic Azad University, Khorramshahr, Iran
2 Assistant Professor, Department of Business Management, South Tehran Branch, Islamic Azad University, Tehran, Iran
3 Assistant Professor, Department of Business Management, Khorramshahr International Branch, Islamic Azad University, Khorramshahr, Iran
Abstract
This research aims to identify the effective factors on sustainable supply chain in the National Iranian South Oil Company (NISOC). The method of data collection was mixed methods. In the qualitative section, data were collected through reviewing 32 previous studies as well as in-depth interviews with 10 faculty members and experts in the field of sustainable supply chain of crude oil and oil products of the NISOC using a Non-Probability Judgmental Sampling. The main dimensions were identified from interviews, and the dimensions were grouped in 13 main categories and 78 sub-categories. 13 main components included organizational factors, production products, sustainable supply chain, industry supply chain, macro policies, technology, customer, economic factors, deficits of supply chain, social factors, political factors, organizational performance, and organizational productivity. Second, through reviewing literature and previous studies, 72 open codes in 9 categories were identified, including strategies, blockchain technology, improving the economic environment, improving the social and environmental policies and laws, internal organizational solutions, external organizational solutions, environmental management, market and capital factors. In the quantitative part, the researcher-made questionnaire was applied in a preliminary study on 10 experts in the field of management, and its relative content validity is examined. Then the final and modified version was provided to 384 employees of the NISOC. SmartPLS software were used for quantitative evaluation of the model. The presented model helps industry managers and decision makers to significantly improve the performance of the supply chain by re-designing the supply chain and taking appropriate measures.
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