Presenting The Model of Internet of Things Drivers in Iran's Oil and Gas and Electric Power Industry Supply Chain With a Fuzzy Total Interpretive Structural Modeling Approach

Document Type : Original Article

Authors

1 Master Graduate, Department of Business Administration Faculty of Financial Science, Management and Entrepreneurship, University of Kashan, Kashan, Iran.

2 assistant professor, department of business administration, faculty of financial science, management and entrepreneurship, university of Kashan, Kashan, Iran

10.22052/eem.2024.253818.1036

Abstract

: In recent years, the Internet of Things (IoT) has emerged as a significant technology in the industrial and academic sectors, particularly in the context of the fourth-generation industry. Encouraging industries, including the oil and gas and electric power sector, to adopt IoT technology expeditiously is crucial due to its numerous advantages. This article focuses on the oil and gas and electric power industry, which is a vital sector in any country, and emphasizes the need for accelerated implementation of IoT to unlock its potential benefits. Despite the potential of IoT to bring about important and innovative changes in the oil and gas and electric power industry, surveys indicate a slow adoption trend in Iran. Thus, this research aims to identify and model the drivers that influence the implementation of IoT in Iran's oil and gas and electric power industry supply chain. The research consists of two stages: drivers identification through literature review and assessment using the Delphi method, followed by the presentation of a drivers model using fuzzy total interpretive structural modeling. The results, comprising 75 drivers categorized into 14 general categories, highlight "government regulations" as the most fundamental driver in Iran's oil and gas and electric power industry supply chain.

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