Analysing IoT Adoption Barriers in the Oil and Gas Industry Using Content Analysis and Fuzzy Cognitive Mapping

The Internet of Things (IoT) is considered a modern concept in the production industry, based on which the physical and digital worlds are connected using the potential of the Internet. Since this technology is new, its acceptance and implementation in the oil and gas industry are associated with barriers. Therefore, this study aims to identify and analyze such barriers.

This study has been performed with a mixed approach in two phases. The qualitative phase was conducted to identify the barriers to using the Internet of Things in the oil and gas industry, and the quantitative phase was conducted to model and analyze the scenario. To identify and extract the barriers to using IoT in the oil and gas industry, it is identified through a literature review and content analysis method. In this research, 58 articles were analyzed and 10 industrial and academic experts analysed extracted barriers by using the Fuzzy cognitive mapping method. To analyze the scenarios, two categories of backward and forward scenarios have been designed. FCMapper software has been used for scenario design.

The results of the content analysis showed 10 barriers, which are subcategories of four aspects. The barriers identified are "limited coordination and collaboration" "lack of automation in organizations", "insufficient leadership support", "weak staff knowledge and abilities", "limited scalability", "opposition to change", "lack of Certain rules and standards for the adoption of the IoT", "the cost of data storage and analysis", "the high cost of implementation and maintenance", "security and confidentiality issues". Finally, these barriers were categorized into four main subcategories: "Organizational", "Integration", "Economic" and "Technical". The results of the fuzzy cognitive mapping method indicate that the barrier "security and confidentiality issues" with a factor of 4.34 has the highest level of influence, and the barrier "high implementation and maintenance cost" with a factor of 4.95 has the highest level of influence, and the barrier "security and confidentiality issues" has the highest centrality with a coefficient of 6.45.

Like any other research, this research has been faced with limitations. Considering that the research was limited to a specific industry, its findings may be affected by the opinions and biases of experts involved in the process, and we cannot generalize the results to another industry. Another limitation of this research is the complexity and time-consuming methods used in it. The methods of content analysis and fuzzy cognitive map may be complex and require sufficient time and a high level of expertise to use them, which can limit their application in some fields.

Considering that the IOT is an emerging and new technology, the adoption of this concept in the oil and gas industry can have several operational advantages, such as reducing testing and calibration time, minimizing production downtime, improving performance, and predicting maintenance performance. One of the important barriers to the use of IoT in the oil and gas industry is "security and data confidentiality" issues in different parts, and this issue causes the unwillingness of stakeholders and decision-makers to use this technology. Conducting research in the field of raising and increasing the security of these systems and ways to increase the security of privacy will greatly help in solving this barrier.

The study offers valuable insights for researchers, managers, and policymakers in the oil and gas industry concerning the adoption of Internet of Things (IoT) technologies. To facilitate the adoption of IoT in the oil and gas industry, companies should invest in comprehensive training programs for managers and stakeholders to improve awareness and understanding of IoT technologies and their benefits.

Since several barriers have prevented the widespread adoption of the IOT in the oil and gas industry, in this research, the identification and extraction of these factors have been done comprehensively for the first time. Modelling and scenario analysis of these barriers with fuzzy cognitive mapping were also performed for the first time.