Analyzing the Critical Parameters for Implementing Sustainable AI Cloud System in an IT Industry Using AHP-ISM-MICMAC Integrated Hybrid MCDM Model

Manideep Yenugula, Shankha Shubhra Goswami (), Subramaniam Kaliappan, Rengaraj Saravanakumar, Areej Alasiry, Mehrez Marzougui, Abdulaziz AlMohimeed and Ahmed Elaraby ()
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Manideep Yenugula: Dvg Tech Solutions Inc., Plainsboro Township, NJ 08536, USA
Shankha Shubhra Goswami: Indira Gandhi Institute of Technology, Sarang 759146, India
Subramaniam Kaliappan: Department of Electrical and Electronics Engineering, Kumaraguru College of Technology, Coimbatore 641049, India
Rengaraj Saravanakumar: Department of Wireless Communication, Institute of ECE, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Science, Chennai 602105, India
Areej Alasiry: College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
Mehrez Marzougui: College of Computer Science, King Khalid University, Abha 61413, Saudi Arabia
Abdulaziz AlMohimeed: College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 13318, Saudi Arabia
Ahmed Elaraby: Cybersecurity Department, College of Engineering and Information Technology, Buraydah Private Colleges, Buraydah 51418, Saudi Arabia

Mathematics, 2023, vol. 11, issue 15, 1-35

Abstract: This study aims to identify the critical parameters for implementing a sustainable artificial intelligence (AI) cloud system in the information technology industry (IT). To achieve this, an AHP-ISM-MICMAC integrated hybrid multi-criteria decision-making (MCDM) model was developed and implemented. The analytic hierarchy process (AHP) was used to determine the importance of each parameter, while interpretive structural modeling (ISM) was used to establish the interrelationships between the parameters. The cross-impact matrix multiplication applied to classification (MICMAC) analysis was employed to identify the driving and dependent parameters. A total of fifteen important parameters categorized into five major groups have been considered for this analysis from previously published works. The results showed that technological, budget, and environmental issues were the most critical parameters in implementing a sustainable AI cloud system. More specifically, the digitalization of innovative technologies is found to be the most crucial among the group from all aspects, having the highest priority degree and strong driving power. ISM reveals that all the factors are interconnected with each other and act as linkage barriers. This study provides valuable insights for IT industries looking to adopt sustainable AI cloud systems and emphasizes the need to consider environmental and economic factors in decision-making processes.

Keywords: AHP; ISM; MICMAC; MCDM; artificial intelligence; IT industry; sustainability; cloud computing barriers (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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