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

Author

Listed:
  • 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
    Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt)

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.

Suggested Citation

  • Manideep Yenugula & Shankha Shubhra Goswami & Subramaniam Kaliappan & Rengaraj Saravanakumar & Areej Alasiry & Mehrez Marzougui & Abdulaziz AlMohimeed & Ahmed Elaraby, 2023. "Analyzing the Critical Parameters for Implementing Sustainable AI Cloud System in an IT Industry Using AHP-ISM-MICMAC Integrated Hybrid MCDM Model," Mathematics, MDPI, vol. 11(15), pages 1-35, August.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:15:p:3367-:d:1208692
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    References listed on IDEAS

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    1. Mazen M. Omer & Ahmed Farouk Kineber & Ayodeji Emmanuel Oke & Chukwuma Kingsley & Ashraf Alyanbaawi & Ehab Farouk Rached & Ali Elmansoury, 2023. "Barriers to Using Cloud Computing in Sustainable Construction in Nigeria: A Fuzzy Synthetic Evaluation," Mathematics, MDPI, vol. 11(4), pages 1-20, February.
    2. Sharma, Mahak & Sehrawat, Rajat & Daim, Tugrul & Shaygan, Amir, 2021. "Technology assessment: Enabling Blockchain in hospitality and tourism sectors," Technological Forecasting and Social Change, Elsevier, vol. 169(C).
    3. Liangliang Song & Qiming Li & George F. List & Yongliang Deng & Ping Lu, 2017. "Using an AHP-ISM Based Method to Study the Vulnerability Factors of Urban Rail Transit System," Sustainability, MDPI, vol. 9(6), pages 1-16, June.
    4. Shankha Shubhra Goswami & Dhiren Kumar Behera, 2021. "An Analysis for Selecting Best Smartphone Model by AHP-TOPSIS Decision-Making Methodology," International Journal of Service Science, Management, Engineering, and Technology (IJSSMET), IGI Global, vol. 12(3), pages 116-137, May.
    5. Seok-Keun Yoo & Bo-Young Kim, 2018. "A Decision-Making Model for Adopting a Cloud Computing System," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
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    Cited by:

    1. Wen-Chin Chen & An-Xuan Ngo & Hui-Pin Chang, 2024. "Enhancing Decision-Making Processes in the Complex Landscape of the Taiwanese Electronics Manufacturing Industry through a Fuzzy MCDM Approach," Mathematics, MDPI, vol. 12(13), pages 1-29, July.
    2. Morteza Hadizadeh & Javad Ghaffari Feyzabadi & Zahra Fardi & Seyed Morteza Mortazavi & Vitor Braga & Aidin Salamzadeh, 2024. "Digital Platforms as a Fertile Ground for the Economic Sustainability of Startups: Assaying Scenarios, Actions, Plans, and Players," Sustainability, MDPI, vol. 16(16), pages 1-27, August.

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