IDEAS home Printed from https://ideas.repec.org/a/igg/jssmet/v12y2021i3p116-137.html
   My bibliography  Save this article

An Analysis for Selecting Best Smartphone Model by AHP-TOPSIS Decision-Making Methodology

Author

Listed:
  • Shankha Shubhra Goswami

    (Indira Gandhi Institute of Technology, India)

  • Dhiren Kumar Behera

    (Indira Gandhi Institute of Technology, India)

Abstract

This article presents the detailed study of integrated AHP-TOPSIS multiple-criteria decision-making (MCDM) methodology. For these purposes, a real-life example is taken where the best smartphone mobile model is proposed among 10 different available models by implementing integrated AHP-TOPSIS methodology. The 10 mobile models selected for this analysis are presently available in the market and are from different brands having different specifications and price range. The selection process is done based on four major criteria (i.e., price, internal storage, RAM, and brand). AHP is applied for the criteria weightage's calculation, whereas TOPSIS is adopted for selecting the best alternative and make a preference ranking order indicating the best model to the worst. The final result shows that Samsung J7 is the best smartphone model followed by Redmi 7A, and Redmi K20 pro occupies the last position; thus, it is the worst model among the group.

Suggested Citation

  • 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.
    • Handle: RePEc:igg:jssmet:v:12:y:2021:i:3:p:116-137
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJSSMET.2021050107
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ke Wang & Ziyi Ying & Shankha Shubhra Goswami & Yongsheng Yin & Yafei Zhao, 2023. "Investigating the Role of Artificial Intelligence Technologies in the Construction Industry Using a Delphi-ANP-TOPSIS Hybrid MCDM Concept under a Fuzzy Environment," Sustainability, MDPI, vol. 15(15), pages 1-42, August.
    2. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jssmet:v:12:y:2021:i:3:p:116-137. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.