IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v59y2022i3d10.1007_s12597-021-00565-x.html
   My bibliography  Save this article

An extension of interpretive structural modeling using linguistic term sets for business decision-making

Author

Listed:
  • Sanjay Kumar Tyagi

    (Higher College of Technology)

  • Sujeet Kumar Sharma

    (Indian Institute of Management)

  • R. Krishankumar

    (Amrita School of Engineering, Coimbatore
    SASTRA University)

  • K. S. Ravichandran

    (Rajiv Gandhi National Institute of Youth Development)

Abstract

This paper presents a new interpretive structural modeling (ISM) technique based on linguistic term sets. In the proposed approach, the linguistic terms will replace binary numbers 0 and 1, representing one attribute's influence on another. The main objective is to introduce the concept of linguistic term sets to the ISM and develop a linguistic interpretive structural modeling (LISM), where the decision-makers (DM) would use linguistic terms such as very high (VH), high (H), low (L), very low (VL) and, no influence (N) to measure the strength of the impact of an attribute on other attributes. Since the linguistic terms are closer to the human cognitive process, it is more convenient and realistic for the decision-makers to use linguistic terms instead of binary numbers to express the pairwise relationship between different attributes. The integration of fuzzy linguistic terms and the ISM would enhance the consistency level and reduce the uncertainty inherent in the decision-maker's choice. The proposed LISM has been demonstrated by identifying the inter-relationships among the key attributes of business analytics methodology (BAM) acceptance in the industry settings.

Suggested Citation

  • Sanjay Kumar Tyagi & Sujeet Kumar Sharma & R. Krishankumar & K. S. Ravichandran, 2022. "An extension of interpretive structural modeling using linguistic term sets for business decision-making," OPSEARCH, Springer;Operational Research Society of India, vol. 59(3), pages 1158-1177, September.
    • Handle: RePEc:spr:opsear:v:59:y:2022:i:3:d:10.1007_s12597-021-00565-x
    DOI: 10.1007/s12597-021-00565-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-021-00565-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s12597-021-00565-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Pfohl, Hans-Christian & Gallus, Philipp & Thomas, David, 2011. "Interpretive structural modeling of supply chain risks," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 55230, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
    2. Vidgen, Richard & Shaw, Sarah & Grant, David B., 2017. "Management challenges in creating value from business analytics," European Journal of Operational Research, Elsevier, vol. 261(2), pages 626-639.
    3. Govindan, Kannan & Palaniappan, Murugesan & Zhu, Qinghua & Kannan, Devika, 2012. "Analysis of third party reverse logistics provider using interpretive structural modeling," International Journal of Production Economics, Elsevier, vol. 140(1), pages 204-211.
    4. Aydiner, Arafat Salih & Tatoglu, Ekrem & Bayraktar, Erkan & Zaim, Selim & Delen, Dursun, 2019. "Business analytics and firm performance: The mediating role of business process performance," Journal of Business Research, Elsevier, vol. 96(C), pages 228-237.
    5. Kajal Chatterjee & Sheikh Ahmed Hossain & Samarjit Kar, 2018. "Prioritization of project proposals in portfolio management using fuzzy AHP," OPSEARCH, Springer;Operational Research Society of India, vol. 55(2), pages 478-501, June.
    6. Mortenson, Michael J. & Doherty, Neil F. & Robinson, Stewart, 2015. "Operational research from Taylorism to Terabytes: A research agenda for the analytics age," European Journal of Operational Research, Elsevier, vol. 241(3), pages 583-595.
    7. Khan, Imran & Rahman, Zillur, 2015. "Brand experience anatomy in retailing: An interpretive structural modeling approach," Journal of Retailing and Consumer Services, Elsevier, vol. 24(C), pages 60-69.
    8. Edmundas Kazimieras Zavadskas & Abbas Mardani & Zenonas Turskis & Ahmad Jusoh & Khalil MD Nor, 2016. "Development of TOPSIS Method to Solve Complicated Decision-Making Problems — An Overview on Developments from 2000 to 2015," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 645-682, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Brinch, Morten & Gunasekaran, Angappa & Fosso Wamba, Samuel, 2021. "Firm-level capabilities towards big data value creation," Journal of Business Research, Elsevier, vol. 131(C), pages 539-548.
    2. Burger, Katharina & White, Leroy & Yearworth, Mike, 2019. "Developing a smart operational research with hybrid practice theories," European Journal of Operational Research, Elsevier, vol. 277(3), pages 1137-1150.
    3. Chen, Yi-Ting & Sun, Edward W. & Lin, Yi-Bing, 2020. "Merging anomalous data usage in wireless mobile telecommunications: Business analytics with a strategy-focused data-driven approach for sustainability," European Journal of Operational Research, Elsevier, vol. 281(3), pages 687-705.
    4. Zhan, Yuanzhu & Tan, Kim Hua, 2020. "An analytic infrastructure for harvesting big data to enhance supply chain performance," European Journal of Operational Research, Elsevier, vol. 281(3), pages 559-574.
    5. Naim Ahmad & Ayman Qahmash, 2021. "SmartISM: Implementation and Assessment of Interpretive Structural Modeling," Sustainability, MDPI, vol. 13(16), pages 1-27, August.
    6. Duan, Yanqing & Cao, Guangming & Edwards, John S., 2020. "Understanding the impact of business analytics on innovation," European Journal of Operational Research, Elsevier, vol. 281(3), pages 673-686.
    7. Osman, Ibrahim H. & Anouze, Abdel Latef & Irani, Zahir & Lee, Habin & Medeni, Tunç D. & Weerakkody, Vishanth, 2019. "A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values," European Journal of Operational Research, Elsevier, vol. 278(2), pages 514-532.
    8. Alisha Lakra & Shubhkirti Gupta & Ravi Ranjan & Sushanta Tripathy & Deepak Singhal, 2022. "The Significance of Machine Learning in the Manufacturing Sector: An ISM Approach," Logistics, MDPI, vol. 6(4), pages 1-15, October.
    9. Kristoffersen, Eivind & Mikalef, Patrick & Blomsma, Fenna & Li, Jingyue, 2021. "Towards a business analytics capability for the circular economy," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
    10. Kim, Jaemin & Dibrell, Clay & Kraft, Ellen & Marshall, David, 2021. "Data analytics and performance: The moderating role of intuition-based HR management in major league baseball," Journal of Business Research, Elsevier, vol. 122(C), pages 204-216.
    11. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    12. Conboy, Kieran & Mikalef, Patrick & Dennehy, Denis & Krogstie, John, 2020. "Using business analytics to enhance dynamic capabilities in operations research: A case analysis and research agenda," European Journal of Operational Research, Elsevier, vol. 281(3), pages 656-672.
    13. Yasmin, Mariam & Tatoglu, Ekrem & Kilic, Huseyin Selcuk & Zaim, Selim & Delen, Dursun, 2020. "Big data analytics capabilities and firm performance: An integrated MCDM approach," Journal of Business Research, Elsevier, vol. 114(C), pages 1-15.
    14. Song, Dong-Wook & Seo, Young-Joon & Kwak, Dong-Wook, 2019. "Learning from Hanjin Shipping’s failure: A holistic interpretation on its causes and reasons," Transport Policy, Elsevier, vol. 82(C), pages 77-87.
    15. Bouzon, Marina & Govindan, Kannan & Rodriguez, Carlos Manuel Taboada, 2015. "Reducing the extraction of minerals: Reverse logistics in the machinery manufacturing industry sector in Brazil using ISM approach," Resources Policy, Elsevier, vol. 46(P1), pages 27-36.
    16. Hindle, Giles A. & Vidgen, Richard, 2018. "Developing a business analytics methodology: A case study in the foodbank sector," European Journal of Operational Research, Elsevier, vol. 268(3), pages 836-851.
    17. Käki, Anssi & Kemppainen, Katariina & Liesiö, Juuso, 2019. "What to do when decision-makers deviate from model recommendations? Empirical evidence from hydropower industry," European Journal of Operational Research, Elsevier, vol. 278(3), pages 869-882.
    18. Tabesh, Pooya & Mousavidin, Elham & Hasani, Sona, 2019. "Implementing big data strategies: A managerial perspective," Business Horizons, Elsevier, vol. 62(3), pages 347-358.
    19. Pavla Vrabcová & Hana Urbancová, 2021. "Use of human resources information system in agricultural companies in the Czech Republic," Agricultural Economics, Czech Academy of Agricultural Sciences, vol. 67(5), pages 173-180.
    20. Sajid, Zaman & Khan, Faisal & Zhang, Yan, 2017. "Integration of interpretive structural modelling with Bayesian network for biodiesel performance analysis," Renewable Energy, Elsevier, vol. 107(C), pages 194-203.

    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:spr:opsear:v:59:y:2022:i:3:d:10.1007_s12597-021-00565-x. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.