IDEAS home Printed from https://ideas.repec.org/a/spr/elcore/v24y2024i2d10.1007_s10660-023-09787-1.html
   My bibliography  Save this article

Improving smart deals system to secure human-centric consumer applications: Internet of things and Markov logic network approaches

Author

Listed:
  • Ali Ala

    (Shanghai Jiao Tong University)

  • Amir Hossein Sadeghi

    (North Carolina State University)

  • Muhammet Deveci

    (National Defence University
    University College London
    Lebanese American University)

  • Dragan Pamucar

    (University of Belgrade
    Yuan Ze University
    Lebanese American University)

Abstract

Considering the increasing inclination of modern consumers to frequent large retail chains capable of promptly fulfilling their diverse needs, there is a noticeable surge in the prevalence of contemporary shopping complexes. Subscription services, customer-focused strategies, and efficient supply management are driving the progression of intelligent commerce within these expansive retail platforms. The Internet of Things (IoT) presents the foundation for “smart” retailers that can monitor inventory levels, diminish equipment failures, and provide better customer experience. Many models, as one of the widely used methods in this domain, Markov Logic Network (MLN), can simultaneously use activity knowledge and data by unifying probability and logic. In this research, we determine a smart deals system (SDS), consider the improved machine learning algorithms to meet performance, and develop secure human-centric consumer applications to render the system workable. From the results, and based on the percentage of efficiency, around 10% of clients are connected randomly, which has a minor impact on the outcomes from LR (logistic regression). Similar outcomes are delivered when the number of customers in the scope of 30–40% is connected for NB (Naive Bayes). Hence, prospective shopping sales will increase along with the efficiency and speed at which it operates.

Suggested Citation

  • Ali Ala & Amir Hossein Sadeghi & Muhammet Deveci & Dragan Pamucar, 2024. "Improving smart deals system to secure human-centric consumer applications: Internet of things and Markov logic network approaches," Electronic Commerce Research, Springer, vol. 24(2), pages 771-797, June.
    • Handle: RePEc:spr:elcore:v:24:y:2024:i:2:d:10.1007_s10660-023-09787-1
    DOI: 10.1007/s10660-023-09787-1
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10660-023-09787-1
    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/s10660-023-09787-1?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. Amir Hossein Sadeghi & Ziyuan Sun & Amirreza Sahebi-Fakhrabad & Hamid Arzani & Robert Handfield, 2023. "A Mixed-Integer Linear Formulation for a Dynamic Modified Stochastic p-Median Problem in a Competitive Supply Chain Network Design," Logistics, MDPI, vol. 7(1), pages 1-24, March.
    2. Edoardo Fonda & Antonella Meneghetti, 2022. "The Human-Centric SMED," Sustainability, MDPI, vol. 14(1), pages 1-13, January.
    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. Amjad, Asim & Ikramullah Butt, Shahid & Agha, Mujtaba Hassan & Ahmad, Ayyaz & Zhang, Faping & Ahmad, Shamraiz, 2023. "Integrating Ergonomics and sustainability: A framework with LDA methodology and implementation roadmap," Technology in Society, Elsevier, vol. 75(C).
    2. Attari, Mahdi Yousfi Nejad & Moslemi Beirami, Ali Asghar & Ala, Ali & Jami, Ensiye Neyshabouri, 2023. "Resolving the practical factors in the healthcare system management by considering a combine approach of AHP and ANP methods," Evaluation and Program Planning, Elsevier, vol. 100(C).
    3. Farzad Kiani & Sajjad Nematzadeh & Fateme Aysin Anka & Mine Afacan Findikli, 2023. "Chaotic Sand Cat Swarm Optimization," Mathematics, MDPI, vol. 11(10), pages 1-47, May.
    4. Borchardt Miriam & Pereira Giancarlo M. & Milan Gabriel S. & Scavarda Annibal R. & Nogueira Edithe O. & Poltosi Leonel C., 2022. "Industry 5.0 Beyond Technology: An Analysis Through the Lens of Business and Operations Management Literature," Organizacija, Sciendo, vol. 55(4), pages 305-321, November.

    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:elcore:v:24:y:2024:i:2:d:10.1007_s10660-023-09787-1. 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.