IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2022i1p25-d1010079.html
   My bibliography  Save this article

E-Commerce Website Usability Analysis Using the Association Rule Mining and Machine Learning Algorithm

Author

Listed:
  • Biresh Kumar

    (Faculty of Computing and Information Technology, Usha Martin University, Ranchi 835103, Jharkhand, India)

  • Sharmistha Roy

    (Faculty of Computing and Information Technology, Usha Martin University, Ranchi 835103, Jharkhand, India)

  • Anurag Sinha

    (Department of Computer Science, IGNOU, New Delhi 110068, India)

  • Celestine Iwendi

    (School of Creative Technologies, University of Bolton, Bolton BL3 5AB, UK)

  • Ľubomíra Strážovská

    (Faculty of Management, Comenius University in Bratislava, Odbojárov 10, 82005 Bratislava, Slovakia)

Abstract

The overall effectiveness of a website as an e-commerce platform is influenced by how usable it is. This study aimed to find out if advanced web metrics, derived from Google Analytics software, could be used to evaluate the overall usability of e-commerce sites and identify potential usability issues. It is simple to gather web indicators, but processing and interpretation take time. This data is produced through several digital channels, including mobile. Big data has proven to be very helpful in a variety of online platforms, including social networking and e-commerce websites, etc. The sheer amount of data that needs to be processed and assessed to be useful is one of the main issues with e-commerce today as a result of the digital revolution. Additionally, on social media a crucial growth strategy for e-commerce is the usage of BDA capabilities as a guideline to boost sales and draw clients for suppliers. In this paper, we have used the KMP algorithm-based multivariate pruning method for web-based web index searching and different web analytics algorithm with machine learning classifiers to achieve patterns from transactional data gathered from e-commerce websites. Moreover, through the use of log-based transactional data, the research presented in this paper suggests a new machine learning-based evaluation method for evaluating the usability of e-commerce websites. To identify the underlying relationship between the overall usability of the eLearning system and its predictor factors, three machine learning techniques and multiple linear regressions are used to create prediction models. This strategy will lead the e-commerce industry to an economically profitable stage. This capability can assist a vendor in keeping track of customers and items they have viewed, as well as categorizing how customers use their e-commerce emporium so the vendor can cater to their specific needs. It has been proposed that machine learning models, by offering trustworthy prognoses, can aid in excellent usability. Such models might be incorporated into an online prognostic calculator or tool to help with treatment selection and possibly increase visibility. However, none of these models have been recommended for use in reusability because of concerns about the deployment of machine learning in e-commerce and technical issues. One problem with machine learning science that needs to be solved is explainability. For instance, let us say B is 10 and all the people in our population are even. The hash function’s behavior is not random since only buckets 0, 2, 4, 6, and 8 can be the value of h(x). However, if B = 11, we would find that 1/11th of the even integers is transmitted to each of the 11 buckets. The hash function would work well in this situation.

Suggested Citation

  • Biresh Kumar & Sharmistha Roy & Anurag Sinha & Celestine Iwendi & Ľubomíra Strážovská, 2022. "E-Commerce Website Usability Analysis Using the Association Rule Mining and Machine Learning Algorithm," Mathematics, MDPI, vol. 11(1), pages 1-24, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:25-:d:1010079
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/1/25/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/1/25/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Shahriar Akter & Samuel Fosso Wamba, 2016. "Big data analytics in E-commerce: a systematic review and agenda for future research," Electronic Markets, Springer;IIM University of St. Gallen, vol. 26(2), pages 173-194, May.
    2. Thi Mai Le & Shu-Yi Liaw, 2017. "Effects of Pros and Cons of Applying Big Data Analytics to Consumers’ Responses in an E-Commerce Context," Sustainability, MDPI, vol. 9(5), pages 1-19, May.
    3. Juan-Pedro Cabrera-Sánchez & Iviane Ramos-de-Luna & Elena Carvajal-Trujillo & Ángel F. Villarejo-Ramos, 2020. "Online Recommendation Systems: Factors Influencing Use in E-Commerce," Sustainability, MDPI, vol. 12(21), pages 1-15, October.
    4. Qiong Jia & Yue Guo & Guanlin Wang & Stuart J. Barnes, 2020. "Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework," IJERPH, MDPI, vol. 17(17), pages 1-21, August.
    5. Thanh Van Do & Hanne Kristine Hallingby & Loc H. Khuong & Natalia Kryvinska, 2014. "A disruption analysis of mobile communication services using business ecosystem concept," International Journal of Services, Economics and Management, Inderscience Enterprises Ltd, vol. 6(3), pages 248-262.
    6. Laith T. Khrais, 2020. "Role of Artificial Intelligence in Shaping Consumer Demand in E-Commerce," Future Internet, MDPI, vol. 12(12), pages 1-14, December.
    7. Chia-Nan Wang & Thanh-Tuan Dang & Ngoc-Ai-Thy Nguyen & Thi-Thu-Hong Le, 2020. "Supporting Better Decision-Making: A Combined Grey Model and Data Envelopment Analysis for Efficiency Evaluation in E-Commerce Marketplaces," Sustainability, MDPI, vol. 12(24), pages 1-24, December.
    8. Polina Fauska & Natalia Kryvinska & Christine Strauss, 2013. "The role of e-commerce in B2B markets of goods and services," International Journal of Services, Economics and Management, Inderscience Enterprises Ltd, vol. 5(1/2), pages 41-71.
    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. de Camargo Fiorini, Paula & Roman Pais Seles, Bruno Michel & Chiappetta Jabbour, Charbel Jose & Barberio Mariano, Enzo & de Sousa Jabbour, Ana Beatriz Lopes, 2018. "Management theory and big data literature: From a review to a research agenda," International Journal of Information Management, Elsevier, vol. 43(C), pages 112-129.
    2. Leogrande, Angelo, 2021. "The Destruction of Price-Representativeness," MPRA Paper 111239, University Library of Munich, Germany.
    3. Shahriar Akter & Saradhi Motamarri & Shahriar Sajib & Ruwan J. Bandara & Shlomo Tarba & Demetris Vrontis, 2024. "Theorising the Microfoundations of analytics empowerment capability for humanitarian service systems," Annals of Operations Research, Springer, vol. 335(3), pages 989-1013, April.
    4. Mykola Odrekhivskyi & Orysya Pshyk-Kovalska & Volodymyr Zhezhukha & Iryna Ivanochko, 2022. "Intelligent Management of Enterprise Business Processes," Mathematics, MDPI, vol. 11(1), pages 1-15, December.
    5. Vijoleta Vrhovac & Stana Vasić & Stevan Milisavljević & Branislav Dudić & Peter Štarchoň & Marina Žižakov, 2023. "Measuring E-Commerce User Experience in the Last-Mile Delivery," Mathematics, MDPI, vol. 11(6), pages 1-21, March.
    6. Jingmei Gao & Zahid Sarwar, 2024. "How do firms create business value and dynamic capabilities by leveraging big data analytics management capability?," Information Technology and Management, Springer, vol. 25(3), pages 283-304, September.
    7. Eric. W. K. See-To & Yang Yang, 2017. "Market sentiment dispersion and its effects on stock return and volatility," Electronic Markets, Springer;IIM University of St. Gallen, vol. 27(3), pages 283-296, August.
    8. Stefan Jovčić & Petr Průša, 2021. "A Hybrid MCDM Approach in Third-Party Logistics (3PL) Provider Selection," Mathematics, MDPI, vol. 9(21), pages 1-19, October.
    9. Sophie Cockcroft & Mark Russell, 2018. "Big Data Opportunities for Accounting and Finance Practice and Research," Australian Accounting Review, CPA Australia, vol. 28(3), pages 323-333, September.
    10. Jeeyoung Lim & Joseph J. Kim & Sunkuk Kim, 2021. "A Holistic Review of Building Energy Efficiency and Reduction Based on Big Data," Sustainability, MDPI, vol. 13(4), pages 1-18, February.
    11. Sharan Srinivas & Surya Ramachandiran, 2024. "Passenger intelligence as a competitive opportunity: unsupervised text analytics for discovering airline-specific insights from online reviews," Annals of Operations Research, Springer, vol. 333(2), pages 1045-1075, February.
    12. Manuel Pérez-Trujillo & Camila Alvayay Torrejón & Maricruz Lacalle Calderón, 2024. "Is E-commerce an opportunity for boosting employment in developing countries? a study for the Chilean case," Asia-Pacific Journal of Regional Science, Springer, vol. 8(4), pages 1077-1110, December.
    13. Sabeen Hussain Bhatti & Wan Mohd Hirwani Wan Hussain & Jabran Khan & Shahbaz Sultan & Alberto Ferraris, 2024. "Exploring data-driven innovation: What’s missing in the relationship between big data analytics capabilities and supply chain innovation?," Annals of Operations Research, Springer, vol. 333(2), pages 799-824, February.
    14. Etienne Thenoz & Solène Juteau & Frantz Rowe, 2024. "E-commerce transformation: A literature review from an institutional and organizational perspective," Electronic Markets, Springer;IIM University of St. Gallen, vol. 34(1), pages 1-20, December.
    15. Rainer Alt, 2020. "Evolution and perspectives of electronic markets," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(1), pages 1-13, March.
    16. Shahriar Akter & Samuel Fosso Wamba, 2019. "Big data and disaster management: a systematic review and agenda for future research," Annals of Operations Research, Springer, vol. 283(1), pages 939-959, December.
    17. Ranjan Chaudhuri & Sheshadri Chatterjee & Demetris Vrontis & Alkis Thrassou, 2024. "Adoption of robust business analytics for product innovation and organizational performance: the mediating role of organizational data-driven culture," Annals of Operations Research, Springer, vol. 339(3), pages 1757-1791, August.
    18. Mathias Eggert & Jens Alberts, 2020. "Frontiers of business intelligence and analytics 3.0: a taxonomy-based literature review and research agenda," Business Research, Springer;German Academic Association for Business Research, vol. 13(2), pages 685-739, July.
    19. Demetris Vrontis & Ranjan Chaudhuri & Sheshadri Chatterjee, 2022. "Adoption of Digital Technologies by SMEs for Sustainability and Value Creation: Moderating Role of Entrepreneurial Orientation," Sustainability, MDPI, vol. 14(13), pages 1-19, June.
    20. Maya Vachkova & Arsalan Ghouri & Haidy Ashour & Normalisa Binti Md Isa & Gregory Barnes, 2023. "Big data and predictive analytics and Malaysian micro-, small and medium businesses," SN Business & Economics, Springer, vol. 3(8), pages 1-28, August.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    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:gam:jmathe:v:11:y:2022:i:1:p:25-:d:1010079. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.