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Empirical analysis in analysing the major factors of machine learning in enhancing the e-business through structural equation modelling (SEM) approach

Author

Listed:
  • Asmat Ara Shaikh

    (Bharati Vidyapeeth’s Institute of Management Studies and Research)

  • K. Santhana Lakshmi

    (SRM Institute of Science and Technology)

  • Korakod Tongkachok

    (Thaksin University)

  • Joel Alanya-Beltran

    (Universidad Tecnológica del Perú)

  • Edwin Ramirez-Asis

    (Universidad Nacional Santiago Antunez de Mayolo)

  • Julian Perez-Falcon

    (Universidad Nacional Santiago Antunez de Mayolo)

Abstract

The main aim of the study is to determine the key variables of implementing Machine learning tools so as to enhance Electronic business (E-business) in the organisation. The researchers has focused in considering critical determinants of machine learning influenced demand forecasting, application of ML in purchase behaviour, crating better customer engagement and support in overall cross selling of the products for enhancing E-business and achieve sustainable development. The implementation of new and advanced machine learning approaches has enabled the organisation to realise more benefits, support in forecasting the customer demand accurately, realise better engagement and cross selling of products. This study intends to source the information through closed ended questionnaire from nearly 155 business managers in retail E-business companies located in India, the study will apply Partial least square (PLS) analysis using the Structural equation modelling and also uses IBM SPSS for making preliminary descriptive analysis based on the data sourced through the questionnaire. The main theme of the paper is to provide critical and comprehensive understanding of implementation of machine learning approaches in enhancing E-business. The dynamic business environment compels business to adopt to new technologies to promote sales, engage with customers and enhance business values. Hence, this study is more involved in understanding the machine learning approaches incorporated by management for supporting their E-business. Based on the analysis it is noted that machine learning driven analysis enable in making better customer engagement, support in analysing the buyer behaviour and track the products from the vendors to reaching the hands of the customers. This research focuses in stating the key determinants of applying machine learning in enhancing the E-business for achieving sustainable growth and advantage. E-commerce business applied ML to analyse the overall buyer behaviour, engage them effectively so as to offer better products and services to meet their needs. The study will be unique as it focuses in analysing the impact of ML in Retail E-business as so to provide better direction for the companies to apply the various aspects of ML in realising the goals and objectives.

Suggested Citation

  • Asmat Ara Shaikh & K. Santhana Lakshmi & Korakod Tongkachok & Joel Alanya-Beltran & Edwin Ramirez-Asis & Julian Perez-Falcon, 2022. "Empirical analysis in analysing the major factors of machine learning in enhancing the e-business through structural equation modelling (SEM) approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(1), pages 681-689, March.
    • Handle: RePEc:spr:ijsaem:v:13:y:2022:i:1:d:10.1007_s13198-021-01590-1
    DOI: 10.1007/s13198-021-01590-1
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    References listed on IDEAS

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    1. Shrutika Mishra & A. R. Tripathi, 2021. "AI business model: an integrative business approach," Journal of Innovation and Entrepreneurship, Springer, vol. 10(1), pages 1-21, December.
    2. Donna L Hoffman & Thomas P Novak & Eileen FischerEditor & Robert KozinetsAssociate Editor, 2018. "Consumer and Object Experience in the Internet of Things: An Assemblage Theory Approach," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 44(6), pages 1178-1204.
    3. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
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    Cited by:

    1. Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023. "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(1), pages 549-568, March.

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