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Big data augmentated business trend identification: the case of mobile commerce

Author

Listed:
  • Ozcan Saritas

    (National Research University Higher School of Economics)

  • Pavel Bakhtin

    (National Research University Higher School of Economics)

  • Ilya Kuzminov

    (National Research University Higher School of Economics)

  • Elena Khabirova

    (National Research University Higher School of Economics)

Abstract

Identifying and monitoring business and technological trends are crucial for innovation and competitiveness of businesses. Exponential growth of data across the world is invaluable for identifying emerging and evolving trends. On the other hand, the vast amount of data leads to information overload and can no longer be adequately processed without the use of automated methods of extraction, processing, and generation of knowledge. There is a growing need for information systems that would monitor and analyse data from heterogeneous and unstructured sources in order to enable timely and evidence-based decision-making. Recent advancements in computing and big data provide enormous opportunities for gathering evidence on future developments and emerging opportunities. The present study demonstrates the use of text-mining and semantic analysis of large amount of documents for investigating in business trends in mobile commerce (m-commerce). Particularly with the on-going COVID-19 pandemic and resultant social isolation, m-commerce has become a large technology and business domain with ever growing market potentials. Thus, our study begins with a review of global challenges, opportunities and trends in the development of m-commerce in the world. Next, the study identifies critical technologies and instruments for the full utilization of the potentials in the sector by using the intelligent big data analytics system based on in-depth natural language processing utilizing text-mining, machine learning, science bibliometry and technology analysis. The results generated by the system can be used to produce a comprehensive and objective web of interconnected technologies, trends, drivers and barriers to give an overview of the whole landscape of m-commerce in one business intelligence (BI) data mart diagram.

Suggested Citation

  • Ozcan Saritas & Pavel Bakhtin & Ilya Kuzminov & Elena Khabirova, 2021. "Big data augmentated business trend identification: the case of mobile commerce," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1553-1579, February.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:2:d:10.1007_s11192-020-03807-9
    DOI: 10.1007/s11192-020-03807-9
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    References listed on IDEAS

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    Cited by:

    1. Roman Shcherbakov & Sofya Privorotskaya & Konstantin Vishnevskiy, 2021. "The Impact Of The Covid-19 Pandemic On Digital Technology Diffusion," HSE Working papers WP BRP 123/STI/2021, National Research University Higher School of Economics.

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