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Application of Machine Learning (ML) in Human Resource Management

In: New Business Models in the Course of Global Crises in South Asia

Author

Listed:
  • Anupriyo Mallick

    (Eastern Institute for Integrated Learning in Management (EIILM), (Affiliated to Vidyasagar University))

Abstract

The present chapter is an attempt to explore the emerging trends of human resource management with reference to digitalization in HRM. One of the significant areas the HR should focus upon is “execution.” The top priority for the HR is to translate this strategy and vision into execution and make it a huge success. It is very pertinent to ensure the workforce in an organization is aligned to its strategy and priority. Like all aspects of modern business, technology is changing the way we operate and function. This applies to all departments in the company, and human resources is no exception. Just because it has the word human in the name does not mean that technology cannot be an invaluable aid. From cloud computing to mobility, big data, VR and augmented reality, block chain technology, “Internet of things” (IoT) and a range of emerging and developing technologies are now finding their way into the more enlightened HR departments of many companies. One technology that is currently making great strides in streamlining and improving the function of HR is machine learning. The technology itself is not new, but the applications for human resources have only recently started to gain traction, and they are already making a significant impact. It is wise to note that the organizations are judged not only on their financial health, quality of service or workforce satisfaction, but also on how effectively they integrate with the external world, customers, partners, and society at large.

Suggested Citation

  • Anupriyo Mallick, 2021. "Application of Machine Learning (ML) in Human Resource Management," Springer Books, in: Amina Omrane & Sudin Bag (ed.), New Business Models in the Course of Global Crises in South Asia, chapter 0, pages 209-220, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-79926-7_12
    DOI: 10.1007/978-3-030-79926-7_12
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