Discovering Trends and Journeys in Knowledge-Based Human Resource Management: Big Data Smart Literature Review Based on Machine Learning Approach

Abstract

The research interest of knowledge in human resource management (HRM) is significant. However, many HRM studies work only with a limited number of analyzed documents. This article is based on processing metadata to get results from 7,318 documents related to human resource management and knowledge, published between 1960 and 2021, retrieved from the Scopus database with research directions linked to knowledge and human resources and the current topic Covid-19 pandemic. Such a broad study has not yet been published in the field of knowledge and HRM. The paper answers three research questions related to trends and innovative journeys in knowledge-based HRM. Descriptive and inferential statistics was used to capture basic trends in knowledge and HRM themes. Latent Dirichlet Allocation (LDA) was used for topic modelling with Gibbs sampling, which we use on a corpus of abstracts. Used method allowed us to identify latent topics which describe a more in-depth relationship between HRM and knowledge. We identified 13 topics related to HRM and knowledge research as the most relevant and showed directions and trends among the authors of HRM and knowledge management. The last part is devoted to the current topic of Covid-19, key areas identified in the literature and their impact on knowledge management from the perspective of HRM.

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