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PurposeThe purpose of this paper is to analyze the extensive literature on blockchain technology (BT) and human resource management (HRM) in enterprises and set the future scope of research in the adoption of BT in HRM.Design/methodology/approachA framework-based review of the literature (Callahan, 2014; Paul and Criado, 2020) is adopted for the present study. The 6 W-Framework developed by Callahan (2014) is used for the development of a conceptual framework on BT and HRM and could address HRM issues through the applications of BT.FindingsThis study focused on the major HR issues, i.e., regulation, staffing and development, and change management. These issues were categorized into sub-categories. The major implementation of BT in HRM is highlighted. The study developed a framework to aid HR professionals in implementing blockchain in the decision-making process of HRM.Research limitations/implicationsThe current study is limited to the bias on the part of employers in providing feedback and data feeding. Blockchain being at its infancy stage did not allow much of pieces of literary works to be introduced.Practical implicationsImplementation of ledger technology in managerial functions will reduce the time, money and effort required by potential recruiters and HR professionals. Using this technology, the time and cost required to verify and sort the right potential can be reduced.Originality/valueThe present work offers benefits to HR professionals and practitioners by expediting the process of effective decision-making of HRM employing BT.

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In machine learning, a Self-Organizing map plays a significant role in finding hidden patterns or intrinsic structures in data. In this study, a new modified expression is arrived at to update the radius of the neighbourhood of BMU in SOM. Further, a new approach is introduced to find the eligible nodes for an update in SOM. We have also incorporated the previous work, such as new initialization algorithms to find the initial weight vectors, a method to place the weight vectors in each node of the grid and a method to identify the number of clusters to improve the performance of the proposed SOM algorithm. The proposed SOM performance in terms of Quantization error (QE), Convergence time (CT) and Modified Semantic Relevance Index (MSRI) are compared with conventional SOM for both class label and non-class label datasets. In addition to the above measure, Classification Accuracy (CA) is also used to evaluate the performance against class label datasets. The proposed SOM algorithm shows better performance in all cases.