Design and data analytics of electronic human resource management activities through Internet of Things in an organization. (3rd March 2020)

• Record Type: Journal Article
• Authors: Nasar, Nasreen; Ray, Sumati; Umer, Saiyed; Mohan Pandey, Hari
• Other Names: Kumar Neeraj guestEditor.; Jindal Anish guestEditor.; Villari Massimo guestEditor.; Srirama Satish Narayana guestEditor.
• Is Part Of: Software, practice & experience. Volume 51:Number 12(2021)
• Page Start: 2411
• Page End: 2427
• Publication Date: 2020-03-03
• Subjects: activities -- data analytics -- electronic human resource management -- IoT; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.2817
• Languages: English
• ISSNs: 0038-0644

Abstract

Summary: A novel design and data analytics for an electronic human resource management (e‐HRM) system has been proposed in this article. E‐HRM software is being widely used in big industries and institutions. This e‐HRM is very cost‐effective, competence, congruence, and commitment for the organization. At present, Internet of Things (IoT) have great impact on e‐HRM, which gives various facilities and supports to e‐HRM functionalities such as securities, standards, privacy, and regulations. The combination of e‐HRM with IoT has wide applications for implementing policies, strategies, and practices within the organization. An e‐HRM has mainly five activities: e‐Selection, e‐Recruitment, e‐Performance, e‐Compensation, and e‐Learning. In this work, the proposed system has two parts. In the first part, the various e‐HRM activities have been discussed and elaborated with examples. In the second part, the description of data analytics based on IoT for each e‐HRM activity has been discussed and demonstrated. Here the data analytics part is divided into four components: (a) data preprocessing; (b) feature selection; (c) data classification; and (d) performance evaluation. Extensive experimentation has been performed for each e‐HRM activity using four HR analytic datasets from Kaggle site, and finally, the performance with proper justifications has been exquisitely done using each dataset respect to each e‐HRM activity.