Enhancing Employee Productivity Through Cloud-Based Digital HRM Solutions

Nathiya, V and Rajini, G (2026) Enhancing Employee Productivity Through Cloud-Based Digital HRM Solutions. International Journal of Computer Information Systems and Industrial Management Applications, 18 (17s): 1. pp. 168-177. ISSN 2150-7988

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Abstract

As digital workplaces evolve, organizations are venturing more into the domain of advanced technology to automate
HR functions as well as improve the performance of employees. The aim of this paper is to explore the effects of digital HRM practices on employees’ productivity through cloud based HR systems, mobile accessibility and automated AI driven HR processes. This paper also discusses the key factors, workflows, challenges, and risks associated with Cloud-based Human Resource Management (Cloud HRM). AMOS Confirmatory Factor Analysis (CFA) is used in this paper to verify reliability and assessment of convergent and discriminant validity, and confirms the robustness of the measurement model. Structural Equation Modeling
(SEM) used to validate the relationship between the important variables and hypotheses testing. The findings indicate that the adoption of cloud based HRM and mobile accessibility positively influence the productivity of employees by improving employees
access to HR services. Organizations that are looking to use AI automation to boost productivity, they might need to invest effort in better implementing strategies, training programs and employee engagement programs to increase its benefits. Robustness of the
framework is confirmed by model fit indices, which indicate the main role of cloud HRM adoption and HR mobile accessibility
systems in enhancing workplace efficiency. It also points out the importance of organization strategies in implementing Artificial
Intelligence driven HR tools, while leveraging digital HRM solutions to improve the productivity of the employees

Item Type: Article
Subjects: Management Studies > Human Resources
Domains: Management Studies
Depositing User: IR Admin
Date Deposited: 01 Sep 2026 11:04
Last Modified: 01 Sep 2026 11:04
URI: https://ir.vistas.ac.in/id/eprint/22271

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