Authors:
P. Sudha, S. Prabhakaran, Sławomira Hajduk, Nadezhda Kunicina, Cristina Dumitru, Amina Omrane
Addresses:
Department of Business Administration, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India, sudhap@dhaanishcollege.in, prabhakaran.s@dhaanishcollege.in. Faculty Engineering of Management, Bialystok University of Technology, Białystok, Poland, s.hajduk@pb.edu.pl. Faculty of Computer Science, Information Technology and Energy, Riga Technical University, Riga, Latvia, nadezda.kunicina@rtu.lv. Department of Education, The National University of Science and Technology Politehnica Bucharest, Pitești University Centre, Romania, cristina.dumitru81@upb.ro. Department of Management Science, University of Sfax, Sfax, Tunisia, amina.omrane@fsegs.usf.tn.
Abstract:
This research explains the employee welfare measures, with a strong focus on Mezcal Steel Industries, and shows how they impact employee satisfaction and well-being. Mezcal Steel employees were surveyed to understand their satisfaction level with the company's welfare facilities. A systematic research methodology was followed, addressing the research question using primary and secondary data sources. A structured questionnaire is applied for primary data collection. Thereafter, responses were received from 120 employees out of 334 employees working within the organization. The official organizational website and record provided secondary data. Because details about the prevalence or existence of welfare practices are available within them, secondary data has added more context to the existing welfare practice. Simple percentage analysis was applied to the data-gathering process. An important observation is that most employees are satisfied with present welfare provisions. However, the observation also suggests areas for improvement. This study also brings out employee welfare as a determinant of organizational success; thus, it is an important consideration for the management of Mezcal Steel Industries.
Keywords: Fire Detection; Visual Monitoring Systems; Using Neural Networks; Diverse Data Collection; Minimal Computational Load; Monitoring Solutions; Standard Fire Data Collections.
Received: 08/02/2024, Revised: 06/04/2024, Accepted: 07/05/2024, Published: 05/06/2024
AVE Trends in Intelligent Technoprise Letters, 2024 Vol. 1 No. 2 , Pages: 100-111