Big Data in Driving Greener Social Welfare and Sustainable Environmental Management

Arumugam, Thangaraja; Ashifa, K. M.; Vinayagalakshmi, V.; Kiran, Uday; Ramya, S.

doi:10.4018/979-8-3693-0049-7.ch022

Advances in Business Information Systems and Analytics

2023

DOI: 10.4018/979-8-3693-0049-7.ch022

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Big Data in Driving Greener Social Welfare and Sustainable Environmental Management

Thangaraja Arumugam,

K. M. Ashifa,

V. Vinayagalakshmi

et al.

Abstract: The emergence of big data (BD) has opened up new opportunities for addressing social and environmental challenges. This chapter examines the potential of BD to drive green social welfare and sustainable environmental management. The chapter highlights how big data can be used to develop new approaches for monitoring and managing natural resources and addressing social issues such as poverty and inequality. It explores how big data can be used to promote sustainable development, from enhancing resource efficien… Show more

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Cited by 5 publications

References 42 publications

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Factors and Interpretable Models in Data-Driven Management Information Systems to Enhance Job Satisfaction of Road Transport Employees

Sabarirajan,

Srivenidevi,

Kuragayala

et al. 2024

Advances in Logistics, Operations, and Management Science

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Adopting interpretable models is crucial in shaping employee job satisfaction within the road transport industry. This research examines the Management Information Systems (MIS) factors influencing the adoption of these models and their subsequent impact on enhancing job satisfaction among employees responsible for ground services. Employing a mixed-methods approach with 200 respondents, the study combines quantitative surveys and qualitative interviews. Key factors explored include training, communication, employee involvement, workload, organizational support, job autonomy, perceived job security impact, and cultural alignment. Data robustness is ensured through validity and reliability tests, complemented by statistical analyses such as independent sample T-tests, ANOVA, regression analysis, and qualitative thematic analysis. The findings provide strategic insights for industry leaders, highlighting the importance of training and communication in fostering a supportive work environment.

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Factors and Interpretable Models in Data-Driven Management Information Systems to Enhance Job Satisfaction of Road Transport Employees

Sabarirajan,

Srivenidevi,

Kuragayala

et al. 2024

Advances in Logistics, Operations, and Management Science

View full text Add to dashboard Cite

show abstract

The Digital Transformation of Work

Sanyal,

2024

Advances in Computational Intelligence and Robotics

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This chapter explores the transformative impact of automation and artificial intelligence on employment, productivity, and socioeconomic dynamics. It addresses critical questions about how automation will redefine job roles and labor markets, the essential skills for an automated economy, and how organizations and employees can adapt to these changes. The study aims to provide a comprehensive analysis of the disruptions and opportunities presented by automation, focusing on sectors likely to be affected, jobs at risk or evolving, and emerging skill requirements. Employing a mixed-methods approach, the research integrates qualitative and quantitative methodologies, including a thorough literature review, data analysis from labor market statistics, and expert interviews. The chapter is structured into key sections: an introduction outlining the significance of the topic, a literature review on existing theories and empirical studies, a methodology section detailing the research design, and a findings section presenting the analysis results. The discussion interprets these findings within broader economic and social contexts, and the conclusion summarizes key insights, policy recommendations, and suggestions for future research.

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Application of Agile Principles for Social Welfare Delivery and Modernization

Rahman,

Channabasaiah,

Hisham

et al. 2025

Advances in Logistics, Operations, and Management Science

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This manuscript explores how Agile principles, originally developed for the software industry, can be applied to modernize social welfare systems. Agile methodologies emphasize flexibility, collaboration, and continuous improvement, offering a dynamic framework for enhancing service delivery and responsiveness to citizens' needs. By adopting Agile, social welfare organizations can foster cross-functional teamwork, improve efficiency, and create more citizen-centric welfare systems. This paper highlights specific Agile principles, such as iterative processes, adaptability, and feedback integration, and their potential to address the challenges faced by traditional welfare systems. The manuscript also discusses the opportunities and barriers to implementing Agile in public and social sectors, including the need for capacity building and overcoming institutional resistance. Through this analysis, the paper aims to provide actionable insights for policymakers and welfare administrators seeking to modernize their systems and improve service outcomes.

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Big Data in Driving Greener Social Welfare and Sustainable Environmental Management

Cited by 5 publications

References 42 publications

Factors and Interpretable Models in Data-Driven Management Information Systems to Enhance Job Satisfaction of Road Transport Employees

Factors and Interpretable Models in Data-Driven Management Information Systems to Enhance Job Satisfaction of Road Transport Employees

The Digital Transformation of Work

Application of Agile Principles for Social Welfare Delivery and Modernization

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