STATISTICS AND DATA: A VIABLE NEXUS INITIATIVE FOR SUSTAINABLE FUTURE OF UNIVERSITIES AND INDUSTRIES
Statistics and Data
DOI:
https://doi.org/10.33003/fjs-2025-0905-3441Keywords:
University, Industries, Sustainability, Data, StatisticsAbstract
The study investigates the impacts of statistical methodology and data on the sustainable future of universities and industries based on goals 4 (Quality education) and 9 (industry, innovation and infrastructure) of sustainable development goals. In the contemporary landscape of academia and industry, the synergy between universities and industries is indispensable for fostering sustainable development. This paper explores the pivotal role of statistics and data in enhancing the sustainability future of both the universities and industries. By employing statistical methodologies and leveraging viable data, universities and industries can collaborate more effectively in addressing societal challenges, optimizing resource utilization, and fostering innovation. The study adopts Partial Least Squares (PLS) to examine the impact of statistics and viable data on the future sustainability of universities and industries. It highlights the transformative potential of statistical insights in guiding evidence-based decision-making, enhancing operational efficiency, and driving sustainable practices in both academic and industrial domains. A sample size of 150 respondents across KU8 and industries is selected for the study. Sustainable development goals 4 and 9 which state the inclusion of both universities education and industry innovation and infrastructure were examined. The study focuses on the importance of interdisciplinary collaboration, capacity building, and ethical considerations in maximizing the impact of statistical approaches for sustainable development. The findings suggest that collaboration between universities and industries, alongside the adoption of data-driven approaches, is essential to achieving long-term sustainability. Therefore, it is recommended that both sectors prioritize integrating statistical methods and data analytics to enhance sustainability efforts.
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