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This study aimed to evaluate the relative efficiencies of the state universities in Sri Lanka from 2017 to 2019 and their capacity to enroll more students.

Data envelopment analysis (DEA) method was used to measure the relative efficiencies. Annual data on expenditure, academic staff, non-academic staff, student enrollments, graduate output and publications in indexed journals were used.

Results from both input oriented DEA and output oriented DEA showed that seven out of 15 (47%) state universities were constantly performing efficiently from 2017 to 2019. Well-established universities are apparently more efficient than emerging universities. Six universities were constantly inefficient. Excess staff and the deficiencies in enrollments, graduate output and publications were the main causes of the inefficiencies. In particular, the total enrollments can be increased by 23%.

The outcomes are thoroughly based on DEA with a selected set of variables and data. Owing to the limitations in the DEA method, variables and data, the results may not reflect the actual efficiencies of the universities. However, this study indicates that the universities can improve their relative efficiencies by accommodating more students and increasing research output.

This study is the first proper investigation of the relative efficiencies of the state universities in Sri Lanka using DEA. This is also the first quantitative assessment of the capacity of Sri Lankan Universities to increase student enrollments.

Some manufacturing processes generate extreme temperatures, noise and other irritating environmental conditions. These environmental factors can have a negative impact on workers’ performance and health. The purpose of this paper is to investigate the impact of temperature, noise and lighting on factory workers’ productivity and day-to-day health in a rubber compound manufacturing plant.

Secondary data on productivity, measurements of factory environmental conditions, and records of medical advice and treatments over a period of three months were used in this study. Causal analysis was performed using statistical techniques ANOVA and regression analysis in SPSS statistical package.

The results revealed that higher levels of temperature and noise inside the factory can cause lower productivity levels. High temperature may not only reduce the productivity but also contribute to illnesses or heat-stress symptoms such as headaches. The results further suggested that both noise and lighting level do not have a statistically significant impact on workers’ health. No statistical evidence was found on lighting level having an impact on workers’ productivity.

The study was particularly focused on a rubber compound manufacturing plant. However, the results were consistent with the results obtained in similar studies which focused on leather and shoe industry and automobile assembly industry.

The impact of environmental conditions on workers’ performance and health has been studied with respect to few industries and regions. This is the first of that kind carried out in Sri Lanka. Clear evidence of productivity losses and health impacts caused by high temperatures, raises the need for improving the working conditions.