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This study sets out to analyze the efficiency and productivity issues of the insurance sector from both the policymakers' and investors' points of view to insulate the business and financial risks of UAE corporate houses.

The paper uses two inputs of “administrative and general expenses”, and “equity and change in legal reserves”, versus two outputs of rate of “return on investments” and “liquid asset to total liabilities ratio” to assess the allocative efficiency of the companies using DEA. Using the the Malmquist productivity index the efficiency is broken down into technical and scale efficiency to evaluate the performance of the insurers.

While the scale of operation of insurers is, by and large, acceptable, there is a considerable degree of managerial inefficiency among the insurers, with the least efficiency in 2000, and higher efficiency in 2004. Further, the insurers on average achieved a mere 0.8 percent annual gain in total factor productivity over the period in question.

The data set is narrow with 19 insurers in the region, which is the limitation.

The results have policy implications for the regulators and managerial implications for the existing insurers to face the growing competition in the region.

This is the first study to investigate the productivity changes of insurance sector operations in a developing economy: the UAE in the Middle‐East region. The study findings help the insurers to take appropriate managerial steps to improve the efficiency of their operations.

This study aims to explore the functions of policy-affiliated intermediaries and their roles in driving innovation in latecomers.

Latecomers have recently recognized the significance of an innovation ecosystem in transitioning to an innovation-driven economy. However, these countries encounter various market and system failures, making policy-affiliated transition intermediaries crucial in orchestrating innovation ecosystems. To achieve this, this study used a case study of a policy-affiliated intermediary and conducted interviews with 19 managers. The results from the content analysis revealed that innovation intermediaries in latecomers perform more comprehensive duties than those mentioned in the literature in the transition to innovation-based development.

Results show that the most important of policy-affiliated innovation intermediaries’ roles is orientation to the government and private sector market, attracting the participation of the private sector through the implementation of tax credits and the development of the corporate innovation ecosystem, developing science and technology parks, developing laboratory research infrastructure, supporting prominent and capable professors to create innovation networks, developing innovation zones around research institutions and leading universities and developing international scientific cooperation. These are new and important functions that innovation intermediaries should play in the transition process.

The research found that the innovation intermediary acts as a change agent in the mental model of other executive and decision-making bodies. Also result shows that this intermediaries can play more roles than what is mentioned in the literature for the development of innovation.

This paper aims to present the most influential factors on classroom indoor PM_2.5 (Particulate Matter < 2.5 µ), determining the level of PM_2.5 concentration in five pre-schools located in the most densely populated district of the Tehran metropolitan area (district 6) as a case study to consider the children's exposure to air pollutants and introducing a suitable model, for the first time, to predict PM_2.5 concentration changes, inside pre-schools.

Indoor and outdoor classes PM_2.5 concentrations were measured using two DUSTTRAK direct-reading instruments. Additional class status information was also recorded; concurrently, urban PM_2.5 concentrations and meteorological data were obtained from the fixed monitoring stations and Meteorological Organization. Then, the predicted concentrations of the indoor PM_2.5, from introduced multiple linear regression model via SPSS, compared with the nearest urban air pollution monitoring stations data.

The average outdoor PM_2.5 concentration (43 ± 0.32 µg m⁻³) was higher than the mean indoor (32 ± 0. 21 µg m⁻³), and both were significantly (p < 0.001) surpassing the 24-h EPA standard level. The indoor PM_2.5 concentrations had the highest level in the autumn (48.7 µg m⁻³) and significantly correlated with the outdoor PM_2.5 (r = 0.94, p < 0.001), the number of pupils, ambient temperature, wind speed, wind direction and open area of the doors and windows (p < 0.001). These parameters, as the main determinants, have led to present a 7-variable regression model, with R² = 0.705, which can predict PM_2.5 concentrations in the pre-school classes with more than 80% accuracy. It can be presumed that the penetration of outdoor PM_2.5 was the main source of indoor PM_2.5 concentrations.

This study faced several limitations, such as accessibility to classrooms, and limitations in technicians' numbers, leading to researchers monitoring indoor and outdoor PM concentrations in schools once a week. Additionally, regarding logistical limitations to using monitoring instruments in pre-schools simultaneously, correction factors by running the instruments were applied to obtain comparable measurements.

The author hereby declares that this submission is his own work and to the best of its knowledge it contains no materials previously published or written by another person.