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This study established a relationship of innovations and employees' job performance in the government sector and also the mediating effect of technology towards the relationship. It adopted quantitative approach where data was collected through questionnaires survey and analyzed statistically. The questionnaire survey utilized simple random sampling methods among UAE government employees. A total of 300 questionnaires were distributed randomly and 265 were returned indicates 88.3% response rate. The collected data was then used to develop and assess SEM-AMOS mediation model. The results found that all the direct relationships reported a positive effect. It also found that the innovation dimension constructs and technology mediating construct having explained 84% variation in employees' job performance constructs. For indirect/mediation effect, it was found that technology does not show any mediation effect on the relationship between innovation and job performance constructs. This implies that technology does not mediate the relationship between innovation strategies and the employees' job performance. It also implies that for an effective employees' job performance core attention to building and managing an innovation approach is required in the UAE government sector. Thus, innovation strategies give more influence to employees' job performance in the government sector through the distinctive capabilities of UAE ministry employees.

This study was intended to establish a relationship between the strategic foresight dimensions with the public policy attributes of the United Arab Emirates (UAE) government. The data was collected through questionnaire survey staffs of UAE government organizations. A total of 397 valid responses were collected and analysed. The collected data was used to develop the relationship using AMOS software. It was found that the measurement model has fulfilled or achieved all the required criteria. For structural model evaluation, it found that the model has achieved all the set criteria of the evaluation. Results of the structural model evaluation found that strategic foresight influence 69 % of the public policy making. It also found that the model has the significant relationship between strategic foresight dimensions and public policy. It was found that the model has 47% predictive relevancy. This result indicates that there is a substantial and direct effect between the strategic foresight and public policy. Hypothesis testing was conducted on the model and found that there is a significant relationship between strategic foresight dimensions and public policy is achieved. This indicates that the model can be applicable to UAE authority and contributes to the body of knowledge for further research.

AbstractRecently, several urban areas are trying to mitigate the environmental impacts of traffic, where noise pollution is one of the main consequences. Thus, studying the determinants of traffic-related noise generation and developing a model that predicts the level of noise by controlling the influencing factors are crucial for transportation planning purposes. This research aims at utilizing the response surface method (RSM) to develop a robust statistical prediction model of traffic-related noise levels and optimize different traffic characteristics' ranges to reduce the expected noise levels. The results indicate that the rate of Leq increase is higher at traffic flow values less than the 1204 veh/h. The interaction effect of flow-speed and flow-heavy vehicle percentage pairs shows that Leq has peak values around 45.8 km/h and 28.71%, respectively, with almost symmetric value distribution about those center points. The main effects study indicates a direct effect of traffic flow, speed, density, and traffic composition on roadside noise levels. The prediction model has good representativeness of observed noise levels by predicted noise levels as the model has a high coefficient of determination (R2 = 95.87% and R2 adj = 92.26%) with a significance level of 0.0036. Then, the research presents a methodology to perform an optimization of the roadside noise level by defining traffic characteristics that can keep the noise level below 65 dB(A) or minimize noise level. Decision-makers could use the proposed method to control the roadside noise level.