PurposeThe present paper seeks to investigate the impact of International Financial Reporting Standards (IFRS) adoption on the foreign direct investment (FDI) in the Gulf Cooperation Council (GCC) region for the period 1980–2017. This study relies on the information asymmetry theory, according to which IFRS adoption, as a positive signal for investors, should attract more FDI. This research is crucial and presents an interesting framework for providing a major motivation for empirical insights since the macroeconomic evidence on the impact of IFRS adoption on FDI is still unclear in the GCC region and no empirical evidence has been provided in the existing related literature.Design/methodology/approachThe analysis was conducted based on panel data from GCC countries over the period 1980–2017 and using the autoregressive distributed lag (ARDL) modeling approach and the pooled mean group (PMG) estimation method.FindingsThe findings indicate that the decision of adopting IFRS in GCC countries has a positive impact of 3% on FDI inflows in the short run. However, the adoption of IFRS in the region leads to a decrease of 10.4 % in FDI inflows in the long run.Practical implicationsThese findings should be of a major interest to regulators and policymakers in GCC countries, practitioners and academic researchers, international investors, managers and any other interested groups about the accounting environment in GCC countries and other developing countries having an interest in the economic consequences of IFRS adoption, as a driver of FDI, in developing countries.Originality/valueThis investigation provides original empirical evidence on the effect of IFRS adoption on FDI inflows within the context of the GCC area. In fact, the current international literature is lacking empirical evidence on the effect of IFRS adoption on FDI inflows for the GCC countries as a whole. Furthermore, this study offers an original methodological contribution to the macroeconomic impact of IFRS adoption literature by using the PMG estimator since there has been no research works to date that has used this method of estimation.

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<p class="Default">Simulating wireless sensor networks; there implementation and evaluation, require the use of a discrete event simulator. Omnet++ is quite a powerful simulator which supports concise and easy modeling of wired as well as wireless sensors environment. Scenarios involving multimedia transmissions with characteristics of video quality control and evaluation must be computed on the basis of Quality of Experience which relies on user’s perception to maintain the video quality. For the multimedia growth and awareness of future WMSNs, it is quite necessary that the performance should be tested for different types of radio models. So varying the radio parameters may allow for the optimization and improvement of the video quality. In this paper we have provided a test bench for the easy evaluation and optimization of the performance of WMSNs using different radio models. The performance is evaluated based on the QoE metrics; i.e. PSNR(Peak Signal-to-Noise ratio) and MoS(Mean Opinion Score), which depend on user’s perception to maintain the video quality.</p>

<p class="Default">Simulating wireless sensor networks; there implementation and evaluation, require the use of a discrete event simulator. Omnet++ is quite a powerful simulator which supports concise and easy modeling of wired as well as wireless sensors environment. Scenarios involving multimedia transmissions with characteristics of video quality control and evaluation must be computed on the basis of Quality of Experience which relies on user’s perception to maintain the video quality. For the multimedia growth and awareness of future WMSNs, it is quite necessary that the performance should be tested for different types of radio models. So varying the radio parameters may allow for the optimization and improvement of the video quality. In this paper we have provided a test bench for the easy evaluation and optimization of the performance of WMSNs using different radio models. The performance is evaluated based on the QoE metrics; i.e. PSNR(Peak Signal-to-Noise ratio) and MoS(Mean Opinion Score), which depend on user’s perception to maintain the video quality.</p>