How does collectively seeking social media community support help employees solve organizational injustice-related problems? : An analysis of Twitter Big Data

The current job market is characterized by turbulence and uncertainty, and employees seem to become highly sensitive to issues related to injustice. When employees perceive organizational injustice (i.e., distributive, procedural, interactional, and informational), they may seek social support. We suggest that employees, instead of approaching their supervisor/manager, colleagues, family, or friends as support providers, resort to social media to seek support from the network community. The communal nature of social media that embodies the possibility of interacting with individuals with similar characteristics makes it possible for those seeking collective support to benefit from the available mechanisms. Seeking community support seems to play a pivotal role, in that it can be focused on actions or emotions depending on what individuals actually need and look for when they seek social support. When seeking community-social support, employees may plan and act collectively. In response to their collective action, the social media community may engage in an intervention either by taking necessary actions (instrumental support) to help employees solve their problems related to organizational injustice or through supporting employees in managing their emotions (emotional support). Emotional community support is most likely as consequential as the instrumental one, given that providing emotional support is a strong indicator of choosing sides. Companies use social media to understand their reputation within society; therefore, when employees reach out to social media to seek support, that community support becomes eligible to help them out.  Twitter, a widely accessible social media that allows exploring different social phenomena, will be utilized for event-specific targeted data collection. Streamed Twitter posts shared by publicly available accounts will be included in the dataset. A broad enough time period to encompass pre-, intra-, and post-event periods will be chosen. The analysis will consist of data cleaning, and structuring, where initial auto-coding will be completed by manually proofing. Plans for the initial analysis involve word frequency, polarization, network analysis, centrality, positive/negative sentiment, and sociogram. Findings are expected to be explorative for a better understanding of the above-detailed mechanisms about the capacity and eligibility of social-media community support that may help employees solve their organizational problems related to injustice. Ej belagd</p

Single Parameter Estimation Approach for Robust Estimation of SIR Model With Limited and Noisy Data : the case for COVID-19

The SIR model and its variants are widely used to predict the progress of COVID-19 worldwide, despite their rather simplistic nature. Nevertheless, robust estimation of the SIR model presents a significant challenge, particularly with limited and possibly noisy data in the initial phase of the pandemic. K-means algorithm is used to perform a cluster analysis of the top ten countries with the highest number of COVID-19 cases, to observe if there are any significant differences among countries in terms of robustness. As a result of model variation tests, the robustness of parameter estimates is found to be particularly problematic in developing countries. The incompatibility of parameter estimates with the observed characteristics of COVID-19 is another potential problem. Hence, a series of research questions are visited. We propose a SPE (“Single Parameter Estimation”) approach to circumvent these potential problems if the basic SIR is the model of choice, and we check the robustness of this new approach by model variation and structured permutation tests. Dissemination of quality predictions is critical for policy and decision-makers in shedding light on the next phases of the pandemic

Predicting the progress of COVID-19 : the case for Turkey

The SIR model is applied to a dataset of 43 days from the beginning of the COVID-19 pandemic in Turkey. Model outputs regarding the estimates of effective reproduction number and peak date of the maximum number of actively infected are presented. Favorable impact of social distancing measures are observed in comparing model outputs on progressive days. Findings are valuable for policy and decision makers in shedding light on the next phases of the pandemic

Instantaneous R for COVID-19 in Turkey : Estimation by Bayesian Statistical Inference

The instantaneous R in Turkey is estimated by Bayesian statistical inference that utilizes a 68-days-long dataset from the beginning of the COVID-19 outbreak in Turkey for monitoring the progression of the pandemic. As it is also globally adapted, enforced social distancing measures help to keep the instantaneous reproduction number below one. The low levels of instantaneous R are referred to as a basis for several countries to relax their country-wide restrictions, while hindsight involves a possible second wave of infections to follow in China, Germany, and South Korea. Thus, policy and decision-makers need to be vigilant regarding the pandemic's progress. It is not yet sure if it is possible to maintain the instantaneous reproduction number below one, even at the lack of societal measures.Türkiye'deki anlık bulaştırma katsayısı COVID-19 salgınının başlangıcından itibaren 68 günlük bir veri seti kullanılarak Bayesyen istatistiksel çıkarım ile tahmin edilmiştir. Salgının kontrol altında tutulabilmesi için anlık bulaştırma katsayısının cari seviyesinin sürekli bir biçimde tahmin edilmesinin önemi vurgulanmıştır. Model çıktılarıyla etkin bulaştırma katsayısı tahminleri sunulmuştur. Zaman ilerledikçe elde edilen model çıktıları karşılaştırıldığında, sosyal mesafe önlemlerinin anlık bulaştırma katsayısının birin altında tutulması yönünde olumlu etkisi gözlemlenmektedir. Bununla birlikte, önlemlerin gevşetilmesi sonrası Çin, Güney Kore ve Almanya gibi ülkelerde salgının ikinci dalgasının başlamış olabileceği de dikkate alındığında, anlık bulaştırma katsayısının kalıcı olarak birin altında tutulup tutulamayacağı belirsizliğini korumaktadır. Bu noktadan hareketle, politika yapıcılar ve karar vericilerin salgının sonraki aşamaları için tetikte olmaları gerekmektedir