Zafar Iqbal

The current research examines the possible outcomes of cronyism like organizational deviance, organizational cynicism, and counterproductive work behavior and also investigates the mediating variable violation of psychological contract among cronyism and its possible outcomes. Many studies have investigated the presence of organizational cronyism at the workplace and its impacts on certain variables. However, the outcomes observed in this study, i.e., OD, OCy, and counter-productive work behavior were not empirically investigated previously as per researchers’ knowledge. The second gap this study fills is the mediating effect of VPC between the studied variables. Thirdly, the study was conducted in Azad Jammu and Kashmir, Pakistan, which is almost the first attempt to investigate this phenomenon in Azad Jammu and Kashmir. Data were collected from the employees working under different ministries of Azad Jammu and Kashmir, Pakistan like education, forest, sports, information, local government, finance, and tourism. The data from 350 employees were collected through convenience sampling. The data collection process was conducted at two distinct time lags. Results show that OC significantly and positively relates with OD, OCy, and counter-productive work behavior, whereas VPC mediates the relationship among OC and OD, OC, and counter-productive work behavior. Employees enjoying special favors from the leadership seem to be more dedicated toward the organization than the employees who do not have this favor, and the ultimate result is negative for the organization.

Automatic incident detection plays a vital role among all the safety-critical applications under the parasol of Intelligent Transportation Systems to provide timely information to passengers and other stakeholders in smart cities. Moreover, accurate classification of these incidents with respect to type and severity assists the Traffic Incident Management Systems and stakeholders in devising better plans for incident site management and avoiding secondary incidents. Most of the AID systems presented in the literature are incident type-specific, i.e., either they are designed for the detection of accident or congestion. While traveling along the road, one may come across different types of traffic incidents, such as accidents, congestion, and reckless driving. This necessitates that the AID system detects and classifies not only all the popular traffic incident types, but severity as well that is associated with these incidents. Therefore, this study aims to propose an efficient incident detection and classification framework for smart cities, by incorporating the efficacy of model stacking, to classify the incidents with respect to their types and severity levels. The experimental results showed that the proposed E-IDC framework achieved performance gains of 5%–56% in terms of incident severity classification and 1%–14% in terms of incident type classification when applied with different classifiers. We have also applied the Wilcoxon test to benchmark the performance of our proposed framework that reflects the significance of our approach over existing individual incident predictors in terms of severity and type classification. Moreover, it has been observed that the proposed E-IDC framework outperforms the existing ensemble technique, such as XGBoost used for the classification of incidents.