Thriving at work is one of the hottest topic of discussion now a days. As it has become one of the most important concern to increase learning and vitality of employees. Practitioners are looking for ways to increase employees thriving at work. This study analyzes the mediating role of civility on perceived organizational support and thriving; and between organizational reward and thriving. The purposive sampling technique was used. Self-reported questionnaires and online survey technique was used for collecting data. This study provides insights about the impact of Perceived Organizational Support and Organizational Reward on thriving and mediating effect of workplace civility. This study provides implications to the professionals as how they can thrive at work and civility is one of the important factor that generates thriving of employees.

The data related to many medical, environmental and ecological variables are often measured in terms of angles wherein its range is defined in [0,π). This type of data is referred to as axial or half circular data. Modeling based on half circular data has not received its due share of attention in statistical literature. In this paper, we introduce a new half circular distribution based on inverse stereographic projection technique on modified Burr−III distribution, called the half circular modified Burr−III (hcMB−III) distribution. The basic properties of the proposed distribution are derived. It is common observation that while estimating the parameters of a model, one usually adopts maximum likelihood estimation method as the starting point. In this paper, we consider seven frequentist methods of estimation, besides using maximum likelihood method for estimating the parameters of the hcMB−III distribution. Monte Carlo simulations are performed for investigating the performances of the considered methods in terms of their biases and mean square errors using small, medium and large sample sizes. Finally, one data set related to posterior corneal curvature of the eyes of 23 patients, is analyzed to check potentiality of the newly proposed model.

A B S T R A C TIn this paper, we proposed a generalized exponential estimator with two auxiliary variables for the estimation of highly clumped population variance under adaptive cluster sampling design. The expressions of approximate bias and minimum mean square error are derived. A family of exponential ratio and exponential product estimator is obtained by using different values of generalized and optimized constants. A numerical study is carried out on real and artificial populations to examine the performance of the proposed estimator over the competing estimators. Related results show that the proposed generalized exponential estimator is able to provide considerably better results over the competing estimators for the estimation of rare and highly clustered population variance.