The focus of the present study was to investigate personality traits as the predictor of emotional intelligence (EI) among the university teachers working as student advisors. A sample of the study comprised 100 student advisors (male = 50; female = 50). The age range of the sample was 21–40 years. Schutte Emotional Intelligence Scale (SEIS) and Big Five Inventory (BFI) were used to measure emotional intelligence (EI) and personality traits. For the statistical analysis of the data, T-test and regression analysis were computed. The findings revealed that three personality traits, extraversion, agreeableness, and openness to experience, emerged as significant predictors of EI. The findings also revealed that conscientiousness and neuroticism have no impact on EI. T-tests indicated that there are no gender differences in EI. Considering the implication of personality traits on EI among university teachers/student advisors, the current research may assist in augmenting the organizational behavior in general and boost the productivity in particular which are both essential ingredients for the deliverance of services to all the stakeholders linked with the educational system in Saudi Arabia.

This research was conducted to ascertain the impact of age and length of service (LOS) on job satisfaction in engineers of Pakistan public sector. Field survey was conducted using job satisfaction survey (JSS) questionnaire having closed-ended questions. Multistage sampling was conducted using a combination of cluster sampling, stratified sampling and random sampling techniques. Power and Precision software was used to determine the sample size. JSS questionnaires were administered amongst 225 electrical and mechanical engineers from five public sector organizations. 158 usable questionnaires were received and data were analyzed in SPSS. Statistical analyses showed existence of an open mouth U-shaped relationship between LOS/age and job satisfaction. It was found that age moderates relationship between LOS and job satisfaction. Non-responsiveness of senior engineers led to one of the limitations of this study. Results of this study can be used for policy-making decisions.

The current study focused on modeling times series using Bayesian Structural Time Series technique (BSTS) on a univariate data-set. Real-life secondary data from stock prices for flying cement covering a period of one year was used for analysis. Statistical results were based on simulation procedures using Kalman filter and Monte Carlo Markov Chain (MCMC). Though the current study involved stock prices data, the same approach can be applied to complex engineering process involving lead times. Results from the current study were compared with classical Autoregressive Integrated Moving Average (ARIMA) technique. For working out the Bayesian posterior sampling distributions BSTS package run with R software was used. Four BSTS models were used on a real data set to demonstrate the working of BSTS technique. The predictive accuracy for competing models was assessed using Forecasts plots and Mean Absolute Percent Error (MAPE). An easyto-follow approach was adopted so that both academicians and practitioners can easily replicate the mechanism. Findings from the study revealed that, for short-term forecasting, both ARIMA and BSTS are equally good but for long term forecasting, BSTS with local level is the most plausible option.

The current research concerns the group acceptance sampling plan in the case where (i) the lifetime of the items follows the Marshall–Olkin Kumaraswamy exponential distribution (MOKw-E) and (ii) a large number of items, considered as a group, can be tested at the same time. When the consumer’s risk and the test terminsation period are defined, the key design parameters are extracted. The values of the operating characteristic function are determined for different quality levels. At the specified producer’s risk, the minimum ratios of the true average life to the specified average life are also calculated. The results of the present study will set the platform for future research on various nano quality level topics when the items follow different probability distributions under the Marshall–Olkin Kumaraswamy scheme. Real-world data are used to explain the technique.

In this paper, we have developed single and double acceptance sampling plans when the product life length follows the power Lindley distribution. The sampling plans have been developed by assuming infinite and finite lot sizes. We have obtained the operating characteristic curves for the resultant sampling plans. The sampling plans have been obtained for various values of the parameters. It has been found that for a finite lot size, the sampling plans provide smaller values of the parameters to achieve the specified acceptance probabilities.