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This paper aims to provide a comprehensive insight into the role of mindfulness in organizational settings. A daily experience sampling method was used to explore the mechanism through which state mindfulness influences employees' work attentiveness. By collecting data from 153 full-time Chinese employees over 5 consecutive days, we used 765 matched cases to conduct a multilevel structural equation model with random slopes to test the conceptual model. The results showed that: 1) daily state mindfulness is positively related to employee work attentiveness; 2) employee moral self-efficacy mediates the indirect relationship between daily state mindfulness and work attentiveness; and 3) moral disengagement moderates the indirect relationship between state mindfulness and work attentiveness through moral self-efficacy, such that the indirect relationship will be weaker in the condition of low moral disengagement than in the condition of high moral disengagement. This research contributes to the work attentiveness literature by incorporating state mindfulness as a critical antecedent. [ABSTRACT FROM AUTHOR]

This paper deals with Monte Carlo simulation in case of dependent input random variables. We propose an algorithm to generate refined descriptive samples from dependent random variables for estimation of expectations of functions of output variables using the Iman and Conover algorithm to transform the dependent variables to independent ones. Therefore, such estimates obtained through a chosen mathematical model are compared with those obtained using the simple random sampling method, which proved that the former are the most efficient. Besides, using already published work on independent input variables, we can deduce in case of dependent input random variables, that asymptotically the variance of the RDS estimator is less than that of SRS estimator for any simulation function having finite second moment. [ABSTRACT FROM AUTHOR]