The influence of family environment and child temperament on work/family role strain for mothers and fathers

This study examined the additive effect of structural variables, child characteristics, and the family environment on mothers' and fathers' work/family role strain. Differences between mothers and fathers on these variables were also examined. The sample consisted of 36 dualearner families whose children had been in daycare from infancy through 4 years of age. Structural variables included work schedules and time spent with child for mothers only, fathers only, and both parents together with child. Child characteristics included temperament and health. Family environment variables included different components of the family environment (conflict, cohesion, expressiveness, organization, and control) and parenting daily hassles. Results showed that mothers' time with child and caregiving for child were greater than fathers'. Mothers reported more expressiveness in the family and more daily hassles with children than fathers. Mothers' level of role strain was also significantly higher than fathers'. For mothers, role strain was associated with hours away from home, child sociability, family conflict, and daily hassles resulting in an R² of 0.57. Fathers' role strain was associated with family expressiveness, organization, and their wives' daily hassles resulting in an R² of 0.37. Data suggest that mothers' and fathers' role strain may be driven by somewhat different factors. For women, aspects of the family and the child and work hours accounted for a considerable portion of the variance while for men, only aspects of the family environment were associated with their level of role strain. Copyright © 2003 John Wiley & Sons, Ltd.     

The Stochastic Goal Programming Model: Theory and Applications

Supported by a network of researchers and practitioners, the goal programming (GP) model is alive today more than ever and is continually fed with theoretical developments and new applications with resounding success. The standard formulation of the GP model was introduced in the earliest of 1960s, and since then, important extensions and numerous applications have been proposed. One of these variants is the stochastic GP model that deals with the uncertainty of some decision‐making situations by using stochastic calculus. In such a situation, the decision maker is not able to assess with certainty the different parameters. However, he or she can provide some information regarding the likelihood of occurrence of the decision‐making parameter values. The aim of this paper is to highlight the main methodological developments of the stochastic GP model and to present an overview of its applications in several domains. Copyright © 2012 John Wiley & Sons, Ltd.