Does Social Connectedness Promote a Greater Sense of Well‐Being in Adolescence Over Time?

This longitudinal study was designed to investigate whether or not social connectedness predicts psychological well‐being over time. Structural equation modeling was used to examine the temporal relations between these constructs assessed yearly for 3 years for a sample of 1,774 10‐ to 15‐year‐olds (at Time 1). Results indicated that global connectedness (i.e., connectedness combined across the domains of family, school, peers, and neighborhood) predicted well‐being, but no reciprocal relation was found. However, reciprocal relations were revealed by analyses that examined connectedness at the domain level, that is, for family and school contexts. The results suggest that youth who reported higher levels of social connectedness at one point in time would subsequently report higher well‐being (i.e., life satisfaction, confidence, positive affect, and aspirations).     

A computerized system to monitor resilience indicators in organizations

The concepts developed by resilience engineering allow the understanding and monitoring the functioning of organizations and, particularly, to map the role of human activities, in success or in failure, enabling a better comprehension about how people make decisions in unexpected situations. The capture of information about human activities in the various organization levels gives managers a deeper real-time understanding of what is influencing the people performance, providing awareness of the factors that influence positively or negatively the organizational goals initially projected. The monitoring is important because the correct functioning of complex systems depends on the knowledge that people have to perform their activities and how the system environment provides tools that actually support the human performance. Therefore, organizations should look forward through precursors in operating signals to identify possible problems or solutions in the structure of tasks and activities, safety, quality, schedule, rework, and maintenance. We apply the concepts of resilience engineering to understand the organization by the analysis of cognitive tasks and activities. The aim is the development of a computerized system to monitor human activities to produce indicators to access system resilience. The validation of the approach was made in a real organization and the results show the successful applicability of the system. Based on findings obtained after the experiment of the system in a real organization, and managers and workers opinions, it was possible to show that the use of system provided an anticipated (real-time) perception about how activities are effectively being performed, allowing managers and workers to make decisions more consistent with daily problems, and also to anticipate solutions to cope with unexpected situations.     

What Is a Social Enterprise? Revising Old Concepts and Interviewing Social Entrepreneurs

The concept of social enterprise (SE) has been developed by an emerging and collective effort of scholars over the world. However, a comprehensive meaning of ‘SE,’ which embeds traditional knowledge of organizational management has potential to be explored further. Due to the relevance of ‘SE’ in the contemporary society, new conceptual and practical insights are desirable. This study proposes to shed light on building up a definition for ‘SE’ that is inspired by existing definitions of organization and ‘SE’ and rely on real-world evidence. Literature review and comprehensive interviews with social entrepreneurs were conducted. Data analysis defines ‘SEs’ as an autonomous organization managed in a participative way and created by and for the community, with a relatively identifiable boundary, that strives to generate social wealth, and for this, produces goods and/or services that guarantee its financial viability, consequently, its continuity. Its integration into its environment generates consequences guided by social, economic, and environmental goals.     

Estimation of a change point in the hazard rate of Lindley model under right censoring

In this article, we consider a single change point model for a sudden change in the hazard rate of Lindley distribution to model right-censored survival data. We derive the quantile function to generate random numbers from the proposed distribution by using the Lambert function. The maximum likelihood estimation method is used to estimate parameters of the change point model. A simulation study is also carried out to analyze the performance of the estimators. To validate our findings, a dataset on bone marrow transplant for patients of acute lymphoblastic leukemia is analyzed using the proposed model and is compared with the existing exponential single change point model.     

A simplified estimation procedure based on the EM algorithm for the power series cure rate model

The family of power series cure rate models provides a flexible modeling framework for survival data of populations with a cure fraction. In this work, we present a simplified estimation procedure for the maximum likelihood (ML) approach. ML estimates are obtained via the expectation-maximization (EM) algorithm where the expectation step involves computation of the expected number of concurrent causes for each individual. It has the big advantage that the maximization step can be decomposed into separate maximizations of two lower-dimensional functions of the regression and survival distribution parameters, respectively. Two simulation studies are performed: the first to investigate the accuracy of the estimation procedure for different numbers of covariates and the second to compare our proposal with the direct maximization of the observed log-likelihood function. Finally, we illustrate the technique for parameter estimation on a dataset of survival times for patients with malignant melanoma.     

A NOTE ON “COMMON DUE WINDOW SCHEDULING”

Kramer and Lee recently addressed a common due window scheduling problem with earliness and tardiness penalties, where earliness and tardiness penalty factors are constant and the common window size is given. They showed that the problem is polynomial when the location of the due window is a decision variable. For the case where the location of the due window is given, the problem is also polynomial when the latest due date is greater than or equal to the makespan, and they proposed a pseudopolynomial dynamic programming algorithm to find an optimal schedule when the latest due date is less than the makespan. In this note we address the problem for the case where the location of the due window is given. Specifically, we show that the problem is polynomial if the window location is unrestricted, and present a more efficient dynamic program algorithm to optimally solve the problem if the window location is restricted. The concepts of unrestricted and restricted window locations are defined in this note.     

VOLUNTARY ACTIVITIES AND DAILY HAPPINESS IN THE UNITED STATES

This article analyzes differences in daily happiness between those individuals in the United States who perform voluntary activities during the day and those who do not. Using the Well‐Being Module of the American Time Use Survey 2010, we find that those who devote any time to voluntary activities during the day report higher levels of daily happiness than those who do not. Comparing the happiness obtained from a range of activities, we find that volunteering is among the most enjoyable, indicating that time spent on voluntary activities is utility‐enhancing. We document that the effect of voluntary activities on the experienced utility of individuals can be decomposed into a “time‐composition” effect and an “activity” effect, with the latter explaining between 11% and 46% of the observed difference. (JEL D13, J16, J22)