Influence of Temporal Distance on the Perceived Importance of Career‐Related Self‐Efficacy and Outcome Expectations

Because all career choices are about future actions, time is an important contextual variable in social cognitive career choice ( Hesketh, 2000 ). However, there is a dearth of research examining the effect of time. The authors examined how temporal distance to career entry influences the perceived importance of self‐efficacy and outcome expectations concerning a career pursuit. They also examined the moderating effects of perceived support and barriers, as well as country. The participants were college students in the United States (n= 180) and in South Korea (n= 215). The results of hierarchical regression analyses did not show main effects of time, but a 3‐way interaction indicated the complex interplay of time and other contextual variables. Implications for career counseling and future research are discussed.     

Using the Constructs Multifinality,Work Hope,and Possible Selves With Urban Minority Youth

This article describes and highlights the potential contributions that the constructs multifinality, work hope, and possible selves make for designing career counseling interventions and for better understanding possible career‐related factors associated with academic engagement and achievement among urban minority youth. Multifinality may serve as a superordinate orientation because it conceptualizes development as discontinuous and relatively plastic, allowing for youth deemed at risk to follow more hopeful pathways. Work hope and possible selves emphasize the utility of providing urban minority youth with space to answer questions related to what they might achieve and do in the future and who they might become. Together, multifinality, work hope, and possible selves provide a hopeful conceptual framework for career researchers and practitioners.     

Evaluating the Efficiency of Governmental Excellence for Social Progress: Focusing on Low- and Lower-Middle-Income Countries

Social Indicators Research - As the “weak-institutions trap” is increasingly recognized as the major hindrance to growth, the advantages offered by e-government may provide an...     

A reference activity model for smart factory design and improvement

Smart manufacturing systems (SMSs) are envisioned to contain highly automated and IT-driven production systems. To address the complexity that arises in such systems, a standard and holistic model for describing its activities and their interrelationships is needed. This paper introduces a factory design and improvement (FDI) activity model and illustrates a case study of FDI in an electromechanical component factory. In essence, FDI is a reference activity model that encompasses a range of manufacturing system activities for designing and improving a factory during its initial development and also its operational phases. The FDI model shows not only the dependency between activities and manufacturing control levels but also the pieces of information and software functions each activity relies on. We envision that the availability of these pieces of information in digital form to integrate across the software functions will increase the agility of factory design and improvement projects. Therefore, our future work lies in contributing to standards for exchanging such information.     

Regularization through variable selection and conditional MLE with application to classification in high dimensions

It is often the case that high-dimensional data consist of only a few informative components. Standard statistical modeling and estimation in such a situation is prone to inaccuracies due to overfitting, unless regularization methods are practiced. In the context of classification, we propose a class of regularization methods through shrinkage estimators. The shrinkage is based on variable selection coupled with conditional maximum likelihood. Using Stein's unbiased estimator of the risk, we derive an estimator for the optimal shrinkage method within a certain class. A comparison of the optimal shrinkage methods in a classification context, with the optimal shrinkage method when estimating a mean vector under a squared loss, is given. The latter problem is extensively studied, but it seems that the results of those studies are not completely relevant for classification. We demonstrate and examine our method on simulated data and compare it to feature annealed independence rule and Fisher's rule.     

Community-Based Services and Depression from Person-Environment Fit Perspective: Focusing on Functional Impairments and Living Alone

ABSTRACT

Guided by the Person-Environment Fit perspective, we investigated the extent to which personal and environmental factors influence depression among community-dwelling adults. The data came from the special section about community-based service utilization in the 2012 Health and Retirement Study (N=1,710). Although community-based service was not significantly associated with depression after controlling for covariates, respondents with functional limitations and living alone were less likely to be depressed when using community-based services. This study demonstrates the different associations between community-based services and depression depending on personal needs. It discusses the importance of community-based services for aging-in-place policy, particularly among vulnerable populations.     

Employee communication,job engagement,and organizational commitment: A study of members of the Millennial Generation

The purpose of this study was to explore the relationship between job engagement and two key components of employee-organization relationships (EOR). Findings from a survey of members of the Millennial Generation (N = 539) in the United States indicate that job engagement mediates the relationship between employee communication and organizational commitment. It is concluded that when employees are engaged in their work, their commitment to the organization is strengthened and the likelihood of them leaving the organization decreases. Furthermore, an argument is made in light of the study’s findings that engagement and commitment work in concert to strengthen EORs overall. To foster engagement, organizations should remove obstacles to internal information flow and provide ongoing feedback to employees about individual and organizational issues.     

Toddlers Involve Their Caregiver to Help Another Person in Need

Three experiments with 18‐ to 35‐month‐old children (n = 169) studied toddler–caregiver interactions when being confronted with another person in need. In particular, we explored whether toddlers would request their caregiver to help a needy other when they are not able to help themselves. Children observed another person who needed help to accomplish a task, but were either not able to provide help as the object was out of reach (Experiment 1) or because an obstacle prevented children from interacting with the other person (Experiments 2, 2b). The experiments revealed the same developmental trend with 2.5‐year‐olds selectively involving their caregiver to help the needy other. The results are interpreted in terms of toddlers’ motivation to see others helped and with respect to their developing ability to actively involve others to regulate their emotions.     

Objective Bayesian analysis based on upper record values from two-parameter Rayleigh distribution with partial information

In the life test, predicting higher failure times than the largest failure time of the observed is an important issue. Although the Rayleigh distribution is a suitable model for analyzing the lifetime of components that age rapidly over time because its failure rate function is an increasing linear function of time, the inference for a two-parameter Rayleigh distribution based on upper record values has not been addressed from the Bayesian perspective. This paper provides Bayesian analysis methods by proposing a noninformative prior distribution to analyze survival data, using a two-parameter Rayleigh distribution based on record values. In addition, we provide a pivotal quantity and an algorithm based on the pivotal quantity to predict the behavior of future survival records. We show that the proposed method is superior to the frequentist counterpart in terms of the mean-squared error and bias through Monte carlo simulations. For illustrative purposes, survival data on lung cancer patients are analyzed, and it is proved that the proposed model can be a good alternative when prior information is not given.