Dyadic Cross-Sectional Associations Between Depressive Mood,Relationship Satisfaction,and Common Dyadic Coping

The aim of this cross-sectional study was to investigate the mediational role of common dyadic coping in the links between depressive mood and relationship satisfaction. We estimated two competing Actor–Partner Mediator Models, in which the dyad is the highest unit of analysis. The first model specified dyadic coping as a mediator in the association between relationship satisfaction and depression, whereas the second model specified dyadic coping as a mediator between depression and relationship satisfaction. Analyzing data from 198 couples, our findings provided a plausible approximation of the second model, representing the interactional model of depression. They showed that common dyadic coping mediates the link between depression and relationship satisfaction for men only.     

Testing the equality of quantiles for several normal populations

Many procedures exist for testing equality of means or medians to compare several independent distributions. However, the mean or median do not determine the entire distribution. In this article, we propose a new small-sample modification of the likelihood ratio test for testing the equality of the quantiles of several normal distributions. The merits of the proposed test are numerically compared with the existing tests—a generalized p-value method and likelihood ratio test—with respect to their sizes and powers. The simulation results demonstrate that proposed method is satisfactory; its actual size is very close to the nominal level. We illustrate these approaches using two real examples.     

Performance measurement and classification data in DEA: Input-oriented model

This paper presents an improved efficiency measurement tool by modifying the existing data envelopment analysis methodology to permit the incorporation of expert knowledge. A previous paper examined the inclusion of such knowledge within the additive model. This information appeared in the form of a binary classification of a subset of the decision making units under study (e.g. good versus poor performers). In the current paper, we extend this logic to the input-oriented radial projection model. We demonstrate that the inclusion of this and other forms of expert judgment can improve the performance of the DEA tool in the sense that the efficiency scores are more in line with expert/management beliefs.     

Performance measurement with classification information: an enhanced additive DEA model

This paper presents an improved measurement tool for evaluating performance of branches within a major Canadian bank. While there have been numerous previous studies of performance in the banking industry, particularly at the branch level, this study is different in a very significant way: specifically two kinds of data are used to develop the model. The first type of data is that related to standard transactions, available from any bank; such have formed the basis of numerous previous studies. The second type of data, obtained from the site studied, is classification information, based on branch consultant/expert judgment as to good and poor performance of branches. The purpose herein is to present a modified version of an existing benchmarking model, data envelopment analysis (DEA), and to show how this tool is applied in the banking industry. The mechanism used herein to incorporate expert knowledge within the DEA framework is to first apply a discriminant or classification tool, to quantify the functional relation that best captures the expert's mental model for performance. The outcome of this first phase is an orientation of variables to aid in the definition of inputs and outputs. The resulting orientation then defines the DEA model that makes up the second phase of the model.     

Investigating Institutional,Economic and Social Determinants of European Regions for Firm Growth Through Employment Generation

This paper examines the influence of the institutional, economic and social characteristics of a region on firm growth through employment generation across 14 European countries for the time period 2010–2013. Theoretically, we utilize the resource‐based view alongside insights from institutional theory to develop a conceptual framework that captures the influence of regional characteristics on firm employment growth. Based on this framework, our empirical results indicate that not only does firm growth depend on the firm‐specific characteristics found in the literature, but regional attributes also significantly impact firm growth in a heterogeneous way for different firm types. In line with the heterogeneous nature of firm growth, our results point to significant differences in the influence of institutional, economic and social characteristics on firm growth in different‐sized groups and across different rates of growth distribution. The implications of our study suggest the importance of managers and policy‐makers realizing which firms are mostly expected to benefit from the external environment, and which in turn can be planned via tailored policy reform by regional governments and firm‐level strategy‐making by managers.     

Big Data‐Savvy Teams’ Skills,Big Data‐Driven Actions and Business Performance

Prior studies on big data analytics have emphasized the importance of specific big data skills and capabilities for organizational success; however, they have largely neglected to investigate the use of cross‐functional teams’ skills and links to the role played by relevant data‐driven actions and business performance. Drawing on the resource‐based view (RBV) of the firm and on unique data collected from 240 big data experts working in global agrifood networks, we examine the links between the use of big data‐savvy (BDS) teams’ skills, big data‐driven (BDD) actions and business performance. BDS teams depend on multi‐disciplinary skills (e.g. computing, mathematics, statistics, machine learning and business domain knowledge) that help them turn their traditional business operations into modern data‐driven insights (e.g. knowing real‐time price changes and customer preferences), leading to BDD actions that enhance business performance. Our results, raised from structural equation modelling, indicate that BDS teams’ skills that produce valuable insights are the key determinants for BDD actions, which ultimately contribute to business performance. We further demonstrate that those organizations that emphasize BDD actions perform better compared to those that do not focus on such applications and relevant insights.     

On the Computation of Non-Central Chi-Square Distribution Function

The cumulative distribution function of the non-central chi-square is very important in calculating the power function of some statistical tests. On the other hand it involves an integral which is difficult to obtain. In literature some workers discussed the evaluation and the approximation of the c.d.f. of the non-central chi-square [see references (2)]. In the present work two computational formulae for computing the cumulative distribution function of the non-central chi-square distribution are given, the first one deals with the case of any degrees of freedom (odd and even), and the second deals with the case of odd degrees of freedom. Numerical illustrations are discussed.     

Do Parent Units Benefit from Reverse Knowledge Transfer?

Emerging market multinationals resort to knowledge acquisitions from their overseas subsidiaries to springboard and realize their global ambitions. Drawing from the knowledge‐based view and social capital perspective, this study explores the effects of organizational collaboration and tacitness on multiple dimensions of reverse knowledge transfer (RKT). Data were collected through a survey, from senior and middle level managers of parent Indian multinationals, pertaining to RKT from their overseas subsidiaries. The hypotheses are analysed using partial least squares modelling. The results demonstrate positive effects between the extent and benefits of RKT. Collaboration was found to have a positive influence on both dimensions of RKT. Tacitness also has a positive impact on the benefits from RKT. The implications of the findings and the limitations of the study are discussed along with suggestions for future research.