The Role of Local Context in the Cross‐border Acquisitions by Emerging Economy Multinational Enterprises

This paper explores the role of local context in cross‐border acquisitions by emerging economy multinational enterprises (EMNEs). It argues that the importance of local context has remained despite the increased global integration of the world economy. Hypotheses are tested using data on Indian acquisitions hosted in 70 countries over an eight‐year period. Results, which are consistent across number and value of cross‐border acquisitions, show that the local context in host countries offers contrasting benefits. Emerging economy multinational enterprises exploited these benefits by embedding in host countries through acquisitions. The acquisition strategy is conventional in the motives underpinning internationalization, but novel in its geographical clustering of host countries, and idiosyncratic owing to the EMNE's ability to draw on home country embeddedness. The paper develops theoretical implications and extends the concept of embeddedness, treating it as a series of internalization or quasi‐internalization decisions across a variety of local contexts by multinationals.     

Generating a smallest binary tree by proper selection of the longest edges to bisect in a unit simplex refinement

In several areas like global optimization using branch-and-bound methods for mixture design, the unit n-simplex is refined by longest edge bisection (LEB). This process provides a binary search tree. For \(n>2\), simplices appearing during the refinement process can have more than one longest edge (LE). The size of the resulting binary tree depends on the specific sequence of bisected longest edges. The questions are how to calculate the size of one of the smallest binary trees generated by LEB and how to find the corresponding sequence of LEs to bisect, which can be represented by a set of LE indices. Algorithms answering these questions are presented here. We focus on sets of LE indices that are repeated at a level of the binary tree. A set of LEs was presented in Aparicio et al. (Informatica 26(1):17–32, 2015), for \(n=3\). An additional question is whether this set is the best one under the so-called \(m_k\)-valid condition.     

Multiphase experiments in practice: A look back

Multiphase experiments are introduced and an overview of their design and analysis as it is currently practised is given via an account of their development since 1955 and a literature survey. Methods that are available for designing and analysing them are outlined, with an emphasis on making explicit the role of the model in their design. The availability of software and its use is described in detail. Overall, while multiphase designs have been applied in areas such as plant breeding, plant pathology, greenhouse experimentation, product storage, gene expression studies, and sensory evaluation, their deployment has been limited.     

The Moderating Effect of Living with a Child Before Incarceration on Postrelease Outcomes Related to a Prison-Based Parent Management Training Program

ABSTRACT

With the rapid growth in incarceration in the United States over the past few decades came dramatic growth in the number of the incarcerated parents with at least one minor child. Parental incarceration places extra stresses and strains on families and children. Almost all of those incarcerated will eventually be released. However, the majority of those released from prison will be rearrested within a year. Finding interventions that can decrease the likelihood of returning to crime and to incarceration are of utmost importance. Using a social bond theory framework, the authors examine the moderating effect of living with a child before incarceration on program outcomes related to a prison-based parent management training program. A significant effect was found. Implications for these findings are discussed for future research and practice.     

Illuminating the Phenomenological Challenges of Cross-Cultural Supervision

ABSTRACT

The authors discuss transference enactments that occur during cross-cultural supervision. Previous research has shown that few mental health professionals willingly talk about race during supervision and that White supervisees experience significant difficulty when directly engaged in discussions about race. They introduce a new phenomenon, “stereotypical” transference enactments, as a means for understanding the countertransference Black female clinical supervisors experience during clinical supervision of White supervisees. The findings indicated that effective cross-cultural supervision should address the intersectionality of race and gender to enhance the development of multicultural clinical skills. The clinical and educational challenges are discussed by using two case vignettes.     

Variations in the Life Histories of Incarcerated Parents by Race and Ethnicity: Implications for Service Provision

ABSTRACT

Incarcerated parents have complex life histories that often remain unresolved during incarceration, can continue to create barriers to prosocial success on release, and present similar intergenerational challenges for their children. This study examines the life histories of incarcerated fathers and mothers from the Pacific Northwest and how their experiences vary based on race and ethnicity. Five areas examined were exposure to trauma, child welfare involvement, mental health and substance abuse problems, juvenile justice and adult criminal justice involvement, and intergenerational criminal justice involvement. The sample comprised 359 incarcerated parents, and their racial/ethnic composition was 59% White, 14% African American, 11% multiracial, 8% Native American, and 7% Latino. Few differences were found across racial and ethnic groups. Mothers appeared more similar to each other across groups than fathers. Results illustrated similarities yet some surprising differences with national trends on key study variables. Implications for future research and intervention and prevention are discussed.     

A copula-based Markov chain model for the analysis of binary longitudinal data

A fully parametric first-order autoregressive (AR(1)) model is proposed to analyse binary longitudinal data. By using a discretized version of a copula, the modelling approach allows one to construct separate models for the marginal response and for the dependence between adjacent responses. In particular, the transition model that is focused on discretizes the Gaussian copula in such a way that the marginal is a Bernoulli distribution. A probit link is used to take into account concomitant information in the behaviour of the underlying marginal distribution. Fixed and time-varying covariates can be included in the model. The method is simple and is a natural extension of the AR(1) model for Gaussian series. Since the approach put forward is likelihood-based, it allows interpretations and inferences to be made that are not possible with semi-parametric approaches such as those based on generalized estimating equations. Data from a study designed to reduce the exposure of children to the sun are used to illustrate the methods.     

Forecast mean squared error reductionin the VAR(1) process

When VAR models are used to predict future outcomes, the forecast error can be substantial. Through imposition of restrictions on the off-diagonal elements of the parameter matrix, however, the information in the process may be condensed to the marginal processes. In particular, if the cross-autocorrelations in the system are small and only a small sample is available, then such a restriction may reduce the forecast mean squared error considerably.

In this paper, we propose three different techniques to decide whether to use the restricted or unrestricted model, i.e. the full VAR(1) model or only marginal AR(1) models. In a Monte Carlo simulation study, all three proposed tests have been found to behave quite differently depending on the parameter setting. One of the proposed tests stands out, however, as the preferred one and is shown to outperform other estimators for a wide range of parameter settings.     

On estimating the distribution of data envelopment analysis efficiency scores: an application to nursing homes’ care planning process

Data envelopment analysis (DEA) is a deterministic econometric model for calculating efficiency by using data from an observed set of decision-making units (DMUs). We propose a method for calculating the distribution of efficiency scores. Our framework relies on estimating data from an unobserved set of DMUs. The model provides posterior predictive data for the unobserved DMUs to augment the frontier in the DEA that provides a posterior predictive distribution for the efficiency scores. We explore the method on a multiple-input and multiple-output DEA model. The data for the example are from a comprehensive examination of how nursing homes complete a standardized mandatory assessment of residents.