45,658 research outputs found
Reducing Causal Ambiguity in Acquisition Integration: Intermediate Goals as Mediators of Integration Decisions and Acquisition Performance
Integration is a difficult process, but one that is vital to acquisition performance. One reason acquirers encounter difficulties is that the integration process exhibits high levels of intrafirm linkage ambiguity – a lack of clarity of the causal link between integration decisions and their performance outcomes. We introduce the construct of intermediate goals as a mechanism that reduces intrafirm linkage ambiguity. Our structural model results, based on a sample of 129 horizontal acquisitions, indicate that the achievement of two intermediate goals (internal reorganization and market expansion) fully mediates the relationships between four integration decisions and acquisition performance
A problem-structuring model for analyzing transportation–environment relationships
This is the post-print version of the final paper published in European Journal of Operational Research. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2009 Elsevier B.V.This study discusses a decision support framework that guides policy makers in their strategic transportation related decisions by using multi-methodology. For this purpose, a methodology for analyzing the effects of transportation policies on environment, society, economy, and energy is proposed. In the proposed methodology, a three-stage problem structuring model is developed. Initially, experts’ opinions are structured by using a cognitive map to determine the relationships between transportation and environmental concepts. Then a structural equation model (SEM) is constructed, based on the cognitive map, to quantify the relations among external transportation and environmental factors. Finally the results of the SEM model are used to evaluate the consequences of possible policies via scenario analysis. In this paper a pilot study that covers only one module of the whole framework, namely transportation–environment interaction module, is conducted to present the applicability and usefulness of the methodology. This pilot study also reveals the impacts of transportation policies on the environment. To achieve a sustainable transportation system, the extent of the relationships between transportation and the environment must be considered. The World Development Indicators developed by the World Bank are used for this purpose
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A Double Error Dynamic Asymptote Model of Associative Learning
In this paper a formal model of associative learning is presented which incorporates representational and computational mechanisms that, as a coherent corpus, empower it to make accurate predictions of a wide variety of phenomena that so far have eluded a unified account in learning theory. In particular, the Double Error Dynamic Asymptote (DDA) model introduces: 1) a fully-connected network architecture in which stimuli are represented as temporally clustered elements that associate to each other, so that elements of one cluster engender activity on other clusters, which naturally implements neutral stimuli associations and mediated learning; 2) a predictor error term within the traditional error correction rule (the double error), which reduces the rate of learning for expected predictors; 3) a revaluation associability rate that operates on the assumption that the outcome predictiveness is tracked over time so that prolonged uncertainty is learned, reducing the levels of attention to initially surprising outcomes; and critically 4) a biologically plausible variable asymptote, which encapsulates the principle of Hebbian learning, leading to stronger associations for similar levels of cluster activity. The outputs of a set of simulations of the DDA model are presented along with empirical results from the literature. Finally, the predictive scope of the model is discussed
Assessment of school performance through a multilevel latent Markov Rasch model
An extension of the latent Markov Rasch model is described for the analysis
of binary longitudinal data with covariates when subjects are collected in
clusters, e.g. students clustered in classes. For each subject, the latent
process is used to represent the characteristic of interest (e.g. ability)
conditional on the effect of the cluster to which he/she belongs. The latter
effect is modeled by a discrete latent variable associated with each cluster.
For the maximum likelihood estimation of the model parameters we outline an EM
algorithm. We show how the proposed model may be used for assessing the
development of cognitive Math achievement. This approach is applied to the
analysis of a dataset collected in the Lombardy Region (Italy) and based on
test scores over three years of middle-school students attending public and
private schools
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
Is there a regulatory trade-off between stability and performance? Evidence from italian banks.
Disentangling the direct causal effect that sanctions exert on bank performance from the indirect
through default risk, we show that a trade-off exists for regulators between banks’ performance and
stability in Italy. Two key findings provide evidence for the nontriviality of the return-risk nexus: (i)
banks’ liquidations are concentrated at the lower-end of the profitability distribution, resulting in
(attrition) biased estimates; (ii) the drop-out is informative since it depends on the unobserved
measurements of profitability. Despite this evidence, while returns are affected by sanctions and
regulatory requirements, default risk is not. However, looking at growth of gross loans, enforcement
actions reduce default risk though at a cost of a significant fall in lending, creating a regulatory tradeoff.
In fact, through loans’ growth, we account for the key dynamics of intermediaries’ soundness,
namely higher profits and less non-performing loans
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