Aspects of quality . . . and world domination!

Training, learning and development tend to be regarded as central to the success of every organization and the nation. However, not all organizations are able or willing to invest resources in such processes. The paper presents a view that training, learning and development occur as a consequence of successful persuasive actions in a chain of talk, which draw on the discursive and rhetorical resources of an emerging learning movement. Utilizing the findings from an examination of data from the UK Department of Education and Skills-sponsored National Training Award winners, the paper provides an employment of actor-network theory and the sociology of translation to show how some organizations make the case for training, learning and development as a virtuous endeavour. It concludes by suggesting that what is needed is a more detailed understanding of how the case for the adoption of training, learning and development, or not, within organizations is made meaningful.     

Liked on Facebook,liked for real?: Nonprofits' online popularity and financial performance

Despite the increasing strategic importance of online popularity, there remain uncertainties about the direct link between nonprofits' online popularity and organizational outcomes and performance. This study examines the relationship between the number of “likes” on a nonprofit arts organization's Facebook page and its financial performance, focusing on how online popularity may reduce the information asymmetry between an organization and its stakeholders. The results show a positive correlation between the number of “likes” and nonprofits' charitable contributions and earned income while controlling for various organizational characteristics, including size. The findings also reveal that the correlation is twice as strong for earned income as for donations.     

西晋末期王浚集团、张轨集团兴衰之比较

本文选择分析王浚集团、张轨集团两个汉族地方武装势力 ,比较它们不同的形成、发展过程 ,探讨它们不同命运的原因 ,进而从一个侧面加深对西晋末期历史的认识     

Nonparametric testing for long-horizon predictability with persistent covariates

We propose a testing procedure for long-horizon predictability via kernel-based nonparametric estimators of long-run covariances between multiperiod returns and persistent covariates. Asymptotic properties of the proposed tests are studied. As for implementation of the test, sieve bootstrap methods are employed to obtain reasonable approximation to the sample distribution of the test statistics. Monte Carlo simulations are conducted to verify the theoretical conjecture. Empirical analysis, using US monthly data from 1929 to 2011, are presented for testing stock return predictability of some forecasting financial variables. Long-term interest rates, unlike default spreads or price-earning ration, are found to show some forecasting power.     

Estimating the probability of simultaneous rainfall extremes within a region: a spatial approach

In this paper we investigate the impact of model mis-specification, in terms of the dependence structure in the extremes of a spatial process, on the estimation of key quantities that are of interest to hydrologists and engineers. For example, it is often the case that severe flooding occurs as a result of the observation of rainfall extremes at several locations in a region simultaneously. Thus, practitioners might be interested in estimates of the joint exceedance probability of some high levels across these locations. It is likely that there will be spatial dependence present between the extremes, and this should be properly accounted for when estimating such probabilities. We compare the use of standard models from the geostatistics literature with max-stables models from extreme value theory. We find that, in some situations, using an incorrect spatial model for our extremes results in a significant under-estimation of these probabilities which – in flood defence terms – could lead to substantial under-protection.     

Finite mixtures of multivariate skew t-distributions: some recent and new results

Finite mixtures of multivariate skew t (MST) distributions have proven to be useful in modelling heterogeneous data with asymmetric and heavy tail behaviour. Recently, they have been exploited as an effective tool for modelling flow cytometric data. A number of algorithms for the computation of the maximum likelihood (ML) estimates for the model parameters of mixtures of MST distributions have been put forward in recent years. These implementations use various characterizations of the MST distribution, which are similar but not identical. While exact implementation of the expectation-maximization (EM) algorithm can be achieved for ‘restricted’ characterizations of the component skew t-distributions, Monte Carlo (MC) methods have been used to fit the ‘unrestricted’ models. In this paper, we review several recent fitting algorithms for finite mixtures of multivariate skew t-distributions, at the same time clarifying some of the connections between the various existing proposals. In particular, recent results have shown that the EM algorithm can be implemented exactly for faster computation of ML estimates for mixtures with unrestricted MST components. The gain in computational time is effected by noting that the semi-infinite integrals on the E-step of the EM algorithm can be put in the form of moments of the truncated multivariate non-central t-distribution, similar to the restricted case, which subsequently can be expressed in terms of the non-truncated form of the central t-distribution function for which fast algorithms are available. We present comparisons to illustrate the relative performance of the restricted and unrestricted models, and demonstrate the usefulness of the recently proposed methodology for the unrestricted MST mixture, by some applications to three real datasets.     

Flexible mixture modelling using the multivariate skew-t-normal distribution

This paper presents a robust probabilistic mixture model based on the multivariate skew-t-normal distribution, a skew extension of the multivariate Student’s t distribution with more powerful abilities in modelling data whose distribution seriously deviates from normality. The proposed model includes mixtures of normal, t and skew-normal distributions as special cases and provides a flexible alternative to recently proposed skew t mixtures. We develop two analytically tractable EM-type algorithms for computing maximum likelihood estimates of model parameters in which the skewness parameters and degrees of freedom are asymptotically uncorrelated. Standard errors for the parameter estimates can be obtained via a general information-based method. We also present a procedure of merging mixture components to automatically identify the number of clusters by fitting piecewise linear regression to the rescaled entropy plot. The effectiveness and performance of the proposed methodology are illustrated by two real-life examples.     

A review of the theories of corporate social responsibility: Its evolutionary path and the road ahead

This study aims to trace the conceptual evolutionary path of theories on corporate social responsibility (CSR) and to reflect on the implications of the development. The retrospection has revealed that the trend has been a progressive rationalization of the concept with a particular focus on tighter coupling with organizations’ financial goals. Rationalization involves two broad shifts in the conceptualization of CSR. First, in terms of the level of analysis, researchers have moved from the discussion of the macro‐social effects of CSR to organizational‐level analysis of CSR's effect on profit. Next, in terms of theoretical orientation, researchers have moved from explicitly normative and ethics‐oriented arguments to implicitly normative and performance‐oriented managerial studies. Based on the retrospection, the limitations of the current state of CSR research that places excessive emphasis on the business case for CSR are outlined, and it is suggested that future research needs to refocus on basic research in order to develop conceptual tools and theoretical mechanisms that explain changing organizational behavior from a broader societal perspective.