Refining individualized consideration: Distinguishing developmental leadership and supportive leadership

This study explores the theoretical and empirical distinction between developmental leadership and supportive leadership, which are currently encompassed in a single sub dimension of transformational leadership, individualized consideration. Items were selected to assess these constructs, and hypotheses regarding the differential effects of developmental and supportive leadership were proposed. Confirmatory factor analyses provided support for the proposed distinction between developmental and supportive leadership, although these leadership factors were very strongly associated. Structural equation modelling and multi-level modelling results indicated that both developmental leadership and supportive leadership displayed unique relationships with theoretically selected outcome measures. Developmental leadership displayed significantly stronger relationships with job satisfaction, career certainty, affective commitment to the organization and role breadth self-efficacy than did supportive leadership. Results provide initial evidence in support of the discriminant validity of these two types of leadership. Discussion focuses on the need to further examine the construct of developmental leadership.

Generalized Perk-Schultz models: solutions of the Yang-Baxter equation associated with quantized orthosymplectic superalgebras

The Perk-Schultz model may be expressed in terms of the solution of the Yang-Baxter equation associated with the fundamental representation of the untwisted affine extension of the general linear quantum superalgebra U-q (gl(m/n)], with a multiparametric coproduct action as given by Reshetikhin. Here, we present analogous explicit expressions for solutions of the Yang-Baxter equation associated with the fundamental representations of the twisted and untwisted affine extensions of the orthosymplectic quantum superalgebras U-q[osp(m/n)]. In this manner, we obtain generalizations of the Perk-Schultz model.

The status and relevance of Vietnamese journalism education: An empirical analysis

Based primarily on data from indepth interviews with senior journalists and journalism educators as well as a content analysis of journalism curricula, this paper sets out to provide an overview of the demand, overall provision structure, teaching materials and methods of Vietnamese journalism education. It first shows that with a fast expansion in both size and substance, the Vietnamese media system is beginning to feel the urgent need for formal journalism education. However, the country's major journalism programs have been criticised for producing hundreds of unqualified journalism graduates a year. In general, the most deplorable aspects of Vietnamese journalism education include its body of outdated and awkward teaching material, its undue focus on theories and politics at the expense of practical training, its lack of qualified teaching staff and its inadequate teaching resources.

What does the community think about lifespan extension technologies? The need for an empirical base for ethical and policy debates

Objectives: This paper examines public understandings of possibilities for increasing life expectancy, interest in taking up lifespan-extending interventions, and motivations influencing these intentions. Methods: Structured interviews were conducted with 31 adults, aged 50 and over. Results: Participants believed that technological advances would increase life expectancy but questioned the value of quantity over quality of life. Life in itself was not considered valuable without the ability to put it to good use. Participants would not use technologies to extend their own lifespan unless the result would also enhance their health. Conclusions: These findings may not be generalisable to the general public but they provide the first empirical evidence on the plausibility of common assumptions about public interest in 'anti-ageing' interventions. Surveys of the views of representative samples of the population are needed to inform the development of a research agenda on the ethical, legal and social implications of lifespan extension.

Statistical racing techniques for improved empirical evaluation of evolutionary algorithms

In empirical studies of Evolutionary Algorithms, it is usually desirable to evaluate and compare algorithms using as many different parameter settings and test problems as possible, in border to have a clear and detailed picture of their performance. Unfortunately, the total number of experiments required may be very large, which often makes such research work computationally prohibitive. In this paper, the application of a statistical method called racing is proposed as a general-purpose tool to reduce the computational requirements of large-scale experimental studies in evolutionary algorithms. Experimental results are presented that show that racing typically requires only a small fraction of the cost of an exhaustive experimental study.

An empirical study of Hoelfding Racing for model selction in K-nearest neighbor classification

Racing algorithms have recently been proposed as a general-purpose method for performing model selection in machine teaming algorithms. In this paper, we present an empirical study of the Hoeffding racing algorithm for selecting the k parameter in a simple k-nearest neighbor classifier. Fifteen widely-used classification datasets from UCI are used and experiments conducted across different confidence levels for racing. The results reveal a significant amount of sensitivity of the k-nn classifier to its model parameter value. The Hoeffding racing algorithm also varies widely in its performance, in terms of the computational savings gained over an exhaustive evaluation. While in some cases the savings gained are quite small, the racing algorithm proved to be highly robust to the possibility of erroneously eliminating the optimal models. All results were strongly dependent on the datasets used.

EXPERIÊNCIAS AFETIVAS NO CONTEXTO ORGANIZACIONAL E SEU IMPACTO SOBRE O BEM-ESTAR NO TRABALHO

Em ambiente de elevada pressão, competição e necessidade de criação de diferenciais consistentes que venham contribuir com a longevidade das organizações, nota-se a busca e, às vezes, radicais transformações nos modelos de gestão de negócios e gestão do ser humano no meio empresarial. No campo central dos estudos atuais acerca do comportamento humano e de suas relações com as diversas instituições em que o homem se vê inserido, figuram os esforços voltados à compreensão do papel e valor da contribuição do ser humano ao ambiente de trabalho e fortalecimento das organizações. Crescentes se mostram a preocupação e o entendimento sobre os fatores que impactam o bem-estar geral, o bem-estar no trabalho, a saúde dos trabalhadores e as variáveis emocionais oriundas das relações interpessoais comuns a todo organismo social. A combinação de temas emergentes e ricos em significância como bem-estar no trabalho, satisfação e envolvimento com o trabalho, comprometimento organizacional afetivo, emoções, afetos e sentimentos, caracterizam-se como um vasto e instigante campo de pesquisa para uma adaptação mais ampla do ser humano ao ambiente organizacional. O presente estudo teve como objetivo submeter ao teste empírico as relações entre experiências afetivas no contexto organizacional e três dimensões de bem-estar no trabalho - satisfação no trabalho, envolvimento com o trabalho e comprometimento organizacional afetivo. A amostra foi composta por 253 profissionais de uma indústria metalúrgica de autopeças na grande São Paulo, sendo 213 do sexo masculino e 29 do sexo feminino, com maior freqüência na faixa etária compreendida entre 26 a 30 anos, distribuída entre solteiros e casados. Para a coleta de dados foi utilizado um questionário de auto-preenchimento com quatro escalas que avaliaram afetos positivos e negativos, satisfação no trabalho, envolvimento com o trabalho e comprometimento organizacional afetivo. A análise dos dados foi feita por meio do SPSS, versão 16.0 e diversos sub-programas permitiram realizar análises descritivas bem como calcular modelos de regressão linear para verificar o impacto de afetos positivos e negativos sobre bem-estar no trabalho. Os resultados deste estudo revelaram que o principal preditor das dimensões de bem-estar no trabalho foram os afetos positivos. Assim, parece ser adequado afirmar que bem-estar no trabalho seja um estado psicológico sustentado, em especial, pela vivência de emoções positivas no contexto organizacional. Sugere-se que a promoção da saúde e do bem-estar dentro das organizações sejam focos de estudos futuros, representando valiosa contribuição aos campos de conhecimento da psicologia da saúde e da psicologia organizacional, bem como ao conseqüente fortalecimento dos vínculos entre empresa e trabalhadores.(AU)

Bayesian invariant measurements of generalisation for continuous distributions

A family of measurements of generalisation is proposed for estimators of continuous distributions. In particular, they apply to neural network learning rules associated with continuous neural networks. The optimal estimators (learning rules) in this sense are Bayesian decision methods with information divergence as loss function. The Bayesian framework guarantees internal coherence of such measurements, while the information geometric loss function guarantees invariance. The theoretical solution for the optimal estimator is derived by a variational method. It is applied to the family of Gaussian distributions and the implications are discussed. This is one in a series of technical reports on this topic; it generalises the results of ¸iteZhu95:prob.discrete to continuous distributions and serve as a concrete example of a larger picture ¸iteZhu95:generalisation.

On the relationship between Bayesian error bars and the input data density

We investigate the dependence of Bayesian error bars on the distribution of data in input space. For generalized linear regression models we derive an upper bound on the error bars which shows that, in the neighbourhood of the data points, the error bars are substantially reduced from their prior values. For regions of high data density we also show that the contribution to the output variance due to the uncertainty in the weights can exhibit an approximate inverse proportionality to the probability density. Empirical results support these conclusions.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

General bounds on Bayes errors for regression with Gaussian processes

Based on a simple convexity lemma, we develop bounds for different types of Bayesian prediction errors for regression with Gaussian processes. The basic bounds are formulated for a fixed training set. Simpler expressions are obtained for sampling from an input distribution which equals the weight function of the covariance kernel, yielding asymptotically tight results. The results are compared with numerical experiments.

Environmental reporting in developing countries:empirical evidence from Bangladesh

Most environmental reporting studies have focused on developed countries. Only a handful number of studies are available on the developing countries, concentrating on the newly industrialized countries and African countries. No studies are available from South Asia except the widely quoted one of Singh and Ahuja (1983). Against this background, it is argued that an empirical study on environmental reporting practices in Bangladesh would make a significant contribution to the environmental reporting literature from the context of developing countries in general, and South Asian countries in particular. The study covers 30 recent annual reports of Bangladeshi companies relating to the year 1996. It shows that very limited environmental disclosure has been made. Although we have noted that 90% of companies made some environmental disclosures, the percentage of companies disclosing environmental information comes down to only 20 if we exclude disclosure related to expenditure on energy usage. In general, the quantity and the quality of disclosures seem to be inadequate and poor as compared to the environmental disclosures in the developed countries. The study concludes with an urge for further research in this regard.

Empirical analysis of human capital development and economic growth in Europe

Recent discussion of the knowledge-based economy draws increasingly attention to the role that the creation and management of knowledge plays in economic development. Development of human capital, the principal mechanism for knowledge creation and management, becomes a central issue for policy-makers and practitioners at the regional, as well as national, level. Facing competition both within and across nations, regional policy-makers view human capital development as a key to strengthening the positions of their economies in the global market. Against this background, the aim of this study is to go some way towards answering the question of whether, and how, investment in education and vocational training at regional level provides these territorial units with comparative advantages. The study reviews literature in economics and economic geography on economic growth (Chapter 2). In growth model literature, human capital has gained increased recognition as a key production factor along with physical capital and labour. Although leaving technical progress as an exogenous factor, neoclassical Solow-Swan models have improved their estimates through the inclusion of human capital. In contrast, endogenous growth models place investment in research at centre stage in accounting for technical progress. As a result, they often focus upon research workers, who embody high-order human capital, as a key variable in their framework. An issue of discussion is how human capital facilitates economic growth: is it the level of its stock or its accumulation that influences the rate of growth? In addition, these economic models are criticised in economic geography literature for their failure to consider spatial aspects of economic development, and particularly for their lack of attention to tacit knowledge and urban environments that facilitate the exchange of such knowledge. Our empirical analysis of European regions (Chapter 3) shows that investment by individuals in human capital formation has distinct patterns. Those regions with a higher level of investment in tertiary education tend to have a larger concentration of information and communication technology (ICT) sectors (including provision of ICT services and manufacture of ICT devices and equipment) and research functions. Not surprisingly, regions with major metropolitan areas where higher education institutions are located show a high enrolment rate for tertiary education, suggesting a possible link to the demand from high-order corporate functions located there. Furthermore, the rate of human capital development (at the level of vocational type of upper secondary education) appears to have significant association with the level of entrepreneurship in emerging industries such as ICT-related services and ICT manufacturing, whereas such association is not found with traditional manufacturing industries. In general, a high level of investment by individuals in tertiary education is found in those regions that accommodate high-tech industries and high-order corporate functions such as research and development (R&D). These functions are supported through the urban infrastructure and public science base, facilitating exchange of tacit knowledge. They also enjoy a low unemployment rate. However, the existing stock of human and physical capital in those regions with a high level of urban infrastructure does not lead to a high rate of economic growth. Our empirical analysis demonstrates that the rate of economic growth is determined by the accumulation of human and physical capital, not by level of their existing stocks. We found no significant effects of scale that would favour those regions with a larger stock of human capital. The primary policy implication of our study is that, in order to facilitate economic growth, education and training need to supply human capital at a faster pace than simply replenishing it as it disappears from the labour market. Given the significant impact of high-order human capital (such as business R&D staff in our case study) as well as the increasingly fast pace of technological change that makes human capital obsolete, a concerted effort needs to be made to facilitate its continuous development.

Effects of government regulations, market orientation and ownership structure on corporate social responsibility in China:an empirical study

Previous research on corporate social responsibility mainly focuses on its nature and impact on business performance. This paper reports on a study that contributes to our understanding of the determinants of corporate social responsibility by focusing specifically on the role played by three strategically important variables, namely government regulation, ownership structure and market orientation. Results of a survey of 586 general managers of hotels in China suggest that the market orientation is the most significant predicator of corporate social responsibility followed by government regulation. In contrast, the ownership structure is found to have little effect. The implications of the findings for managers in China are discussed.

Approximate Bayesian techniques for inference in stochastic dynamical systems

This thesis is concerned with approximate inference in dynamical systems, from a variational Bayesian perspective. When modelling real world dynamical systems, stochastic differential equations appear as a natural choice, mainly because of their ability to model the noise of the system by adding a variant of some stochastic process to the deterministic dynamics. Hence, inference in such processes has drawn much attention. Here two new extended frameworks are derived and presented that are based on basis function expansions and local polynomial approximations of a recently proposed variational Bayesian algorithm. It is shown that the new extensions converge to the original variational algorithm and can be used for state estimation (smoothing). However, the main focus is on estimating the (hyper-) parameters of these systems (i.e. drift parameters and diffusion coefficients). The new methods are numerically validated on a range of different systems which vary in dimensionality and non-linearity. These are the Ornstein-Uhlenbeck process, for which the exact likelihood can be computed analytically, the univariate and highly non-linear, stochastic double well and the multivariate chaotic stochastic Lorenz '63 (3-dimensional model). The algorithms are also applied to the 40 dimensional stochastic Lorenz '96 system. In this investigation these new approaches are compared with a variety of other well known methods such as the ensemble Kalman filter / smoother, a hybrid Monte Carlo sampler, the dual unscented Kalman filter (for jointly estimating the systems states and model parameters) and full weak-constraint 4D-Var. Empirical analysis of their asymptotic behaviour as a function of observation density or length of time window increases is provided.