Influência dos investimentos sociais na classificação de empresas no Índice de Sustentabilidade Empresarial da BMF&BOVESPA

O mercado global estimula a competitividade para a busca de retornos financeiros. Entretanto, para a obtenção destes retornos financeiros, as empresas não podem atuar sem considerar critérios de sustentabilidade empresarial, condição sem a qual as empresas perderão competitividade. A BM&FBOVESPA trouxe grande contribuição às empresas que operam em seu mercado através do ISE (Índice de Sustentabilidade Empresarial). No entanto é justificado o questionamento se a dimensão que contempla os investimentos relativos à responsabilidade social tem peso suficiente para classificar melhor uma empresa no ISE ou se a concepção de sustentabilidade do ISE é multifocal e cuja ênfase seja distribuída nos conceitos do triple bottom line. Para verificar tal fato, este trabalho procurou comprovar o peso real da dimensão social dentro do ISE por meio de regressão logística, valendo-se de amostragem adequada de empresas participantes ou não na carteira do ISE nos anos 2011 e 2012. Demonstrou-se que não há diferenças significativas entre os grupos que justifiquem a classificação ou não no ISE somente baseando-se nas variáveis da dimensão social, bem como das mesmas com a adição de variáveis financeiras. Nota-se, portanto, que a dimensão social não é suficientemente relevante para diferenciar a participação ou não de uma empresa na carteira do ISE.

Explaining employee turnover in an Asian context

Employee turnover is giving sleepless nights to HR managers in many countries in Asia. A widely-held belief in these countries is that employees have developed 'bad' attitudes due to the labour shortage. Employees are believed to job-hop for no reason, or even for fun. Unfortunately, despite employee turnover being such a serious problem in Asia, there is a dearth of studies investigating it; in particular studies using a comprehensive set of variables are rare. This study examines three sets of antecedents of turnover intention in companies in Singapore: demographic, controllable and uncontrollable. Singapore companies provide an appropriate setting as their turnover rates are among the highest in Asia. Findings of the study suggest that organisational commitment, procedural justice and a job-hopping attitude were three main factors associated with turnover intention in Singapore companies.

A DEA model for units with negative data

In this paper we propose an alternative method for measuring efficiency of Decision making Units, which allows the presence of variables with both negative and positive values. The model is applied to data on the notional effluent processing system to compare the results with recent developed methods; Modified Slacks Based Model as suggested by Sharp et al (2007) and Range Directional Measures developed by Silva Portela et al (2004). A further example explores advantages of using the new model.

Parent-child interaction and road behaviour:An exploratory study

The relationship between parent-child interaction and child pedestrian behaviour was investigated by comparing parent-child communication to road-crossing behaviour. Forty-four children and their parents were observed carrying out a communication task (the Map Task), and were covertly filmed crossing roads around a university campus. The Map Task provided measures of task focus and sensitivity to another's current knowledge, which we predicted would be reflected in road-crossing behaviour. We modelled indices of road behaviour with factor scores derived from a principal-component analysis of communication features, and background variables including the age, sex and traffic experience of the child, and parental education. A number of variables were significantly related to road crossing, including the age and sex of the child, the length of the conversation, and specific conversational features such as the checking and clarification of uncertain information by both parent and child. The theoretical and practical implications of the findings are discussed.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Inference by belief propagation in composite systems

We devise a message passing algorithm for probabilistic inference in composite systems, consisting of a large number of variables, that exhibit weak random interactions among all variables and strong interactions with a small subset of randomly chosen variables; the relative strength of the two interactions is controlled by a free parameter. We examine the performance of the algorithm numerically on a number of systems of this type for varying mixing parameter values.

Data analysis methods in optometry Part 7: multiple linear regression

Multiple regression analysis is a complex statistical method with many potential uses. It has also become one of the most abused of all statistical procedures since anyone with a data base and suitable software can carry it out. An investigator should always have a clear hypothesis in mind before carrying out such a procedure and knowledge of the limitations of each aspect of the analysis. In addition, multiple regression is probably best used in an exploratory context, identifying variables that might profitably be examined by more detailed studies. Where there are many variables potentially influencing Y, they are likely to be intercorrelated and to account for relatively small amounts of the variance. Any analysis in which R squared is less than 50% should be suspect as probably not indicating the presence of significant variables. A further problem relates to sample size. It is often stated that the number of subjects or patients must be at least 5-10 times the number of variables included in the study.5 This advice should be taken only as a rough guide but it does indicate that the variables included should be selected with great care as inclusion of an obviously unimportant variable may have a significant impact on the sample size required.

Higher-order co-occurrences for exploratory point pattern analysis and decision tree clustering on spatial data

Analyzing geographical patterns by collocating events, objects or their attributes has a long history in surveillance and monitoring, and is particularly applied in environmental contexts, such as ecology or epidemiology. The identification of patterns or structures at some scales can be addressed using spatial statistics, particularly marked point processes methodologies. Classification and regression trees are also related to this goal of finding "patterns" by deducing the hierarchy of influence of variables on a dependent outcome. Such variable selection methods have been applied to spatial data, but, often without explicitly acknowledging the spatial dependence. Many methods routinely used in exploratory point pattern analysis are2nd-order statistics, used in a univariate context, though there is also a wide literature on modelling methods for multivariate point pattern processes. This paper proposes an exploratory approach for multivariate spatial data using higher-order statistics built from co-occurrences of events or marks given by the point processes. A spatial entropy measure, derived from these multinomial distributions of co-occurrences at a given order, constitutes the basis of the proposed exploratory methods. © 2010 Elsevier Ltd.

Statistical analysis in microbiology:statnotes

This book is aimed primarily at microbiologists who are undertaking research and who require a basic knowledge of statistics to analyse their experimental data. Computer software employing a wide range of data analysis methods is widely available to experimental scientists. The availability of this software, however, makes it essential that investigators understand the basic principles of statistics. Statistical analysis of data can be complex with many different methods of approach, each of which applies in a particular experimental circumstance. Hence, it is possible to apply an incorrect statistical method to data and to draw the wrong conclusions from an experiment. The purpose of this book, which has its origin in a series of articles published in the Society for Applied Microbiology journal ‘The Microbiologist’, is an attempt to present the basic logic of statistics as clearly as possible and therefore, to dispel some of the myths that often surround the subject. The 28 ‘Statnotes’ deal with various topics that are likely to be encountered, including the nature of variables, the comparison of means of two or more groups, non-parametric statistics, analysis of variance, correlating variables, and more complex methods such as multiple linear regression and principal components analysis. In each case, the relevant statistical method is illustrated with examples drawn from experiments in microbiological research. The text incorporates a glossary of the most commonly used statistical terms and there are two appendices designed to aid the investigator in the selection of the most appropriate test.

Statnote 28:Canonical Variate Analysis (CVA)

The pattern of correlation between two sets of variables can be tested using canonical variate analysis (CVA). CVA, like principal components analysis (PCA) and factor analysis (FA) (Statnote 27, Hilton & Armstrong, 2011b), is a multivariate analysis Essentially, as in PCA/FA, the objective is to determine whether the correlations between two sets of variables can be explained by a smaller number of ‘axes of correlation’ or ‘canonical roots’.

Technical innovation in industrial production

New techniques in manufacturing, popularly referred to as mechanization and automation, have been a preoccupation of social and economic theorists since the industrial revolution. A selection of relevant literature is reviewed, including the neoclassical economic treatment of technical change. This incorporates alterations to the mathematical production function and an associated increase in the efficiency with which the factors of production are converted into output. Other work emphasises the role of research and development and the process of diffusion, whereby new production techniques are propagated throughout industry. Some sociological writings attach importance to the type of production technology and its effect on the organisational structure and social relations within the factory. Nine detailed case studies are undertaken of examples of industrial innovation in the rubber, automobile, vehicle components, confectionery and clothing industries. The old and new techniques are compared for a range of variables, including capital equipment, labour employed, raw materials used, space requirements and energy consumption, which in most cases exhibit significant change with the innovation. The rate of output, labour productivity, product quality, maintenance requirements and other aspects are also examined. The process by which the change in production method was achieved is documented, including the development of new equipment and the strategy of its introduction into the factory, where appropriate. The firm, its environment, and the attitude of different sectors of the workforce are all seen to play a part in determining the motives for and consequences which flow from the innovations. The traditional association of technical progress with its labour-saving aspect, though an accurate enough description of the cases investigated, is clearly seen to afford an inadequate perspective for the proper understanding of this complex phenomenon, which also induces change in a wide range of other social, economic and technical variables.

Finding structures in information networks using the affinity network

This thesis proposes a novel graphical model for inference called the Affinity Network,which displays the closeness between pairs of variables and is an alternative to Bayesian Networks and Dependency Networks. The Affinity Network shares some similarities with Bayesian Networks and Dependency Networks but avoids their heuristic and stochastic graph construction algorithms by using a message passing scheme. A comparison with the above two instances of graphical models is given for sparse discrete and continuous medical data and data taken from the UCI machine learning repository. The experimental study reveals that the Affinity Network graphs tend to be more accurate on the basis of an exhaustive search with the small datasets. Moreover, the graph construction algorithm is faster than the other two methods with huge datasets. The Affinity Network is also applied to data produced by a synchronised system. A detailed analysis and numerical investigation into this dynamical system is provided and it is shown that the Affinity Network can be used to characterise its emergent behaviour even in the presence of noise.

How do public private partnerships influence business school performance in English-speaking Caribbean?

This thesis addresses the question of how business schoolsestablished as public privatepartnerships (PPPs) within a regional university in the English-speaking Caribbean survived for over twenty-one years and achieved legitimacy in their environment. The aim of the study was to examine how public and private sector actors contributed to the evolution of the PPPs. A social network perspective provided a broad relational focus from which to explore the phenomenon and engage disciplinary and middle-rangetheories to develop explanations. Legitimacy theory provided an appropriate performance dimension from which to assess PPP success. An embedded multiple-case research design, with three case sites analysed at three levels including the country and university environment, the PPP as a firm and the subgroup level constituted the methodological framing of the research process. The analysis techniques included four methods but relied primarily on discourse and social network analysis of interview data from 40 respondents across the three sites. A staged analysis of the evolution of the firm provided the ‘time and effects’ antecedents which formed the basis for sense-making to arrive at explanations of the public-private relationship-influenced change. A conceptual model guided the study and explanations from the cross-case analysis were used to refine the process model and develop a dynamic framework and set of theoretical propositions that would underpin explanations of PPP success and legitimacy in matched contexts through analytical generalisation. The study found that PPP success was based on different models of collaboration and partner resource contribution that arose from a confluence of variables including the development of shared purpose, private voluntary control in corporate governance mechanisms and boundary spanning leadership. The study contributes a contextual theory that explains how PPPs work and a research agenda of ‘corporate governance as inspiration’ from a sociological perspective of ‘liquid modernity’. Recommendations for policy and management practice were developed.

Comparing methods to separate treatment from self-selection effects in an online banking setting

The literature discusses several methods to control for self-selection effects but provides little guidance on which method to use in a setting with a limited number of variables. The authors theoretically compare and empirically assess the performance of different matching methods and instrumental variable and control function methods in this type of setting by investigating the effect of online banking on product usage. Hybrid matching in combination with the Gaussian kernel algorithm outperforms the other methods with respect to predictive validity. The empirical finding of large self-selection effects indicates the importance of controlling for these effects when assessing the effectiveness of marketing activities.

Enhanced renewable energy adoption for sustainable development in India:interpretive structural modeling approach

Poverty alleviation and social upliftment of rural India is closely linked with the availability and use of energy for development. At the same time, sustainable supply of clean and affordable renewable energy sources is required if development is to be sustainable, so that it does not cause any environmental problems. The purpose of this paper is to determine the key variables of renewable energy implementation for sustainable development, on which the top management should focus. In this paper, an interpretive structural modeling (ISM) - based approach has been employed to model the implementation variables of renewable energy for sustainable development. These variables have been categorized under ‘enablers’ that help to increase the implementation of renewable energy for sustainable development. A major finding of this research is that public awareness regarding renewable energy for sustainable development is a very significant enabler. In this paper, an interpretation of variables of renewable energy for sustainable development in terms of their driving and dependence powers has been examined. For better results, top management should focus on improving the high-driving power enablers such as leadership, strategic planning, public awareness, top management support, availability of finance, government support, and support from interest groups.