Book review : Ethics and organizational practice : questioning the moral foundations of management

The arena of ethics and business is a colossus: thousands of books, multiple dedicated journals, de rigueur organizational ethics policies and CSR initiatives - most of which we can be fairly confident would receive poor reviews by the editors and authors of Ethics and organizational practice: Questioning the moral foundations of management. This edited collection is a self-identified ‘critical’ take on business ethics, one that according to the editors’ introduction wishes to ‘expose business ethics to its crises’ and ‘critically investigate(s) what ethics means’. The ‘critical’ which Muhr, Sørensen and Vallentin invoke is one that would be familiar to authors and readers of Critical Management Studies – that is, to use Fournier and Grey’s (2000) oft-referenced depiction, the study of management and organization that is non-performative with regards to managerialist concerns of efficiency and profitability, that seeks to denaturalize taken-for-granted legitimations, and the normalization of current organizational practices and ideologies, and one which demonstrates significant reflexivity with regard to the philosophies and methodologies it deploys. To the above, we may also add pluralism, and indeed some playfulness, with a wide diversity of conceptual, theoretical, historical and popular sources mined for their potential to help us reconsider the organizational present.

Hybrid neural-evolutionary model for electricity price forecasting

Evolving artificial neural networks has attracted much attention among researchers recently, especially in the fields where plenty of data exist but explanatory theories and models are lacking or based upon too many simplifying assumptions. Financial time series forecasting is one of them. A hybrid model is used to forecast the hourly electricity price from the California Power Exchange. A collaborative approach is adopted to combine ANN and evolutionary algorithm. The main contributions of this thesis include: Investigated the effect of changing values of several important parameters on the performance of the model, and selected the best combination of these parameters; good forecasting results have been obtained with the implemented hybrid model when the best combination of parameters is used. The lowest MAPE through a single run is 5. 28134%. And the lowest averaged MAPE over 10 runs is 6.088%, over 30 runs is 6.786%; through the investigation of the parameter period, it is found that by including future values of the homogenous moments of the instant being forecasted into the input vector, forecasting accuracy is greatly enhanced. A comparison of results with other works reported in the literature shows that the proposed model gives superior performance on the same data set.

Using job strain and organizational justice models to predict multiple forms of employee performance behaviours among Australian policing personnel

The overall purpose of this investigation was to examine the relationship between stress-related working conditions and three forms of employee performance behaviours: in-role behaviours, citizenship behaviours directed at other individuals and citizenship behaviours directed at the organization. The potentially stressful working conditions were based on the job strain model (incorporating job demands, job control and social support) as well as organizational justice theory. A sample of Australian-based police officers (n = 640) took part in this study and the data were collected via a mail-out survey. Multiple regression analyses were undertaken to assess both the strength and the nature of the relationships between the working conditions and employee performance and these analyses included tests for additive, interactional and curvilinear effects. The overall results indicated that a significant proportion of the explained variance in all three outcome measures was attributed to the additive effects of demand, control and support. The level of variance associated with the organizational justice dimensions was relatively small, although there were signs that specific dimensions of justice may provide unique insights into the relationship between job stressors and employee performance. The implications of these and other notable findings are discussed.

Using causal mapping to find new avenue for organizational fit research

Although organizational fit is strongly linked to important psychological outcomes such as motivation, satisfaction, turnover and performance, there is still a lot of confusion about definitions and conceptualizations of the construct. One reason for this is that fit researchers have almost exclusively conducted theory-driven nomothetic studies that have utilized varying approaches to the term. In this paper, we call for exploratory research that listens to how workers construct their own sense of fit and suggest that researchers should adopt idiographic data gathering techniques, coupled with nomothetic analysis tools, to do so. To enable this, we explain how fit researchers might use causal maps and thereby develop a stronger understanding of organizational fit that is grounded in how people conceive it.

Lost in translation? Emotional intelligence, affective economies, leadership and organizational change

Not until the late 1990s did the rational/emotional binary embedded in mainstream literature on educational leadership and management come under challenge. Now the emotional dimensions of organisational change and leadership are widely recognised in the leadership, organisational change and school improvement literature. However, the dissolution of the binary did not draw from feminist social theory, critical organisational theory, the sociology of emotions or critical pedagogy. Instead, the strongest influence in educational leadership and administration has been from psychological theory, management theory and brain science, mobilised particularly through Goleman's notion of emotional intelligence. This article undertakes a feminist deconstruction of two texts: one from organisational theory by Goleman and the other on educational leadership and school improvement, in order to explore how ‘emotion’ has been translated into educational leadership. As a counterpoint, I identify the gaps and silences, appropriations and marginalisation identified from feminist perspectives. I argue that the emotional labour of teaching and leading cannot be individualised because emotion is both relational and contextual.

Organizational factors associated with decreased mortality among veterans affairs patients with an ICU stay

In-hospital mortality rates associated with an ICU stay are high and vary widely among units. This variation may be related to organizational factors such as staffing patterns, ICU structure, and care processes. We aimed to identify organizational factors associated with variation in in-hospital mortality for patients with an ICU stay. This was a retrospective observational cross-sectional study using administrative data from 34 093 patients from 171 ICUs in 119 Veterans Health Administration hospitals. Staffing and patient data came from Veterans Health Administration national databases. ICU characteristics came from a survey in 2004 of ICUs within the Veterans Health Administration. We conducted multilevel multivariable estimation with patient-, unit-, and hospital-level data. The primary outcome was in-hospital mortality. Of 34 093 patients, 2141 (6.3%)died in the hospital. At the patient level, risk of complications and having a medical diagnosis were significantly associated with a higher risk of mortality. At the unit level, having an interface with the electronic medical record was significantly associated with a lower risk of mortality. The finding that electronic medical records integrated with ICU information systems are associated with lower in-hospital mortality adds support to existing evidence on organizational characteristics associated with in-hospital mortality among ICU patients.

Short term load forecasting using interval type-2 fuzzy logic systems

Accurate Short Term Load Forecasting (STLF) is essential for a variety of decision making processes. However, forecasting accuracy may drop due to presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with extra degrees of freedom, are an excellent tool for handling prevailing uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models appropriately approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks used in this study.

Does the choice of estimator matter when forecasting returns?

While the literature concerned with the predictability of stock returns is huge, surprisingly little is known when it comes to role of the choice of estimator of the predictive regression. Ideally, the choice of estimator should be rooted in the salient features of the data. In case of predictive regressions of returns there are at least three such features; (i) returns are heteroskedastic, (ii) predictors are persistent, and (iii) regression errors are correlated with predictor innovations. In this paper we examine if the accounting of these features in the estimation process has any bearing on our ability to forecast future returns. The results suggest that it does.

Interval type-2 fuzzy logic systems for load forecasting : a comparative study

Accurate short term load forecasting (STLF) is essential for a variety of decision-making processes. However, forecasting accuracy can drop due to the presence of uncertainty in the operation of energy systems or unexpected behavior of exogenous variables. This paper proposes the application of Interval Type-2 Fuzzy Logic Systems (IT2 FLSs) for the problem of STLF. IT2 FLSs, with additional degrees of freedom, are an excellent tool for handling uncertainties and improving the prediction accuracy. Experiments conducted with real datasets show that IT2 FLS models precisely approximate future load demands with an acceptable accuracy. Furthermore, they demonstrate an encouraging degree of accuracy superior to feedforward neural networks and traditional type-1 Takagi-Sugeno-Kang (TSK) FLSs.

Short term load forecasting using a hybrid neural network

Short Term Load Forecasting (STLF) is very important from the power systems grid operation point of view. STLF involves forecasting load demand in a short term time frame. The short term time frame may consist of half hourly prediction up to weekly prediction. Accurate forecasting would benefit the utility in terms of reliability and stability of the grid ensuring adequate supply is present to meet with the load demand. Apart from that it would also affect the financial performance of the utility company. An accurate forecast would result in better savings while maintaining the security of the grid. This paper outlines the STLF using a novel hybrid online learning neural network, known as the Gaussian Regression (GR). This new hybrid neural network is a combination of two existing online learning neural networks which are the Gaussian Adaptive Resonance Theory (GA) and the Generalized Regression Neural Network (GRNN). Both GA and GRNN implemented online learning, but each of them suffers from limitation. Originally GA is used for unsupervised clustering by compressing the training samples into several categories. A supervised version of GA is available, namely Gaussian ARTMAP (GAM). However, the GAM is still not capable on solving regression problem. On the other hand, GRNN is designed for solving real value estimation (regression) problem, but the learning process would involve of memorizing all training samples, hence high computational cost. The hybrid GR is considered an enhanced version of GRNN with compression ability while still maintains online learning properties. Simulation results show that GR has comparable prediction accuracy and has less prototype as compared to the original GRNN as well as the Support Vector Regression.