Getting the most out of multidisciplinary teams: a multi-sample study of team innovation in health care

Driven by the assumption that multidisciplinarity contributes positively to team outcomes teams are often deliberately staffed such that they comprise multiple disciplines. However, the diversity literature suggests that multidisciplinarity may not always benefit a team. This study departs from the notion of a linear, positive effect of multidisciplinarity and tests its contingency on the quality of team processes. It was assumed that multidisciplinarity only contributes to team outcomes if the quality of team processes is high. This hypothesis was tested in two independent samples of health care workers (N = 66 and N = 95 teams), using team innovation as the outcome variable. Results support the hypothesis for the quality of innovation, rather than the number of innovations introduced by the teams.

Mean reversion in the short horizon returns of UK portfolios

This paper will show that short horizon stock returns for UK portfolios are more predictable than suggested by sample autocorrelation co-efficients. Four capitalisation based portfolios are constructed for the period 1976–1991. It is shown that the first order autocorrelation coefficient of monthly returns can explain no more than 10% of the variation in monthly portfolio returns. Monthly autocorrelation coefficients assume that each weekly return of the previous month contains the same amount of information. However, this will not be the case if short horizon returns contain predictable components which dissipate rapidly. In this case, the return of the most recent week would say a lot more about the future monthly portfolio return than other weeks. This suggests that when predicting future monthly portfolio returns more weight should be given to the most recent weeks of the previous month, because, the most recent weekly returns provide the most information about the subsequent months' performance. We construct a model which exploits the mean reverting characteristics of monthly portfolio returns. Using this model we forecast future monthly portfolio returns. When compared to forecasts that utilise the autocorrelation statistic the model which exploits the mean reverting characteristics of monthlyportfolio returns can forecast future returns better than the autocorrelation statistic, both in and out of sample.

Density forecasts of crude-oil prices using option-implied and ARCH-type models

The predictive accuracy of competing crude-oil price forecast densities is investigated for the 1994–2006 period. Moving beyond standard ARCH type models that rely exclusively on past returns, we examine the benefits of utilizing the forward-looking information that is embedded in the prices of derivative contracts. Risk-neutral densities, obtained from panels of crude-oil option prices, are adjusted to reflect real-world risks using either a parametric or a non-parametric calibration approach. The relative performance of the models is evaluated for the entire support of the density, as well as for regions and intervals that are of special interest for the economic agent. We find that non-parametric adjustments of risk-neutral density forecasts perform significantly better than their parametric counterparts. Goodness-of-fit tests and out-of-sample likelihood comparisons favor forecast densities obtained by option prices and non-parametric calibration methods over those constructed using historical returns and simulated ARCH processes. © 2010 Wiley Periodicals, Inc. Jrl Fut Mark 31:727–754, 2011

A comparison of linear forecasting models and neural networks:an application to Euro inflation and Euro Divisia

Linear models reach their limitations in applications with nonlinearities in the data. In this paper new empirical evidence is provided on the relative Euro inflation forecasting performance of linear and non-linear models. The well established and widely used univariate ARIMA and multivariate VAR models are used as linear forecasting models whereas neural networks (NN) are used as non-linear forecasting models. It is endeavoured to keep the level of subjectivity in the NN building process to a minimum in an attempt to exploit the full potentials of the NN. It is also investigated whether the historically poor performance of the theoretically superior measure of the monetary services flow, Divisia, relative to the traditional Simple Sum measure could be attributed to a certain extent to the evaluation of these indices within a linear framework. Results obtained suggest that non-linear models provide better within-sample and out-of-sample forecasts and linear models are simply a subset of them. The Divisia index also outperforms the Simple Sum index when evaluated in a non-linear framework. © 2005 Taylor & Francis Group Ltd.

An illustration of variable precision rough sets model:an analysis of the findings of the UK Monopolies and Mergers Commission

This paper introduces a new technique in the investigation of limited-dependent variable models. This paper illustrates that variable precision rough set theory (VPRS), allied with the use of a modern method of classification, or discretisation of data, can out-perform the more standard approaches that are employed in economics, such as a probit model. These approaches and certain inductive decision tree methods are compared (through a Monte Carlo simulation approach) in the analysis of the decisions reached by the UK Monopolies and Mergers Committee. We show that, particularly in small samples, the VPRS model can improve on more traditional models, both in-sample, and particularly in out-of-sample prediction. A similar improvement in out-of-sample prediction over the decision tree methods is also shown.

Effect of sample preparation on the thermal degradation of metal-added biomass

The present study investigates the effect of different sample preparation methods on the pyrolysis behaviour of metal-added biomass; Willow samples were compared in the presence of two salts of zinc and lead containing sulphate and nitrate anions which were added to the wood samples with three different techniques as dry-mixing, impregnation and ion-exchange. The effect of acid and water wash as common demineralisation pre-treatments were also analysed to evaluate their roles in the thermal degradation of the biomass. Results from thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FT-IR) and pyrolysis-mass spectrometry (Py-MS) measurements indicated that these pre-treatments change the matrix and the physical-chemical properties of wood. Results suggested that these structural changes increase the thermal stability of cellulose during pyrolysis. Sample preparation was also found to be a crucial factor during pyrolysis; different anions of metal salts changed the weight loss rate curves of wood material, which indicates changes in the primary degradation process of the biomass. Results also showed that dry-mixing, impregnation or ion-exchange influence the thermal behaviour of wood in different ways when a chosen metal salt was and added to the wood material. © 2011 Elsevier B.V. All rights reserved.

Speaking out of turn? Taking the initiative in teacher-fronted classroom interaction.

Teacher-fronted interaction is generally seen to place limitations on the contributions that learners can make to classroom discourse and the conclusion is that learners are unable to experiment with, for example, turn-taking mechanisms. This article looks at teacher-fronted interaction in the language classroom from the perspective of learner talk by examining how learners might take the initiative during this apparently more rigid form of interaction. Detailed microanalysis of classroom episodes, using a conversation analysis institutional discourse approach, shows how learners orient to the institutional context to make sophisticated and effective use of turn-taking mechanisms to take the initiative and direct the interaction, even in the controlled environment of teacher-fronted talk. The article describes some of the functions of such learner initiative, examines how learners and teachers co-construct interaction and how learners can create learning opportunities for themselves. It also briefly looks at teacher reactions to such initiative. The article concludes that learner initiative in teacher-fronted interaction may constitute a significant opportunity for learning and that teachers should find ways of encouraging such interaction patterns.

Selectivity and timing performance of UK investment trusts

This study examines the selectivity and timing performance of 218 UK investment trusts over the period July 1981 to June 2009. We estimate the Treynor and Mazuy (1966) and Henriksson and Merton (1981) models augmented with the size, value, and momentum factors, either under the OLS method adjusted with the Newey-West procedure or under the GARCH(1,1)-in-mean method following the specification of Glosten et al. (1993; hereafter GJR-GARCH-M). We find that the OLS method provides little evidence in favour of the selectivity and timing ability, consistent with previous studies. Interestingly, the GJR-GARCH-M method reverses this result, showing some relatively strong evidence on favourable selectivity ability, particularly for international funds, as well as favourable timing ability, particularly for domestic funds. We conclude that the GJR-GARCH-M method performs better in evaluating fund performance compared with the OLS method and the non-parametric approach, as it essentially accounts for the time-varying characteristics of factor loadings and hence obtains more reliable results, in particular, when the high frequency data, such as the daily returns, are used in the analysis. Our results are robust to various in-sample and out-of-sample tests and have valuable implications for practitioners in making their asset allocation decisions across different fund styles. © 2012 Elsevier B.V.

Response to out of stock produce and its underlying economic considerations

The goal of this research is to investigate consumer response to out-of-stock product in the produce category. We do this by comparing results from a survey conducted in Greece and the United States to previous research on consumer response to out-of-stock situations for other perishable and non-perishable products. We further examined the underlying economic reasoning as well as the cultural and physical differences between the United States and Greece as explanations of different reactions. Out of Stock produce response proved different in produce than in other perishables and non-perishables. There is some evidence that produce does follow previous the suggested economic reasoning from the previous research, especially within transaction costs. Finally, the respondent’s country proved very significant in dictating response.

Breaks in the UK household sector money demand function

We use non-parametric procedures to identify breaks in the underlying series of UK household sector money demand functions. Money demand functions are estimated using cointegration techniques and by employing both the Simple Sum and Divisia measures of money. P-star models are also estimated for out-of-sample inflation forecasting. Our findings suggest that the presence of breaks affects both the estimation of cointegrated money demand functions and the inflation forecasts. P-star forecast models based on Divisia measures appear more accurate at longer horizons and the majority of models with fundamentals perform better than a random walk model.

Dynamic models of exchange rate dependence using option prices and historical returns

Models for the conditional joint distribution of the U.S. Dollar/Japanese Yen and Euro/Japanese Yen exchange rates, from November 2001 until June 2007, are evaluated and compared. The conditional dependency is allowed to vary across time, as a function of either historical returns or a combination of past return data and option-implied dependence estimates. Using prices of currency options that are available in the public domain, risk-neutral dependency expectations are extracted through a copula repre- sentation of the bivariate risk-neutral density. For this purpose, we employ either the one-parameter \Normal" or a two-parameter \Gumbel Mixture" specification. The latter provides forward-looking information regarding the overall degree of covariation, as well as, the level and direction of asymmetric dependence. Specifications that include option-based measures in their information set are found to outperform, in-sample and out-of-sample, models that rely solely on historical returns.

Taking the carbon out of the car

Effective measures are being taken to reduce emissions from cars, which are now emerging as a major contributor to climate change. Developed countries will need to reduce emissions by at least 80% by 2050 to achieve stabilization of atmospheric CO2 concentration between 450 and 550 ppm, and have a unique opportunity to avoid the most damaging effects of climate change. The UK is aiming at completely decarbonising transport by 2050 through a combination of more efficient vehicles, cleaner fuels, and smart driving choices. The European Commission has proposed a mandatory CO2 target on new car CO 2 efficiency, which is an urgent needed development. The nation is also using regulatory targets for local schemes, such as free parking or congestion charging, break points for company car tax, and vehicle excise duty. Car ownership and use should thereby continue to drive economic growth and enhance quality of life around the world without destroying the planet.

Breaking out of the silo mentality

The return to methods focusing on language and experience following the dominance of experimental methods has in the last few decades led to debate, dialogue, and disagreement regarding the status of qualitative and quantitative methods. However, a recent focus on impact has brought an air of pragmatism to the research arena. In what ways, then, is psychology moving from entrenched mono methods approaches that have epitomised its development until recently, to describing and discussing ways in which mixed and pluralistic research can advance and contribute to further, deeper psychological understanding?.

Implementing a knowledge management system within an NHS hospital:a case study exploring the roll-out of an Electronic Patient Record (EPR)

This research aims to contribute to understanding the implementation of knowledge management systems (KMS) in the field of health through a case study, leading to theory building and theory extension. We use the concept of the business process approach to knowledge management as a theoretical lens to analyse and explore how a large teaching hospital developed, executed and practically implemented a KMS. A qualitative study was conducted over a 2.5 year period with data collected from semi-structured interviews with eight members of the strategic management team, 12 clinical users and 20 patients in addition to non-participant observation of meetings and documents. The theoretical propositions strategy was used as the overarching approach for data analysis. Our case study provides evidence that true patient centred approaches to supporting care delivery with a KMS benefit from process thinking at both the planning and implementation stages, and an emphasis on the knowledge demands resulting from: the activities along the care pathways; where cross-overs in care occur; and knowledge sharing for the integration of care. The findings also suggest that despite the theoretical awareness of KMS implementation methodologies, the actual execution of such systems requires practice and learning. Flexible, fluid approaches through rehearsal are important and communications strategies should focus heavily on transparency incorporating both structured and unstructured communication methods.