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The routine use of intrapartum electronic fetal monitoring (EFM) has resulted in an increased burden of operative and vaginal instrumental deliveries for women at low obstetric risk. Such modes of delivery increase maternal mortality and morbidity risks. This study aims to explore midwives' values, attitudes and beliefs when using intrapartum fetal monitoring techniques in clinical practice.

A total of 58 registered midwives across two NHS Trusts in one region in the north of England were interviewed using a qualitative approach.

Midwives attempted to manage the psychological burden of the threat from clinical negligence by using EFM. This meant that some midwives used electronic monitoring regardless of clinical need. Midwives lack confidence in the ability of EFM to accurately detect fetal compromise but are aware that the visual monitoring record is recognised as a valuable piece of legal evidence. The midwives' perceptions of professional self‐efficacy in seeking to avoid a claim in clinical negligence contributed to defensive practice. Research limitations/implications – The study was conducted in only two hospitals in one region of England; however the Trust demographics were similar and midwifery practice within the unit reflects national maternity standards of care.

Multidisciplinary strategies may be required to overcome barriers to the effective implementation of clinical guidelines where intrapartum fetal monitoring is concerned and Trust audit departments must undertake regular audit cycles in order to ascertain practice compliance with best evidence.

The paper provides information so that midwives' knowledge regarding the limitations of EFM can be improved.

Value at risk (VaR) and expected shortfall (ES) are popular market risk measurements. The former is not coherent but robust, whereas the latter is coherent but less interpretable, only conditionally backtestable and less robust. In this chapter, we compare an innovative artificial neural network (ANN) model with a time series model in the context of forecasting VaR and ES of the univariate time series of four asset classes: US large capitalization equity index, European large cap equity index, US bond index, and US dollar versus euro exchange rate price index for the period of January 4, 1999, to December 31, 2018. In general, the ANN model has more favorable backtesting results as compared to the autoregressive moving average, generalized autoregressive conditional heteroscedasticity (ARMA-GARCH) time series model. In terms of forecasting accuracy, the ANN model has much fewer in-sample and out-of-sample exceptions than those of the ARMA-GARCH model.

An artificial neural network methodology is used to develop a new measure of contagion using exchange rate data from the Asian Crisis of 1997 and beyond. Connection weight changes during retraining of networks used to forecast exchange rates form the basis of this measure. These weight changes are used in obtaining a contribution factor for independent variables used in a forecasting process. Volatilities of contribution factors form the basis of the measure of contagion obtained. These volatilities are statistically validated through a series of simulations where critical values for them are derived. The measures of contagion obtained are then matched to concurrent economic and financial shocks that occurred during the crisis. It is found that there is good correlation between these events and the contagion measures obtained.

The following classified, annotated list of titles is intended to provide reference librarians with a current checklist of new reference books, and is designed to supplement the RSR review column, “Recent Reference Books,” by Frances Neel Cheney. “Reference Books in Print” includes all additional books received prior to the inclusion deadline established for this issue. Appearance in this column does not preclude a later review in RSR. Publishers are urged to send a copy of all new reference books directly to RSR as soon as published, for immediate listing in “Reference Books in Print.” Reference books with imprints older than two years will not be included (with the exception of current reprints or older books newly acquired for distribution by another publisher). The column shall also occasionally include library science or other library related publications of other than a reference character.

To provide insights into the role of formal and informal accounts in preventing the liquidation of a professional football club and in post-crisis rebuilding.

This case study, framed as a conflict arena, covers an eight-year period of a high-profile struggle over the future of a professional football club. It uses a mixed methods design, including direct engagement with key actors involved in administration proceedings and transformation to a hybrid supporter-owned organisation.

Our findings suggest that within the arena:• formal accounting and governance were of limited use in managing the complex network of relationships and preventing the abuse of power or existential crises. • informal accounting helped mobilise critical resources and maintain supporters’ emotional investment during periods of conflict. • informal accounts enabled both resistance and coalition-building in response to perceived abuse of power. • informal accounts were used by the Club as part of its legitimation activities.

This study provides theoretical and empirical insights into an unfolding crisis with evidence gathered directly from actors involved in the process. The conceptual framework developed in this paper creates new visibilities and possibilities for developing more effective accounting practices in settings that enable continuing emotional investment from supporters.

The purpose of this paper is to validate Tsiotsou’s (2012) sport team brand personality scale in a new country (Australia) and segment (Generation Y) and extend its use from sport teams to individual athletes. This paper also explores the scale’s predictive power in an athlete’s role model influence.

An online panel survey with 560 responses from Australian Generation Y consumers was obtained. Structural equation modelling was used to test and validate the scale.

This paper confirmed the sport team brand personality scale can be applied to an individual athlete. Three dimensions of the scale, namely, competitiveness, morality and authenticity, were found to have predictive power in an athlete’s role model influence. Authenticity was the strongest predictor, followed by morality and competitiveness.

Individual athlete brand personality is powerful in influencing how Australian Generation Y consumers identify with their favourite athlete as a role model. These insights assist brand and marketing managers to use athletes as part of their communication strategies regarding brand and product endorsements.

This research addresses Tsiotsou’s (2012) call to test the robustness and external validity of the sport team brand personality scale and to empirically test how the scale can predict outcomes. The authors succeeded in validating and extending the sport brand personality scale to an individual athlete level in a new country and consumer segment. The scale was found to have predictive power in an athlete’s role model influence.

An appropriate use of neural computing techniques is to apply them to corporate bankruptcy prediction, where conventional solutions can be hard to obtain. Having said that, choosing an appropriate Artificial Neural Network topology (ANN) for predicting corporate bankruptcy would remain a daunting prospect. The context of the problem is that there are no fixed rules in determining the ANN structure or its parameter values, a large number of ANN topologies may have to be constructed with different structures and parameters before determining an acceptable model. The trial‐and‐error process can be tedious, and the experience of the ANN user in constructing the topologies is invaluable in the search for a good model. Yet, a permanent solution does not exist. This paper identifies a non trivial novel approach for implementing artificial neural networks for the prediction of corporate bankruptcy by applying inter‐connected neural networks. The proposed approach is to produce a neural network architecture that captures the underlying characteristics of the problem domain. The research primarily employed financial data sets from the London Stock Exchange and Jordans financial database of major public and private British companies. Early results indicate that an ANN appears to outperform the traditional approach in forecasting corporate bankruptcy.

This paper investigates the relationship between divestiture activity and subsequent acquisition deal-making. We argue that the divestiture activity of firms influences their acquisition behavior through corporate restructuring learning effects and enhanced strategic flexibility. These organizational spillovers affect not only the degree of risk acquirers are ready to take but also their ability to effectively negotiate with the target firm. We test the existence of organizational spillovers for an international sample of 4,795 acquirers for the period 1990–2008 and get support for our theoretical predictions.