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The purpose of this paper is to investigate the impact of the public pension governance practices on the public defined benefit pension (DBP) fund performance.

To provide a holistic evaluation of public DBP performance, this study first employs the Data Envelopment Analysis (DEA) approach to construct a relative performance measure that simultaneously takes into account the association between investment inputs and performance outputs across DBPs in our sample. A DEA regression model is then constructed to empirically examine the impact of pension governance on public DBP performance.

Using 1,544 hand-collected observations in the USA from 2002 to 2013, the findings show that the public DBP plans with a small board, appointed board trustees, and a separate investment council exhibit better performance.

The effectiveness of pension governance has increasingly drawn public attention, as it affects the performance of the public DBP plans that especially matter to public employees. The empirical findings of this research offer insights into recent calls to reexamine public DBP management practices and to carry out related public pension fund policy reforms.

The examination of public DBP governance practices in this study enriches the governance literature, particularly research on public pension funds, by using public sector data. Second, by applying the DEA method to evaluate the relative performance of public DBP funds, this study obtains a more comprehensive analysis of the public pension governance.

The role of audit committees in ensuring the quality of corporate financial reporting has come under considerable scrutiny due to recent high‐profile “earnings management” cases and the collapse of Enron. The purpose of this paper is to examine the association between the characteristics of audit committees (size, independence, financial expertise, activity, and stock ownership) and earnings restatement – a direct measure of earnings management.

Univariate correlations and multivariate statistical analyses are performed. In particular, a multivariate logistic regression model is used.

Evidence suggests a negative association between the size of audit committees and the occurrence of earnings restatement. The remaining four audit committee characteristics are not found to have a significant impact on the quality of reported earnings.

This study focuses on the fiscal year 2000 only. As data become available for more fiscal years, future studies may re‐examine the issue.

Results of this research provide useful information for the accounting profession, the regulators and corporations on the effective practice of audit committees.

The purpose of this paper is to measure the performance and relative operating efficiency of mini branches of a commercial bank for internal benchmarking.

The authors employ data envelopment analysis (DEA) for measuring the relative efficiency of mini bank branches in a region of a state in the USA. In addition, the authors use Tobit regression analysis for finding determinants of the efficiency.

The authors identified efficient and inefficient branches using pertinent variables along with potential improvements for the inefficient branches. Among the variables, the authors confirmed that number of full-time employees and service charges were statistically significant for explaining the efficiency of bank branches.

The major limitation of this study is the one time measure of efficiency using observations for a month and selected variables. To improve this study: first, the authors need to test other variables related to, but not limited to, assets, bad loans, and number of new account opened; second, it is necessary to measure the efficiency of the branches over time.

The contributions of this study include an application of DEA for measuring and benchmarking the performance of bank branches, especially mini branches located in grocery stores and the use of internal data, which is rarely available to the public.

While financial reporting fraud has become more prevalent and costly in recent years, fraud detection has been badly lagging. Several recent studies have examined the feasibility of various computer techniques in business and industrial applications. The purpose of this study is to evaluate the utility of an integrated fuzzy neural network (FNN) for fraud detection. The FNN developed in this research outperformed most statistical models and artificial neural networks (ANN) reported in prior studies. Its performance also compared favorably with a baseline Logit model, especially in the prediction of fraud cases.

This study aims to examine whether US oil and gas companies engaged in earnings management during the 2011 Arab Spring, which resulted in significant increases in both crude oil and gasoline prices.

Following a similar research methodology from prior research, this study tests the existence of earnings management based on discretionary total accruals, current accruals and non-current accruals to determine whether both large petroleum refining firms and relatively small oil and gas-producing firms, jointly and separately, lowered reported earnings.

The results show that, overall, US oil and gas companies as a group engaged in income-decreasing earnings management during the Arab Spring. The results seem to support the political cost hypothesis. However, further analyses indicate that the results are driven by abnormal income-decreasing accruals of the relatively small oil and gas-producing firms, which are politically less sensitive.

The findings suggest that there may be other non-political cost incentives, such as income smoothing, for the relatively small oil and gas-producing firms managing earnings downward during periods of large oil price increases. However, the possibility for firms with reversals of income-increasing activity from other quarters is not ruled out.

This study not only is the first empirical study of earnings management by oil and gas companies during the Arab Spring, but also contributes to extant earnings management literature regarding political cost hypothesis, which still remains a major concern for US oil and gas companies.

This study aims to develop a data fusion method for powder-bed fusion (PBF) process monitoring based on process image information. The data fusion method can help improve process condition identification performance, which can provide guidance for further PBF process monitoring and control system development.

Design of reliable process monitoring systems is an essential approach to solve PBF built quality. A data fusion framework based on support vector machine (SVM), convolutional neural network (CNN) and Dempster-Shafer (D-S) evidence theory are proposed in the study. The process images which include the information of melt pool, plume and spatters were acquired by a high-speed camera. The features were extracted based on an appropriate image processing method. The three feature vectors corresponding to the three objects, respectively, were used as the inputs of SVM classifiers for process condition identification. Moreover, raw images were also used as the input of a CNN classifier for process condition identification. Then, the information fusion of the three SVM classifiers and the CNN classifier by an improved D-S evidence theory was studied.

The results demonstrate that the sensitivity of information sources is different for different condition identification. The feature fusion based on D-S evidence theory can improve the classification performance, with feature fusion and classifier fusion, the accuracy of condition identification is improved more than 20%.

An improved D-S evidence theory is proposed for PBF process data fusion monitoring, which is promising for the development of reliable PBF process monitoring systems.

Aim of the present monograph is the economic analysis of the role of MNEs regarding globalisation and digital economy and in parallel there is a reference and examination of some legal aspects concerning MNEs, cyberspace and e‐commerce as the means of expression of the digital economy. The whole effort of the author is focused on the examination of various aspects of MNEs and their impact upon globalisation and vice versa and how and if we are moving towards a global digital economy.

We investigate the link between firm volatility and risk-taking (RT) among 4232 institutions across 11 countries during the period of 2000–2017 and find RT is negatively correlated with volatility measures. Second, a decomposition of the primary risk measure, the Z score and Merton distance-to-default, reveals that high RT contributed to lower stock return volatility mainly through better corporate governance, firm size, higher information efficiency, and strong BOD. Third, Australia firms engage in more RT compared to other countries. Finally, majority of the selected countries show the negative impact of RT in firm volatility in the pre-crises period (2002–2006) and during the crises period (2007–2009) but not in the post-crises period (2010–2014).

Gives an in depth view of the strategies pursued by the world’s leading chief executive officers in an attempt to provide guidance to new chief executives of today. Considers the marketing strategies employed, together with the organizational structures used and looks at the universal concepts that can be applied to any product. Uses anecdotal evidence to formulate a number of theories which can be used to compare your company with the best in the world. Presents initial survival strategies and then looks at ways companies can broaden their boundaries through manipulation and choice. Covers a huge variety of case studies and examples together with a substantial question and answer section.