Chief Audit Executives’ Evaluations of Whistle-Blowing Allegations

This study examines the effects of the source of whistle-blowing allegations and potential for allegations to trigger concerns about reputation threats on Chief Audit Executives’ handling of whistle-blowing allegations. The participants for this study, 79 Chief Audit Executives (CAEs) and deputy CAEs, evaluated whistle-blowing reports related to financial reporting malfeasance that were received from either an anonymous or a non-anonymous source. The whistle-blowing reports alleged that the wrongdoing resulted from either the exploitation of substantial weaknesses in internal controls (suggesting higher responsibility of the CAE and internal audit) or the circumvention of internal controls (suggesting lower responsibility of the CAE or internal audit). Findings indicate that CAEs believe anonymous whistle-blowing reports to be significantly less credible than non-anonymous reports. Although CAEs assessed lower credibility ratings for the reports alleging wrongdoing by the exploitation of substantial weaknesses in internal controls, they allocated more resources to investigating these allegations.

Visualization Techniques For The Analysis Of Network Simulation Results

The Simulation Automation Framework for Experiments (SAFE) streamlines the de- sign and execution of experiments with the ns-3 network simulator. SAFE ensures that best practices are followed throughout the workflow a network simulation study, guaranteeing that results are both credible and reproducible by third parties. Data analysis is a crucial part of this workflow, where mistakes are often made. Even when appearing in highly regarded venues, scientific graphics in numerous network simulation publications fail to include graphic titles, units, legends, and confidence intervals. After studying the literature in network simulation methodology and in- formation graphics visualization, I developed a visualization component for SAFE to help users avoid these errors in their scientific workflow. The functionality of this new component includes support for interactive visualization through a web-based interface and for the generation of high-quality, static plots that can be included in publications. The overarching goal of my contribution is to help users create graphics that follow best practices in visualization and thereby succeed in conveying the right information about simulation results.

Determining the Socially Optimal Recycling Rate

What municipal recycling rate is socially optimal? One credible answer would consider the recycling rate that minimizes the overall social costs of managing municipal waste. Such social costs are comprised of all budgetary costs and revenues associated with operating municipal waste and recycling programs, all costs to recycling households associated with preparing and storing recyclable materials for collection, all external disposal costs associated with waste disposed at landfills or incinerators, and all external benefits associated with the provision of recycled materials that foster environmentally efficient production processes. This paper discusses how to estimate these four components of social cost to then estimate the optimal recycling rate. (C) 2013 Elsevier B.V. All rights reserved.