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The purpose of this paper is to apply signal detection theory (SDT) to the problem of detecting management fraud. The use of SDT methodology significantly strengthens understanding of the relationships among audit technology, base rates of management fraud, costs of Type I and Type II errors, extensions of audit procedures, and risk assessments prior and during the audit. The analysis suggests that the auditor must accept disproportionate false alarm rates in order to maintain audit effectiveness in the presence of management fraud. This condition becomes even stronger as the costs of Type II errors increase compared to costs of Type I errors. The study also provides policy implications for auditor practice and standard‐setters.

The purpose of this paper is to investigate how the effectiveness of systems for ensuring cooperation in online transactions is impacted by a positivity bias in the evaluation of the work that is produced. The presence of this bias can reduce the informativeness of the reputation system and negatively impact its ability to ensure quality.

This research combines survey and experimental methods, collecting data from 1,875 Mechanical Turk (MTurk) workers in five studies designed to investigate the informativeness of the MTurk reputation system.

The findings demonstrate the presence of a positivity bias in evaluations of workers on MTurk, which leaves them undifferentiated, except at the extremity of the reputation system and by status markers.

Because MTurk workers self-select tasks, the findings are limited in that they may only be generalizable to those who are interested in research-related work. Further, the tasks used in this research are largely subjective in nature, which may decrease their sensitivity to differences in quality.

For researchers, the results suggest that requiring 99 per cent approval rates (rather than the previously advised 95 per cent) should be used to identify high-quality workers on MTurk.

The research provides insights into the design and use of reputation systems and demonstrates how design decisions can exacerbate the effect of naturally occurring biases in evaluations to reduce the utility of these systems.

American police trace their initial brush with handgun training to efforts taken by New York City in 1895. Developing proficiency did not become a widely held priority until beginning in the mid‐1920s when the reform era’s focus upon training understandably led them to desire being not just trained, but “qualified” with their handguns. Qualification is a military‐derived status introduced in large part by the National Rifle Association’s police firearms training programme between the two World Wars. Today, as then, formal qualification expectations imply that officers exceeding various minimum performance levels are competent to employ handguns during armed confrontations. An examination of police field marksmanship in armed confrontations ‐ within the context of firearms training developments, the nature of and role played by “qualification”, and the basis for threshold scores ‐ suggests otherwise.

Due‐date performance (DDP) is a very important performance indicator for the companies. Thus, companies with a high hit rate would have greater competitive advantage; on the contrary, companies that delay customers' orders frequently would lose sales opportunities and reputations. Therefore, there were many academic studies and practical efforts to improve DDP in the past, but the problem of low hit rate still exists. In order to increase the hit rate, some companies have focused on reducing the variation, while others focus on production management, but is the real problem affecting the low rate variability or production management? This is indeed difficult to be validated through practice. Therefore, this study designed three scenarios, tested each scenario for 30 times, each test involved seven subjects. The tests were to provide counter‐evidence in the Job Shop environment without variation. If the variation is the main factor of affecting hit rate, the rate at this time should be good; otherwise, the assumption that variation is the main cause is rebutted. The results demonstrated that production management planning is the main cause, and the method of enhancing the hit rate is obtained during the test.

This paper aims to propose a scenario-based approach for measuring interest rate risks. Many regulatory capital standards in banking and insurance make use of similar approaches. The authors provide a theoretical justification and extensive backtesting of our approach.

The authors theoretically derive a scenario-based value-at-risk for interest rate risks based on a principal component analysis. The authors calibrate their approach based on the Nelson–Siegel model, which is modified to account for lower bounds for interest rates. The authors backtest the model outcomes against historical yield curve changes for a large number of generated asset–liability portfolios. In addition, the authors backtest the scenario-based value-at-risk against the stochastic model.

The backtesting results of the adjusted Nelson–Siegel model (accounting for a lower bound) are similar to those of the traditional Nelson–Siegel model. The suitability of the scenario-based value-at-risk can be substantially improved by allowing for correlation parameters in the aggregation of the scenario outcomes. Implementing those parameters is straightforward with the replacement of Pearson correlations by value-at-risk-implied tail correlations in situations where risk factors are not elliptically distributed.

The paper assumes deterministic cash flow patterns. The authors discuss the applicability of their approach, e.g. for insurance companies.

The authors’ approach can be used to better communicate interest rate risks using scenarios. Discussing risk measurement results with decision makers can help to backtest stochastic-term structure models.

The authors’ adjustment of the Nelson–Siegel model to account for lower bounds makes the model more useful in the current low-yield environment when unjustifiably high negative interest rates need to be avoided. The proposed scenario-based value-at-risk allows for a pragmatic measurement of interest rate risks, which nevertheless closely approximates the value-at-risk according to the stochastic model.

Purpose – Health information technology (HIT) holds promise for improving the quality of health care and reducing health care system inefficiencies. Numerous studies have examined HIT availability, specifically electronic health records (EHRs), and utilization among physicians in individual countries. However, no one has examined EHR use among physicians who train in one country and move to practice in another country. In the United States, physicians who complete medical school outside the country but practice within the United States are commonly referred to as International Medical Graduates (IMGs). IMGs have a growing presence in the United States, yet little is known about the availability and use of HIT among these physicians. The purpose of this study is to explore the availability and use of HIT among IMGs practicing in United States.

Design/methodology/approach – The Health Tracking Physician Survey (2008) was used to examine the relationship between availability and use of HIT and IMG status controlling for several physician and practice characteristics. Our analysis included responses from 4,720 physicians, 20.7% of whom were IMGs.

Findings – Using logistic regression, controlling for physician gender, specialty, years in practice, practice type, ownership status and geographical location, we found IMGs were significantly less likely to have a comprehensive EHR in their practices (OR=0.84; p=0.005). In addition, findings indicate that IMGs are more likely to have and use several so-called first generation HIT capabilities, such as reminders for clinicians about preventive services (OR=1.31; p=0.001) and other needed patient follow-up (OR=1.26; p=0.007).

Originality/value – This study draws attention to the need for further research regarding barriers to HIT adoption and use among IMGs.

This study aims to ensure the operation safety of high speed trains, it is necessary to carry out nondestructive monitoring of the tensile damage of the gearbox housing material in rail time, yet the traditional tests of mechanical property can hardly meet this requirement.

In this study the acoustic emission (AE) technology is applied in the tensile tests of the gearbox housing material of an high-speed rail (HSR) train, during which the acoustic signatures are acquired for parameter analysis. Afterward, the support vector machine (SVM) classifier is introduced to identify and classify the characteristic parameters extracted, on which basis the SVM is improved and the weighted support vector machine (WSVM) method is applied to effectively reduce the misidentification of the SVM classifier. Through the study of the law of relations between the characteristic values and the tensile life, a degradation model of the gearbox housing material amid tensile is built.

The results show that the growth rate of the logarithmic hit count of AE signals and that of logarithmic amplitude can well characterize the stage of the material tensile process, and the WSVM method can improve the classification accuracy of the imbalanced data to above 94%. The degradation model built can identify the damage occurred to the HSR gearbox housing material amid the tensile process and predict the service life remains.

The results of this study provide new concepts for the life prediction of tensile samples, and more further tests should be conducted to verify the conclusion of this research.

The economic and administrative conditions of countries normatively have an effect on the economy and level of market development. Moreover, it is of great importance for a healthy economy whether the public institutions and organizations are transparent and functioning in accordance with their purpose. The aim of this study is to show whether there is a relationship between transparency and market efficiency.

Correlation analysis has been conducted between prediction accuracy rates, which are obtained by seven different machine learning algorithms and Corruption Perception Index (CPI) levels.

It has been statistically shown that the indices of countries with low corruption levels are harder to predict, which, in turn, can be interpreted as having higher weak-form market efficiency. According to that, an intermediate negative correlation has been found between CPI scores and predictability levels of stock indices. Considering the findings, it can be interpreted that the markets of countries with relatively more transparent and well-functioning public sector have more weak-form market efficiency.

The study can be extended with cutting-edge machine learning and deep learning techniques in future studies. There are very few studies which try to explain factors related to market efficiency. Thus, the authors claim that there is still room for further research in order to determine the factors related to market efficiency, implying that current literature is still far from explaining the causation behind the inefficiencies.

According to findings, the markets of countries with relatively more transparent and well-functioning public sector have more weak-form market efficiency. Based on these findings, in practice, it can be said that more successful predictions can be made using machine learning algorithms in countries with relatively lower CPI scores.

In literature, the factors related to market efficiency are still far from explaining the causation behind the inefficiencies. Thus, it has been investigated whether transparent and well-functioning public institutions and organizations have any relation with market efficiency.

The purpose of this paper is to show that despite the critical importance of using accurate data when identifying geographic patterns and studying hotspots, few have explored the data quality issues introduced by Geographic Information Systems (GIS) software applications. While software manufacturers provide some information about the address matching process, critical details are left out or are buried in technical, and sometimes proprietary, jargon. The purpose of this paper is to address these issues.

The paper demonstrates, with three datasets of 100 cases each, how the assumptions built into popular GIS software produce systematically missing data during the data importing process commonly referred to as address matching.

Inclusion of directional indicators and zip codes are more important than previously thought. The results highlight the critical need to provide complete descriptions of research methodology. All geographic analyses must be accompanied with: information about the hit rate (percent of cases plotted), details about the software and process used to import tabular crime data, information about the software parameters set for the importation process (geocoding preferences), reference information about the street file used; and, an examination of the missing cases to identify some of the sampling error. When forecasting crime issues or identifying hot spots, analysts must be cognizant of the differential impact this bias will have on the generalizability of the results.

The paper explores previously neglected issues in data quality introduced by GIS software applications.

The purpose of this paper is to examine the influence of reference data, input address quality, and crime type on completeness and positional accuracy of street geocoded crime events.

Existing data were analyzed using ArcGIS, including crime incident information, street network reference data, and address point and/or parcel reference data. Geocoding completeness was determined by the overall match rate. Positional accuracy was determined by comparing the Euclidian distance between street geocoded locations of crime events to the corresponding address point/parcel geocoded location.

Results indicate that match rates vary by reference data, input address quality, and crime type. Local street centerline files consistently produced match rates that were as good as – and in many cases superior to – other types of reference data, including commercial data. Greater variability in positional accuracy was observed across reference data when crime type and input address quality was considered, but results were consistent with positional accuracy analysis conducted using data from other disciplines.

Results provide researchers and practitioners with valuable guidance and insight into one of the most basic – albeit fundamental – procedures related to the spatio‐temporal analysis of crime, suggesting that reference data required to produce geocoded crime incidents successfully and of high quality does not necessarily mean a large financial investment on the part of law enforcement agencies or researchers interested in the geospatial analysis of crime.

Prior to this investigation, a comprehensive examination of the impact of data quality on geocoded crime events was absent from the literature.