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A conflict exists between the way statistics is practiced in contemporary business environments and the way statistics is taught in schools of management. While businesses are embracing programs, such as six sigma and TQM, that bring statistical methods to the forefront of management decision making, students do not graduate with the skills to apply these methods effectively. Based on the concept of process thinking, it is argued that evolutionary rather than revolutionary changes should be made in the way statistics is taught. The difference between the process thinking approach and the classic statistical approach is illustrated using several business‐related examples.

A practical model based on basic probability theory is developed to evaluate the operational and financial performance of mammography systems. The model is intended to be used by decision makers to evaluate overall sensitivity, overall specificity, positive and negative predictive values, and expected cost. As an illustration, computer aided detection (CAD) systems that support a radiologist's diagnosis are compared with standard mammography to determine conditions that would support their use. The model's input parameters include the operational performance of mammography (with and without CAD), the age of the patient, the cost of administering the mammogram and the expected costs associated with false positive and false negative outcomes. Sensitivity analyses are presented that show the CAD system projecting financial benefit over ranges of uncertainty associated with each model parameter.

This research updates and expands upon Decker’s article “Citizen attitudes toward the police: a review of past findings and suggestions for future policy” by summarizing the findings from more than 100 articles on perceptions of and attitudes toward the police. Initially, the value of research on attitudes toward the police is discussed. Then the research pertaining to the impact of individual level variables (e.g. race) and contextual level variables (e.g. neighborhood) on perceptions of the police is reviewed. Studies of juveniles’ attitudes toward the police, perceptions of police policies and practices, methodological issues and conceptual issues are also discussed. This review of the literature indicates that only four variables (age, contact with police, neighborhood, and race) have consistently been proven to affect attitudes toward the police. However, there are interactive effects between these and other variables which are not yet understood; a finding which indicates that theoretical generalizations about attitudes toward police should be made with caution.

The librarian and researcher have to be able to uncover specific articles in their areas of interest. This Bibliography is designed to help. Volume IV, like Volume III, contains features to help the reader to retrieve relevant literature from MCB University Press' considerable output. Each entry within has been indexed according to author(s) and the Fifth Edition of the SCIMP/SCAMP Thesaurus. The latter thus provides a full subject index to facilitate rapid retrieval. Each article or book is assigned its own unique number and this is used in both the subject and author index. This Volume indexes 29 journals indicating the depth, coverage and expansion of MCB's portfolio.

The purpose of this paper is to examine public perceptions of police efforts to control crime in South Korea.

The data were gathered from surveys administered to college students in the Seoul-Gyeonggi Province metropolitan area. Logistic regression analyses were performed to assess the impact of gender, fear of crime and perceived risk of victimization on diffuse and specific perceptions of police performance.

The respondents did not view the police favorably. Fewer than half the respondents reported that the police do a good job of controlling drunk driving, approximately a quarter reported that the police do a good job of controlling burglary and investigating homicide and roughly a fifth reported believing that the police effectively control crime. Violent victimization and fear of violent victimization had a significant negative impact on confidence in the police.

The data suggest that informing the public about the low risk of violent victimization and other publicity campaigns designed to reduce fear of violence may foster confidence in the police.

This study identifies subtle similarities and differences in the structure of public perceptions of the police between Eastern and Western nations. Additionally, the data indicate there is a need for greater specificity in measures of public perceptions of the police.

The purpose of this paper is to examine how celebrated cases affect attitudes toward police, controlling for key demographic, police contact, and neighborhood contextual variables.

The paper presents two waves of public opinion data measuring attitudes toward police, police services, police harassment, and officer guilt before and after a celebrated police misconduct trial. Data were collected by telephone from residents living in three areas.

The findings in the paper suggest that news consumption of this celebrated case had no significant effects on general attitudes toward police, police services, and concerns about police harassment. Media coverage, however, did effect citizen evaluation of the guilt of the officers involved in the case. The more a citizen read a newspaper or read about the case, the more likely she was to think that the officers were guilty. Concern about crime in the neighborhood was an important predictor of attitudes toward the police, and race effects were much more pronounced after media coverage of the case.

This paper highlights the need to examine more closely media coverage of celebrated cases and the effects of such high profile cases. In addition, it illustrates that public opinion research must be careful of contextual variables when conducting a study at a single point in time.

These findings also have critical implications for law enforcement agencies. The findings highlight the importance of police departments being prepared to respond to crisis events.

This paper is valuable to scholars and police practitioners because of its close examination of the effects of a celebrated case on various measures of public opinion of the police. Although there have many studies examining this general topic, research has ignored the impact of media coverage generally and coverage of high profile incidents.

Purpose: The main aim of the study is to provide a tool for non-financial information in decision-making. We analysed the non-financial data in the annual reports in order to show the usage of this information in financial decision processes.

Need for the Study: Main financial reports such as balance sheets and income statements can be analysed by statistical methods. However, an expanded financial reporting framework needs new analysing methods due to unstructured and big data. The study offers a solution to the analysis problem that comes with non-financial reporting, which is an essential communication tool in corporate reporting.

Methodology: Text mining analysis of annual reports is conducted using software named R. To simplify the problem, we try to predict the companies’ corporate governance qualifications using text mining. K Nearest Neighbor, Naive Bayes and Decision Tree machine learning algorithms were used.

Findings: Our analysis illustrates that K Nearest Neighbor has classified the highest number of correct classifications by 85%, compared to 50% for the random walk. The empirical evidence suggests that text mining can be used by all stakeholders as a financial analysis method.

Practical Implications: Combining financial statement analyses with financial reporting analyses will decrease the information asymmetry between the company and stakeholders. So stakeholders can make more accurate decisions. Analysis of non-financial data with text mining will provide a decisive competitive advantage, especially for investors to make the right decisions. This method will lead to allocating scarce resources more effectively. Another contribution of the study is that stakeholders can predict the corporate governance qualification of the company from the annual reports even if it does not include in the Corporate Governance Index (CGI).

This paper aims to investigate what factors influence women’s meaningful and equitable persistence in computing and technology fields. It draws on theories of learning and equity from the learning sciences to inform the understanding of women’s underrepresentation in computing as it investigates young women who showed an interest in computing in high school and followed-up with them in their college and careers.

The mixed-methods approach compares data from quantitative surveys and qualitative focus groups and interviews. The sample comes from database of 1,500 young women who expressed interest in computing by applying for an award for high schoolers. These women were surveyed in 2013 and then again in 2016, with 511 women identifying themselves as high schoolers in 2013 and then having graduated and pursued college or careers in the second survey. The authors also conducted qualitative interviews and focus groups with 90 women from the same sample.

The findings show that multiple factors influence women’s persistence in computing, but the best predictor of women’s persistence is access to early computing and programming opportunities. However, access and opportunities must be evaluated within broader social and contextual factors.

The main limitation is that the authors measure women’s persistence in computing according to their chosen major or profession. This study does not measure the impact of computational thinking in women’s everyday lives.

Educators and policymakers should consider efforts to make Computer Science-for-All a reality.

Few longitudinal studies of a large sample of women exist that follow women interested in computing from high school into college and careers particularly from a critical educational equity perspective.

Cost transparency is of central importance to reach a consensus between supply chain partners. The purpose of this paper is to contribute to the instrument of cost analysis which supports the link between buyers and suppliers.

Based on a detailed literature review in the area of cost analysis and purchasing, intelligent decision support systems for cost estimation are identified. Subsequently, expert interviews are conducted to determine the application possibilities for managers. The application potential is derived from the synthesis of motivation, identified applications and challenges in the industry. Management recommendations are to be derived by bringing together scientific and practical approaches in the industry.

On the one hand, the results of this study show that machine learning (ML) is a complex technology that poses many challenges for cost and purchasing managers. On the other hand, ML methods, especially in combination with expert knowledge and other analytical methods, offer immense added value for cost analysis in purchasing.

Digital transformation allows to facilitate the cost calculation process in purchasing decisions. In this context, the application of ML approaches has gained increased attention. While such approaches can lead to high cost reductions on the side of both suppliers and buyers, an intelligent cost analysis is very demanding.