A risk/cost framework for logistics policy evaluation: Hazardous waste management

The management of hazardous waste disposal operations is extremely complex involving a multitude of environmental, engineering, economic, social and political concerns. This article proposes a framework to assist policy makers in the evaluation of logistic policies. A spatial general equilibrium based policy evaluation model is developed to calculate risk, cost, and risk equity tradeoff curves. This framework provides policy makers a tool with which they can relate resulting logistics patterns and their associated risk, cost, and equity attributes to original policy goals

Proctored vs. Un-Proctored Exams in a Hybrid Course: A Brief Comparison of Student Results

The research aims to examine whether there is a difference in undergraduate student performance on skill-based exams in an introductory computer literacy course at a state comprehensive university when exams are administered in-class vs. online. Two samples, each consisting of approximately 107 students, are considered for this study. A comparison of exam scores will be used to identify differences in exam performance between the two groups

Assessing Technology Skills in an Undergraduate Business Course

This article focuses on how an undergraduate program of an Association to Advance Collegiate Schools of Business (AACSB), an accredited business school, incorporates assessment on the use of information technology in a computer business course. To meet the new AACSB standards regarding assessment and adequately determine if and what students are learning? This article presents the technology learning goals, the associated learning objectives and the specific technology-related behaviors and actions that are assessed. In addition, specific examples of student assignments are presented as well as how these assignments are designed and assessed in relation to the learning objectives for the course is discussed

Proctored Versus Unproctored Online Exams: Studying the Impact of Exam Environment on Student Performance

Increasing numbers of universities are offering courses in online and hybrid formats. One challenge in online assessment is the maintenance of academic integrity. We present a thorough statistical analysis to uncover differences in student performance when online exams are administered in a proctored environment (i.e., in class) versus an unproctored environment (i.e., offsite). Controlling for student grade point average (GPA), no significant differences in mean overall course performance or exam performance between the two groups were found, nor were there any differences in the mean vectors of individual exam scores. The study reveals that the group taking online exams in the unproctored environment has significantly more variation in their performance results. In examining potential causes of the greater variation, analyses were performed to assess whether an increased level of possible cheating behavior could be observed from performance results for students in the unproctored section. No evidence of cheating behavior was found

Retention Assessment Of Core Operations Management Topics For Business Administration Students

To meet the new AACSB International standards regarding retention assessment and adequately determine if and what students are learning, this research presents a framework within which expected learning outcomes and specific learning are assessed. This paper presents the framework and describes how the process can be implemented with an application to retention assessment for the core operations management (OM) topics. Assessing core OM topics partially fulfills the AACSB International mandate of topical coverage and outcome assessment in the area of analytical decision making, which includes business statistics, financial analysis, and operations management. The study is of value to those who desire to better understand and implement assurance of learning or assessment in their programs

Using Van Valens Procedure In Business Research To Assess Consistent Differences In Multidimensional Variability In Two Or More Groups

Much business research involves comparisons in two or more groups on many dimensions.  This paper primarily focuses on demonstrating and providing guidance as to how researchers should approach a multivariate analysis in the comparison of sets of corresponding characteristics in two or more independent groups.  In particular, this paper demonstrates the utility of a simple but not widely known procedure developed by Van Valen (1978) that should be employed to test for the significance of differences in overall variability in the sets of corresponding characteristics in two or more groups, a test that enjoys much statistical power in detecting significant subtle group differences when the set of characteristics in one group consistently demonstrates greater variability than the corresponding set of characteristics in the other group(s).

Using Van Valens Procedure In Business Research To Assess Consistent Differences In Multidimensional Variability In Two Or More Groups

Much business research involves comparisons in two or more groups on many dimensions. This paper primarily focuses on demonstrating and providing guidance as to how researchers should approach a multivariate analysis in the comparison of sets of corresponding characteristics in two or more independent groups. In particular, this paper demonstrates the utility of a simple but not widely known procedure developed by Van Valen (1978) that should be employed to test for the significance of differences in overall variability in the sets of corresponding characteristics in two or more groups, a test that enjoys much statistical power in detecting significant subtle group differences when the set of characteristics in one group consistently demonstrates greater variability than the corresponding set of characteristics in the other group(s)

A Relative Comparison of Leading Supply Chain Management Software Packages

Supply Chain Management (SCM) has proven to be an effective tool that aids companies in the development of competitive advantages. SCM Systems are relied on to manage warehouses, transportation, trade logistics and various other issues concerning the coordinated movement of products and services from suppliers to customers. Although in today’s fast paced business environment, numerous supply chain solution tools are readily available to companies, choosing the right SCM software is not an easy task. The complexity of SCM systems creates a multifaceted issue when selecting the right software, particularly in light of the speed at which technology evolves. In this paper, we use the approach of Analytic Hierarchy Process (AHP) to determine which SCM software best meets the needs of a company. The AHP approach outlined in this paper can be easily transferred to the comparison of other SCM software packages

Chapter XII: A Comparison and Scenario Analysis of Leading Data Mining Software

Finding the right software is often hindered by different criteria as well as by technology changes. We performed an analytic hierarchy process (AHP) analysis using Expert Choice to determine which data mining package was best suitable for us. Deliberating a dozen alternatives and objectives led us to a series of pair-wise comparisons. When further synthesizing the results, Expert Choice helped us provide a clear rationale for the decision. The issue is that data mining technology is changing very rapidly. Our article focused only on the major suppliers typically available in the market place. The method and the process that we have used can be easily applied to analyze and compare other data mining software or knowledge management initiatives

Accelerating drug discovery for Alzheimer's disease: best practices for preclinical animal studies

Animal models have contributed significantly to our understanding of the underlying biological mechanisms of Alzheimer's disease (AD). As a result, over 300 interventions have been investigated and reported to mitigate pathological phenotypes or improve behavior in AD animal models or both. To date, however, very few of these findings have resulted in target validation in humans or successful translation to disease-modifying therapies. Challenges in translating preclinical studies to clinical trials include the inability of animal models to recapitulate the human disease, variations in breeding and colony maintenance, lack of standards in design, conduct and analysis of animal trials, and publication bias due to under-reporting of negative results in the scientific literature. The quality of animal model research on novel therapeutics can be improved by bringing the rigor of human clinical trials to animal studies. Research communities in several disease areas have developed recommendations for the conduct and reporting of preclinical studies in order to increase their validity, reproducibility, and predictive value. To address these issues in the AD community, the Alzheimer's Drug Discovery Foundation partnered with Charles River Discovery Services (Morrisville, NC, USA) and Cerebricon Ltd. (Kuopio, Finland) to convene an expert advisory panel of academic, industry, and government scientists to make recommendations on best practices for animal studies testing investigational AD therapies. The panel produced recommendations regarding the measurement, analysis, and reporting of relevant AD targets, th choice of animal model, quality control measures for breeding and colony maintenance, and preclinical animal study design. Major considerations to incorporate into preclinical study design include a priori hypotheses, pharmacokinetics-pharmacodynamics studies prior to proof-of-concept testing, biomarker measurements, sample size determination, and power analysis. The panel also recommended distinguishing between pilot 'exploratory' animal studies and more extensive 'therapeutic' studies to guide interpretation. Finally, the panel proposed infrastructure and resource development, such as the establishment of a public data repository in which both positive animal studies and negative ones could be reported. By promoting best practices, these recommendations can improve the methodological quality and predictive value of AD animal studies and make the translation to human clinical trials more efficient and reliable